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Spacekime Analytics (Time Complexity and Inferential Uncertainty)
Structured Big Data Analytics Case-Study (UKBB)
SOCR Team
01/18/2022
# Get this figure: fig <- get_figure("MattSundquist", 4064)
# Get this figure's data: data <- get_figure("MattSundquist", 4064)$data
# Add data to this figure: p <- add_trace(p, x=c(4, 5), y=c(4, 5), kwargs=list(filename="Klein bottle", fileopt="extend"))
# Get y data of first trace: y1 <- get_figure("MattSundquist", 4064)$data[[1]]$y
library(plotly)1 Structured Big Data Analytics Case-Study
Next, we will look at another interesting example of a large structured tabular dataset. The goal remains the same - examine the effects of indexing complex data only using kime-order (time) and comparing the data representations as well as the subsequent data analytics. In this case-study, we will use the UK Biobank (UKBB) data.
A previous investigation Predictive Big Data Analytics using the UK Biobank Data, based on \(7,614\) imaging, clinical, and phenotypic features and neuroimaging data of \(9,914\) UKBB subjects reported the twenty most salient derived imaging biomarkers. By jointly representing and modeling the significant clinical and demographic variables along with specific salient neuroimaging features, the researchers predicted the presence and progression of depression and mental health of participating volunteers. We will explore the effects of kime-direction on the findings based on the same data and methods. For ease of demonstration, efficient calculations, and direct interpretation, we start by transforming the data into a tighter computable object of dimensions \(9,914\times 107\).
UKBB_data <- get(load("E:/Ivo.dir/Research/UMichigan/Publications_Books/2019/DataScience_Book_Value_Uncertainty_Kime_2019/other/UKBB_data_cluster_label.Rdata"))
# str(UKBB_data)
UKBB_Colnames <- colnames(UKBB_data); View(UKBB_Colnames); dim(UKBB_data) # 9914 7615## [1] 9914 7615# Extract the top-50 derived NI biomarkers (data_summary_cluster_2.xlsx), per
# https://drive.google.com/drive/folders/1SdAtefp_taabNL70JvwJZSexTkzXiEKD
top50_NI_Biomarkers <- c("lh_BA_exvivo_area__lh_WhiteSurfArea_area", "rh_BA_exvivo_area__rh_WhiteSurfArea_area", "rh_aparc.a2009s_area__rh_WhiteSurfArea_area", "rh_aparc_area__rh_WhiteSurfArea_area", "lh_aparc_area__lh_WhiteSurfArea_area", "lh_aparc.a2009s_area__lh_WhiteSurfArea_area", "aseg__SupraTentorialVol", "aseg__SupraTentorialVolNotVent", "aseg__SupraTentorialVolNotVentVox", "aseg__BrainSegVol", "aseg__BrainSegVolNotVentSurf", "aseg__BrainSegVolNotVent", "aseg__CortexVol", "aseg__rhCortexVol", "aseg__lhCortexVol", "aseg__TotalGrayVol", "aseg__MaskVol", "rh_aparc.DKTatlas_area__rh_superiortemporal_area", "rh_aparc.DKTatlas_area__rh_superiorfrontal_area", "lh_aparc.DKTatlas_area__lh_superiorfrontal_area", "lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area", "lh_aparc.DKTatlas_area__lh_superiortemporal_area", "aseg__EstimatedTotalIntraCranialVol", "lh_aparc_area__lh_lateralorbitofrontal_area", "rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area", "lh_aparc_area__lh_superiorfrontal_area", "rh_aparc_area__rh_superiortemporal_area", "rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area", "rh_aparc_area__rh_superiorfrontal_area", "lh_aparc_area__lh_rostralmiddlefrontal_area", "wmparc__wm.lh.lateralorbitofrontal", "wmparc__wm.lh.insula", "rh_aparc_area__rh_medialorbitofrontal_area", "lh_BA_exvivo_area__lh_BA3b_exvivo_area", "lh_aparc.DKTatlas_area__lh_postcentral_area", "lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume", "lh_aparc.DKTatlas_area__lh_insula_area", "aseg__SubCortGrayVol", "lh_aparc.a2009s_area__lh_G_orbital_area", "lh_aparc_area__lh_superiortemporal_area", "rh_aparc.DKTatlas_area__rh_insula_area", "lh_aparc.DKTatlas_area__lh_precentral_area", "lh_aparc.pial_area__lh_lateralorbitofrontal_area", "lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area", "lh_aparc_area__lh_postcentral_area", "lh_aparc.pial_area__lh_superiorfrontal_area", "rh_aparc_area__rh_rostralmiddlefrontal_area", "wmparc__wm.lh.superiortemporal", "lh_aparc.pial_area__lh_rostralmiddlefrontal_area", "rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume")
# Extract the main clinical features (binary/dichotomous and categorical/polytomous)
#### binary
top25_BinaryClinical_Biomarkers <- c("X1200.0.0", "X1200.2.0", "X1170.0.0", "X1190.2.0", "X1170.2.0", "X2080.0.0", "X6138.2.2", "X20117.0.0", "X6138.0.2", "X2877.0.0", "X20117.2.0", "X2877.2.0", "X1190.0.0", "X4968.2.0", "X1249.2.0", "X1190.1.0", "X1170.1.0", "X2080.2.0", "X4292.2.0", "X2050.0.0", "X1628.0.0", "X1200.1.0", "X20018.2.0", "X4292.0.0", "X3446.0.0")
#### polytomous
top31_PolytomousClinical_Biomarkers <- c("X31.0.0", "X22001.0.0", "X1950.0.0", "X1950.2.0", "X1980.0.0", "X2040.2.0", "X1980.2.0", "X2030.0.0", "X2090.0.0", "X2040.0.0", "X1618.2.0", "X1618.0.0", "X1210.0.0", "X2030.2.0", "X2000.0.0", "X1930.0.0", "X2090.2.0", "X2000.2.0", "X1210.2.0", "X1618.1.0", "X4653.2.0", "X1970.2.0", "X1970.0.0", "X1980.1.0", "X1930.2.0", "X4598.2.0", "X4598.0.0", "X4653.0.0", "X2090.1.0", "X2040.1.0", "X4631.2.0")
# Extract derived computed phenotype
derivedComputedPhenotype <- UKBB_Colnames[length(UKBB_Colnames)]
# Construct the Computable data object including all salient predictors and derived cluster phenotype
ColNameList <- c(top50_NI_Biomarkers, top25_BinaryClinical_Biomarkers,
top31_PolytomousClinical_Biomarkers, derivedComputedPhenotype); length(ColNameList)## [1] 107col.index <- which(colnames(UKBB_data) %in% ColNameList); length(col.index)## [1] 107tight107_UKBB_data <- UKBB_data[ , col.index]; dim(tight107_UKBB_data)## [1] 9914 107## View(tight107_UKBB_data[1:10, ]) # Confirm the tight data object organization We can investigate the effects of the kime-phase on the resulting data analytic inference obtained using the UKBB data.
library(caret)## Warning: package 'caret' was built under R version 4.1.2## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.1.2################################
# 1. First deal with the missing values # summary(tight107_UKBB_data)
##### "mi" imputation - LONG
#library(mi) # use pmm (predictive mean matching) imputation method for the missing variables.
#mdf <- missing_data.frame(tight107_UKBB_data)
#show(mdf) # ; head(mdf); dim(mdf)
#options(mc.cores = 4)
#imputations <- mi(mdf, n.iter=4, n.chains=1, verbose=T)
#imp.data.frames <- complete(imputations, 1)
#imp_tight107_UKBB_data <- imp.data.frames[[1]]
# split-off the computed phenotype (avoid manipulationg hte outcome that will be predicted during analysis phase
colnames(tight107_UKBB_data)[length(tight107_UKBB_data)] <- "cluster_2_cluster"
colnames(tight107_UKBB_data)[length(tight107_UKBB_data)] # fix the problem with $ in variable name## [1] "cluster_2_cluster"y_pheno <- tight107_UKBB_data[ , length(tight107_UKBB_data)]
tight106_UKBB_data <- tight107_UKBB_data[, -length(tight107_UKBB_data)]; dim(tight106_UKBB_data)## [1] 9914 106# MICE imputation (With parallel core computing)
# all predictors with absolute correlation over 0.4 AND at least 30% usable cases
library(mice)## Warning: package 'mice' was built under R version 4.1.2##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(foreach)
library(doParallel)## Loading required package: iterators## Loading required package: parallel# set-up local parallel cluster
number_cores <- detectCores() - 2
clustUKBB <- makeCluster(number_cores)
clusterSetRNGStream(clustUKBB, 1234)
registerDoParallel(clustUKBB)
imp_tight106_UKBB_data <-
foreach(no = 1:number_cores,
.combine = ibind,
.export = "tight106_UKBB_data",
.packages = "mice") %dopar%
{
mice(tight106_UKBB_data, m=2,maxit=3, printFlag=T, seed=1234, method = 'cart')
}## Warning in e$fun(obj, substitute(ex), parent.frame(), e$data): already exporting
## variable(s): tight106_UKBB_datastr(imp_tight106_UKBB_data)## List of 21
## $ data :'data.frame': 9914 obs. of 106 variables:
## ..$ lh_aparc_area__lh_lateralorbitofrontal_area : int [1:9914] 2350 2646 2431 2124 2851 2741 2907 2611 2626 2602 ...
## ..$ lh_aparc_area__lh_postcentral_area : int [1:9914] 3819 4123 4201 3712 4807 3801 4269 3736 4376 3877 ...
## ..$ lh_aparc_area__lh_rostralmiddlefrontal_area : int [1:9914] 5114 4990 4753 4841 5530 5673 6421 5161 5104 5117 ...
## ..$ lh_aparc_area__lh_superiorfrontal_area : int [1:9914] 6282 6679 6955 6705 7688 6959 7990 6382 6095 6967 ...
## ..$ lh_aparc_area__lh_superiortemporal_area : int [1:9914] 3648 3604 4016 3368 3703 3920 4819 3725 4155 3949 ...
## ..$ lh_aparc_area__lh_WhiteSurfArea_area : num [1:9914] 75300 85056 79993 79557 87467 ...
## ..$ lh_aparc.a2009s_area__lh_G_orbital_area : int [1:9914] 1336 1621 1496 1270 1838 1653 1770 1649 1596 1582 ...
## ..$ lh_aparc.a2009s_area__lh_WhiteSurfArea_area : num [1:9914] 75311 85083 80002 79593 87479 ...
## ..$ lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area : int [1:9914] 2668 2899 2648 2450 3193 3165 3215 2865 2888 2935 ...
## ..$ lh_aparc.DKTatlas_area__lh_postcentral_area : int [1:9914] 4234 4671 4782 4229 5476 4379 4844 4319 5047 4333 ...
## ..$ lh_aparc.DKTatlas_area__lh_precentral_area : int [1:9914] 4114 5242 4066 4589 4717 4953 5348 4530 4408 4441 ...
## ..$ lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area : int [1:9914] 3694 3639 3271 3497 4006 4202 4655 3873 3507 3647 ...
## ..$ lh_aparc.DKTatlas_area__lh_superiorfrontal_area : int [1:9914] 6804 7010 7467 7326 8026 7337 8505 6697 6824 7524 ...
## ..$ lh_aparc.DKTatlas_area__lh_superiortemporal_area : int [1:9914] 4925 4852 5154 4555 4910 5125 6033 4849 5272 5213 ...
