R code follows
#Data with added effect codes
ScaledData <- read.csv(
"C:/Users/woodph/OneDrive - University of Missouri/Documents/bartholow/May202025/ScaledVars.csv")
summary(ScaledData)
## subid ISD Drink ISA
## Min. :8001 Min. :0.0000 Min. : 1.0 Min. :0.0000
## 1st Qu.:8203 1st Qu.:0.0000 1st Qu.: 1.0 1st Qu.:0.0000
## Median :8404 Median :0.0000 Median : 10.0 Median :0.0000
## Mean :8403 Mean :0.4928 Mean : 37.3 Mean :0.3357
## 3rd Qu.:8601 3rd Qu.:1.0000 3rd Qu.:100.0 3rd Qu.:1.0000
## Max. :8801 Max. :1.0000 Max. :100.0 Max. :1.0000
##
## ISC ISP ISE1 ISE2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3401 Mean :0.3242 Mean :0.3256 Mean :0.3372
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## ISE3 exp Antisac1 Antisac2
## Min. :0.0000 Min. :1.000 Min. : 6.981 Min. : 7.372
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.: 9.173 1st Qu.: 9.252
## Median :0.0000 Median :2.000 Median : 9.878 Median :10.270
## Mean :0.3372 Mean :2.012 Mean : 9.792 Mean :10.113
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:10.583 3rd Qu.:10.896
## Max. :1.0000 Max. :3.000 Max. :11.914 Max. :11.992
## NA's :5 NA's :502
## Antisac3 PerSSRT1 PerSSRT2 PerSSRT3
## Min. : 7.395 Min. : 7.266 Min. : 7.386 Min. : 8.168
## 1st Qu.: 9.719 1st Qu.: 9.198 1st Qu.: 9.260 1st Qu.: 9.391
## Median :10.426 Median : 9.661 Median : 9.687 Median : 9.886
## Mean :10.321 Mean : 9.972 Mean : 9.888 Mean :10.104
## 3rd Qu.:11.053 3rd Qu.:10.619 3rd Qu.:10.505 3rd Qu.:10.706
## Max. :11.992 Max. :13.136 Max. :12.683 Max. :12.959
## NA's :314 NA's :88 NA's :528 NA's :355
## Stroop1 Stroop2 Stroop3 Keeptrk1
## Min. : 6.605 Min. : 7.505 Min. : 6.201 Min. : 6.881
## 1st Qu.: 9.315 1st Qu.: 9.626 1st Qu.: 9.579 1st Qu.: 9.223
## Median : 9.982 Median :10.242 Median :10.240 Median : 9.851
## Mean : 9.875 Mean :10.161 Mean :10.144 Mean : 9.846
## 3rd Qu.:10.539 3rd Qu.:10.758 3rd Qu.:10.794 3rd Qu.:10.585
## Max. :13.146 Max. :12.524 Max. :12.284 Max. :12.053
## NA's :3 NA's :499 NA's :315
## Keeptrk2 Keeptrk3 Letmem1 Letmem2
## Min. : 6.992 Min. : 7.288 Min. : 7.099 Min. : 7.444
## 1st Qu.: 9.422 1st Qu.: 9.718 1st Qu.: 9.244 1st Qu.: 9.370
## Median :10.090 Median :10.355 Median : 9.775 Median :10.079
## Mean :10.014 Mean :10.271 Mean : 9.792 Mean :10.120
## 3rd Qu.:10.660 3rd Qu.:10.886 3rd Qu.:10.296 3rd Qu.:10.843
## Max. :12.053 Max. :12.478 Max. :12.251 Max. :12.251
## NA's :500 NA's :311 NA's :497
## Letmem3 Nback1 Nback2 Nback3
## Min. : 7.54 Min. : 7.350 Min. : 8.026 Min. : 7.519
## 1st Qu.: 9.50 1st Qu.: 9.165 1st Qu.: 9.545 1st Qu.: 9.545
## Median :10.24 Median : 9.672 Median :10.178 Median :10.305
## Mean :10.31 Mean : 9.793 Mean :10.173 Mean :10.278
## 3rd Qu.:10.84 3rd Qu.:10.432 3rd Qu.:10.906 3rd Qu.:11.065
## Max. :12.25 Max. :12.078 Max. :12.078 Max. :12.078
## NA's :308 NA's :14 NA's :496 NA's :311
## Catswt1 Catswt2 Catswt3 Colswt1
## Min. : 6.633 Min. : 6.915 Min. : 6.940 Min. : 6.386
## 1st Qu.: 9.330 1st Qu.: 9.522 1st Qu.: 9.673 1st Qu.: 9.359
## Median :10.114 Median :10.352 Median :10.396 Median :10.048
## Mean : 9.913 Mean :10.051 Mean :10.131 Mean : 9.858
## 3rd Qu.:10.670 3rd Qu.:10.779 3rd Qu.:10.816 3rd Qu.:10.585
## Max. :11.873 Max. :11.837 Max. :12.251 Max. :13.207
## NA's :3 NA's :500 NA's :309 NA's :3
## Colswt2 Colswt3 Numswt1 Numswt2
## Min. : 7.132 Min. : 7.332 Min. : 6.237 Min. : 7.210
## 1st Qu.: 9.694 1st Qu.: 9.813 1st Qu.: 9.249 1st Qu.: 9.755
## Median :10.300 Median :10.367 Median : 9.924 Median :10.307
## Mean :10.071 Mean :10.215 Mean : 9.794 Mean :10.186
## 3rd Qu.:10.705 3rd Qu.:10.794 3rd Qu.:10.548 3rd Qu.:10.810
## Max. :12.013 Max. :12.931 Max. :11.635 Max. :11.562
## NA's :497 NA's :302 NA's :5 NA's :504
## Numswt3
## Min. : 6.947
## 1st Qu.: 9.880
## Median :10.502
## Mean :10.280
## 3rd Qu.:10.925
## Max. :12.228
## NA's :313
#drink coded off of dummy vectors of beverage
#100 is alcohol 10 is control 1 is placebo
#grouping is drink (100 = a 10=c 1=p);
#create group dummy codes
ScaledData$A <- 0
ScaledData$A <- 1*(ScaledData$Drink==100)
ScaledData$P <- 0
ScaledData$P <- 1*(ScaledData$Drink==1)
#ISD is zero if they did both arms, 1 if they did just descending
ScaledData$Both <- 0
ScaledData$Both <- 1*(ScaledData$ISD==0)
ScaledData$IX <- ScaledData$A*ScaledData$Both
library(lavaan);
## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.
require(lavaan);
library(semTools)
##
## ###############################################################################
## This is semTools 0.5-7
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
summary(ScaledData)
## subid ISD Drink ISA
## Min. :8001 Min. :0.0000 Min. : 1.0 Min. :0.0000
## 1st Qu.:8203 1st Qu.:0.0000 1st Qu.: 1.0 1st Qu.:0.0000
## Median :8404 Median :0.0000 Median : 10.0 Median :0.0000
## Mean :8403 Mean :0.4928 Mean : 37.3 Mean :0.3357
## 3rd Qu.:8601 3rd Qu.:1.0000 3rd Qu.:100.0 3rd Qu.:1.0000
## Max. :8801 Max. :1.0000 Max. :100.0 Max. :1.0000
##
## ISC ISP ISE1 ISE2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3401 Mean :0.3242 Mean :0.3256 Mean :0.3372
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## ISE3 exp Antisac1 Antisac2
## Min. :0.0000 Min. :1.000 Min. : 6.981 Min. : 7.372
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.: 9.173 1st Qu.: 9.252
## Median :0.0000 Median :2.000 Median : 9.878 Median :10.270
## Mean :0.3372 Mean :2.012 Mean : 9.792 Mean :10.113
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:10.583 3rd Qu.:10.896
