twin script
# Set Starting Values
# set them close to means, and paths at whatever???
# weekend dur, weekday dur, chrono, jet lag, weekend dur act, weekday dur act, weekend eff, weekday eff, var, att, int, ext, prodromal
svMe <- c(9, 8, 16, 2, 9, 8, 1, 1, 1, 2, 4, 4, 1) # start value for means
svPa <- .5 # start value for path coefficient a
svPc <- .5 # start value for path coefficient c
svPe <- .5 # start value for path coefficient for e
# ----------------------------------------------------------------------------------------------------------------------
# PREPARE MODEL
# Create Algebra for expected Mean Matrices
meanG <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svMe, labels=labVars("mean",vars), name="meanG" )
# Create Matrices for Variance Components
covA <- mxMatrix( type="Symm", nrow=nv, ncol=nv, free=TRUE, values=valDiag(svPa,nv), labels=labLower("VA",nv), name="VA" )
covC <- mxMatrix( type="Symm", nrow=nv, ncol=nv, free=TRUE, values=valDiag(svPc,nv), labels=labLower("VC",nv), name="VC" )
covE <- mxMatrix( type="Symm", nrow=nv, ncol=nv, free=TRUE, values=valDiag(svPe,nv), labels=labLower("VE",nv), name="VE" )
# Create Algebra for expected Variance/Covariance Matrices in MZ & DZ twins
covP <- mxAlgebra( expression= VA+VC+VE, name="V" )
covMZ <- mxAlgebra( expression= VA+VC, name="cMZ" )
covDZ <- mxAlgebra( expression= 0.5%x%VA+ VC, name="cDZ" )
expCovMZ <- mxAlgebra( expression= rbind( cbind(V, cMZ), cbind(t(cMZ), V)), name="expCovMZ" )
expCovDZ <- mxAlgebra( expression= rbind( cbind(V, cDZ), cbind(t(cDZ), V)), name="expCovDZ" )
# Create Data Objects for Multiple Groups
dataMZ <- mxData( observed=mzData, type="raw" )
dataDZ <- mxData( observed=dzData, type="raw" )
# Create Expectation Objects for Multiple Groups
expMZ <- mxExpectationNormal( covariance="expCovMZ", means="meanG", dimnames=selVars )
expDZ <- mxExpectationNormal( covariance="expCovDZ", means="meanG", dimnames=selVars )
funML <- mxFitFunctionML()
# Create Model Objects for Multiple Groups
pars <- list( meanG, covA, covC, covE, covP )
modelMZ <- mxModel( pars, covMZ, expCovMZ, dataMZ, expMZ, funML, name="MZ" )
modelDZ <- mxModel( pars, covDZ, expCovDZ, dataDZ, expDZ, funML, name="DZ" )
multi <- mxFitFunctionMultigroup( c("MZ","DZ") )
# Create Algebra for Standardization
matI <- mxMatrix( type="Iden", nrow=nv, ncol=nv, name="I" )
invSD <- mxAlgebra( expression=solve(sqrt(I*V)), name="iSD" )
# Calculate standardized variance components
unstA <- mxAlgebra( expression=diag2vec(VA), name="unstA")
unstC <- mxAlgebra( expression=diag2vec(VC), name="unstC")
unstE <- mxAlgebra( expression=diag2vec(VE), name="unstE")
stndA <- mxAlgebra( expression=diag2vec(VA/V), name="stndA")
stndC <- mxAlgebra( expression=diag2vec(VC/V), name="stndC")
stndE <- mxAlgebra( expression=diag2vec(VE/V), name="stndE")
# Calculate genetic and environmental correlations
corA <- mxAlgebra( expression=solve(sqrt(I*VA))%&%VA, name ="rA" ) #cov2cor()