## ..$ lh_aparc.DKTatlas_area__lh_insula_area : int [1:9914] 1893 1962 2075 1849 2100 1624 2208 1788 1911 1900 ...
## ..$ lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume: int [1:9914] 8109 9048 8004 7191 9712 9537 8814 8080 9332 8922 ...
## ..$ lh_aparc.pial_area__lh_lateralorbitofrontal_area : int [1:9914] 2864 3199 2613 2402 3518 3255 3325 2852 2980 3125 ...
## ..$ lh_aparc.pial_area__lh_rostralmiddlefrontal_area : int [1:9914] 6190 6032 5367 5747 6053 7007 7232 6043 5891 6683 ...
## ..$ lh_aparc.pial_area__lh_superiorfrontal_area : int [1:9914] 7988 8299 8443 8000 9050 8397 9127 7536 7270 8741 ...
## ..$ lh_BA_exvivo_area__lh_BA3b_exvivo_area : int [1:9914] 1477 1634 1519 1433 1631 1520 1565 1438 1568 1506 ...
## ..$ lh_BA_exvivo_area__lh_WhiteSurfArea_area : num [1:9914] 80694 90563 85253 85222 92610 ...
## ..$ rh_aparc_area__rh_medialorbitofrontal_area : int [1:9914] 1706 1729 1783 1888 2124 2032 2008 2000 1734 1886 ...
## ..$ rh_aparc_area__rh_rostralmiddlefrontal_area : int [1:9914] 4957 5040 4820 5546 5664 6033 6232 5594 5553 5566 ...
## ..$ rh_aparc_area__rh_superiorfrontal_area : int [1:9914] 5866 6505 6709 6422 7271 6404 7039 5688 6317 6288 ...
## ..$ rh_aparc_area__rh_superiortemporal_area : int [1:9914] 3442 3704 3280 3624 3431 3580 4203 3505 3806 3282 ...
## ..$ rh_aparc_area__rh_WhiteSurfArea_area : num [1:9914] 75996 83699 80262 80435 86398 ...
## ..$ rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area : int [1:9914] 1702 1602 1841 1966 2161 1884 2313 1904 1942 1906 ...
## ..$ rh_aparc.a2009s_area__rh_WhiteSurfArea_area : num [1:9914] 76001 83753 80267 80447 86413 ...
## ..$ rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area : int [1:9914] 2309 2678 2493 2471 3019 3004 3063 2734 3089 2830 ...
## ..$ rh_aparc.DKTatlas_area__rh_superiorfrontal_area : int [1:9914] 7542 8166 8588 8281 9155 8314 8955 7646 8021 8354 ...
## ..$ rh_aparc.DKTatlas_area__rh_superiortemporal_area : int [1:9914] 4388 4741 4304 4644 4687 4641 5424 4627 4844 4285 ...
## ..$ rh_aparc.DKTatlas_area__rh_insula_area : int [1:9914] 2014 2019 2112 1981 2077 1816 2237 1798 2075 1900 ...
## ..$ rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume: int [1:9914] 7447 7830 7859 7614 9241 8685 8212 7782 9114 9287 ...
## ..$ rh_BA_exvivo_area__rh_WhiteSurfArea_area : num [1:9914] 81191 89124 85315 86163 91575 ...
## ..$ aseg__BrainSegVol : int [1:9914] 1103218 1121279 1053890 1119779 1112684 1169717 1226114 1089037 1148694 1087378 ...
## ..$ aseg__BrainSegVolNotVent : int [1:9914] 1063264 1070975 1028244 1065158 1076242 1105100 1205772 1062760 1121631 1063590 ...
## ..$ aseg__BrainSegVolNotVentSurf : num [1:9914] 1062099 1069818 1027862 1064310 1075692 ...
## ..$ aseg__lhCortexVol : num [1:9914] 228606 230880 209613 222804 230371 ...
## ..$ aseg__rhCortexVol : num [1:9914] 230274 209291 210212 227798 232323 ...
## ..$ aseg__CortexVol : num [1:9914] 458879 440171 419825 450602 462694 ...
## ..$ aseg__SubCortGrayVol : int [1:9914] 50800 50573 51371 54863 48979 52781 56751 50079 55256 50591 ...
## ..$ aseg__TotalGrayVol : num [1:9914] 613718 624125 570302 601374 602811 ...
## ..$ aseg__SupraTentorialVol : num [1:9914] 974971 958416 932012 999701 994856 ...
## ..$ aseg__SupraTentorialVolNotVent : num [1:9914] 939417 912893 909538 949265 962710 ...
## ..$ aseg__SupraTentorialVolNotVentVox : int [1:9914] 936915 909761 906943 945946 960353 959397 1063889 924416 980829 930046 ...
## ..$ aseg__MaskVol : int [1:9914] 1552381 1631227 1460936 1512763 1532661 1610154 1632023 1514226 1630776 1514167 ...
## ..$ aseg__EstimatedTotalIntraCranialVol : num [1:9914] 1450837 1435991 1428100 1434827 1365251 ...
## ..$ wmparc__wm.lh.lateralorbitofrontal : num [1:9914] 6157 6389 6138 6211 7170 ...
## ..$ wmparc__wm.lh.superiortemporal : num [1:9914] 7972 7137 8994 7371 7205 ...
## ..$ wmparc__wm.lh.insula : num [1:9914] 9878 9077 10518 9782 10755 ...
## ..$ X31.0.0 : int [1:9914] 0 1 0 0 1 1 0 0 1 0 ...
## ..$ X1170.0.0 : int [1:9914] 3 3 2 3 3 3 3 3 3 2 ...
## ..$ X1170.1.0 : int [1:9914] NA NA NA NA 3 3 4 NA NA NA ...
## ..$ X1170.2.0 : int [1:9914] 2 3 2 3 3 3 4 4 3 2 ...
## ..$ X1190.0.0 : int [1:9914] 1 1 1 2 1 2 2 1 2 1 ...
## ..$ X1190.1.0 : int [1:9914] NA NA NA NA 1 2 1 NA NA NA ...
## ..$ X1190.2.0 : int [1:9914] 2 1 2 2 1 2 1 1 2 1 ...
## ..$ X1200.0.0 : int [1:9914] 3 2 2 2 2 3 2 2 2 2 ...
## ..$ X1200.1.0 : int [1:9914] NA NA NA NA 1 2 2 NA NA NA ...
## ..$ X1200.2.0 : int [1:9914] 3 2 3 3 2 3 3 2 2 2 ...
## ..$ X1210.0.0 : int [1:9914] NA 1 2 2 1 1 2 2 2 2 ...
## ..$ X1210.2.0 : int [1:9914] 1 1 2 NA 2 1 2 2 2 2 ...
## ..$ X1249.2.0 : int [1:9914] 4 1 4 2 1 4 2 1 3 4 ...
## ..$ X1618.0.0 : int [1:9914] 1 0 1 0 1 1 1 NA NA 1 ...
## ..$ X1618.1.0 : int [1:9914] NA NA NA NA NA 1 1 NA NA NA ...
## ..$ X1618.2.0 : int [1:9914] 1 0 NA NA NA 1 1 0 NA 1 ...
## ..$ X1628.0.0 : int [1:9914] 2 2 2 3 1 2 3 2 1 3 ...
## ..$ X1930.0.0 : int [1:9914] 1 1 0 1 1 0 1 0 1 0 ...
## ..$ X1930.2.0 : int [1:9914] 1 0 0 1 NA 0 1 0 1 0 ...
## ..$ X1950.0.0 : int [1:9914] 0 1 1 0 1 0 NA 0 1 1 ...
## ..$ X1950.2.0 : int [1:9914] NA 1 1 1 1 0 NA 0 1 0 ...
## ..$ X1970.0.0 : int [1:9914] 0 0 0 1 1 0 0 0 0 0 ...
## ..$ X1970.2.0 : int [1:9914] 0 0 0 1 0 0 0 0 0 0 ...
## ..$ X1980.0.0 : int [1:9914] 1 0 NA 1 1 0 1 0 1 1 ...
## ..$ X1980.1.0 : int [1:9914] NA NA NA NA 1 0 1 NA NA NA ...
## ..$ X1980.2.0 : int [1:9914] 1 0 1 1 1 0 1 0 1 1 ...
## ..$ X2000.0.0 : int [1:9914] 1 1 0 1 1 0 NA NA 1 1 ...
## ..$ X2000.2.0 : int [1:9914] 1 1 0 1 1 0 NA 1 1 1 ...
## ..$ X2030.0.0 : int [1:9914] 1 0 0 1 1 0 0 0 0 0 ...
## ..$ X2030.2.0 : int [1:9914] 0 0 1 1 0 0 0 0 0 0 ...
## ..$ X2040.0.0 : int [1:9914] 0 1 NA 0 1 0 1 1 1 0 ...
## ..$ X2040.1.0 : int [1:9914] NA NA NA NA 0 0 0 NA NA NA ...
## ..$ X2040.2.0 : int [1:9914] 1 1 0 0 0 0 1 1 1 0 ...
## ..$ X2050.0.0 : int [1:9914] 3 1 4 2 2 1 NA 1 2 1 ...
## ..$ X2080.0.0 : int [1:9914] 2 1 2 2 2 3 1 1 1 2 ...
## ..$ X2080.2.0 : int [1:9914] 2 1 2 2 2 3 NA 1 2 1 ...
## ..$ X2090.0.0 : int [1:9914] 1 0 1 1 1 0 0 0 1 0 ...
## ..$ X2090.1.0 : int [1:9914] NA NA NA NA 0 0 0 NA NA NA ...
## ..$ X2090.2.0 : int [1:9914] 1 0 1 1 0 0 0 0 0 0 ...
## ..$ X2877.0.0 : int [1:9914] NA 1 NA NA 1 NA NA 1 NA NA ...
## ..$ X2877.2.0 : int [1:9914] NA 1 NA NA 1 NA NA 1 NA NA ...
## ..$ X3446.0.0 : int [1:9914] NA NA NA NA NA NA NA NA NA NA ...
## ..$ X4292.0.0 : int [1:9914] 0 NA 3 3 NA NA NA NA 3 NA ...
## ..$ X4292.2.0 : int [1:9914] 3 3 3 3 0 3 3 3 3 3 ...
## ..$ X4598.0.0 : int [1:9914] 1 NA 1 1 NA NA NA NA 1 NA ...
## ..$ X4598.2.0 : int [1:9914] 1 0 1 1 1 1 0 0 1 0 ...
## ..$ X4631.2.0 : int [1:9914] 1 0 1 1 NA 0 0 0 0 0 ...
## ..$ X4653.0.0 : int [1:9914] 0 NA 0 1 NA NA NA NA 0 NA ...
## ..$ X4653.2.0 : int [1:9914] 0 0 0 0 0 0 0 0 0 0 ...
## .. [list output truncated]
## $ imp :List of 106
## ..$ lh_aparc_area__lh_lateralorbitofrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc_area__lh_postcentral_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc_area__lh_rostralmiddlefrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc_area__lh_superiorfrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc_area__lh_superiortemporal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc_area__lh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.a2009s_area__lh_G_orbital_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
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## ..$ lh_aparc.a2009s_area__lh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_postcentral_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_precentral_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 1: logi(0)
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## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_superiorfrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_superiortemporal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_area__lh_insula_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume:'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.pial_area__lh_lateralorbitofrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.pial_area__lh_rostralmiddlefrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_aparc.pial_area__lh_superiorfrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_BA_exvivo_area__lh_BA3b_exvivo_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ lh_BA_exvivo_area__lh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc_area__rh_medialorbitofrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc_area__rh_rostralmiddlefrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
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## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc_area__rh_superiorfrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc_area__rh_superiortemporal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc_area__rh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.a2009s_area__rh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.DKTatlas_area__rh_superiorfrontal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.DKTatlas_area__rh_superiortemporal_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.DKTatlas_area__rh_insula_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume:'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ rh_BA_exvivo_area__rh_WhiteSurfArea_area :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__BrainSegVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__BrainSegVolNotVent :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__BrainSegVolNotVentSurf :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__lhCortexVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__rhCortexVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__CortexVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__SubCortGrayVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__TotalGrayVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__SupraTentorialVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__SupraTentorialVolNotVent :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__SupraTentorialVolNotVentVox :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__MaskVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ aseg__EstimatedTotalIntraCranialVol :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
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## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ wmparc__wm.lh.lateralorbitofrontal :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ wmparc__wm.lh.superiortemporal :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
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## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ wmparc__wm.lh.insula :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ X31.0.0 :'data.frame': 0 obs. of 12 variables:
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## .. ..$ 1: logi(0)
## .. ..$ 2: logi(0)
## ..$ X1170.0.0 :'data.frame': 137 obs. of 12 variables:
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## .. ..$ 1: num [1:137] 4 4 3 4 4 3 4 2 4 3 ...