## Max. :1.0000 Max. :3.000 Max. :11.914 Max. :11.992
## NA's :5 NA's :502
## Antisac3 PerSSRT1 PerSSRT2 PerSSRT3
## Min. : 7.395 Min. : 7.266 Min. : 7.386 Min. : 8.168
## 1st Qu.: 9.719 1st Qu.: 9.198 1st Qu.: 9.260 1st Qu.: 9.391
## Median :10.426 Median : 9.661 Median : 9.687 Median : 9.886
## Mean :10.321 Mean : 9.972 Mean : 9.888 Mean :10.104
## 3rd Qu.:11.053 3rd Qu.:10.619 3rd Qu.:10.505 3rd Qu.:10.706
## Max. :11.992 Max. :13.136 Max. :12.683 Max. :12.959
## NA's :314 NA's :88 NA's :528 NA's :355
## Stroop1 Stroop2 Stroop3 Keeptrk1
## Min. : 6.605 Min. : 7.505 Min. : 6.201 Min. : 6.881
## 1st Qu.: 9.315 1st Qu.: 9.626 1st Qu.: 9.579 1st Qu.: 9.223
## Median : 9.982 Median :10.242 Median :10.240 Median : 9.851
## Mean : 9.875 Mean :10.161 Mean :10.144 Mean : 9.846
## 3rd Qu.:10.539 3rd Qu.:10.758 3rd Qu.:10.794 3rd Qu.:10.585
## Max. :13.146 Max. :12.524 Max. :12.284 Max. :12.053
## NA's :3 NA's :499 NA's :315
## Keeptrk2 Keeptrk3 Letmem1 Letmem2
## Min. : 6.992 Min. : 7.288 Min. : 7.099 Min. : 7.444
## 1st Qu.: 9.422 1st Qu.: 9.718 1st Qu.: 9.244 1st Qu.: 9.370
## Median :10.090 Median :10.355 Median : 9.775 Median :10.079
## Mean :10.014 Mean :10.271 Mean : 9.792 Mean :10.120
## 3rd Qu.:10.660 3rd Qu.:10.886 3rd Qu.:10.296 3rd Qu.:10.843
## Max. :12.053 Max. :12.478 Max. :12.251 Max. :12.251
## NA's :500 NA's :311 NA's :497
## Letmem3 Nback1 Nback2 Nback3
## Min. : 7.54 Min. : 7.350 Min. : 8.026 Min. : 7.519
## 1st Qu.: 9.50 1st Qu.: 9.165 1st Qu.: 9.545 1st Qu.: 9.545
## Median :10.24 Median : 9.672 Median :10.178 Median :10.305
## Mean :10.31 Mean : 9.793 Mean :10.173 Mean :10.278
## 3rd Qu.:10.84 3rd Qu.:10.432 3rd Qu.:10.906 3rd Qu.:11.065
## Max. :12.25 Max. :12.078 Max. :12.078 Max. :12.078
## NA's :308 NA's :14 NA's :496 NA's :311
## Catswt1 Catswt2 Catswt3 Colswt1
## Min. : 6.633 Min. : 6.915 Min. : 6.940 Min. : 6.386
## 1st Qu.: 9.330 1st Qu.: 9.522 1st Qu.: 9.673 1st Qu.: 9.359
## Median :10.114 Median :10.352 Median :10.396 Median :10.048
## Mean : 9.913 Mean :10.051 Mean :10.131 Mean : 9.858
## 3rd Qu.:10.670 3rd Qu.:10.779 3rd Qu.:10.816 3rd Qu.:10.585
## Max. :11.873 Max. :11.837 Max. :12.251 Max. :13.207
## NA's :3 NA's :500 NA's :309 NA's :3
## Colswt2 Colswt3 Numswt1 Numswt2
## Min. : 7.132 Min. : 7.332 Min. : 6.237 Min. : 7.210
## 1st Qu.: 9.694 1st Qu.: 9.813 1st Qu.: 9.249 1st Qu.: 9.755
## Median :10.300 Median :10.367 Median : 9.924 Median :10.307
## Mean :10.071 Mean :10.215 Mean : 9.794 Mean :10.186
## 3rd Qu.:10.705 3rd Qu.:10.794 3rd Qu.:10.548 3rd Qu.:10.810
## Max. :12.013 Max. :12.931 Max. :11.635 Max. :11.562
## NA's :497 NA's :302 NA's :5 NA's :504
## Numswt3 A P Both
## Min. : 6.947 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 9.880 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :10.502 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :10.280 Mean :0.3357 Mean :0.3242 Mean :0.5072
## 3rd Qu.:10.925 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :12.228 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :313
## IX
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1758
## 3rd Qu.:0.0000
## Max. :1.0000
##
data.frame(Column_Number = seq_along(ScaledData), Column_Name = names(ScaledData))
## Column_Number Column_Name
## 1 1 subid
## 2 2 ISD
## 3 3 Drink
## 4 4 ISA
## 5 5 ISC
## 6 6 ISP
## 7 7 ISE1
## 8 8 ISE2
## 9 9 ISE3
## 10 10 exp
## 11 11 Antisac1
## 12 12 Antisac2
## 13 13 Antisac3
## 14 14 PerSSRT1
## 15 15 PerSSRT2
## 16 16 PerSSRT3
## 17 17 Stroop1
## 18 18 Stroop2
## 19 19 Stroop3
## 20 20 Keeptrk1
## 21 21 Keeptrk2
## 22 22 Keeptrk3
## 23 23 Letmem1
## 24 24 Letmem2
## 25 25 Letmem3
## 26 26 Nback1
## 27 27 Nback2
## 28 28 Nback3
## 29 29 Catswt1
## 30 30 Catswt2
## 31 31 Catswt3
## 32 32 Colswt1
## 33 33 Colswt2
## 34 34 Colswt3
## 35 35 Numswt1
## 36 36 Numswt2
## 37 37 Numswt3
## 38 38 A
## 39 39 P
## 40 40 Both
## 41 41 IX
rmat <- cor(ScaledData[ , 11:37], use = "pairwise.complete.obs")
round(rmat, 2)
## Antisac1 Antisac2 Antisac3 PerSSRT1 PerSSRT2 PerSSRT3 Stroop1 Stroop2
## Antisac1 1.00 0.74 0.68 -0.03 0.03 -0.05 0.19 0.16
## Antisac2 0.74 1.00 0.85 -0.11 0.06 -0.18 0.08 0.18
## Antisac3 0.68 0.85 1.00 -0.05 0.02 -0.06 0.14 0.17
## PerSSRT1 -0.03 -0.11 -0.05 1.00 0.02 0.11 -0.06 0.11
## PerSSRT2 0.03 0.06 0.02 0.02 1.00 0.04 0.02 0.05
## PerSSRT3 -0.05 -0.18 -0.06 0.11 0.04 1.00 -0.05 -0.08
## Stroop1 0.19 0.08 0.14 -0.06 0.02 -0.05 1.00 0.51
## Stroop2 0.16 0.18 0.17 0.11 0.05 -0.08 0.51 1.00
## Stroop3 0.24 0.18 0.20 -0.05 0.12 -0.06 0.46 0.49
## Keeptrk1 0.10 0.08 0.14 0.00 -0.02 0.10 0.06 0.24
## Keeptrk2 0.20 0.39 0.35 0.09 -0.07 0.28 0.13 0.29
## Keeptrk3 0.20 0.36 0.38 0.08 0.01 0.15 0.08 0.20
## Letmem1 0.22 0.31 0.25 -0.01 0.09 0.05 0.13 0.11
## Letmem2 0.20 0.32 0.28 -0.06 0.08 -0.23 0.22 0.12
## Letmem3 0.22 0.27 0.21 -0.04 0.06 -0.02 0.15 0.02
## Nback1 0.25 0.18 0.22 -0.05 0.07 0.07 0.08 -0.04
## Nback2 0.21 0.36 0.35 0.08 0.00 -0.01 0.04 0.05
## Nback3 0.22 0.27 0.36 0.04 0.04 0.07 0.07 0.03
## Catswt1 0.20 0.13 0.18 -0.12 0.04 -0.02 0.15 0.10
## Catswt2 0.20 0.27 0.21 -0.11 -0.11 0.00 0.20 0.26
## Catswt3 0.24 0.34 0.36 -0.08 -0.08 -0.19 0.23 0.15
## Colswt1 0.16 0.13 0.15 -0.01 -0.01 0.03 0.11 0.13
## Colswt2 0.25 0.19 0.23 0.05 0.12 -0.01 0.12 0.11
## Colswt3 0.15 0.18 0.24 0.05 0.04 -0.10 0.18 0.18
## Numswt1 0.19 0.10 0.13 -0.05 0.06 -0.08 0.19 0.11
## Numswt2 0.30 0.40 0.40 0.03 -0.08 -0.01 0.15 0.16
## Numswt3 0.23 0.42 0.31 -0.06 0.05 -0.23 0.19 -0.03
## Stroop3 Keeptrk1 Keeptrk2 Keeptrk3 Letmem1 Letmem2 Letmem3 Nback1
## Antisac1 0.24 0.10 0.20 0.20 0.22 0.20 0.22 0.25
## Antisac2 0.18 0.08 0.39 0.36 0.31 0.32 0.27 0.18
## Antisac3 0.20 0.14 0.35 0.38 0.25 0.28 0.21 0.22
## PerSSRT1 -0.05 0.00 0.09 0.08 -0.01 -0.06 -0.04 -0.05
## PerSSRT2 0.12 -0.02 -0.07 0.01 0.09 0.08 0.06 0.07
## PerSSRT3 -0.06 0.10 0.28 0.15 0.05 -0.23 -0.02 0.07
## Stroop1 0.46 0.06 0.13 0.08 0.13 0.22 0.15 0.08
## Stroop2 0.49 0.24 0.29 0.20 0.11 0.12 0.02 -0.04
## Stroop3 1.00 0.17 0.22 0.14 0.14 0.19 0.09 0.07
## Keeptrk1 0.17 1.00 0.60 0.48 0.31 0.30 0.25 0.19
## Keeptrk2 0.22 0.60 1.00 0.69 0.36 0.39 0.35 0.17
## Keeptrk3 0.14 0.48 0.69 1.00 0.31 0.39 0.29 0.14
## Letmem1 0.14 0.31 0.36 0.31 1.00 0.60 0.50 0.30
## Letmem2 0.19 0.30 0.39 0.39 0.60 1.00 0.66 0.30
## Letmem3 0.09 0.25 0.35 0.29 0.50 0.66 1.00 0.26
## Nback1 0.07 0.19 0.17 0.14 0.30 0.30 0.26 1.00
## Nback2 0.12 0.23 0.37 0.23 0.35 0.28 0.27 0.63
## Nback3 0.15 0.21 0.23 0.22 0.26 0.35 0.24 0.64
## Catswt1 0.19 0.08 0.10 0.11 0.07 0.04 0.09 0.07
## Catswt2 0.32 0.02 0.10 0.12 0.03 0.02 0.13 0.03
## Catswt3 0.20 0.07 -0.03 0.10 0.05 0.01 0.08 0.10
## Colswt1 0.07 0.06 0.06 0.06 -0.01 0.06 0.01 0.02
## Colswt2 0.26 0.02 0.07 0.04 