corC <- mxAlgebra( expression=solve(sqrt(I*VC))%&%VC, name ="rC" )
corE <- mxAlgebra( expression=solve(sqrt(I*VE))%&%VE, name ="rE" )
# Calculate Phenotypic Correlation
corP <- mxAlgebra (expression=solve(sqrt(I*V)) %*% V %*% solve(sqrt(I*V)), name="rP")
# Create matrix for Variance Components
colACEs <- cbind('A','C','E','SA','SC','SE')
estACEs <- mxAlgebra( expression=cbind(unstA,unstC,unstE,stndA,stndC,stndE), name="ACEs", dimnames=list(vars,colACEs))
# Create Confidence Interval Objects
ciACE <- mxCI( c("rA","rC","rE","ACEs" ) )
# Build Model with Confidence Intervals
calc <- list( matI,invSD,corA,corC,corE,corP,unstA,unstC,unstE,stndA,stndC,stndE,estACEs,ciACE)
modelACE <- mxModel( "twoACEvc", pars, modelMZ, modelDZ, multi, calc )
# ----------------------------------------------------------------------------------------------------------------------
# RUN MODEL
# Run ACE Model
#can turn off intervals, will still estimate model but not CIs
fitACE <- mxRun( modelACE, intervals=F)
## Running twoACEvc with 286 parameters
## Warning: In model 'twoACEvc' Optimizer returned a non-zero status code 5. The Hessian at the solution does not appear
## to be convex. See ?mxCheckIdentification for possible diagnosis (Mx status RED).
# Print Covariance & Correlation Matrices
fitACE$ACEs$result
## A C E SA SC SE
## weekend_dur_mcq_wave_2 0.61010550 0.54871914 0.95491980 0.28863730 0.25959578 0.45176691
## weekday_dur_mcq_wave_2 0.62652821 0.56591279 0.96942186 0.28980941 0.26177090 0.44841968
## chronotype_wave_2 2.08360457 1.69307439 4.22957697 0.26024706 0.21146893 0.52828401
## social_jet_lag_wave_2 0.66063765 0.59552177 1.01649375 0.29069004 0.26203812 0.44727183
## avg_weekend_dur 0.49578155 0.50096937 0.47904001 0.33594294 0.33945823 0.32459883
## avg_weekday_dur 0.45227845 0.46156143 0.42755165 0.33717110 0.34409150 0.31873740
## avg_weekend_effic 0.38307131 0.40932678 0.29650991 0.35179401 0.37590575 0.27230024
## avg_weekday_effic 0.38082348 0.40800529 0.29082014 0.35272900 0.37790553 0.26936548
## variability 0.42813835 0.44161693 0.38863565 0.34022683 0.35093778 0.30883539
## cbcl_scr_syn_attention_r_wave_2 0.94236597 0.67293503 1.61533960 0.29169632 0.20829771 0.50000597
## cbcl_scr_syn_internal_r_wave_2 1.36078595 1.16904569 2.28034590 0.28289724 0.24303587 0.47406689
## cbcl_scr_syn_external_r_wave_2 1.52536654 1.29029818 2.29867549 0.29825285 0.25229025 0.44945690
## total_score_wave_2 0.99518498 0.78155985 1.80059943 0.27819100 0.21847488 0.50333412
fitACE$rA$result ### <--- genetic corrs
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1.0000000000 0.248130026 0.060601892 0.112103187 0.170493149 0.255747355 0.1922970320 0.1906690682