## .. ..$ 2: num [1:137] 3 4 3 4 3 2 3 1 3 4 ...
## ..$ X1170.1.0 :'data.frame': 6696 obs. of 12 variables:
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## .. ..$ 1: num [1:6696] 2 3 2 3 4 3 2 1 3 3 ...
## .. ..$ 2: num [1:6696] 2 3 2 3 4 3 2 2 3 3 ...
## ..$ X1170.2.0 :'data.frame': 74 obs. of 12 variables:
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## .. ..$ 1: num [1:74] 4 4 4 3 4 2 4 4 4 4 ...
## .. ..$ 2: num [1:74] 4 3 4 2 2 3 4 4 3 4 ...
## ..$ X1190.0.0 :'data.frame': 3 obs. of 12 variables:
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## .. ..$ 1: num [1:3] 1 2 2
## .. ..$ 2: num [1:3] 1 2 2
## ..$ X1190.1.0 :'data.frame': 6696 obs. of 12 variables:
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 1: num [1:6696] 2 1 2 1 1 2 1 2 3 1 ...
## .. ..$ 2: num [1:6696] 1 1 2 1 1 2 1 2 3 1 ...
## ..$ X1190.2.0 :'data.frame': 72 obs. of 12 variables:
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## .. ..$ 1: num [1:72] 2 3 1 1 2 1 1 2 1 1 ...
## .. ..$ 2: num [1:72] 1 2 1 2 2 2 1 1 2 1 ...
## ..$ X1200.0.0 :'data.frame': 6 obs. of 12 variables:
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## .. ..$ 1: num [1:6] 1 1 2 2 3 2
## .. ..$ 2: num [1:6] 1 2 1 3 3 2
## ..$ X1200.1.0 :'data.frame': 6697 obs. of 12 variables:
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## .. ..$ 1: num [1:6697] 3 1 3 2 2 3 2 2 2 2 ...
## .. ..$ 2: num [1:6697] 3 2 3 2 2 2 2 1 2 1 ...
## ..$ X1200.2.0 :'data.frame': 76 obs. of 12 variables:
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## .. ..$ 1: num [1:76] 2 2 2 2 1 2 3 2 2 1 ...
## .. ..$ 2: num [1:76] 2 3 2 2 1 1 2 2 2 1 ...
## ..$ X1210.0.0 :'data.frame': 583 obs. of 12 variables:
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## .. ..$ 1: num [1:583] 1 2 1 2 2 2 1 1 2 2 ...
## .. ..$ 2: num [1:583] 1 2 1 1 2 2 1 2 2 2 ...
## ..$ X1210.2.0 :'data.frame': 752 obs. of 12 variables:
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 1: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## .. ..$ 2: num [1:752] 2 1 1 1 1 2 2 2 2 2 ...
## ..$ X1249.2.0 :'data.frame': 351 obs. of 12 variables:
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## .. ..$ 1: num [1:351] 2 4 3 4 4 2 4 4 4 3 ...
## .. ..$ 2: num [1:351] 3 4 4 3 4 4 4 2 1 4 ...
## ..$ X1618.0.0 :'data.frame': 4771 obs. of 12 variables:
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:4771] 0 0 0 1 0 1 0 0 0 1 ...
## .. ..$ 2: num [1:4771] 0 0 1 0 0 0 1 0 1 1 ...
## ..$ X1618.1.0 :'data.frame': 7900 obs. of 12 variables:
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## .. ..$ 1: num [1:7900] 1 0 1 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:7900] 1 0 1 0 1 0 0 1 1 1 ...
## ..$ X1618.2.0 :'data.frame': 3846 obs. of 12 variables:
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## .. ..$ 1: num [1:3846] 1 0 1 0 0 1 1 1 0 0 ...
## .. ..$ 2: num [1:3846] 1 1 1 0 1 1 0 1 0 1 ...
## ..$ X1628.0.0 :'data.frame': 517 obs. of 12 variables:
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## .. ..$ 1: num [1:517] 3 3 2 1 2 3 2 2 2 3 ...
## .. ..$ 2: num [1:517] 2 3 1 1 3 3 3 3 1 2 ...
## ..$ X1930.0.0 :'data.frame': 123 obs. of 12 variables:
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## .. ..$ 1: num [1:123] 0 0 0 1 1 0 1 0 1 0 ...
## .. ..$ 2: num [1:123] 1 0 1 1 0 0 1 0 1 1 ...
## ..$ X1930.2.0 :'data.frame': 181 obs. of 12 variables:
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## .. ..$ 1: num [1:181] 1 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 2: num [1:181] 0 1 0 0 0 1 1 0 0 0 ...
## ..$ X1950.0.0 :'data.frame': 266 obs. of 12 variables:
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:266] 1 0 1 1 1 0 1 1 1 1 ...
## .. ..$ 2: num [1:266] 0 0 1 1 0 1 1 1 0 1 ...
## ..$ X1950.2.0 :'data.frame': 301 obs. of 12 variables:
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## .. ..$ 1: num [1:301] 0 1 0 0 1 0 0 1 1 0 ...
## .. ..$ 2: num [1:301] 0 1 1 0 1 0 1 1 1 0 ...
## ..$ X1970.0.0 :'data.frame': 214 obs. of 12 variables:
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:214] 0 1 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:214] 0 0 0 1 1 0 0 0 1 1 ...
## ..$ X1970.2.0 :'data.frame': 253 obs. of 12 variables:
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## .. ..$ 1: num [1:253] 0 1 0 1 0 0 1 0 0 1 ...
## .. ..$ 2: num [1:253] 0 0 0 1 1 0 1 0 0 1 ...
## ..$ X1980.0.0 :'data.frame': 201 obs. of 12 variables:
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## .. ..$ 1: num [1:201] 1 0 0 1 1 1 1 0 1 0 ...
## .. ..$ 2: num [1:201] 1 0 1 1 1 0 0 1 0 0 ...
## ..$ X1980.1.0 :'data.frame': 6756 obs. of 12 variables:
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 1: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:6756] 1 0 1 1 0 0 1 0 0 0 ...
## ..$ X1980.2.0 :'data.frame': 289 obs. of 12 variables:
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## .. ..$ 1: num [1:289] 1 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 2: num [1:289] 1 0 0 0 1 1 0 0 1 0 ...
## ..$ X2000.0.0 :'data.frame': 308 obs. of 12 variables:
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## .. ..$ 1: num [1:308] 1 1 0 1 0 0 1 0 0 0 ...
## .. ..$ 2: num [1:308] 0 0 0 0 0 1 1 1 1 1 ...
## ..$ X2000.2.0 :'data.frame': 412 obs. of 12 variables:
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## .. ..$ 1: num [1:412] 1 1 0 1 1 1 1 0 1 1 ...
## .. ..$ 2: num [1:412] 0 1 0 1 0 0 1 1 1 0 ...
## ..$ X2030.0.0 :'data.frame': 164 obs. of 12 variables:
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## .. ..$ 1: num [1:164] 0 1 0 1 0 0 0 0 1 1 ...
## .. ..$ 2: num [1:164] 0 1 0 1 1 0 0 0 1 1 ...
## ..$ X2030.2.0 :'data.frame': 236 obs. of 12 variables:
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## .. ..$ 1: num [1:236] 1 1 0 1 0 0 0 1 1 1 ...
## .. ..$ 2: num [1:236] 1 0 1 1 1 0 0 0 0 0 ...
## ..$ X2040.0.0 :'data.frame': 296 obs. of 12 variables:
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## .. ..$ 1: num [1:296] 1 0 0 0 0 0 0 1 0 1 ...
## .. ..$ 2: num [1:296] 0 1 0 0 0 1 0 1 0 0 ...
## ..$ X2040.1.0 :'data.frame': 6804 obs. of 12 variables:
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## .. ..$ 1: num [1:6804] 0 1 0 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:6804] 0 1 0 0 0 1 0 0 1 0 ...
## ..$ X2040.2.0 :'data.frame': 385 obs. of 12 variables:
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ 1: num [1:385] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ 2: num [1:385] 0 0 0 0 0 0 0 1 0 0 ...
## ..$ X2050.0.0 :'data.frame': 295 obs. of 12 variables:
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## .. ..$ 1: num [1:295] 1 4 1 1 2 1 1 1 1 1 ...
## .. ..$ 2: num [1:295] 1 2 1 1 1 1 1 1 1 1 ...
## ..$ X2080.0.0 :'data.frame': 175 obs. of 12 variables:
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## .. ..$ 1: num [1:175] 3 1 1 1 1 3 1 1 1 2 ...
## .. ..$ 2: num [1:175] 3 1 1 2 1 1 2 2 1 1 ...
## ..$ X2080.2.0 :'data.frame': 266 obs. of 12 variables:
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## .. ..$ 1: num [1:266] 1 1 1 1 2 1 1 2 2 1 ...
## .. ..$ 2: num [1:266] 1 1 1 1 2 2 1 1 4 1 ...
## ..$ X2090.0.0 :'data.frame': 41 obs. of 12 variables:
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## .. ..$ 1: num [1:41] 0 0 1 0 1 1 0 0 0 0 ...
## .. ..$ 2: num [1:41] 0 0 0 0 1 1 0 0 0 1 ...
## ..$ X2090.1.0 :'data.frame': 6707 obs. of 12 variables:
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6707] 1 0 1 1 0 1 0 0 0 0 ...
## .. ..$ 2: num [1:6707] 1 0 1 1 0 0 0 0 0 0 ...
## ..$ X2090.2.0 :'data.frame': 113 obs. of 12 variables:
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## .. ..$ 1: num [1:113] 0 1 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:113] 1 1 1 0 1 1 0 0 0 0 ...
## ..$ X2877.0.0 :'data.frame': 7610 obs. of 12 variables:
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## .. ..$ 1: num [1:7610] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7610] 1 1 1 1 2 1 1 1 2 1 ...