0.00 0.04 0.08 0.05
## Colswt3 0.09 0.06 0.08 0.14 -0.02 0.10 0.06 0.08
## Numswt1 0.15 0.05 0.12 0.17 0.01 0.03 0.07 0.09
## Numswt2 0.31 0.04 0.15 0.19 0.02 0.04 0.12 0.16
## Numswt3 0.11 0.01 0.14 0.22 -0.01 -0.01 0.01 0.07
## Nback2 Nback3 Catswt1 Catswt2 Catswt3 Colswt1 Colswt2 Colswt3 Numswt1
## Antisac1 0.21 0.22 0.20 0.20 0.24 0.16 0.25 0.15 0.19
## Antisac2 0.36 0.27 0.13 0.27 0.34 0.13 0.19 0.18 0.10
## Antisac3 0.35 0.36 0.18 0.21 0.36 0.15 0.23 0.24 0.13
## PerSSRT1 0.08 0.04 -0.12 -0.11 -0.08 -0.01 0.05 0.05 -0.05
## PerSSRT2 0.00 0.04 0.04 -0.11 -0.08 -0.01 0.12 0.04 0.06
## PerSSRT3 -0.01 0.07 -0.02 0.00 -0.19 0.03 -0.01 -0.10 -0.08
## Stroop1 0.04 0.07 0.15 0.20 0.23 0.11 0.12 0.18 0.19
## Stroop2 0.05 0.03 0.10 0.26 0.15 0.13 0.11 0.18 0.11
## Stroop3 0.12 0.15 0.19 0.32 0.20 0.07 0.26 0.09 0.15
## Keeptrk1 0.23 0.21 0.08 0.02 0.07 0.06 0.02 0.06 0.05
## Keeptrk2 0.37 0.23 0.10 0.10 -0.03 0.06 0.07 0.08 0.12
## Keeptrk3 0.23 0.22 0.11 0.12 0.10 0.06 0.04 0.14 0.17
## Letmem1 0.35 0.26 0.07 0.03 0.05 -0.01 0.00 -0.02 0.01
## Letmem2 0.28 0.35 0.04 0.02 0.01 0.06 0.04 0.10 0.03
## Letmem3 0.27 0.24 0.09 0.13 0.08 0.01 0.08 0.06 0.07
## Nback1 0.63 0.64 0.07 0.03 0.10 0.02 0.05 0.08 0.09
## Nback2 1.00 0.77 0.12 0.22 0.23 0.02 0.17 0.21 0.08
## Nback3 0.77 1.00 0.04 0.05 0.18 0.05 0.12 0.01 -0.02
## Catswt1 0.12 0.04 1.00 0.61 0.59 0.41 0.30 0.35 0.45
## Catswt2 0.22 0.05 0.61 1.00 0.55 0.44 0.42 0.45 0.41
## Catswt3 0.23 0.18 0.59 0.55 1.00 0.42 0.24 0.35 0.47
## Colswt1 0.02 0.05 0.41 0.44 0.42 1.00 0.49 0.50 0.39
## Colswt2 0.17 0.12 0.30 0.42 0.24 0.49 1.00 0.64 0.43
## Colswt3 0.21 0.01 0.35 0.45 0.35 0.50 0.64 1.00 0.49
## Numswt1 0.08 -0.02 0.45 0.41 0.47 0.39 0.43 0.49 1.00
## Numswt2 0.18 0.14 0.53 0.48 0.47 0.28 0.41 0.39 0.67
## Numswt3 0.10 0.00 0.41 0.49 0.48 0.34 0.45 0.56 0.71
## Numswt2 Numswt3
## Antisac1 0.30 0.23
## Antisac2 0.40 0.42
## Antisac3 0.40 0.31
## PerSSRT1 0.03 -0.06
## PerSSRT2 -0.08 0.05
## PerSSRT3 -0.01 -0.23
## Stroop1 0.15 0.19
## Stroop2 0.16 -0.03
## Stroop3 0.31 0.11
## Keeptrk1 0.04 0.01
## Keeptrk2 0.15 0.14
## Keeptrk3 0.19 0.22
## Letmem1 0.02 -0.01
## Letmem2 0.04 -0.01
## Letmem3 0.12 0.01
## Nback1 0.16 0.07
## Nback2 0.18 0.10
## Nback3 0.14 0.00
## Catswt1 0.53 0.41
## Catswt2 0.48 0.49
## Catswt3 0.47 0.48
## Colswt1 0.28 0.34
## Colswt2 0.41 0.45
## Colswt3 0.39 0.56
## Numswt1 0.67 0.71
## Numswt2 1.00 0.73
## Numswt3 0.73 1.00
print(rmat)
## Antisac1 Antisac2 Antisac3 PerSSRT1 PerSSRT2
## Antisac1 1.00000000 0.74391961 0.67883014 -0.0280017804 0.034063330
## Antisac2 0.74391961 1.00000000 0.85059804 -0.1093691298 0.055346061
## Antisac3 0.67883014 0.85059804 1.00000000 -0.0494645075 0.023736805
## PerSSRT1 -0.02800178 -0.10936913 -0.04946451 1.0000000000 0.024872734
## PerSSRT2 0.03406333 0.05534606 0.02373681 0.0248727336 1.000000000
## PerSSRT3 -0.05083305 -0.17605769 -0.05723182 0.1112674818 0.040420230
## Stroop1 0.19205727 0.08099529 0.14197408 -0.0596173660 0.021164329
## Stroop2 0.16182843 0.17543498 0.17072099 0.1089781140 0.047023469
## Stroop3 0.23911183 0.18114787 0.19642362 -0.0475661807 0.119483630
## Keeptrk1 0.09590240 0.08392529 0.14232808 0.0004494688 -0.016564326
## Keeptrk2 0.20421147 0.38534910 0.34888173 0.0903058480 -0.072669382
## Keeptrk3 0.20064941 0.36358999 0.38145298 0.0788578795 0.009239997
## Letmem1 0.21926807 0.30755378 0.24735969 -0.0147223319 0.092406434
## Letmem2 0.20244833 0.32269310 0.27513776 -0.0568958934 0.079104831
## Letmem3 0.21857116 0.26910162 0.21246316 -0.0361242260 0.060867649
## Nback1 0.25449721 0.17841281 0.22050982 -0.0516934733 0.066507628
## Nback2 0.20803363 0.35846665 0.34866843 0.0838596907 -0.001891785
## Nback3 0.22330311 0.27170163 0.35718899 0.0416187036 0.040531848
## Catswt1 0.19940749 0.12893591 0.18057127 -0.1179511340 0.037169000
## Catswt2 0.20187602 0.27135772 0.21459654 -0.1119825127 -0.105049419
## Catswt3 0.24480811 0.33818076 0.36143043 -0.0837090069 -0.082057273
## Colswt1 0.16296981 0.12982593 0.15143124 -0.0077426164 -0.009775657
## Colswt2 0.24860724 0.19445793 0.23169598 0.0487849757 0.122121559
## Colswt3 0.14976377 0.18303757 0.24054137 0.0533171810 0.043690416
## Numswt1 0.18527237 0.10225677 0.12649990 -0.0531196231 0.055915311
## Numswt2 0.30183457 0.39887065 0.39885420 0.0327865426 -0.075251048
## Numswt3 0.23314179 0.41517616 0.31013825 -0.0608691962 0.045428213
## PerSSRT3 Stroop1 Stroop2 Stroop3 Keeptrk1
## Antisac1 -0.050833049 0.19205727 0.16182843 0.23911183 0.0959024007
## Antisac2 -0.176057688 0.08099529 0.17543498 0.18114787 0.0839252917
## Antisac3 -0.057231825 0.14197408 0.17072099 0.19642362 0.1423280767
## PerSSRT1 0.111267482 -0.05961737 0.10897811 -0.04756618 0.0004494688
## PerSSRT2 0.040420230 0.02116433 0.04702347 0.11948363 -0.0165643258
## PerSSRT3 1.000000000 -0.04745126 -0.07629676 -0.05889066 0.1014016469
## Stroop1 -0.047451262 1.00000000 0.51355730 0.45780809 0.0550457888
## Stroop2 -0.076296765 0.51355730 1.00000000 0.48764340 0.2410682186
## Stroop3 -0.058890665 0.45780809 0.48764340 1.00000000 0.1675925004
## Keeptrk1 0.101401647 0.05504579 0.24106822 0.16759250 1.0000000000
## Keeptrk2 0.279656058 0.12897541 0.29464011 0.21803949 0.6027757924
## Keeptrk3 0.153759172 0.08111108 0.20115008 0.14251431 0.4841399124
## Letmem1 0.053898959 0.12798672 0.10945253 0.13533987 0.3094702876
## Letmem2 -0.229625127 0.21609554 0.12261652 0.18646544 0.3009256227
## Letmem3 -0.021230660 0.14898145 0.02328405 0.08909035 0.2549630160
## Nback1 0.071839882 0.08180357 -0.03760695 0.06701946 0.1904195766
## Nback2 -0.009824291 0.04394208 0.04648467 0.12074790 0.2331297192
## Nback3 0.070089832 0.06963892 0.02791751 0.14857317 0.2143846137
## Catswt1 -0.024598507 0.15404381 0.10245774 0.18765170 0.0836629470
## Catswt2 -0.003459916 0.19695392 0.25950024 0.32193493 0.0194936558
## Catswt3 -0.186482606 0.22504289 0.15296196 0.20242567 0.0658332196
## Colswt1 0.026495381 0.10920756 0.13373069 0.07462849 0.0586335433
## Colswt2 -0.014412937 0.12467028 0.11108797 0.25946418 0.0178418606
## Colswt3 -0.099911998 