## [2,] 0.2481300264 1.000000000 0.190314711 0.115853137 0.185094551 0.288642725 0.1779975834 0.1827763204
## [3,] 0.0606018924 0.190314711 1.000000000 0.144516662 -0.021519331 -0.028638666 -0.0667097884 -0.0643417494
## [4,] 0.1121031868 0.115853137 0.144516662 1.000000000 0.120198674 0.068796955 0.1790883087 0.1808470681
## [5,] 0.1704931485 0.185094551 -0.021519331 0.120198674 1.000000000 0.334438057 0.3574402755 0.3552221320
## [6,] 0.2557473554 0.288642725 -0.028638666 0.068796955 0.334438057 1.000000000 0.3680295055 0.3761075563
## [7,] 0.1922970320 0.177997583 -0.066709788 0.179088309 0.357440275 0.368029505 1.0000000000 0.5341027122
## [8,] 0.1906690682 0.182776320 -0.064341749 0.180847068 0.355222132 0.376107556 0.5341027122 1.0000000000
## [9,] 0.2136022757 0.131937044 -0.074638885 0.203286317 0.214224601 0.307996446 0.4391717105 0.4405826955
## [10,] 0.0514000051 0.104761352 0.067952235 0.087962818 0.058781268 0.061128308 0.0852108326 0.0841641032
## [11,] 0.0856880755 0.082965504 0.100283004 0.083635152 -0.021788280 0.018497903 0.0068194065 0.0067537154
## [12,] -0.0092212667 0.042189926 0.108311089 0.079354568 0.058643004 -0.019794877 0.0091058967 0.0105743959
## [13,] -0.0376863912 0.040145584 0.122230106 0.200704611 0.025836077 0.045676350 0.0937437711 0.0952596401
## [,9] [,10] [,11] [,12] [,13]
## [1,] 0.2136022757 0.051400005 0.0856880755 -0.0092212667 -0.037686391
## [2,] 0.1319370437 0.104761352 0.0829655043 0.0421899262 0.040145584
## [3,] -0.0746388852 0.067952235 0.1002830037 0.1083110892 0.122230106
## [4,] 0.2032863170 0.087962818 0.0836351517 0.0793545680 0.200704611
## [5,] 0.2142246006 0.058781268 -0.0217882801 0.0586430041 0.025836077
## [6,] 0.3079964456 0.061128308 0.0184979025 -0.0197948769 0.045676350
## [7,] 0.4391717105 0.085210833 0.0068194065 0.0091058967 0.093743771
## [8,] 0.4405826955 0.084164103 0.0067537154 0.0105743959 0.095259640
## [9,] 1.0000000000 0.080821393 0.0455421442 -0.0026594129 0.112751427
## [10,] 0.0808213931 1.000000000 0.1663297767 0.1445551668 0.136203735
## [11,] 0.0455421442 0.166329777 1.0000000000 0.1958705568 0.076128950
## [12,] -0.0026594129 0.144555167 0.1958705568 1.0000000000 0.133946009
## [13,] 0.1127514271 0.136203735 0.0761289500 0.1339460088 1.000000000
fitACE$rC$result
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1.000000000 0.321626568 0.093811451 0.132966039 0.182176400 0.274657178 0.200140060 0.199428459
## [2,] 0.321626568 1.000000000 0.170156226 0.042693874 0.195108932 0.321661740 0.200192785 0.203467717
## [3,] 0.093811451 0.170156226 1.000000000 0.101834570 0.025325281 0.021134575 -0.024655776 -0.022408638
## [4,] 0.132966039 0.042693874 0.101834570 1.000000000 0.146132055 0.068696820 0.205819591 0.205903149
## [5,] 0.182176400 0.195108932 0.025325281 0.146132055 1.000000000 0.296702276 0.321155427 0.320006065
## [6,] 0.274657178 0.321661740 0.021134575 0.068696820 0.296702276 1.000000000 0.319324900 0.323041921
## [7,] 0.200140060 0.200192785 -0.024655776 0.205819591 0.321155427 0.319324900 1.000000000 0.431414344
## [8,] 0.199428459 0.203467717 -0.022408638 0.205903149 0.320006065 0.323041921 0.431414344 