## ..$ X2877.2.0 :'data.frame': 7561 obs. of 12 variables:
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## .. ..$ 1: num [1:7561] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ 2: num [1:7561] 1 1 1 1 1 1 1 1 2 1 ...
## ..$ X3446.0.0 :'data.frame': 9480 obs. of 12 variables:
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## .. ..$ 1: num [1:9480] 1 2 1 1 3 1 1 1 1 1 ...
## .. ..$ 2: num [1:9480] 1 1 1 1 3 1 1 1 1 1 ...
## ..$ X4292.0.0 :'data.frame': 6408 obs. of 12 variables:
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## .. ..$ 1: num [1:6408] 3 0 3 3 3 3 3 3 3 3 ...
## .. ..$ 2: num [1:6408] 0 0 0 3 3 3 3 3 3 0 ...
## ..$ X4292.2.0 :'data.frame': 96 obs. of 12 variables:
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## .. ..$ 1: num [1:96] 3 3 3 3 3 0 3 3 3 3 ...
## .. ..$ 2: num [1:96] 3 3 3 3 3 0 3 3 3 0 ...
## ..$ X4598.0.0 :'data.frame': 6468 obs. of 12 variables:
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## .. ..$ 1: num [1:6468] 0 0 1 1 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6468] 0 0 1 1 0 0 0 0 1 0 ...
## ..$ X4598.2.0 :'data.frame': 214 obs. of 12 variables:
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## .. ..$ 1: num [1:214] 0 1 1 1 1 1 0 1 1 1 ...
## .. ..$ 2: num [1:214] 0 1 0 0 1 0 1 1 0 0 ...
## ..$ X4631.2.0 :'data.frame': 374 obs. of 12 variables:
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## .. ..$ 1: num [1:374] 0 0 0 1 0 1 1 1 0 1 ...
## .. ..$ 2: num [1:374] 1 1 0 1 0 1 1 1 0 1 ...
## ..$ X4653.0.0 :'data.frame': 6465 obs. of 12 variables:
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## .. ..$ 1: num [1:6465] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ 2: num [1:6465] 0 0 1 0 0 0 0 0 0 0 ...
## ..$ X4653.2.0 :'data.frame': 224 obs. of 12 variables:
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. ..$ 1: num [1:224] 0 0 0 1 0 0 0 1 1 0 ...
## .. ..$ 2: num [1:224] 0 0 0 0 1 0 0 0 1 0 ...
## .. [list output truncated]
## $ m : num 12
## $ where : logi [1:9914, 1:106] FALSE FALSE FALSE FALSE FALSE FALSE ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:9914] "1" "2" "3" "4" ...
## .. ..$ : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ blocks :List of 106
## ..$ lh_aparc_area__lh_lateralorbitofrontal_area : chr "lh_aparc_area__lh_lateralorbitofrontal_area"
## ..$ lh_aparc_area__lh_postcentral_area : chr "lh_aparc_area__lh_postcentral_area"
## ..$ lh_aparc_area__lh_rostralmiddlefrontal_area : chr "lh_aparc_area__lh_rostralmiddlefrontal_area"
## ..$ lh_aparc_area__lh_superiorfrontal_area : chr "lh_aparc_area__lh_superiorfrontal_area"
## ..$ lh_aparc_area__lh_superiortemporal_area : chr "lh_aparc_area__lh_superiortemporal_area"
## ..$ lh_aparc_area__lh_WhiteSurfArea_area : chr "lh_aparc_area__lh_WhiteSurfArea_area"
## ..$ lh_aparc.a2009s_area__lh_G_orbital_area : chr "lh_aparc.a2009s_area__lh_G_orbital_area"
## ..$ lh_aparc.a2009s_area__lh_WhiteSurfArea_area : chr "lh_aparc.a2009s_area__lh_WhiteSurfArea_area"
## ..$ lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area : chr "lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area"
## ..$ lh_aparc.DKTatlas_area__lh_postcentral_area : chr "lh_aparc.DKTatlas_area__lh_postcentral_area"
## ..$ lh_aparc.DKTatlas_area__lh_precentral_area : chr "lh_aparc.DKTatlas_area__lh_precentral_area"
## ..$ lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area : chr "lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area"
## ..$ lh_aparc.DKTatlas_area__lh_superiorfrontal_area : chr "lh_aparc.DKTatlas_area__lh_superiorfrontal_area"
## ..$ lh_aparc.DKTatlas_area__lh_superiortemporal_area : chr "lh_aparc.DKTatlas_area__lh_superiortemporal_area"
## ..$ lh_aparc.DKTatlas_area__lh_insula_area : chr "lh_aparc.DKTatlas_area__lh_insula_area"
## ..$ lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume: chr "lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume"
## ..$ lh_aparc.pial_area__lh_lateralorbitofrontal_area : chr "lh_aparc.pial_area__lh_lateralorbitofrontal_area"
## ..$ lh_aparc.pial_area__lh_rostralmiddlefrontal_area : chr "lh_aparc.pial_area__lh_rostralmiddlefrontal_area"
## ..$ lh_aparc.pial_area__lh_superiorfrontal_area : chr "lh_aparc.pial_area__lh_superiorfrontal_area"
## ..$ lh_BA_exvivo_area__lh_BA3b_exvivo_area : chr "lh_BA_exvivo_area__lh_BA3b_exvivo_area"
## ..$ lh_BA_exvivo_area__lh_WhiteSurfArea_area : chr "lh_BA_exvivo_area__lh_WhiteSurfArea_area"
## ..$ rh_aparc_area__rh_medialorbitofrontal_area : chr "rh_aparc_area__rh_medialorbitofrontal_area"
## ..$ rh_aparc_area__rh_rostralmiddlefrontal_area : chr "rh_aparc_area__rh_rostralmiddlefrontal_area"
## ..$ rh_aparc_area__rh_superiorfrontal_area : chr "rh_aparc_area__rh_superiorfrontal_area"
## ..$ rh_aparc_area__rh_superiortemporal_area : chr "rh_aparc_area__rh_superiortemporal_area"
## ..$ rh_aparc_area__rh_WhiteSurfArea_area : chr "rh_aparc_area__rh_WhiteSurfArea_area"
## ..$ rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area : chr "rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area"
## ..$ rh_aparc.a2009s_area__rh_WhiteSurfArea_area : chr "rh_aparc.a2009s_area__rh_WhiteSurfArea_area"
## ..$ rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area : chr "rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area"
## ..$ rh_aparc.DKTatlas_area__rh_superiorfrontal_area : chr "rh_aparc.DKTatlas_area__rh_superiorfrontal_area"
## ..$ rh_aparc.DKTatlas_area__rh_superiortemporal_area : chr "rh_aparc.DKTatlas_area__rh_superiortemporal_area"
## ..$ rh_aparc.DKTatlas_area__rh_insula_area : chr "rh_aparc.DKTatlas_area__rh_insula_area"
## ..$ rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume: chr "rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume"
## ..$ rh_BA_exvivo_area__rh_WhiteSurfArea_area : chr "rh_BA_exvivo_area__rh_WhiteSurfArea_area"
## ..$ aseg__BrainSegVol : chr "aseg__BrainSegVol"
## ..$ aseg__BrainSegVolNotVent : chr "aseg__BrainSegVolNotVent"
## ..$ aseg__BrainSegVolNotVentSurf : chr "aseg__BrainSegVolNotVentSurf"
## ..$ aseg__lhCortexVol : chr "aseg__lhCortexVol"
## ..$ aseg__rhCortexVol : chr "aseg__rhCortexVol"
## ..$ aseg__CortexVol : chr "aseg__CortexVol"
## ..$ aseg__SubCortGrayVol : chr "aseg__SubCortGrayVol"
## ..$ aseg__TotalGrayVol : chr "aseg__TotalGrayVol"
## ..$ aseg__SupraTentorialVol : chr "aseg__SupraTentorialVol"
## ..$ aseg__SupraTentorialVolNotVent : chr "aseg__SupraTentorialVolNotVent"
## ..$ aseg__SupraTentorialVolNotVentVox : chr "aseg__SupraTentorialVolNotVentVox"
## ..$ aseg__MaskVol : chr "aseg__MaskVol"
## ..$ aseg__EstimatedTotalIntraCranialVol : chr "aseg__EstimatedTotalIntraCranialVol"
## ..$ wmparc__wm.lh.lateralorbitofrontal : chr "wmparc__wm.lh.lateralorbitofrontal"
## ..$ wmparc__wm.lh.superiortemporal : chr "wmparc__wm.lh.superiortemporal"
## ..$ wmparc__wm.lh.insula : chr "wmparc__wm.lh.insula"
## ..$ X31.0.0 : chr "X31.0.0"
## ..$ X1170.0.0 : chr "X1170.0.0"
## ..$ X1170.1.0 : chr "X1170.1.0"
## ..$ X1170.2.0 : chr "X1170.2.0"
## ..$ X1190.0.0 : chr "X1190.0.0"
## ..$ X1190.1.0 : chr "X1190.1.0"
## ..$ X1190.2.0 : chr "X1190.2.0"
## ..$ X1200.0.0 : chr "X1200.0.0"
## ..$ X1200.1.0 : chr "X1200.1.0"
## ..$ X1200.2.0 : chr "X1200.2.0"
## ..$ X1210.0.0 : chr "X1210.0.0"
## ..$ X1210.2.0 : chr "X1210.2.0"
## ..$ X1249.2.0 : chr "X1249.2.0"
## ..$ X1618.0.0 : chr "X1618.0.0"
## ..$ X1618.1.0 : chr "X1618.1.0"
## ..$ X1618.2.0 : chr "X1618.2.0"
## ..$ X1628.0.0 : chr "X1628.0.0"
## ..$ X1930.0.0 : chr "X1930.0.0"
## ..$ X1930.2.0 : chr "X1930.2.0"
## ..$ X1950.0.0 : chr "X1950.0.0"
## ..$ X1950.2.0 : chr "X1950.2.0"
## ..$ X1970.0.0 : chr "X1970.0.0"
## ..$ X1970.2.0 : chr "X1970.2.0"
## ..$ X1980.0.0 : chr "X1980.0.0"
## ..$ X1980.1.0 : chr "X1980.1.0"
## ..$ X1980.2.0 : chr "X1980.2.0"
## ..$ X2000.0.0 : chr "X2000.0.0"
## ..$ X2000.2.0 : chr "X2000.2.0"
## ..$ X2030.0.0 : chr "X2030.0.0"
## ..$ X2030.2.0 : chr "X2030.2.0"
## ..$ X2040.0.0 : chr "X2040.0.0"
## ..$ X2040.1.0 : chr "X2040.1.0"
## ..$ X2040.2.0 : chr "X2040.2.0"
## ..$ X2050.0.0 : chr "X2050.0.0"
## ..$ X2080.0.0 : chr "X2080.0.0"
## ..$ X2080.2.0 : chr "X2080.2.0"
## ..$ X2090.0.0 : chr "X2090.0.0"
## ..$ X2090.1.0 : chr "X2090.1.0"
## ..$ X2090.2.0 : chr "X2090.2.0"
## ..$ X2877.0.0 : chr "X2877.0.0"
## ..$ X2877.2.0 : chr "X2877.2.0"
## ..$ X3446.0.0 : chr "X3446.0.0"
## ..$ X4292.0.0 : chr "X4292.0.0"
## ..$ X4292.2.0 : chr "X4292.2.0"
## ..$ X4598.0.0 : chr "X4598.0.0"
## ..$ X4598.2.0 : chr "X4598.2.0"
## ..$ X4631.2.0 : chr "X4631.2.0"
## ..$ X4653.0.0 : chr "X4653.0.0"
## ..$ X4653.2.0 : chr "X4653.2.0"
## .. [list output truncated]
## ..- attr(*, "calltype")= Named chr [1:106] "type" "type" "type" "type" ...
## .. ..- attr(*, "names")= chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ call :List of 6
## ..$ : language mice(data = tight106_UKBB_data, m = 2, method = "cart", maxit = 3, printFlag = T, seed = 1234)
## ..$ : language fun(x = result.1, y = result.2)
## ..$ : language fun(x = accum, y = result.3)
## ..$ : language fun(x = accum, y = result.4)
## ..$ : language fun(x = accum, y = result.5)
## ..$ : language fun(x = accum, y = result.6)
## $ nmis : Named int [1:106] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "names")= chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ method : Named chr [1:106] "" "" "" "" ...