0.18183264 0.18018461 0.09395503 0.0631856432
## Numswt1 -0.082305898 0.19038216 0.10877315 0.14573830 0.0509379647
## Numswt2 -0.008464722 0.15237340 0.16322360 0.30712628 0.0352832505
## Numswt3 -0.232126051 0.19224439 -0.02943070 0.10922793 0.0094828921
## Keeptrk2 Keeptrk3 Letmem1 Letmem2 Letmem3
## Antisac1 0.20421147 0.200649411 0.219268073 0.202448325 0.218571159
## Antisac2 0.38534910 0.363589990 0.307553781 0.322693097 0.269101619
## Antisac3 0.34888173 0.381452975 0.247359688 0.275137757 0.212463161
## PerSSRT1 0.09030585 0.078857880 -0.014722332 -0.056895893 -0.036124226
## PerSSRT2 -0.07266938 0.009239997 0.092406434 0.079104831 0.060867649
## PerSSRT3 0.27965606 0.153759172 0.053898959 -0.229625127 -0.021230660
## Stroop1 0.12897541 0.081111078 0.127986720 0.216095543 0.148981455
## Stroop2 0.29464011 0.201150084 0.109452530 0.122616523 0.023284051
## Stroop3 0.21803949 0.142514308 0.135339874 0.186465439 0.089090348
## Keeptrk1 0.60277579 0.484139912 0.309470288 0.300925623 0.254963016
## Keeptrk2 1.00000000 0.693354418 0.364157346 0.390157453 0.346782016
## Keeptrk3 0.69335442 1.000000000 0.313960124 0.387417535 0.285428578
## Letmem1 0.36415735 0.313960124 1.000000000 0.602643545 0.497720398
## Letmem2 0.39015745 0.387417535 0.602643545 1.000000000 0.657945337
## Letmem3 0.34678202 0.285428578 0.497720398 0.657945337 1.000000000
## Nback1 0.17490692 0.141894475 0.295661661 0.301695179 0.255960014
## Nback2 0.36676192 0.233611699 0.347252056 0.282834623 0.272740788
## Nback3 0.23293064 0.220990151 0.263700465 0.353149100 0.238309687
## Catswt1 0.09622364 0.113945140 0.070066552 0.041435727 0.091932595
## Catswt2 0.09844831 0.120449692 0.025022730 0.016304483 0.133501073
## Catswt3 -0.03387565 0.104580713 0.052431387 0.011283507 0.075319912
## Colswt1 0.06460274 0.058937693 -0.008283719 0.055671085 0.007979003
## Colswt2 0.06528612 0.035318171 0.004930345 0.038846227 0.075047438
## Colswt3 0.08465626 0.136729994 -0.022029084 0.103314549 0.063454816
## Numswt1 0.11594717 0.174094558 0.006528441 0.033483266 0.072362712
## Numswt2 0.15185915 0.185441255 0.019237201 0.036544717 0.116694550
## Numswt3 0.14406380 0.222001647 -0.005771993 -0.005600785 0.010112642
## Nback1 Nback2 Nback3 Catswt1 Catswt2
## Antisac1 0.25449721 0.208033633 0.223303112 0.19940749 0.201876016
## Antisac2 0.17841281 0.358466651 0.271701629 0.12893591 0.271357717
## Antisac3 0.22050982 0.348668435 0.357188988 0.18057127 0.214596542
## PerSSRT1 -0.05169347 0.083859691 0.041618704 -0.11795113 -0.111982513
## PerSSRT2 0.06650763 -0.001891785 0.040531848 0.03716900 -0.105049419
## PerSSRT3 0.07183988 -0.009824291 0.070089832 -0.02459851 -0.003459916
## Stroop1 0.08180357 0.043942078 0.069638922 0.15404381 0.196953916
## Stroop2 -0.03760695 0.046484667 0.027917509 0.10245774 0.259500236
## Stroop3 0.06701946 0.120747899 0.148573170 0.18765170 0.321934926
## Keeptrk1 0.19041958 0.233129719 0.214384614 0.08366295 0.019493656
## Keeptrk2 0.17490692 0.366761917 0.232930640 0.09622364 0.098448308
## Keeptrk3 0.14189448 0.233611699 0.220990151 0.11394514 0.120449692
## Letmem1 0.29566166 0.347252056 0.263700465 0.07006655 0.025022730
## Letmem2 0.30169518 0.282834623 0.353149100 0.04143573 0.016304483
## Letmem3 0.25596001 0.272740788 0.238309687 0.09193260 0.133501073
## Nback1 1.00000000 0.631415501 0.639382638 0.06800507 0.029282853
## Nback2 0.63141550 1.000000000 0.765289965 0.11578242 0.217553401
## Nback3 0.63938264 0.765289965 1.000000000 0.04084436 0.048054558
## Catswt1 0.06800507 0.115782418 0.040844358 1.00000000 0.606988795
## Catswt2 0.02928285 0.217553401 0.048054558 0.60698879 1.000000000
## Catswt3 0.10347510 0.227062282 0.177170509 0.59170453 0.550904428
## Colswt1 0.01511064 0.020944396 0.047077831 0.41049273 0.436872604
## Colswt2 0.05240629 0.165329823 0.118527405 0.30166747 0.420720121
## Colswt3 0.07708710 0.205624040 0.008330784 0.35423127 0.450616013
## Numswt1 0.09475154 0.075177679 -0.022146718 0.44578688 0.408479010
## Numswt2 0.15578271 0.183603752 0.142912084 0.53146047 0.475826587
## Numswt3 0.07096910 0.096610573 0.002952774 0.40616581 0.492872916
## Catswt3 Colswt1 Colswt2 Colswt3 Numswt1
## Antisac1 0.24480811 0.162969810 0.248607236 0.149763771 0.185272367
## Antisac2 0.33818076 0.129825930 0.194457934 0.183037575 0.102256772
## Antisac3 0.36143043 0.151431241 0.231695978 0.240541372 0.126499902
## PerSSRT1 -0.08370901 -0.007742616 0.048784976 0.053317181 -0.053119623
## PerSSRT2 -0.08205727 -0.009775657 0.122121559 0.043690416 0.055915311
## PerSSRT3 -0.18648261 0.026495381 -0.014412937 -0.099911998 -0.082305898
## Stroop1 0.22504289 0.109207562 0.124670277 0.181832643 0.190382158
## Stroop2 0.15296196 0.133730691 0.111087967 0.180184609 0.108773154
## Stroop3 0.20242567 0.074628490 0.259464176 0.093955035 0.145738302
## Keeptrk1 0.06583322 0.058633543 0.017841861 0.063185643 0.050937965
## Keeptrk2 -0.03387565 0.064602740 0.065286118 0.084656256 0.115947166
## Keeptrk3 0.10458071 0.058937693 0.035318171 0.136729994 0.174094558
## Letmem1 0.05243139 -0.008283719 0.004930345 -0.022029084 0.006528441
## Letmem2 0.01128351 0.055671085 0.038846227 0.103314549 0.033483266
## Letmem3 0.07531991 0.007979003 0.075047438 0.063454816 0.072362712
## Nback1 0.10347510 0.015110636 0.052406292 0.077087104 0.094751539
## Nback2 0.22706228 0.020944396 0.165329823 0.205624040 0.075177679
## Nback3 0.17717051 0.047077831 0.118527405 0.008330784 -0.022146718
## Catswt1 0.59170453 0.410492731 0.301667469 0.354231268 0.445786879
## Catswt2 0.55090443 0.436872604 0.420720121 0.450616013 0.408479010
## Catswt3 1.00000000 0.421721459 0.238974939 0.353793151 0.466243395
## Colswt1 0.42172146 1.000000000 0.487234627 0.503440116 0.391765036
## Colswt2 0.23897494 0.487234627 1.000000000 0.639799313 0.425439140
## Colswt3 0.35379315 0.503440116 0.639799313 1.000000000 0.489078984
## Numswt1 0.46624339 0.391765036 0.425439140 0.489078984 1.000000000
## Numswt2 0.47203017 0.277358901 0.408500440 0.388019706 0.669532216
## Numswt3 0.48410947 0.342125187 0.447873088 0.564276975 0.710641248