1.000000000
## [9,] 0.235350991 0.155365389 -0.056043250 0.220820104 0.195524916 0.292119066 0.379007854 0.378428650
## [10,] 0.090775769 0.155075462 0.078681044 0.115605221 0.082676530 0.116895107 0.124999226 0.126071819
## [11,] 0.093524066 0.051703282 0.096095124 0.031396256 -0.014142353 0.044563347 0.021228561 0.020249001
## [12,] -0.010463913 -0.010292075 0.112963307 0.087500675 0.100585165 -0.017707366 0.020904897 0.022542547
## [13,] 0.038091146 0.070998079 0.096221851 0.222385361 0.056112229 0.096168334 0.137173719 0.137175431
## [,9] [,10] [,11] [,12] [,13]
## [1,] 0.2353509914 0.090775769 0.093524066 -0.0104639132 0.038091146
## [2,] 0.1553653892 0.155075462 0.051703282 -0.0102920748 0.070998079
## [3,] -0.0560432500 0.078681044 0.096095124 0.1129633074 0.096221851
## [4,] 0.2208201037 0.115605221 0.031396256 0.0875006755 0.222385361
## [5,] 0.1955249160 0.082676530 -0.014142353 0.1005851652 0.056112229
## [6,] 0.2921190662 0.116895107 0.044563347 -0.0177073655 0.096168334
## [7,] 0.3790078542 0.124999226 0.021228561 0.0209048965 0.137173719
## [8,] 0.3784286498 0.126071819 0.020249001 0.0225425466 0.137175431
## [9,] 1.0000000000 0.123821569 0.053812025 0.0054172996 0.158570321
## [10,] 0.1238215693 1.000000000 0.292185240 0.2953708544 0.169027192
## [11,] 0.0538120254 0.292185240 1.000000000 0.3006653408 0.114987042
## [12,] 0.0054172996 0.295370854 0.300665341 1.0000000000 0.114731966
## [13,] 0.1585703207 0.169027192 0.114987042 0.1147319664 1.000000000
fitACE$rE$result
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1.0000000000 0.0088438226 -0.034176136 -0.033321079 0.159893210 0.172485364 0.1739689281 0.1779318889
## [2,] 0.0088438226 1.0000000000 0.235081925 0.361520126 0.105997896 0.163607535 0.0894530402 0.0989845797
## [3,] -0.0341761356 0.2350819253 1.000000000 0.278635367 -0.155745118 -0.163866670 -0.2059871964 -0.2045110471
## [4,] -0.0333210785 0.3615201261 0.278635367 1.000000000 0.013465890 -0.027737257 0.0537321191 0.0591482823
## [5,] 0.1598932095 0.1059978959 -0.155745118 0.013465890 1.000000000 0.493801630 0.5356318645 0.5262911602
## [6,] 0.1724853639 0.1636075348 -0.163866670 -0.027737257 0.493801630 1.000000000 0.6070440457 0.6289514273
## [7,] 0.1739689281 0.0894530402 -0.205987196 0.053732119 0.535631865 0.607044046 1.0000000000 0.9989515727
## [8,] 0.1779318889 0.0989845797 -0.204511047 0.059148282 0.526291160 0.628951427 0.9989515727 1.0000000000
## [9,] 0.1324350760 0.0402741914 -0.168696403 0.097526390 0.325599616 0.373879093 0.7308904185 0.7411082468
## [10,] -0.0371242759 0.0965615594 0.049907928 0.146809567 -0.032979184 -0.044489677 0.0036173845 0.0039412555
## [11,] 0.0032276426 0.1597575459 0.078536178 0.188193900 -0.054399602 -0.046507648 -0.0582083660 -0.0552420281
## [12,] -0.0645675344 0.1898576241 0.129200787 0.247506067 -0.063868184 -0.066401285 -0.0738561966 -0.0722563288
## [13,] -0.1668259199 0.1345026818 0.226730740 