## ..- attr(*, "names")= chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ predictorMatrix: num [1:106, 1:106] 0 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## .. ..$ : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ visitSequence : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ formulas :List of 106
## ..$ lh_aparc_area__lh_lateralorbitofrontal_area :Class 'formula' language lh_aparc_area__lh_lateralorbitofrontal_area ~ lh_aparc_area__lh_postcentral_area + lh_aparc_area__lh_rostral| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc_area__lh_postcentral_area :Class 'formula' language lh_aparc_area__lh_postcentral_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_rostral| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc_area__lh_rostralmiddlefrontal_area :Class 'formula' language lh_aparc_area__lh_rostralmiddlefrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc_area__lh_superiorfrontal_area :Class 'formula' language lh_aparc_area__lh_superiorfrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_pos| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc_area__lh_superiortemporal_area :Class 'formula' language lh_aparc_area__lh_superiortemporal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_po| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc_area__lh_WhiteSurfArea_area :Class 'formula' language lh_aparc_area__lh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postc| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.a2009s_area__lh_G_orbital_area :Class 'formula' language lh_aparc.a2009s_area__lh_G_orbital_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_po| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.a2009s_area__lh_WhiteSurfArea_area :Class 'formula' language lh_aparc.a2009s_area__lh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_apar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_postcentral_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_postcentral_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_precentral_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_precentral_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_apar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_superiorfrontal_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_superiorfrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_are| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_superiortemporal_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_superiortemporal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_ar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_area__lh_insula_area :Class 'formula' language lh_aparc.DKTatlas_area__lh_insula_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_pos| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume:Class 'formula' language lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.pial_area__lh_lateralorbitofrontal_area :Class 'formula' language lh_aparc.pial_area__lh_lateralorbitofrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_ar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.pial_area__lh_rostralmiddlefrontal_area :Class 'formula' language lh_aparc.pial_area__lh_rostralmiddlefrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_ar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_aparc.pial_area__lh_superiorfrontal_area :Class 'formula' language lh_aparc.pial_area__lh_superiorfrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_BA_exvivo_area__lh_BA3b_exvivo_area :Class 'formula' language lh_BA_exvivo_area__lh_BA3b_exvivo_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_pos| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ lh_BA_exvivo_area__lh_WhiteSurfArea_area :Class 'formula' language lh_BA_exvivo_area__lh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_p| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc_area__rh_medialorbitofrontal_area :Class 'formula' language rh_aparc_area__rh_medialorbitofrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc_area__rh_rostralmiddlefrontal_area :Class 'formula' language rh_aparc_area__rh_rostralmiddlefrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc_area__rh_superiorfrontal_area :Class 'formula' language rh_aparc_area__rh_superiorfrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_pos| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc_area__rh_superiortemporal_area :Class 'formula' language rh_aparc_area__rh_superiortemporal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_po| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc_area__rh_WhiteSurfArea_area :Class 'formula' language rh_aparc_area__rh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postc| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area :Class 'formula' language rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.a2009s_area__rh_WhiteSurfArea_area :Class 'formula' language rh_aparc.a2009s_area__rh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__l| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area :Class 'formula' language rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_apar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.DKTatlas_area__rh_superiorfrontal_area :Class 'formula' language rh_aparc.DKTatlas_area__rh_superiorfrontal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_are| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.DKTatlas_area__rh_superiortemporal_area :Class 'formula' language rh_aparc.DKTatlas_area__rh_superiortemporal_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_ar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.DKTatlas_area__rh_insula_area :Class 'formula' language rh_aparc.DKTatlas_area__rh_insula_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_pos| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume:Class 'formula' language rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ rh_BA_exvivo_area__rh_WhiteSurfArea_area :Class 'formula' language rh_BA_exvivo_area__rh_WhiteSurfArea_area ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_p| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__BrainSegVol :Class 'formula' language aseg__BrainSegVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_ap| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__BrainSegVolNotVent :Class 'formula' language aseg__BrainSegVolNotVent ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area | __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__BrainSegVolNotVentSurf :Class 'formula' language aseg__BrainSegVolNotVentSurf ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_a| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__lhCortexVol :Class 'formula' language aseg__lhCortexVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_ap| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__rhCortexVol :Class 'formula' language aseg__rhCortexVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_ap| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__CortexVol :Class 'formula' language aseg__CortexVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_apar| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__SubCortGrayVol :Class 'formula' language aseg__SubCortGrayVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__TotalGrayVol :Class 'formula' language aseg__TotalGrayVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_a| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__SupraTentorialVol :Class 'formula' language aseg__SupraTentorialVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area +| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__SupraTentorialVolNotVent :Class 'formula' language aseg__SupraTentorialVolNotVent ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__SupraTentorialVolNotVentVox :Class 'formula' language aseg__SupraTentorialVolNotVentVox ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcent| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__MaskVol :Class 'formula' language aseg__MaskVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ aseg__EstimatedTotalIntraCranialVol :Class 'formula' language aseg__EstimatedTotalIntraCranialVol ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postce| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ wmparc__wm.lh.lateralorbitofrontal :Class 'formula' language wmparc__wm.lh.lateralorbitofrontal ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcen| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ wmparc__wm.lh.superiortemporal :Class 'formula' language wmparc__wm.lh.superiortemporal ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ wmparc__wm.lh.insula :Class 'formula' language wmparc__wm.lh.insula ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X31.0.0 :Class 'formula' language X31.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area__| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1170.0.0 :Class 'formula' language X1170.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1170.1.0 :Class 'formula' language X1170.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1170.2.0 :Class 'formula' language X1170.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1190.0.0 :Class 'formula' language X1190.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1190.1.0 :Class 'formula' language X1190.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1190.2.0 :Class 'formula' language X1190.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1200.0.0 :Class 'formula' language X1200.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1200.1.0 :Class 'formula' language X1200.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1200.2.0 :Class 'formula' language X1200.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1210.0.0 :Class 'formula' language X1210.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1210.2.0 :Class 'formula' language X1210.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1249.2.0 :Class 'formula' language X1249.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1618.0.0 :Class 'formula' language X1618.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1618.1.0 :Class 'formula' language X1618.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1618.2.0 :Class 'formula' language X1618.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1628.0.0 :Class 'formula' language X1628.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1930.0.0 :Class 'formula' language X1930.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1930.2.0 :Class 'formula' language X1930.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1950.0.0 :Class 'formula' language X1950.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1950.2.0 :Class 'formula' language X1950.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1970.0.0 :Class 'formula' language X1970.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1970.2.0 :Class 'formula' language X1970.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1980.0.0 :Class 'formula' language X1980.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1980.1.0 :Class 'formula' language X1980.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X1980.2.0 :Class 'formula' language X1980.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2000.0.0 :Class 'formula' language X2000.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2000.2.0 :Class 'formula' language X2000.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2030.0.0 :Class 'formula' language X2030.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2030.2.0 :Class 'formula' language X2030.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2040.0.0 :Class 'formula' language X2040.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2040.1.0 :Class 'formula' language X2040.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2040.2.0 :Class 'formula' language X2040.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2050.0.0 :Class 'formula' language X2050.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2080.0.0 :Class 'formula' language X2080.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2080.2.0 :Class 'formula' language X2080.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2090.0.0 :Class 'formula' language X2090.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2090.1.0 :Class 'formula' language X2090.1.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2090.2.0 :Class 'formula' language X2090.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2877.0.0 :Class 'formula' language X2877.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X2877.2.0 :Class 'formula' language X2877.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X3446.0.0 :Class 'formula' language X3446.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4292.0.0 :Class 'formula' language X4292.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4292.2.0 :Class 'formula' language X4292.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4598.0.0 :Class 'formula' language X4598.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4598.2.0 :Class 'formula' language X4598.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4631.2.0 :Class 'formula' language X4631.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4653.0.0 :Class 'formula' language X4653.0.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## ..$ X4653.2.0 :Class 'formula' language X4653.2.0 ~ lh_aparc_area__lh_lateralorbitofrontal_area + lh_aparc_area__lh_postcentral_area + lh_aparc_area| __truncated__ ...