## Numswt2 Numswt3
## Antisac1 0.301834569 0.233141790
## Antisac2 0.398870654 0.415176161
## Antisac3 0.398854203 0.310138246
## PerSSRT1 0.032786543 -0.060869196
## PerSSRT2 -0.075251048 0.045428213
## PerSSRT3 -0.008464722 -0.232126051
## Stroop1 0.152373403 0.192244386
## Stroop2 0.163223600 -0.029430697
## Stroop3 0.307126285 0.109227934
## Keeptrk1 0.035283250 0.009482892
## Keeptrk2 0.151859155 0.144063800
## Keeptrk3 0.185441255 0.222001647
## Letmem1 0.019237201 -0.005771993
## Letmem2 0.036544717 -0.005600785
## Letmem3 0.116694550 0.010112642
## Nback1 0.155782705 0.070969098
## Nback2 0.183603752 0.096610573
## Nback3 0.142912084 0.002952774
## Catswt1 0.531460475 0.406165807
## Catswt2 0.475826587 0.492872916
## Catswt3 0.472030170 0.484109465
## Colswt1 0.277358901 0.342125187
## Colswt2 0.408500440 0.447873088
## Colswt3 0.388019706 0.564276975
## Numswt1 0.669532216 0.710641248
## Numswt2 1.000000000 0.729655205
## Numswt3 0.729655205 1.000000000
#library(corrplot)
#corrplot(rmat, method = "circle")
#ct <- corr.test(ScaledData)
#r_mat <- ct$r
#p_mat <- ct$p
# Step 2: Plot correlations, only showing significant ones
#corrplot(r_mat,
# method = "circle",
# type = "upper",
# p.mat = p_mat, # p-values matrix
# sig.level = 0.05, # significance threshold
# insig = "blank") # options: "blank", "pch", "n"
BevScreenData <- read.csv(
"C:/Users/woodph/OneDrive - University of Missouri/Documents/bartholow/May202025/BeverageScreened.csv")
#rescale variables
BevScreenData$Antisac1= BevScreenData$Antisac1/.15;
BevScreenData$Antisac2= BevScreenData$Antisac2/.15;
BevScreenData$Antisac3= BevScreenData$Antisac3/.15;
BevScreenData$Stroop1 = -1*BevScreenData$Stroop1/60;
BevScreenData$Stroop2 = -1*BevScreenData$Stroop2/60;
BevScreenData$Stroop3 = -1*BevScreenData$Stroop3/60;
BevScreenData$PerSSRT1 = BevScreenData$PerSSRT1/100;
BevScreenData$PerSSRT2 = BevScreenData$PerSSRT2/100;
BevScreenData$PerSSRT3 = BevScreenData$PerSSRT3/100;
BevScreenData$Keeptrk1= BevScreenData$Keeptrk1/.1;
BevScreenData$Keeptrk2= BevScreenData$Keeptrk2/.1;
BevScreenData$Keeptrk3= BevScreenData$Keeptrk3/.1;
BevScreenData$Letmem1= BevScreenData$Letmem1/.3;
BevScreenData$Letmem2= BevScreenData$Letmem2/.3;
BevScreenData$Letmem3= BevScreenData$Letmem3/.3;
BevScreenData$Nback1= BevScreenData$Nback1/.08;
BevScreenData$Nback2= BevScreenData$Nback2/.08;
BevScreenData$Nback3= BevScreenData$Nback3/.08;
BevScreenData$Catswt1= BevScreenData$Catswt1/120;
BevScreenData$Catswt2= BevScreenData$Catswt2/120;
BevScreenData$Catswt3= BevScreenData$Catswt3/120;
BevScreenData$Colswt1= BevScreenData$Colswt1/150;
BevScreenData$Colswt2= BevScreenData$Colswt2/150;
BevScreenData$Colswt3= BevScreenData$Colswt3/150;
BevScreenData$Numswt1= BevScreenData$Numswt1/160;
BevScreenData$Numswt2= BevScreenData$Numswt2/160;
BevScreenData$Numswt3= BevScreenData$Numswt3/160;
#drink coded off of dummy vectors of beverage
#100 is alcohol 10 is control 1 is placebo
#grouping is drink (100 = a 10=c 1=p);
#create group dummy codes
BevScreenData$A <- 0
BevScreenData$A <- 1*(BevScreenData$Drink==100)
BevScreenData$P <- 0
BevScreenData$P <- 1*(BevScreenData$Drink==1)
#ISD is zero if they did both arms, 1 if they did just descending
BevScreenData$Both <- 0
BevScreenData$Both <- 1*(BevScreenData$ISD==0)
BevScreenData$IX <- BevScreenData$A*BevScreenData$Both
#cor_mat <- cor(BevScreenData)
#p_mat <- cor.mtest(BevScreenData) # from earlier example
summary(BevScreenData)
## subid ISD Drink ISA
## Min. :8001 Min. :0.0000 Min. : 1.0 Min. :0.0000
## 1st Qu.:8203 1st Qu.:0.0000 1st Qu.: 1.0 1st Qu.:0.0000
## Median :8404 Median :0.0000 Median : 10.0 Median :0.0000
## Mean :8403 Mean :0.4928 Mean : 37.3 Mean :0.3357
## 3rd Qu.:8601 3rd Qu.:1.0000 3rd Qu.:100.0 3rd Qu.:1.0000
## Max. :8801 Max. :1.0000 Max. :100.0 Max. :1.0000
##
## ISC ISP ISE1 ISE2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3401 Mean :0.3242 Mean :0.3256 Mean :0.3372
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## ISE3 exp Antisac1 Antisac2
## Min. :0.0000 Min. :1.000 Min. :1.429 Min. :1.825
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:3.651 1st Qu.:3.730
## Median :0.0000 Median :2.000 Median :4.365 Median :4.762
## Mean :0.3372 Mean :2.012 Mean :4.278 Mean :4.603
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:5.079 3rd Qu.:5.397
## Max. :1.0000 Max. :3.000 Max. :6.429 Max. :6.508
## NA's :5 NA's :502
## Antisac3 PerSSRT1 PerSSRT2 PerSSRT3
## Min. :1.848 Min. :0.376 Min. :0.5055 Min. :1.347
## 1st Qu.:4.204 1st Qu.:2.456 1st Qu.:2.5217 1st Qu.:2.663
## Median :4.921 Median :2.954 Median :2.9816 Median :3.195
## Mean :4.814 Mean :3.288 Mean :3.1977 Mean :3.430
## 3rd Qu.:5.556 3rd Qu.:3.984 3rd Qu.:3.8616 3rd Qu.:4.077
## Max. :6.508 Max. :6.693 Max. :6.2051 Max. :6.502
## NA's :314 NA's :88 NA's :528 NA's :355
## Stroop1 Stroop2 Stroop3 Keeptrk1
## Min. :-5.876 Min. :-4.8918 Min. :-6.3179 Min. :4.140
## 1st Qu.:-2.911 1st Qu.:-2.5711 1st Qu.:-2.6232 1st Qu.:6.593
## Median :-2.182 Median :-1.8974 Median :-1.9002 Median :7.250
## Mean :-2.299 Mean :-1.9859 Mean :-2.0046 Mean :7.245
## 3rd Qu.:-1.573 3rd Qu.:-1.3334 3rd Qu.:-1.2941 3rd Qu.:8.019
## Max. : 1.279 Max. : 0.5987 Max. : 0.3363 Max. :9.556
## NA's :3 NA's :499 NA's :315
## Keeptrk2 Keeptrk3 Letmem1 Letmem2
## Min. :4.256 Min. : 4.566 Min. :0.2228 Min. :0.5582
## 1st Qu.:6.801 1st Qu.: 7.111 1st Qu.:2.3102 1st Qu.:2.4324
## Median :7.500 Median : 7.778 Median :2.8269 Median :3.1220
## Mean :7.421 Mean : 7.690 Mean :2.8434 Mean :3.1625
## 3rd Qu.:8.097 3rd Qu.: 8.333 3rd Qu.:3.3339 3rd Qu.:3.8655
## Max. :9.556 Max. :10.000 Max. :5.2360 Max. :5.2360
## NA's :500 NA's :311 NA's :497
## Letmem3 Nback1 Nback2 Nback3
## Min. :0.6523 Min. : 7.639 Min. : 8.333 Min. : 7.812
## 1st Qu.:2.5588 1st Qu.: 9.505 1st Qu.: 9.896 1st Qu.: 9.896
## Median :3.2837 Median :10.026 Median :10.547 Median :10.677
## Mean :3.3488 Mean :10.150 Mean :10.542 Mean :10.650
## 3rd Qu.:3.8655 3rd Qu.:10.807 3rd Qu.:11.296 3rd Qu.:11.458
## Max. :5.2360 Max. :12.500 Max. :12.500 Max. :12.500
## NA's :308 NA's :14 NA's :496 