0.340421653 -0.094482160 -0.078093421 -0.0594901109 -0.0599185374
## [,9] [,10] [,11] [,12] [,13]
## [1,] 0.1324350760 -0.0371242759 0.0032276426 -0.064567534 -0.166825920
## [2,] 0.0402741914 0.0965615594 0.1597575459 0.189857624 0.134502682
## [3,] -0.1686964030 0.0499079276 0.0785361775 0.129200787 0.226730740
## [4,] 0.0975263902 0.1468095673 0.1881938996 0.247506067 0.340421653
## [5,] 0.3255996156 -0.0329791838 -0.0543996023 -0.063868184 -0.094482160
## [6,] 0.3738790925 -0.0444896771 -0.0465076478 -0.066401285 -0.078093421
## [7,] 0.7308904185 0.0036173845 -0.0582083660 -0.073856197 -0.059490111
## [8,] 0.7411082468 0.0039412555 -0.0552420281 -0.072256329 -0.059918537
## [9,] 1.0000000000 0.0086079041 -0.0198583037 -0.070466847 -0.038369948
## [10,] 0.0086079041 1.0000000000 0.0717874903 0.045798928 0.122106397
## [11,] -0.0198583037 0.0717874903 1.0000000000 -0.029828975 0.139585408
## [12,] -0.0704668473 0.0457989280 -0.0298289748 1.000000000 0.224938731
## [13,] -0.0383699478 0.1221063967 0.1395854077 0.224938731 1.000000000
fitACE$rP$result ### <--- pheno corrs
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1.000000000 0.159587330 0.021893410 0.052173151 0.168399568 0.22732330812 0.184814143 0.185372010
## [2,] 0.159587330 1.000000000 0.206718783 0.206713142 0.156355138 0.24861906834 0.150891252 0.156835226
## [3,] 0.021893410 0.206718783 1.000000000 0.199163525 -0.064071911 -0.07002442084 -0.105262880 -0.102976437
## [4,] 0.052173151 0.206713142 0.199163525 1.000000000 0.086276185 0.03169327040 0.140618166 0.143233919
## [5,] 0.168399568 0.156355138 -0.064071911 0.086276185 1.000000000 0.37279398569 0.396846410 0.392516654
## [6,] 0.227323308 0.248619068 -0.070024421 0.031693270 0.372793986 1.00000000000 0.420433812 0.430486209
## [7,] 0.184814143 0.150891252 -0.105262880 0.140618166 0.396846410 0.42043381217 1.000000000 0.621290524
## [8,] 0.185372010 0.156835226 -0.102976437 0.143233919 0.392516654 0.43048620881 0.621290524 1.000000000
## [9,] 0.187441186 0.103507035 -0.105617112 0.167140517 0.243001158 0.32313131119 0.501548290 0.504195418
## [10,] 0.018378811 0.112393907 0.060886001 0.122049619 0.027099168 0.03270469018 0.063608638 0.063814628
## [11,] 0.049470628 0.110455422 0.088298276 0.118565411 -0.032118747 0.00052148602 -0.012345844 -0.011470539
## [12,] -0.034478269 0.094993245 0.119224808 0.156836297 0.023603537 -0.03662708328 -0.016450389 -0.014751115
## [13,] -0.081159258 0.092277899 0.170486203 0.271806041 -0.015010921 0.00907714554 0.046613056 0.047193019
## [,9] [,10] [,11] [,12] [,13]
## [1,] 0.187441186 0.018378811 0.04947062760 -0.034478269 -0.0811592584
## [2,] 0.103507035 0.112393907 0.11045542160 0.094993245 0.0922778985
## [3,] -0.105617112 0.060886001 0.08829827636 0.119224808 0.1704862031
## [4,] 0.167140517 0.122049619 0.11856541081 0.156836297 0.2718060414
## [5,] 0.243001158 0.027099168 -0.03211874730 0.023603537 -0.0150109214