## .. .. ..- attr(*, ".Environment")=<environment: 0x0000000046bf2e78>
## .. [list output truncated]
## $ post : Named chr [1:106] "" "" "" "" ...
## ..- attr(*, "names")= chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## $ blots :List of 106
## ..$ lh_aparc_area__lh_lateralorbitofrontal_area : list()
## ..$ lh_aparc_area__lh_postcentral_area : list()
## ..$ lh_aparc_area__lh_rostralmiddlefrontal_area : list()
## ..$ lh_aparc_area__lh_superiorfrontal_area : list()
## ..$ lh_aparc_area__lh_superiortemporal_area : list()
## ..$ lh_aparc_area__lh_WhiteSurfArea_area : list()
## ..$ lh_aparc.a2009s_area__lh_G_orbital_area : list()
## ..$ lh_aparc.a2009s_area__lh_WhiteSurfArea_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_lateralorbitofrontal_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_postcentral_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_precentral_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_rostralmiddlefrontal_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_superiorfrontal_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_superiortemporal_area : list()
## ..$ lh_aparc.DKTatlas_area__lh_insula_area : list()
## ..$ lh_aparc.DKTatlas_volume__lh_lateralorbitofrontal_volume: list()
## ..$ lh_aparc.pial_area__lh_lateralorbitofrontal_area : list()
## ..$ lh_aparc.pial_area__lh_rostralmiddlefrontal_area : list()
## ..$ lh_aparc.pial_area__lh_superiorfrontal_area : list()
## ..$ lh_BA_exvivo_area__lh_BA3b_exvivo_area : list()
## ..$ lh_BA_exvivo_area__lh_WhiteSurfArea_area : list()
## ..$ rh_aparc_area__rh_medialorbitofrontal_area : list()
## ..$ rh_aparc_area__rh_rostralmiddlefrontal_area : list()
## ..$ rh_aparc_area__rh_superiorfrontal_area : list()
## ..$ rh_aparc_area__rh_superiortemporal_area : list()
## ..$ rh_aparc_area__rh_WhiteSurfArea_area : list()
## ..$ rh_aparc.a2009s_area__rh_G.S_cingul.Ant_area : list()
## ..$ rh_aparc.a2009s_area__rh_WhiteSurfArea_area : list()
## ..$ rh_aparc.DKTatlas_area__rh_lateralorbitofrontal_area : list()
## ..$ rh_aparc.DKTatlas_area__rh_superiorfrontal_area : list()
## ..$ rh_aparc.DKTatlas_area__rh_superiortemporal_area : list()
## ..$ rh_aparc.DKTatlas_area__rh_insula_area : list()
## ..$ rh_aparc.DKTatlas_volume__rh_lateralorbitofrontal_volume: list()
## ..$ rh_BA_exvivo_area__rh_WhiteSurfArea_area : list()
## ..$ aseg__BrainSegVol : list()
## ..$ aseg__BrainSegVolNotVent : list()
## ..$ aseg__BrainSegVolNotVentSurf : list()
## ..$ aseg__lhCortexVol : list()
## ..$ aseg__rhCortexVol : list()
## ..$ aseg__CortexVol : list()
## ..$ aseg__SubCortGrayVol : list()
## ..$ aseg__TotalGrayVol : list()
## ..$ aseg__SupraTentorialVol : list()
## ..$ aseg__SupraTentorialVolNotVent : list()
## ..$ aseg__SupraTentorialVolNotVentVox : list()
## ..$ aseg__MaskVol : list()
## ..$ aseg__EstimatedTotalIntraCranialVol : list()
## ..$ wmparc__wm.lh.lateralorbitofrontal : list()
## ..$ wmparc__wm.lh.superiortemporal : list()
## ..$ wmparc__wm.lh.insula : list()
## ..$ X31.0.0 : list()
## ..$ X1170.0.0 : list()
## ..$ X1170.1.0 : list()
## ..$ X1170.2.0 : list()
## ..$ X1190.0.0 : list()
## ..$ X1190.1.0 : list()
## ..$ X1190.2.0 : list()
## ..$ X1200.0.0 : list()
## ..$ X1200.1.0 : list()
## ..$ X1200.2.0 : list()
## ..$ X1210.0.0 : list()
## ..$ X1210.2.0 : list()
## ..$ X1249.2.0 : list()
## ..$ X1618.0.0 : list()
## ..$ X1618.1.0 : list()
## ..$ X1618.2.0 : list()
## ..$ X1628.0.0 : list()
## ..$ X1930.0.0 : list()
## ..$ X1930.2.0 : list()
## ..$ X1950.0.0 : list()
## ..$ X1950.2.0 : list()
## ..$ X1970.0.0 : list()
## ..$ X1970.2.0 : list()
## ..$ X1980.0.0 : list()
## ..$ X1980.1.0 : list()
## ..$ X1980.2.0 : list()
## ..$ X2000.0.0 : list()
## ..$ X2000.2.0 : list()
## ..$ X2030.0.0 : list()
## ..$ X2030.2.0 : list()
## ..$ X2040.0.0 : list()
## ..$ X2040.1.0 : list()
## ..$ X2040.2.0 : list()
## ..$ X2050.0.0 : list()
## ..$ X2080.0.0 : list()
## ..$ X2080.2.0 : list()
## ..$ X2090.0.0 : list()
## ..$ X2090.1.0 : list()
## ..$ X2090.2.0 : list()
## ..$ X2877.0.0 : list()
## ..$ X2877.2.0 : list()
## ..$ X3446.0.0 : list()
## ..$ X4292.0.0 : list()
## ..$ X4292.2.0 : list()
## ..$ X4598.0.0 : list()
## ..$ X4598.2.0 : list()
## ..$ X4631.2.0 : list()
## ..$ X4653.0.0 : list()
## ..$ X4653.2.0 : list()
## .. [list output truncated]
## $ seed : num 1234
## $ iteration : num 3
## $ lastSeedValue : int [1:626] 10403 544 -1464851769 -965606226 1156730613 -486092514 -188017815 -647554440 1348862306 307241230 ...
## $ chainMean : num [1:106, 1:3, 1:12] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## .. ..$ : chr [1:3] "1" "2" "3"
## .. ..$ : chr [1:12] "Chain 1" "Chain 2" "Chain 3" "Chain 4" ...
## $ chainVar : num [1:106, 1:3, 1:12] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:106] "lh_aparc_area__lh_lateralorbitofrontal_area" "lh_aparc_area__lh_postcentral_area" "lh_aparc_area__lh_rostralmiddlefrontal_area" "lh_aparc_area__lh_superiorfrontal_area" ...
## .. ..$ : chr [1:3] "1" "2" "3"
## .. ..$ : chr [1:12] "Chain 1" "Chain 2" "Chain 3" "Chain 4" ...
## $ loggedEvents :'data.frame': 336 obs. of 5 variables:
## ..$ it : num [1:336] 0 0 0 0 0 0 1 1 1 1 ...
## ..$ im : num [1:336] 0 0 0 0 0 0 1 1 1 1 ...
## ..$ dep : chr [1:336] "" "" "" "" ...
## ..$ meth: chr [1:336] "collinear" "collinear" "collinear" "collinear" ...
## ..$ out : chr [1:336] "lh_aparc.a2009s_area__lh_WhiteSurfArea_area" "lh_BA_exvivo_area__lh_WhiteSurfArea_area" "rh_aparc.a2009s_area__rh_WhiteSurfArea_area" "rh_BA_exvivo_area__rh_WhiteSurfArea_area" ...
## $ version :Classes 'package_version', 'numeric_version' hidden list of 1
## ..$ : int [1:3] 3 14 0
## $ date : Date[1:1], format: "2022-01-18"
## - attr(*, "class")= chr "mids"# save(imp_tight106_UKBB_data, file = "C:/Users/Dinov/Desktop/imp_tight106_UKBB_data.Rdata")
# test <- load(file = "C:/Users/Dinov/Desktop/imp_tight106_UKBB_data.Rdata")
# imp_tight106_UKBB_data <- mice(tight106_UKBB_data, m=2,maxit=3, printFlag=T, seed=1234, method = 'cart')
### MICE NOTE: When the column features have a number of unbalanced factors, these categorical variables are transformed into dummy indicator-variables.
### There is a high probability that the resulting columns may be linear combinations of one-another.
### The default MICE imputation methods, linear regression, may not be able to solve the linear matrix equations due to matrix low rank, i.e., matrices may cannot be inverted causing errors like "system is computationally singular". RTo solve that problem, we can change the method to "cart".
### Also use seed before/during imputation for reproducible results.
comp_imp_tight106_UKBB_data <- as.matrix(complete(imp_tight106_UKBB_data),
dimnames = list(NULL, colnames(tight106_UKBB_data)))
################################################
## 2. Split the UKBB into 9 epochs
# First normalize all 50 derived NI biomarkers *using scale* as the NI biomarkers have vastly different distributions
for (i in 1:50) {
comp_imp_tight106_UKBB_data[, i] <- scale(comp_imp_tight106_UKBB_data[, i])
}
# Next configure the 11 epochs
comp_imp_tight106_UKBB_data <- comp_imp_tight106_UKBB_data[1:9900, ] # remove the last 14 cases to make the 11 epochs of size 900 observations yeach
is.matrix(comp_imp_tight106_UKBB_data); dim(comp_imp_tight106_UKBB_data)## [1] TRUE## [1] 9900 106dim(comp_imp_tight106_UKBB_data) <- c(11, 900, dim(comp_imp_tight106_UKBB_data)[2])
dim(comp_imp_tight106_UKBB_data)## [1] 11 900 106# double check epoch split worked
# identical(comp_imp_tight106_UKBB_data[10, 900, 12], as.matrix(complete(imp_tight106_UKBB_data))[10*900, 12])
epochs_tight106_UKBB_data_1 <- comp_imp_tight106_UKBB_data[1, , ]; dim(epochs_tight106_UKBB_data_1)## [1] 900 106# 3. Transform all 9 epochs (Big datasets/signals) to k-space (Fourier domain)
x1 <- c(1:900)
FT_epochs_tight106_UKBB <- array(complex(), c(11, 900, dim(comp_imp_tight106_UKBB_data)[3]))
mag_FT_epochs_tight106_UKBB <- array(complex(), c(11, 900, dim(comp_imp_tight106_UKBB_data)[3]))
phase_FT_epochs_tight106_UKBB <- array(complex(), c(11, 900, dim(comp_imp_tight106_UKBB_data)[3]))
for (i in 1:11) {
FT_epochs_tight106_UKBB[i, , ] <- fft(comp_imp_tight106_UKBB_data[i, , ])
X2 <- FT_epochs_tight106_UKBB[i, , ]
# plot(fftshift1D(log(Re(X2)+2)), main = "log(fftshift1D(Re(FFT(tight106_UKBB))))")
mag_FT_epochs_tight106_UKBB[i, , ] <- sqrt(Re(X2)^2+Im(X2)^2);
# plot(log(fftshift1D(Re(X2_mag))), main = "log(Magnitude(FFT(tight106_UKBB)))")
phase_FT_epochs_tight106_UKBB[i, , ] <- atan2(Im(X2), Re(X2));
# plot(fftshift1D(X2_phase), main = "Shift(Phase(FFT(tight106_UKBB)))")
}
# Compute the Average Phase of all 11 epochs (this will be needed later to confirm better data analytics)
avgPhase_FT_epochs_tight106_UKBB <- apply(phase_FT_epochs_tight106_UKBB, c(2,3), mean)
dim(avgPhase_FT_epochs_tight106_UKBB)## [1] 900 106### Test the process to confirm calculations
# X2<-FT_epochs_tight106_UKBB[1,,];X2_mag<-mag_FT_epochs_tight106_UKBB[1,,];X2_phase<-phase_FT_epochs_tight106_UKBB[1,,]
# Real2 = X2_mag * cos(X2_phase)
# Imaginary2 = X2_mag * sin(X2_phase)
# man_hat_X2 = Re(fft(Real2 + 1i*Imaginary2, inverse = T)/length(X2))
# ifelse(abs(man_hat_X2[5,10] - comp_imp_tight106_UKBB_data[1, 5, 10]) < 0.001, "Perfect Syntesis", "Problems!!!")
#######
##### 4. Invert back to spacetime the epochs_tight106_UKBB_data_1 signal with NIL and Average phase
# Start with Nil Phase
Real = mag_FT_epochs_tight106_UKBB[1, , ] * cos(0) # cos(mag_FT_epochs_tight106_UKBB[1, , ])
Imaginary = mag_FT_epochs_tight106_UKBB[1, , ] * sin(0) # sin(mag_FT_epochs_tight106_UKBB[1, , ])
ift_NilPhase_X2mag = Re(fft(Real+1i*Imaginary, inverse = T)/length(mag_FT_epochs_tight106_UKBB[1,,]))
# display(ift_NilPhase_X2mag, method = "raster")
# dim(ift_NilPhase_X2mag); View(ift_NilPhase_X2mag); # compare to View(comp_imp_tight106_UKBB_data[1, , ])
# Next, synthesize the data using Average Phase
Real_Avg = mag_FT_epochs_tight106_UKBB[1, , ] * cos(avgPhase_FT_epochs_tight106_UKBB)
Imaginary_Avg = mag_FT_epochs_tight106_UKBB[1, , ] * sin(avgPhase_FT_epochs_tight106_UKBB)
ift_AvgPhase_X2mag = Re(fft(Real_Avg+1i*Imaginary_Avg, inverse = T)/length(mag_FT_epochs_tight106_UKBB[1,,]))
# is.complex(ift_AvgPhase_X2mag); dim(ift_AvgPhase_X2mag)
# To Transform the entire UKBB data to k-space (Fourier domain)
# library(EBImage)
#FT_UKBB_data <- fft(comp_imp_tight106_UKBB_data)
#X2 <- FT_UKBB_data # display(FT_UKBB_data, method = "raster")
#mag_FT_UKBB_data <- sqrt(Re(X2)^2+Im(X2)^2)
### # plot(log(fftshift1D(Re(X2_mag))), main = "log(Magnitude(FFT(timeseries)))")
#phase_FT_UKBB_data <- atan2(Im(X2), Re(X2))