NA's :311
## Catswt1 Catswt2 Catswt3 Colswt1
## Min. :-4.6981 Min. :-4.4096 Min. :-4.3835 Min. :-5.3375
## 1st Qu.:-1.9345 1st Qu.:-1.7380 1st Qu.:-1.5834 1st Qu.:-2.0493
## Median :-1.1317 Median :-0.8877 Median :-0.8429 Median :-1.2872
## Mean :-1.3376 Mean :-1.1960 Mean :-1.1145 Mean :-1.4975
## 3rd Qu.:-0.5621 3rd Qu.:-0.4499 3rd Qu.:-0.4125 3rd Qu.:-0.6937
## Max. : 0.6707 Max. : 0.6340 Max. : 1.0581 Max. : 2.2062
## NA's :3 NA's :500 NA's :309 NA's :3
## Colswt2 Colswt3 Numswt1 Numswt2
## Min. :-4.5118 Min. :-4.2911 Min. :-5.9078 Min. :-4.8392
## 1st Qu.:-1.6789 1st Qu.:-1.5471 1st Qu.:-2.6004 1st Qu.:-2.0448
## Median :-1.0083 Median :-0.9349 Median :-1.8589 Median :-1.4384
## Mean :-1.2623 Mean :-1.1028 Mean :-2.0024 Mean :-1.5713
## 3rd Qu.:-0.5606 3rd Qu.:-0.4621 3rd Qu.:-1.1744 3rd Qu.:-0.8863
## Max. : 0.8862 Max. : 1.9011 Max. : 0.0194 Max. :-0.0607
## NA's :497 NA's :302 NA's :5 NA's :504
## Numswt3 A P Both
## Min. :-5.1276 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:-1.9073 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :-1.2244 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :-1.4678 Mean :0.3357 Mean :0.3242 Mean :0.5072
## 3rd Qu.:-0.7596 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. : 0.6707 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :313
## IX
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1758
## 3rd Qu.:0.0000
## Max. :1.0000
##
df_subset <- BevScreenData[, 11:37]
#ct <- corr.test(df_subset)
#r_mat <- ct$r
#p_mat <- ct$p
# Step 2: Plot correlations, only showing significant ones
#corrplot(r_mat,
# method = "circle",
# type = "upper",
# p.mat = p_mat, # p-values matrix
# sig.level = 0.05, # significance threshold
# insig = "blank") # options: "blank", "pch", "n"
#Implicit covariates "CFASuperFactorModel"
GrowthModel<-"
! regressions
AS=~1.0*Antisac1+AN2*Antisac2+AN3*Antisac3
STR=~1.0*Stroop1+S2*Stroop2+S3*Stroop3
Keep=~1.0*Keeptrk1+K2*Keeptrk2+K3*Keeptrk3
Let=~1.0*Letmem1+L2*Letmem2+L3*Letmem3
NB=~1.0*Nback1+N2*Nback2+N3*Nback3
cat=~1.0*Catswt1+A2*Catswt2+A3*Catswt3
col=~1.0*Colswt1+O2*Colswt2+O3*Colswt3
Num=~1.0*Numswt1+U2*Numswt2+U3*Numswt3
EF1=~EFAntisac*Antisac1+EFStroop*Stroop1+EFKeeptrk*Keeptrk1+EFLetmem*Letmem1+EFNback*Nback1+
EFCatswt*Catswt1+EFColswt*Colswt1+EFNumswt*Numswt1
EF2=~EFAntisac*Antisac2+EFStroop*Stroop2+EFKeeptrk*Keeptrk2+EFLetmem*Letmem2+EFNback*Nback2+
EFCatswt*Catswt2+EFColswt*Colswt2+EFNumswt*Numswt2
EF3=~EFAntisac*Antisac3+EFStroop*Stroop3+EFKeeptrk*Keeptrk3+EFLetmem*Letmem3+EFNback*Nback3+
EFCatswt*Catswt3+EFColswt*Colswt3+EFNumswt*Numswt3
UP1=~UPKeeptrk*Keeptrk1+start(0.38)*Keeptrk1+UPLetmem*Letmem1+UPNback*Nback1
UP2=~UPKeeptrk*Keeptrk2+start(0.65)*Keeptrk2+UPLetmem*Letmem2+UPNback*Nback2
UP3=~UPKeeptrk*Keeptrk3+UPLetmem*Letmem3+UPNback*Nback3
SW1=~SWCatswt*Catswt1+SWColswt*Colswt1+SWNumswt*Numswt1
SW2=~SWCatswt*Catswt2+SWColswt*Colswt2+SWNumswt*Numswt2
SW3=~SWCatswt*Catswt3+SWColswt*Colswt3+SWNumswt*Numswt3
EF1 ~ A__EF1*A
EF2 ~ A__EF2*A
EF3 ~ A__EF3*A
EF1 ~ P__EF1*P
EF2 ~ P__EF2*P
EF3 ~ P__EF3*P
EF3 ~ Both__EF3*Both
EF3 ~ IX__EF3*IX
UP1 ~ A__UP1*A
UP1 ~ P__UP1*P
UP2 ~ A__UP2*A
UP2 ~ P__UP2*P
UP3 ~ A__UP3*A
UP3 ~ P__UP3*P
UP3 ~ Both__UP3*Both
UP3 ~ IX__UP3*IX
SW1 ~ A__SW1*A
SW1 ~ P__SW1*P
SW2 ~ A__SW2*A
SW3 ~ P__SW3*P
SW2 ~ P__SW2*P
SW3 ~ A__SW3*A
SW3 ~ IX__SW3*IX
SW3 ~ Both__SW3*Both
# Adjust each observed variable for predictors A, P, Both, and IX
Antisac1 ~ A + P
Stroop1 ~ A + P
Keeptrk1 ~ A + P
Letmem1 ~ A + P
Nback1 ~ A + P
Catswt1 ~ A + P
Colswt1 ~ A + P
Numswt1 ~ A + P
! residuals, variances and covariances
!manifest variances
Antisac1 ~~ VAR_Antisac1*Antisac1
Antisac2 ~~ VAR_Antisac2*Antisac2
Antisac3 ~~ VAR_Antisac3*Antisac3
Stroop1 ~~ VAR_Stroop1*Stroop1
Stroop2 ~~ VAR_Stroop2*Stroop2
Stroop3 ~~ VAR_Stroop3*Stroop3
Keeptrk1 ~~ VAR_Keeptrk1*Keeptrk1
Keeptrk2 ~~ VAR_Keeptrk2*Keeptrk2
Keeptrk3 ~~ VAR_Keeptrk3*Keeptrk3
Letmem1 ~~ VAR_Letmem1*Letmem1
Letmem2 ~~ VAR_Letmem2*Letmem2
Letmem3 ~~ VAR_Letmem3*Letmem3
!Covariances of Letmem
!Letmem1 ~~ C_Letmem1_Letmem2*Letmem2
!Letmem1 ~~ C_Letmem1_Letmem3*Letmem3
!Letmem2 ~~ C_Letmem2_Letmem3*Letmem3
Nback1 ~~ VAR_Nback1*Nback1
Nback2 ~~ VAR_Nback2*Nback2
Nback3 ~~ VAR_Nback3*Nback3
Catswt1 ~~ VAR_Catswt1*Catswt1
Catswt2 ~~ VAR_Catswt2*Catswt2
Catswt3 ~~ VAR_Catswt3*Catswt3
Colswt1 ~~ VAR_Colswt1*Colswt1
Colswt2 ~~ VAR_Colswt2*Colswt2
Colswt3 ~~ VAR_Colswt3*Colswt3
Numswt1 ~~ VAR_Numswt1*Numswt1
Numswt2 ~~ VAR_Numswt2*Numswt2
Numswt3 ~~ VAR_Numswt3*Numswt3
!Task variances
AS ~~ VAR_AS*AS
STR ~~ VAR_STR*STR
Keep ~~ VAR_Keep*Keep
Let ~~ VAR_Let*Let
NB ~~ VAR_NB*NB
cat ~~ VAR_cat*cat
col ~~ VAR_col*col
Num ~~ VAR_Num*Num
!Superfactor Variances & Covariances
EF1 ~~ 1.0*EF1
EF2 ~~ 1.0*EF2
EF3 ~~ 1.0*EF3
EF1 ~~ 1*EF2
EF1 ~~ 1*EF3
EF2 ~~ 1*EF3
UP1 ~~ 1.0*UP1
UP2 ~~ 1.0*UP2
UP3 ~~ 1.0*UP3
UP1 ~~ 1*UP2
UP2 ~~ 1*UP3
UP1 ~~ 1*UP3
SW1 ~~ 1.0*SW1
SW2 ~~ 1.0*SW2
SW3 ~~ 1.0*SW3
SW1 ~~ 1*SW2
SW1 ~~ 1*SW3
SW2 ~~ 1*SW3
!Covariances of Updating with Switch
UP1 ~~ 0*SW1
UP2 ~~ 0*SW2
UP3 ~~ 0*SW3
! means
!Superfactor means for Ascending and Descending
!EF2~const__EF2*1
!EF3~const__EF3*1
!UP2~const__UP2*1
!UP3~const__UP3*1
!SW2~const__SW2*1
!SW3~const__SW3*1
!Task factor means
AS~NA*1
STR~NA*1
Keep~NA*1
Let~NA*1
NB~NA*1
cat~NA*1
col~NA*1
Num~NA*1
!First manifest variable free, others fixed to equality
Antisac1~IAntisac1*1;
Antisac2~0*1;
Antisac3~0*1;
Stroop1~IStroop1*1;
Stroop2~0*1;
Stroop3~0*1;
Keeptrk1~IKeeptrk1*1;
Keeptrk2~0*1;
Keeptrk3~0*1;
Letmem1~ILetmem1*1;
Letmem2~0*1;
Letmem3~0*1;
Nback1~INback1*1;
Nback2~0*1;
Nback3~0*1;
Catswt1~ICatswt1*1;
Catswt2~0*1;
Catswt3~0*1;
Colswt1~IColswt1*1;
Colswt2~0*1;
Colswt3~0*1;
Numswt1~INumswt1*1;
Numswt2~0*1;
Numswt3~0*1;
!Baseline superfactor means zeroed out
EF1~0*1;
EF2~0*1;
EF3~0*1;
UP1~0*1;
UP2~0*1;
UP3~0*1;
SW1~0*1;
SW2~0*1;
SW3~0*1;
";
GrowthModelResult<-lavaan(GrowthModel, data=ScaledData, fixed.x=TRUE, missing="FIML",
control = list(
optim.method = "NLMINB", # try "NLMINB" if needed
optim.maxit = 20000, # raise iteration cap
rel.tol = 1e-10, # tighter convergence tolerance