## [6,] 0.323131311 0.032704690 0.00052148602 -0.036627083 0.0090771455
## [7,] 0.501548290 0.063608638 -0.01234584401 -0.016450389 0.0466130557
## [8,] 0.504195418 0.063814628 -0.01147053880 -0.014751115 0.0471930189
## [9,] 1.000000000 0.062321166 0.02224611397 -0.025489030 0.0634672394
## [10,] 0.062321166 1.000000000 0.14847203922 0.132059961 0.1361141050
## [11,] 0.022246114 0.148472039 1.00000000000 0.117576933 0.1160379244
## [12,] -0.025489030 0.132059961 0.11757693274 1.000000000 0.1725072738
## [13,] 0.063467239 0.136114105 0.11603792441 0.172507274 1.0000000000
### combine both into one matrix and plot
table of all descriptives
means <- abcd %>%
dplyr::summarize(meanweekend_dur_mcq_wave_2 = mean(weekend_dur_mcq_wave_2, na.rm = TRUE),
meanweekday_dur_mcq_wave_2 = mean(weekday_dur_mcq_wave_2, na.rm = TRUE),
meanchronotype_wave_2 = mean(chronotype_wave_2, na.rm = TRUE),
meansocial_jet_lag_wave_2 = mean(social_jet_lag_wave_2, na.rm = TRUE),
meanavg_weekend_dur = mean(avg_weekend_dur,na.rm=T),
meanavg_weekday_dur = mean(avg_weekday_dur, na.rm=T),
meanavg_weekend_effic = mean(avg_weekend_effic, na.rm=T),
meanavg_weekday_effic = mean(avg_weekday_effic, na.rm=T),
meanvariability = mean(variability, na.rm=T),
meancbcl_scr_syn_attention_r_wave_2 = mean(cbcl_scr_syn_attention_r_wave_2, na.rm = TRUE),
meancbcl_scr_syn_internal_r_wave_2 = mean(cbcl_scr_syn_internal_r_wave_2, na.rm = TRUE),
meancbcl_scr_syn_external_r_wave_2 = mean(cbcl_scr_syn_external_r_wave_2, na.rm = TRUE),
meantotal_score_wave_2 = mean(total_score_wave_2, na.rm = TRUE)) %>%
t %>%
as.data.frame %>%
rename(Mean=V1)
sds <- abcd %>%
dplyr::summarize(sdweekend_dur_mcq_wave_2 = sd(weekend_dur_mcq_wave_2, na.rm = TRUE),
sdweekday_dur_mcq_wave_2 = sd(weekday_dur_mcq_wave_2, na.rm = TRUE),
sdchronotype_wave_2 = sd(chronotype_wave_2, na.rm = TRUE),
sdsocial_jet_lag_wave_2 = sd(social_jet_lag_wave_2, na.rm = TRUE),
sd(avg_weekend_dur,na.rm=T),
SDavg_weekday_dur = sd(avg_weekday_dur, na.rm=T),
SDavg_weekend_effic = sd(avg_weekend_effic, na.rm=T),
SDavg_weekday_effic = sd(avg_weekday_effic, na.rm=T),
SDvariability = sd(variability, na.rm=T),
SDcbcl_scr_syn_attention_r_wave_2 = sd(cbcl_scr_syn_attention_r_wave_2, na.rm = TRUE),
SDcbcl_scr_syn_internal_r_wave_2 = sd(cbcl_scr_syn_internal_r_wave_2, na.rm = TRUE),
SDcbcl_scr_syn_external_r_wave_2 = sd(cbcl_scr_syn_external_r_wave_2, na.rm = TRUE),
SDtotal_score_wave_2 = sd(total_score_wave_2, na.rm = TRUE)) %>%
t %>%
as.data.frame %>%
rename(SD=V1)
Ns <- abcd %>%
dplyr::summarize(Nweekend_dur_mcq_wave_2 = sum(!is.na(weekend_dur_mcq_wave_2)),
Nweekday_dur_mcq_wave_2 = sum(!is.na(weekday_dur_mcq_wave_2)),
Nchronotype_wave_2 = sum(!is.na(chronotype_wave_2)),
Nsocial_jet_lag_wave_2 = sum(!is.na(social_jet_lag_wave_2)),
Navg_weekend_dur = sum(!is.na(avg_weekend_dur)),
Navg_weekday_dur = sum(!is.na(avg_weekday_dur)),
Navg_weekend_effic = sum(!is.na(avg_weekend_effic)),