### Test the process to confirm calculations
# X2<-FT_UKBB_data; X2_mag <- mag_FT_UKBB_data; X2_phase<-phase_FT_UKBB_data
# Real2 = X2_mag * cos(X2_phase)
# Imaginary2 = X2_mag * sin(X2_phase)
# man_hat_X2 = Re(fft(Real2 + 1i*Imaginary2, inverse = T)/length(X2))
# ifelse(abs(man_hat_X2[5,10] - comp_imp_tight106_UKBB_data[5, 10]) < 0.001, "Perfect Syntesis", "Problems!!!")
#######
# Then we can Invert back the complete UKBB FT data into spacetime using nil phase
#Real = mag_FT_UKBB_data * cos(0) # cos(phase_FT_UKBB_data)
#Imaginary = mag_FT_UKBB_data * sin(0) # sin(phase_FT_UKBB_data)
#ift_NilPhase_X2mag = Re(fft(Real+1i*Imaginary, inverse = T)/length(FT_UKBB_data))
# display(ift_NilPhase_X2mag, method = "raster")
# dim(ift_NilPhase_X2mag); View(ift_NilPhase_X2mag); # compare to View(aqi_data1)
#summary(comp_imp_tight106_UKBB_data); summary(ift_NilPhase_X2mag)
# 5. Epoch 1: Perform Random Forest prediction (based on ift_TruePhase_X2mag==Original==epochs_tight106_UKBB_data_1) of:
##### Ever depressed for a whole week 1 #########################################
library("randomForest")## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin# y_pheno <- comp_imp_tight106_UKBB_data[,"X4598.2.0"] ### Ever depressed for a whole week 1
# y_pheno <- as.factor(y_pheno)
colnames(epochs_tight106_UKBB_data_1) <- colnames(tight106_UKBB_data)
set.seed(1234)
rf_depressed <- randomForest(as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"]) ~ . ,
data=epochs_tight106_UKBB_data_1[ , !(colnames(epochs_tight106_UKBB_data_1) %in% c("X4598.0.0", "X4598.2.0"))])
rf_depressed##
## Call:
## randomForest(formula = as.factor(epochs_tight106_UKBB_data_1[, "X4598.2.0"]) ~ ., data = epochs_tight106_UKBB_data_1[, !(colnames(epochs_tight106_UKBB_data_1) %in% c("X4598.0.0", "X4598.2.0"))])
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 10
##
## OOB estimate of error rate: 21.78%
## Confusion matrix:
## 0 1 class.error
## 0 370 72 0.1628959
## 1 124 334 0.2707424pred1 = predict(rf_depressed, type="class")
confusionMatrix(pred1, as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"]))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 370 124
## 1 72 334
##
## Accuracy : 0.7822
## 95% CI : (0.7538, 0.8088)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5652
##
## Mcnemar's Test P-Value : 0.0002696
##
## Sensitivity : 0.8371
## Specificity : 0.7293
## Pos Pred Value : 0.7490
## Neg Pred Value : 0.8227
## Prevalence : 0.4911
## Detection Rate : 0.4111
## Detection Prevalence : 0.5489
## Balanced Accuracy : 0.7832
##
## 'Positive' Class : 0
## ############## Accuracy : 0.7911 #############################
##### plot a simple decision tree
library(rpart); library(rpart.plot)
## there is an error in installing the rattle package
library(rattle) ## this is for the fancyRpartPlot## Loading required package: tibble## Warning: package 'tibble' was built under R version 4.1.2## Loading required package: bitops## Rattle: A free graphical interface for data science with R.
## Version 5.4.0 Copyright (c) 2006-2020 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.##
## Attaching package: 'rattle'## The following object is masked from 'package:randomForest':
##
## importancelabel <- as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"])
data1 <- as.data.frame(epochs_tight106_UKBB_data_1[ , !(colnames(epochs_tight106_UKBB_data_1) %in%
c("X4598.0.0", "X4598.2.0", "cluster_2_cluster"))])
data1$label <- label
depress_tree <- rpart(label ~ ., control=rpart.control(minsplit=30, cp=0.001, maxdepth=30), data=data1)
rpart.plot(depress_tree, type = 4, extra = 1, clip.right.labs = F, tweak=4)## Warning: labs do not fit even at cex 0.15, there may be some overplotting
pred1 = predict(depress_tree, type="class")
# table(pred1, label)
library(caret)
confusionMatrix(pred1, label)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 367 55
## 1 75 403
##
## Accuracy : 0.8556
## 95% CI : (0.8309, 0.8779)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.7108
##
## Mcnemar's Test P-Value : 0.09563
##
## Sensitivity : 0.8303
## Specificity : 0.8799
## Pos Pred Value : 0.8697
## Neg Pred Value : 0.8431
## Prevalence : 0.4911
## Detection Rate : 0.4078
## Detection Prevalence : 0.4689
## Balanced Accuracy : 0.8551
##
## 'Positive' Class : 0
## ########################### Accuracy : 0.8511 ##############################
### Prune decision tree
prune_depress_tree <- prune(depress_tree,
cp=depress_tree$cptable[which.min(depress_tree$cptable[,"xerror"]),"CP"])
# plot the pruned tree
plot(prune_depress_tree, uniform=TRUE, main="Pruned UKBB Decision Tree")
text(prune_depress_tree, use.n=TRUE, all=TRUE, cex=.8)
rpart.plot(prune_depress_tree, type = 4, extra = 1, clip.right.labs = F, tweak=2)
pred2 = predict(prune_depress_tree, type="class")
confusionMatrix(pred2, label)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 391 128
## 1 51 330
##
## Accuracy : 0.8011
## 95% CI : (0.7735, 0.8267)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6033
##
## Mcnemar's Test P-Value : 1.343e-08
##
## Sensitivity : 0.8846
## Specificity : 0.7205
## Pos Pred Value : 0.7534
## Neg Pred Value : 0.8661
## Prevalence : 0.4911
## Detection Rate : 0.4344
## Detection Prevalence : 0.5767
## Balanced Accuracy : 0.8026
##
## 'Positive' Class : 0
## ######################### Accuracy : 0.7867 ###############################
###### 6. FOR the COMPLETE UKBB data: plot a simple decision tree
#library(rpart); library(rpart.plot)
#library(rattle) ## this is for the fancyRpartPlot
dim(comp_imp_tight106_UKBB_data) <- c(11*900, dim(comp_imp_tight106_UKBB_data)[3])
colnames(comp_imp_tight106_UKBB_data) <- colnames(tight106_UKBB_data)
label3 <- as.factor(comp_imp_tight106_UKBB_data[ ,"X4598.2.0"])
data3 <- as.data.frame(comp_imp_tight106_UKBB_data[ , !(colnames(comp_imp_tight106_UKBB_data) %in%
c("X4598.0.0", "X4598.2.0", "cluster_2_cluster"))])
depress_tree3 <- rpart(label3 ~ ., control=rpart.control(minsplit=30, cp=0.001, maxdepth=30), data=data3)
rpart.plot(depress_tree3, type = 4, extra = 1, clip.right.labs = F, tweak=2)
pred3 = predict(depress_tree3, type="class")
confusionMatrix(pred3, label3)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 4355 1214
## 1 532 3799
##
## Accuracy : 0.8236
## 95% CI : (0.816, 0.8311)
## No Information Rate : 0.5064
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6478
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8911
## Specificity : 0.7578
## Pos Pred Value : 0.7820
## Neg Pred Value : 0.8772
## Prevalence : 0.4936
## Detection Rate : 0.4399
## Detection Prevalence : 0.5625
## Balanced Accuracy : 0.8245
##
## 'Positive' Class : 0
## #################### Accuracy : 0.8238 ###########################
### Prune decision tree
prune_depress_tree3 <- prune(depress_tree3,
cp=depress_tree3$cptable[which.min(depress_tree3$cptable[,"xerror"]),"CP"])
rpart.plot(prune_depress_tree3, type = 4, extra = 1, clip.right.labs = F, tweak=2, main="Pruned UKBB Decision Tree")
pred3 = predict(prune_depress_tree3, type="class")
confusionMatrix(pred3, label3)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 4390 1359
## 1 497 3654
##
## Accuracy : 0.8125
## 95% CI : (0.8047, 0.8202)
## No Information Rate : 0.5064
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6258
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8983
## Specificity : 0.7289
## Pos Pred Value : 0.7636
## Neg Pred Value : 0.8803
## Prevalence : 0.4936
## Detection Rate : 0.4434
## Detection Prevalence : 0.5807
## Balanced Accuracy : 0.8136
##
## 'Positive' Class : 0
## #################### Accuracy : 0.8132 ###########################
# RF
rf_depressed_Complete <- randomForest(label3 ~ . , data=data3)
rf_depressed_Complete##
## Call:
## randomForest(formula = label3 ~ ., data = data3)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 10
##
## OOB estimate of error rate: 19.46%
## Confusion matrix:
## 0 1 class.error
## 0 4231 656 0.1342337
## 1 1271 3742 0.2535408pred3 = predict(rf_depressed_Complete, type="class")
confusionMatrix(pred3, label3)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 4231 1271
## 1 656 3742
##
## Accuracy : 0.8054
## 95% CI : (0.7974, 0.8131)
## No Information Rate : 0.5064
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6113
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8658
## Specificity : 0.7465
## Pos Pred Value : 0.7690
## Neg Pred Value : 0.8508
## Prevalence : 0.4936
## Detection Rate : 0.4274
## Detection Prevalence : 0.5558
## Balanced Accuracy : 0.8061
##
## 'Positive' Class : 0
## # In random forests, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. The out-of-bag (oob) error estimate is internally computed during the run... In particular, when newdata is not provided, the *predict.randomForest()==predict()* method automatically returns the out-of-bag prediction.