ftol = 1e-10 # function tolerance (if available in your lavaan)
));
summary(GrowthModelResult, fit.measures=TRUE,standardized=TRUE);
## Warning in pchisq(X2, df = df, ncp = ncp): NaNs produced
## Warning in pchisq(X2, df = df, ncp = ncp): NaNs produced
## lavaan 0.6-19 ended normally after 1520 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 146
## Number of equality constraints 28
##
## Number of observations 694
## Number of missing patterns 103
##
## Model Test User Model:
##
## Test statistic 395.752
## Degrees of freedom 302
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 4046.313
## Degrees of freedom 372
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.974
## Tucker-Lewis Index (TLI) 0.969
##
## Robust Comparative Fit Index (CFI) 0.006
## Robust Tucker-Lewis Index (TLI) -0.224
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12347.940
## Loglikelihood unrestricted model (H1) -12150.064
##
## Akaike (AIC) 24931.880
## Bayesian (BIC) 25467.892
## Sample-size adjusted Bayesian (SABIC) 25093.221
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.021
## 90 Percent confidence interval - lower 0.015
## 90 Percent confidence interval - upper 0.027
## P-value H_0: RMSEA <= 0.050 1.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Robust RMSEA 2.490
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NaN
## P-value H_0: Robust RMSEA >= 0.080 NaN
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AS =~
## Antisc1 1.000 0.359 0.367
## Antisc2 (AN2) 1.363 0.247 5.527 0.000 0.489 0.511
## Antisc3 (AN3) 1.385 0.250 5.535 0.000 0.497 0.509
## STR =~
## Stroop1 1.000 0.634 0.615
## Stroop2 (S2) 0.887 0.133 6.671 0.000 0.563 0.648
## Stroop3 (S3) 0.883 0.132 6.687 0.000 0.560 0.592
## Keep =~
## Keptrk1 1.000 0.521 0.527
## Keptrk2 (K2) 1.207 0.140 8.649 0.000 0.629 0.676
## Keptrk3 (K3) 1.213 0.140 8.639 0.000 0.632 0.641
## Let =~
## Letmem1 1.000 0.018 0.020
## Letmem2 (L2) 18.713 0.098 191.798 0.000 0.342 0.340
## Letmem3 (L3) 18.499 0.099 186.904 0.000 0.338 0.317
## NB =~
## Nback1 1.000 0.588 0.630
## Nback2 (N2) 1.312 0.090 14.569 0.000 0.772 0.782
## Nback3 (N3) 1.301 0.089 14.663 0.000 0.765 0.732
## cat =~
## Catswt1 1.000 0.552 0.548
## Catswt2 (A2) 0.544 0.238 2.291 0.022 0.300 0.305
## Catswt3 (A3) 0.538 0.235 2.293 0.022 0.297 0.308
## col =~
## Colswt1 1.000 0.280 0.268
## Colswt2 (O2) 1.610 0.433 3.718 0.000 0.450 0.471
## Colswt3 (O3) 1.606 0.431 3.722 0.000 0.449 0.503
## Num =~
## Numswt1 1.000 0.466 0.461
## Numswt2 (U2) 0.987 0.142 6.929 0.000 0.460 0.484
## Numswt3 (U3) 0.978 0.142 6.906 0.000 0.455 0.481
## EF1 =~
## Antisc1 (EFAn) 0.706 0.064 11.086 0.000 1.606 1.643
## Stroop1 (EFSt) 0.248 0.043 5.744 0.000 0.565 0.548
## Keptrk1 (EFKp) 0.216 0.048 4.534 0.000 0.491 0.497
## Letmem1 (EFLt) 0.284 0.042 6.702 0.000 0.645 0.716
## Nback1 (EFNb) 0.334 0.044 7.557 0.000 0.761 0.815
## Catswt1 (EFCt) 0.313 0.044 7.042 0.000 0.712 0.707
## Colswt1 (EFCl) 0.196 0.042 4.697 0.000 0.446 0.428
## Numswt1 (EFNm) 0.278 0.047 5.888 0.000 0.632 0.625
## EF2 =~
## Antisc2 (EFAn) 0.706 0.064 11.086 0.000 0.736 0.769
## Stroop2 (EFSt) 0.248 0.043 5.744 0.000 0.259 0.298
## Keptrk2 (EFKp) 0.216 0.048 4.534 0.000 0.225 0.242
## Letmem2 (EFLt) 0.284 0.042 6.702 0.000 0.296 0.294
## Nback2 (EFNb) 0.334 0.044 7.557 0.000 0.349 0.353
## Catswt2 (EFCt) 0.313 0.044 7.042 0.000 0.326 0.332
## Colswt2 (EFCl) 0.196 0.042 4.697 0.000 0.205 0.214
## Numswt2 (EFNm) 0.278 0.047 5.888 0.000 0.290 0.305
## EF3 =~
## Antisc3 (EFAn) 0.706 0.064 11.086 0.000 0.747 0.765
## Stroop3 (EFSt) 0.248 0.043 5.744 0.000 0.263 0.278
## Keptrk3 (EFKp) 0.216 0.048 4.534 0.000 0.229 0.232
## Letmem3 (EFLt) 0.284 0.042 6.702 0.000 0.300 0.282
## Nback3 (EFNb) 0.334 0.044 7.557 0.000 0.354 0.338
## Catswt3 (EFCt) 0.313 0.044 7.042 0.000 0.331 0.343
## Colswt3 (EFCl) 0.196 0.042 4.697 0.000 0.208 0.233
## Numswt3 (EFNm) 0.278 0.047 5.888 0.000 0.294 0.311
## UP1 =~
## Keptrk1 (UPKp) 0.360 0.043 8.333 0.000 0.427 0.432
## Letmem1 (UPLt) 0.641 0.033 19.251 0.000 0.762 0.845
## Nback1 (UPNb) 0.249 0.043 5.816 0.000 0.296 0.317
## UP2 =~
## Keptrk2 (UPKp) 0.360 0.043 8.333 0.000 0.380 0.408
## Letmem2 (UPLt) 0.641 0.033 19.251 0.000 0.677 0.674
## Nback2 (UPNb) 0.249 0.043 5.816 0.000 0.263 0.266
## UP3 =~
## Keptrk3 (UPKp) 0.360 0.043 8.333 0.000 0.382 0.388
## Letmem3 (UPLt) 0.641 0.033 19.251 0.000 0.681 0.641
## Nback3 (UPNb) 0.249 0.043 5.816 0.000 0.265 0.253
## SW1 =~
## Catswt1 (SWCt) 0.552 0.038 14.341 0.000 0.707 0.703
## Colswt1 (SWCl) 0.555 0.038 14.669 0.000 0.711 0.681
## Numswt1 (SWNm) 0.627 0.041 15.190 0.000 0.804 0.795
## SW2 =~
## Catswt2 (SWCt) 0.552 0.038 14.341 0.000 0.557 0.567
## Colswt2 (SWCl) 0.555 0.038 14.669 0.000 0.560 0.587
## Numswt2 (SWNm) 0.627 0.041 15.190 0.000 0.633 0.666
## SW3 =~
## Catswt3 (SWCt) 0.552 0.038 14.341 0.000 0.566 0.587
## Colswt3 (SWCl) 0.555 0.038 14.669 0.000 0.569 0.638
## Numswt3 (SWNm) 0.627 0.041 15.190 0.000 0.643 0.679
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EF1 ~
## A (A__EF1) 3.709 0.099 37.394 0.000 1.631 0.770
## EF2 ~
## A (A__EF2) -0.686 0.173 -3.968 0.000 -0.659 -0.311
## EF3 ~
## A (A__EF3) -0.809 0.181 -4.463 0.000 -0.764 -0.361
## EF1 ~
## P (P__EF1) 4.746 0.117 40.442 0.000 2.087 0.977
## EF2 ~
## P (P__EF2) -0.165 0.163 -1.009 0.313 -0.158 -0.074
## EF3 ~
## P (P__EF3) -0.059 0.142 -0.418 0.676 -0.056 -0.026
## Both (B__E) 0.100 0.107 0.929 0.353 0.094 0.047
## IX (IX__E) 0.122 0.188 0.649 0.516 0.115 0.044
## UP1 ~
## A (A__UP1) -1.309 0.123 -10.667 0.000 -1.102 -0.520
## P (P__UP1) -1.399 0.149 -9.384 0.000 -1.177 -0.551
## UP2 ~
## A (A__UP2) -0.699 0.209 -3.339 0.001 -0.662 -0.313
## P (P__UP2) 0.034 0.204 0.167 0.867 0.032 0.015
## UP3 ~
## A (A__UP3) -0.333 0.233 -1.431 0.153 -0.313 -0.148
## P (P__UP3) 0.170 0.174 0.979 0.327 0.160 0.075
## Both (B__U) 0.597 0.156 3.825 0.000 0.562 0.281
## IX (IX__U) 0.038 0.259 0.145 0.884 0.035 0.013
## SW1 ~
## A (A__SW1) -1.072 0.139 -7.691 0.000 -0.836 -0.395
## P (P__SW1) -1.961 0.178 -11.026 0.000 -1.531 -0.717
## SW2 ~
## A (A__SW2) -0.227 0.172 -1.318 