Navg_weekday_effic = sum(!is.na(avg_weekday_effic)),
Nvariability = sum(!is.na(variability)),
Ncbcl_scr_syn_attention_r_wave_2 = sum(!is.na(cbcl_scr_syn_attention_r_wave_2)),
Ncbcl_scr_syn_internal_r_wave_2 = sum(!is.na(cbcl_scr_syn_internal_r_wave_2)),
Ncbcl_scr_syn_external_r_wave_2 = sum(!is.na(cbcl_scr_syn_external_r_wave_2)),
Ntotal_score_wave_2 = sum(!is.na(total_score_wave_2))) %>%
t %>%
as.data.frame %>%
rename(N=V1)
descriptives_abcd<- cbind(means,sds, Ns, mz.cors, dz.cors)
row.names(descriptives_abcd)<- abcd_vars
knitr::kable(
descriptives_abcd
)
| Weekend Duration |
9.32673976 |
1.91352646 |
10054 |
0.28100095 |
0.20758001 |
| Weekday Duration |
8.54452457 |
1.41427930 |
10054 |
0.38994938 |
0.23888195 |
| Chronotype |
15.32381480 |
11.34404908 |
8771 |
0.34241742 |
0.34005529 |
| Social Jet Lag |
1.85889661 |
1.60919017 |
10054 |
0.63493913 |
0.53983471 |
| Weekend Duration* |
8.41128426 |
0.97318906 |
4755 |
0.68461260 |
0.55797745 |
| Weekday Duration* |
8.36232514 |
0.76430909 |
4793 |
0.67806418 |
0.49964729 |
| Weekend Efficiency* |
0.97527427 |
0.02128015 |
4755 |
0.41163490 |
0.33735259 |
| Weekday Efficiency* |
0.97635205 |
0.01506695 |
4793 |
0.25908447 |
0.22885810 |
| Variability* |
1.23736890 |
0.54335484 |
4788 |
0.33078063 |
0.50273038 |
| Attention |
2.70018553 |
3.30929242 |
8085 |
0.43146668 |
0.22926676 |
| Internalizing |
4.94038343 |
5.61664207 |
8085 |
0.41527737 |
0.36062441 |
| Externalizing |
3.92875696 |
5.52128001 |
8085 |
0.64468363 |
0.26270670 |
| Prodromal |
1.55905512 |
2.78768162 |
10414 |
0.23694189 |
0.28529727 |
print table!! all descriptives!
all<- rbind(descriptives_lts, descriptives_abcd)
knitr::kable(all, digits = 3, align=c('l','c','c','c','c'))
| Insomnia |
0.209 |
0.407 |
2108 |
0.438 |
0.213 |
| Satisfaction |
2.159 |
1.216 |
1901 |
0.266 |
0.022 |
| Weekday Duration |
7.082 |
1.383 |
2082 |
0.279 |
0.077 |
| Weekend Duration |
8.004 |
1.656 |
1595 |
0.273 |
0.062 |
| Chronotype |
50.125 |
9.532 |
2112 |
0.527 |
0.184 |
| Alertness |
17.187 |
3.692 |
2113 |
0.328 |
0.150 |
| Internalizing |
0.036 |
0.487 |
2113 |
0.425 |
0.272 |
| Externalizing |
0.013 |
0.643 |
2113 |
0.606 |
0.267 |
| Weekend Duration1 |
9.327 |
1.914 |
10054 |
0.281 |
0.208 |
| Weekday Duration1 |
8.545 |
1.414 |
10054 |
0.390 |
0.239 |
| Chronotype1 |
15.324 |
11.344 |
8771 |
0.342 |
0.340 |
| Social Jet Lag |
1.859 |
1.609 |
10054 |
0.635 |
0.540 |
| Weekend Duration* |
8.411 |
0.973 |
4755 |
0.685 |
0.558 |
| Weekday Duration* |
8.362 |
0.764 |
4793 |
0.678 |
0.500 |
| Weekend Efficiency* |
0.975 |
0.021 |
4755 |
0.412 |
0.337 |
| Weekday Efficiency* |
0.976 |
0.015 |
4793 |
0.259 |
0.229 |
| Variability* |
1.237 |
0.543 |
4788 |
0.331 |
0.503 |
| Attention |
2.700 |
3.309 |
8085 |
0.431 |
0.229 |
| Internalizing1 |
4.940 |
5.617 |
8085 |
0.415 |
0.361 |
| Externalizing1 |
3.929 |
5.521 |
8085 |
0.645 |
0.263 |
| Prodromal |
1.559 |
2.788 |
10414 |
0.237 |
0.285 |