##################### Accuracy : 0.808 #########################
# 7. Perform Random Forest prediction (based on ift_AvgPhase_X2mag) of:
##### Ever depressed for a whole week 1 #########################################
# library("randomForest")
# y_pheno <- comp_imp_tight106_UKBB_data[,"X4598.2.0"] ### Ever depressed for a whole week 1
# y_pheno <- as.factor(y_pheno)
label2 <- as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"])
# use the real outcome not synthesized (as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"]))
colnames(ift_AvgPhase_X2mag) <- colnames(tight106_UKBB_data)
data2 <- as.data.frame(ift_AvgPhase_X2mag[ , !(colnames(ift_AvgPhase_X2mag) %in%
c("X4598.0.0", "X4598.2.0", "cluster_2_cluster"))])
set.seed(1234)
rf_depressed_AvgPhase <- randomForest(label2 ~ . , importance=T, nodesize=30, mtry=100, ntree= 1000, data=data2)
rf_depressed_AvgPhase##
## Call:
## randomForest(formula = label2 ~ ., data = data2, importance = T, nodesize = 30, mtry = 100, ntree = 1000)
## Type of random forest: classification
## Number of trees: 1000
## No. of variables tried at each split: 100
##
## OOB estimate of error rate: 48.67%
## Confusion matrix:
## 0 1 class.error
## 0 218 224 0.5067873
## 1 214 244 0.4672489pred1_AvgPhase <- predict(rf_depressed_AvgPhase, type="class")
confusionMatrix(pred1_AvgPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 218 214
## 1 224 244
##
## Accuracy : 0.5133
## 95% CI : (0.4801, 0.5465)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : 0.4078
##
## Kappa : 0.026
##
## Mcnemar's Test P-Value : 0.6672
##
## Sensitivity : 0.4932
## Specificity : 0.5328
## Pos Pred Value : 0.5046
## Neg Pred Value : 0.5214
## Prevalence : 0.4911
## Detection Rate : 0.2422
## Detection Prevalence : 0.4800
## Balanced Accuracy : 0.5130
##
## 'Positive' Class : 0
## ##### plot a simple decision tree
# library(rpart); library(rpart.plot)
## there is an error in installing the rattle package
# library(rattle) ## this is for the fancyRpartPlot
set.seed(1234)
depress_tree_AvgPhase <- rpart(label2 ~ ., control=rpart.control(minsplit=30,cp=0.001,maxdepth=30),data=data2)
pred1_AvgPhase <- predict(depress_tree_AvgPhase, type="class")
# library(caret)
confusionMatrix(pred1_AvgPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 337 86
## 1 105 372
##
## Accuracy : 0.7878
## 95% CI : (0.7596, 0.8141)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.5751
##
## Mcnemar's Test P-Value : 0.1928
##
## Sensitivity : 0.7624
## Specificity : 0.8122
## Pos Pred Value : 0.7967
## Neg Pred Value : 0.7799
## Prevalence : 0.4911
## Detection Rate : 0.3744
## Detection Prevalence : 0.4700
## Balanced Accuracy : 0.7873
##
## 'Positive' Class : 0
## rpart.plot(depress_tree_AvgPhase, type = 2, extra = 1, clip.right.labs = F, varlen=5, faclen=5, tweak=4)
################### Accuracy : 0.7956 #############################
### Prune decision tree
prune_depress_tree_AvgPhase <- prune(depress_tree_AvgPhase,
cp=depress_tree_AvgPhase$cptable[which.min(depress_tree_AvgPhase$cptable[,"xerror"]),"CP"]/(1.5))
# plot the pruned tree
#plot(prune_depress_tree_AvgPhase, uniform=TRUE, main="Pruned UKBB Decision Tree (Avg-Phase Synthesis)")
#text(prune_depress_tree_AvgPhase, use.n=TRUE, all=TRUE, cex=.8)
rpart.plot(prune_depress_tree_AvgPhase, type = 4, extra = 1, clip.right.labs = F, varlen=5, faclen=5, tweak=1.5)
pred2_AvgPhase = predict(prune_depress_tree_AvgPhase, type="class")
confusionMatrix(pred2_AvgPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 340 95
## 1 102 363
##
## Accuracy : 0.7811
## 95% CI : (0.7526, 0.8077)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.562
##
## Mcnemar's Test P-Value : 0.669
##
## Sensitivity : 0.7692
## Specificity : 0.7926
## Pos Pred Value : 0.7816
## Neg Pred Value : 0.7806
## Prevalence : 0.4911
## Detection Rate : 0.3778
## Detection Prevalence : 0.4833
## Balanced Accuracy : 0.7809
##
## 'Positive' Class : 0
## # 8. Perform Random Forest prediction (based on ift_NilPhase_X2mag) of:
##### Ever depressed for a whole week 1 #########################################
# library("randomForest")
# y_pheno <- comp_imp_tight106_UKBB_data[,"X4598.2.0"] ### Ever depressed for a whole week 1
# y_pheno <- as.factor(y_pheno)
label2 <- as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"])
# use the real outcome not synthesized (as.factor(epochs_tight106_UKBB_data_1[ ,"X4598.2.0"]))
colnames(ift_NilPhase_X2mag) <- colnames(tight106_UKBB_data)
data2 <- as.data.frame(ift_NilPhase_X2mag[ , !(colnames(ift_NilPhase_X2mag) %in%
c("X4598.0.0", "X4598.2.0", "cluster_2_cluster"))])
set.seed(1234)
rf_depressed_NilPhase <- randomForest(label2 ~ . , importance=T, nodesize=30, mtry=100, ntree= 1000, data=data2)
rf_depressed_NilPhase##
## Call:
## randomForest(formula = label2 ~ ., data = data2, importance = T, nodesize = 30, mtry = 100, ntree = 1000)
## Type of random forest: classification
## Number of trees: 1000
## No. of variables tried at each split: 100
##
## OOB estimate of error rate: 52.33%
## Confusion matrix:
## 0 1 class.error
## 0 199 243 0.5497738
## 1 228 230 0.4978166pred1_NilPhase <- predict(rf_depressed_NilPhase, type="class")
confusionMatrix(pred1_NilPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 199 228
## 1 243 230
##
## Accuracy : 0.4767
## 95% CI : (0.4436, 0.5099)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : 0.9754
##
## Kappa : -0.0476
##
## Mcnemar's Test P-Value : 0.5189
##
## Sensitivity : 0.4502
## Specificity : 0.5022
## Pos Pred Value : 0.4660
## Neg Pred Value : 0.4863
## Prevalence : 0.4911
## Detection Rate : 0.2211
## Detection Prevalence : 0.4744
## Balanced Accuracy : 0.4762
##
## 'Positive' Class : 0
## ##### plot a simple decision tree
# library(rpart); library(rpart.plot)
## there is an error in installing the rattle package
# library(rattle) ## this is for the fancyRpartPlot
set.seed(1234)
depress_tree_NilPhase <- rpart(label2 ~ ., control=rpart.control(minsplit=30,cp=0.001,maxdepth=30),data=data2)
pred1_NilPhase <- predict(depress_tree_NilPhase, type="class")
# library(caret)
confusionMatrix(pred1_NilPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 337 77
## 1 105 381
##
## Accuracy : 0.7978
## 95% CI : (0.77, 0.8236)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.595
##
## Mcnemar's Test P-Value : 0.04535
##
## Sensitivity : 0.7624
## Specificity : 0.8319
## Pos Pred Value : 0.8140
## Neg Pred Value : 0.7840
## Prevalence : 0.4911
## Detection Rate : 0.3744
## Detection Prevalence : 0.4600
## Balanced Accuracy : 0.7972
##
## 'Positive' Class : 0
## rpart.plot(depress_tree_NilPhase, type = 2, extra = 1, clip.right.labs = F, varlen=5, faclen=5, tweak=2)
################### Accuracy : 0.79 #############################
### Prune decision tree
prune_depress_tree_NilPhase <- prune(depress_tree_NilPhase,
cp=depress_tree_NilPhase$cptable[which.min(depress_tree_NilPhase$cptable[,"xerror"]),"CP"]/(1.5))
# plot the pruned tree
#plot(prune_depress_tree_NilPhase, uniform=TRUE, main="Pruned UKBB Decision Tree (Nil-Phase Synthesis)")
#text(prune_depress_tree_NilPhase, use.n=TRUE, all=TRUE, cex=.8)
rpart.plot(prune_depress_tree_NilPhase, type = 4, extra = 1, clip.right.labs = F, varlen=5, faclen=5, tweak=2)
pred2_NilPhase = predict(prune_depress_tree_AvgPhase, type="class")
confusionMatrix(pred2_NilPhase, label2)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 340 95
## 1 102 363
##
## Accuracy : 0.7811
## 95% CI : (0.7526, 0.8077)
## No Information Rate : 0.5089
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.562
##
## Mcnemar's Test P-Value : 0.669
##
## Sensitivity : 0.7692
## Specificity : 0.7926
## Pos Pred Value : 0.7816
## Neg Pred Value : 0.7806
## Prevalence : 0.4911
## Detection Rate : 0.3778
## Detection Prevalence : 0.4833
## Balanced Accuracy : 0.7809
##
## 'Positive' Class : 0
## # 9. Compare the analytics results from #3, ..., #8!
# Compare the original against nil-phase and avg-phase synthesized data
origNilNil_6rows_Compare <- rbind(head(epochs_tight106_UKBB_data_1), head(ift_NilPhase_X2mag), head(ift_AvgPhase_X2mag))
x1 <- 1:dim(epochs_tight106_UKBB_data_1)[2]
orig.mean <- apply(epochs_tight106_UKBB_data_1, 2, mean)
nilPhase.mean <- apply(ift_NilPhase_X2mag, 2, mean)
avgPhase.mean <- apply(ift_AvgPhase_X2mag, 2, mean)
plot(x1, orig.mean, main = "Comparing original UKBB against nil-phase and avg-phase synthesized data",
col="green", lwd = 3, type="l", lty=1, xlab = "Features", ylab = "Averages across cases")
lines(x1, nilPhase.mean, col = "red", lwd = 3, lty=1)
lines(x1, avgPhase.mean, col = "blue", lwd = 3, lty=1)
legend("top", bty="n", legend=c(
sprintf("Original"), sprintf("Nil-Phase Reconstruction"),
sprintf("Average-Phase Reconstruction")),
col=c("green", "red", "blue"), lty=c(1,1,1), lwd=c(3,3,3), cex=0.9)
stopCluster(clustUKBB)
################### Plot_ly Figure 6.15
time <- x1[1:length(orig.mean)]
p <- plot_ly()
p <- add_lines(p, x=~time, y=~orig.mean,
name = "Original.Mean", type = 'scatter', mode = 'lines',
hoverinfo = 'name', line=list(color='green', width=4))
p <- add_lines(p, x=~time, y=~nilPhase.mean,
name = "ift_NilPhase.Mean", type = 'scatter', mode = 'lines',
hoverinfo = 'name',
line=list(color='red', width = 2))
p <- add_lines(p, x=~time, y=~avgPhase.mean,
name = "ift_AvgPhase.Mean", type = 'scatter', mode = 'lines',
hoverinfo = 'name',
line=list(color='blue', width = 2)) %>%
layout(xaxis = list(range = c(0,108), title="Features"),
yaxis = list(range = c(0,6), title="Averages across cases"),
title=sprintf('Comparison: Corr(Real, NilPhase) = %s; Comparison: Corr(Real, AvgPhase) = %s',
format(cor(orig.mean, nilPhase.mean), digits=3),
format(cor(orig.mean, avgPhase.mean), digits=3)),
legend = list(orientation = "h", # show entries horizontally
xanchor = "center", # use center of legend as anchor
x = 0.5, y=-0.1,
size = 30))
pAs we have the benefit of hind-side, the prior study Predictive Big Data Analytics using the UK Biobank Data had identified the most salient derived neuroimaging and clinical biomarkers (\(k=107\)) in this study, including the physician identified clinical outcomes and the computed phenotypes using unsupervised machine learning methods. Therefore, for simplicity, this demonstration only focuses on these features, without a previous feature-selection preprocessing step. We are examining the effects of the kime phase on the scientific inference.
Note that averaging the phases in the Fourier domain assumes that either we have a very large number of samples that effectively span the range of kime-angles, or that the sampling is uniform on the kime-angles space. When these assumptions are violated, other phase-aggregation or phase-ensembling methods (e.g., weighted mean, non-parametric measures of centrality, or Bayesian strategies) may need to utilized to ensure the reliability of the final inference.