0.188 -0.225 -0.106
## SW3 ~
## P (P__SW3) -0.041 0.133 -0.306 0.760 -0.040 -0.019
## SW2 ~
## P (P__SW2) 0.109 0.162 0.668 0.504 0.107 0.050
## SW3 ~
## A (A__SW3) 0.232 0.175 1.324 0.186 0.226 0.107
## IX (IX__S) -0.345 0.177 -1.956 0.050 -0.337 -0.128
## Both (B__S) 0.531 0.103 5.148 0.000 0.518 0.259
## Antisac1 ~
## A -2.627 0.220 -11.937 0.000 -2.627 -1.269
## P -3.341 0.265 -12.628 0.000 -3.341 -1.600
## Stroop1 ~
## A -0.938 0.187 -5.010 0.000 -0.938 -0.430
## P -1.174 0.223 -5.266 0.000 -1.174 -0.533
## Keeptrk1 ~
## A -0.297 0.193 -1.535 0.125 -0.297 -0.142
## P -0.520 0.230 -2.259 0.024 -0.520 -0.246
## Letmem1 ~
## A -0.211 0.125 -1.690 0.091 -0.211 -0.111
## P -0.394 0.151 -2.609 0.009 -0.394 -0.204
## Nback1 ~
## A -0.907 0.187 -4.844 0.000 -0.907 -0.459
## P -1.223 0.223 -5.490 0.000 -1.223 -0.613
## Catswt1 ~
## A -0.685 0.145 -4.706 0.000 -0.685 -0.321
## P -0.449 0.183 -2.455 0.014 -0.449 -0.209
## Colswt1 ~
## A -0.182 0.142 -1.280 0.201 -0.182 -0.082
## P 0.088 0.178 0.494 0.621 0.088 0.039
## Numswt1 ~
## A -0.289 0.145 -1.991 0.046 -0.289 -0.135
## P -0.084 0.186 -0.453 0.651 -0.084 -0.039
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .EF1 ~~
## .EF2 1.000 1.000 1.000
## .EF3 1.000 1.000 1.000
## .EF2 ~~
## .EF3 1.000 1.000 1.000
## .UP1 ~~
## .UP2 1.000 1.000 1.000
## .UP2 ~~
## .UP3 1.000 1.000 1.000
## .UP1 ~~
## .UP3 1.000 1.000 1.000
## .SW1 ~~
## .SW2 1.000 1.000 1.000
## .SW3 1.000 1.000 1.000
## .SW2 ~~
## .SW3 1.000 1.000 1.000
## .UP1 ~~
## .SW1 0.000 0.000 0.000
## .UP2 ~~
## .SW2 0.000 0.000 0.000
## .UP3 ~~
## .SW3 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AS 7.535 1.362 5.533 0.000 20.996 20.996
## STR 11.544 1.726 6.687 0.000 18.203 18.203
## Keep 8.427 0.977 8.625 0.000 16.181 16.181
## Let 0.554 0.004 138.720 0.000 30.328 30.328
## NB 7.931 0.543 14.612 0.000 13.482 13.482
## cat 18.677 8.143 2.294 0.022 33.847 33.847
## col 6.290 1.691 3.720 0.000 22.502 22.502
## Num 10.416 1.509 6.902 0.000 22.358 22.358
## .Antisc1 (IAn1) 2.253 1.363 1.653 0.098 2.253 2.305
## .Antisc2 0.000 0.000 0.000
## .Antisc3 0.000 0.000 0.000
## .Stroop1 (ISt1) -1.663 1.726 -0.964 0.335 -1.663 -1.614
## .Stroop2 0.000 0.000 0.000
## .Stroop3 0.000 0.000 0.000
## .Keptrk1 (IKp1) 1.407 0.981 1.435 0.151 1.407 1.424
## .Keptrk2 0.000 0.000 0.000
## .Keptrk3 0.000 0.000 0.000
## .Letmem1 (ILt1) 9.220 0.057 162.332 0.000 9.220 10.230
## .Letmem2 0.000 0.000 0.000
## .Letmem3 0.000 0.000 0.000
## .Nback1 (INb1) 1.846 0.544 3.394 0.001 1.846 1.978
## .Nback2 0.000 0.000 0.000
## .Nback3 0.000 0.000 0.000
## .Catswt1 (ICt1) -8.708 8.143 -1.069 0.285 -8.708 -8.651
## .Catswt2 0.000 0.000 0.000
## .Catswt3 0.000 0.000 0.000
## .Colswt1 (ICl1) 3.610 1.692 2.133 0.033 3.610 3.458
## .Colswt2 0.000 0.000 0.000
## .Colswt3 0.000 0.000 0.000
## .Numswt1 (INm1) -0.650 1.509 -0.431 0.666 -0.650 -0.643
## .Numswt2 0.000 0.000 0.000
## .Numswt3 0.000 0.000 0.000
## .EF1 0.000 0.000 0.000
## .EF2 0.000 0.000 0.000
## .EF3 0.000 0.000 0.000
## .UP1 0.000 0.000 0.000
## .UP2 0.000 0.000 0.000
## .UP3 0.000 0.000 0.000
## .SW1 0.000 0.000 0.000
## .SW2 0.000 0.000 0.000
## .SW3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Ant1 (VAR_A1) 0.328 0.030 11.064 0.000 0.328 0.343
## .Ant2 (VAR_A2) 0.136 0.025 5.408 0.000 0.136 0.148
## .Ant3 (VAR_A3) 0.149 0.024 6.177 0.000 0.149 0.156
## .Str1 (VAR_S1) 0.598 0.077 7.745 0.000 0.598 0.563
## .Str2 (VAR_S2) 0.371 0.057 6.549 0.000 0.371 0.492
## .Str3 (VAR_S3) 0.512 0.057 9.024 0.000 0.512 0.572
## .Kpt1 (VAR_K1) 0.530 0.050 10.637 0.000 0.530 0.542
## .Kpt2 (VAR_K2) 0.260 0.044 5.898 0.000 0.260 0.301
## .Kpt3 (VAR_K3) 0.361 0.045 8.070 0.000 0.361 0.372
## .Ltm1 (VAR_L1) 0.320 0.035 9.206 0.000 0.320 0.394
## .Ltm2 (VAR_L2) 0.312 0.051 6.123 0.000 0.312 0.309
## .Ltm3 (VAR_L3) 0.434 0.051 8.498 0.000 0.434 0.383
## .Nbc1 (VAR_Nb1) 0.352 0.033 10.811 0.000 0.352 0.403
## .Nbc2 (VAR_Nb2) 0.173 0.035 4.987 0.000 0.173 0.177
## .Nbc3 (VAR_Nb3) 0.299 0.036 8.279 0.000 0.299 0.273
## .Cts1 (VAR_Ct1) 0.304 0.132 2.298 0.022 0.304 0.300
## .Cts2 (VAR_Ct2) 0.448 0.059 7.592 0.000 0.448 0.463
## .Cts3 (VAR_Ct3) 0.412 0.047 8.727 0.000 0.412 0.443
## .Cls1 (VAR_Cl1) 0.664 0.047 14.115 0.000 0.664 0.610
## .Cls2 (VAR_Cl2) 0.346 0.048 7.261 0.000 0.346 0.379
## .Cls3 (VAR_Cl3) 0.227 0.037 6.086 0.000 0.227 0.285
## .Nms1 (VAR_Nm1) 0.333 0.037 9.107 0.000 0.333 0.326
## .Nms2 (VAR_Nm2) 0.195 0.030 6.452 0.000 0.195 0.216
## .Nms3 (VAR_Nm3) 0.189 0.029 6.560 0.000 0.189 0.211
## AS (VAR_AS) 0.129 0.081 1.593 0.111 1.000 1.000
## STR (VAR_ST) 0.402 0.085 4.723 0.000 1.000 1.000
## Keep (VAR_Kp) 0.271 0.055 4.965 0.000 1.000 1.000
## Let (VAR_Lt) 0.000 0.000 2.378 0.017 1.000 1.000
## NB (VAR_NB) 0.346 0.044 7.784 0.000 1.000 1.000
## cat (VAR_ct) 0.304 0.137 2.220 0.026 1.000 1.000
## col (VAR_cl) 0.078 0.041 1.923 0.054 1.000 1.000
## Num (VAR_Nm) 0.217 0.054 4.042 0.000 1.000 1.000
## .EF1 1.000 0.193 0.193
## .EF2 1.000 0.920 0.920
## .EF3 1.000 0.893 0.893
## .UP1 1.000 0.708 0.708
## .UP2 1.000 0.897 0.897
## .UP3 1.000 0.885 0.885
## .SW1 1.000 0.609 0.609
## .SW2 1.000 0.981 0.981
## .SW3 1.000 0.951 0.951
clipboard(GrowthModelResult)
## File saved in the clipboard; please paste it in any program you wish.
clipboard(GrowthModelResult,
what = "est", # <- estimates, not matrices
standardized = TRUE,
digits = 3, ci = TRUE)
## File saved in the clipboard; please paste it in any program you wish.
clipboard(GrowthModelResult,
what = "est", # estimates
standardized = TRUE, # include standardized columns
ci = TRUE, # optional: confidence intervals
digits = 2) # rounding
## File saved in the clipboard; please paste it in any program you wish.
clipboard(GrowthModelResult, "fit", "coef", standardized=TRUE)
## Warning in pchisq(X2, df = df, ncp = ncp): NaNs produced
## Warning in pchisq(X2, df = df, ncp = ncp): NaNs produced
## File saved in the clipboard; please paste it in any program you wish.
clipboard(GrowthModelResult, "coef", standardized=TRUE)
## File saved in the clipboard; please paste it in any program you wish.