library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(sandwich)
library(clubSandwich)
## Registered S3 method overwritten by 'clubSandwich':
## method from
## bread.mlm sandwich
library(lme4)
## Loading required package: Matrix
library(lmerTest)
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(interactions)
library(sjPlot)
data0 <- read.delim("C:/Users/Lenovo/Dropbox/STATISTICS/R DATA/KNCAST_PDC_RECODE_R2.dat", row.names=1)
vars <- c("BLYCOGC", "PDC24", "NCIIA_24",
"BBSEX", "RAN", "INC_IMP_R",
"EDU_B", "MSTATUS", "SEFKOR")
data0$complete_flag <- as.integer(complete.cases(data0[, vars]))
table(data0$complete_flag)
##
## 0 1
## 110 722
data<- data0[data0$complete_flag == 1, ]
#######################Centering
data$PDC24_c <- data$PDC24 - mean(data$PDC24, na.rm = TRUE)
data$PDC12_c <- data$PDC12 - mean(data$PDC12, na.rm = TRUE)
data$NCIIA24_c <- data$NCIIA_24 - mean(data$NCIIA_24, na.rm = TRUE)
data$NCIIA12_c <- data$NCIIA_12 - mean(data$NCIIA_12, na.rm = TRUE)
data$INC_IMP_RC <- data$INC_IMP_R - mean(data$INC_IMP_R, na.rm = TRUE)
########################FACTOR
data <- data %>%
mutate(
BBSEX = factor(BBSEX), # 아동 성별
EDU_B = factor(EDU_B), # 엄마 교육수준
MSTATUS = factor(MSTATUS), # 혼인상태
SEFKOR = factor(SEFKOR)) # 국적/한국인 여부
data <- data %>%mutate(BBSEX = factor(BBSEX,levels = c(0, 1),labels = c("Female", "Male")),
EDU_B = factor(EDU_B,levels = c(0, 1), labels = c("High school or less", "College or higher")),
MSTATUS = factor(MSTATUS, levels = c(0, 1),labels = c("Not married", "Married")),
RAN = factor(RAN, levels = c(0, 1),labels = c("Control", "Intervention")),
SEFKOR = factor(SEFKOR, levels = c(0, 1),labels = c("Non-Korean", "Korean")))
COG_B<-lmer(BLYCOGC~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN_B<-lmer(BLYLANC~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN_R_B<-lmer(BLYLREB~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN_E_B<-lmer(BLYLEXB~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT_B<-lmer(BLYMOTC~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT_F_B<-lmer(BLYMOTFB~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT_G_B<-lmer(BLYMOTGB~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
SOC_B<-lmer(BLYSEC~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
ADP_B<-lmer(BLYADC~PDC24_c+NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
round(summary(COG_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 94.190 10.485 681.270 8.983 0.000
## PDC24_c -1.075 0.371 706.826 -2.895 0.004
## NCIIA24_c -0.380 0.486 702.093 -0.783 0.434
## BBSEXMale -3.874 1.006 674.232 -3.851 0.000
## RANIntervention -0.664 1.035 667.292 -0.642 0.521
## INC_IMP_RC 0.798 0.368 674.053 2.167 0.031
## EDU_BCollege or higher 3.250 1.924 660.320 1.690 0.092
## MSTATUSMarried -1.070 9.694 687.265 -0.110 0.912
## SEFKORKorean 9.220 3.527 632.420 2.614 0.009
round(summary(LAN_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 67.832 13.156 679.345 5.156 0.000
## PDC24_c -1.056 0.456 656.358 -2.314 0.021
## NCIIA24_c 0.886 0.595 639.799 1.488 0.137
## BBSEXMale -7.277 1.224 568.597 -5.946 0.000
## RANIntervention -0.611 1.301 671.693 -0.470 0.639
## INC_IMP_RC 1.901 0.462 674.679 4.111 0.000
## EDU_BCollege or higher 6.676 2.420 668.340 2.759 0.006
## MSTATUSMarried 14.222 12.154 681.941 1.170 0.242
## SEFKORKorean 16.209 4.450 655.019 3.642 0.000
round(summary(LAN_R_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.684 2.349 679.334 1.994 0.047
## PDC24_c -0.175 0.082 684.287 -2.125 0.034
## NCIIA24_c 0.196 0.108 673.182 1.824 0.069
## BBSEXMale -1.014 0.222 620.091 -4.575 0.000
## RANIntervention -0.222 0.232 669.613 -0.958 0.338
## INC_IMP_RC 0.329 0.083 673.852 3.987 0.000
## EDU_BCollege or higher 1.103 0.432 665.078 2.554 0.011
## MSTATUSMarried 1.609 2.171 683.154 0.741 0.459
## SEFKORKorean 3.143 0.793 647.126 3.963 0.000
round(summary(LAN_E_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.438 2.623 680.723 1.692 0.091
## PDC24_c -0.182 0.091 662.555 -1.998 0.046
## NCIIA24_c 0.088 0.119 647.284 0.736 0.462
## BBSEXMale -1.412 0.245 580.430 -5.775 0.000
## RANIntervention 0.047 0.259 673.162 0.180 0.857
## INC_IMP_RC 0.317 0.092 676.161 3.436 0.001
## EDU_BCollege or higher 1.163 0.482 669.817 2.410 0.016
## MSTATUSMarried 3.212 2.424 683.347 1.325 0.186
## SEFKORKorean 2.255 0.887 656.529 2.541 0.011
round(summary(MOT_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 96.718 11.365 686.571 8.510 0.000
## PDC24_c -0.697 0.403 707.690 -1.731 0.084
## NCIIA24_c 1.136 0.526 703.687 2.159 0.031
## BBSEXMale -3.304 1.090 680.060 -3.032 0.003
## RANIntervention -0.366 1.122 674.901 -0.326 0.744
## INC_IMP_RC 0.765 0.399 680.547 1.917 0.056
## EDU_BCollege or higher 1.883 2.085 669.055 0.903 0.367
## MSTATUSMarried -4.830 10.507 691.549 -0.460 0.646
## SEFKORKorean 10.944 3.824 645.473 2.862 0.004
round(summary(MOT_F_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.175 1.957 684.640 5.712 0.000
## PDC24_c -0.036 0.069 704.534 -0.523 0.601
## NCIIA24_c 0.172 0.090 699.279 1.906 0.057
## BBSEXMale -1.083 0.187 669.992 -5.792 0.000
## RANIntervention -0.099 0.193 673.324 -0.513 0.608
## INC_IMP_RC 0.190 0.069 678.698 2.764 0.006
## EDU_BCollege or higher 0.003 0.359 667.736 0.009 0.993
## MSTATUSMarried -1.089 1.809 689.433 -0.602 0.547
## SEFKORKorean 1.392 0.659 645.337 2.113 0.035
round(summary(MOT_G_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.741 2.627 691.472 2.947 0.003
## PDC24_c -0.185 0.094 712.994 -1.976 0.049
## NCIIA24_c 0.201 0.123 712.704 1.634 0.103
## BBSEXMale -0.059 0.255 705.832 -0.230 0.818
## RANIntervention -0.018 0.259 674.373 -0.070 0.944
## INC_IMP_RC 0.064 0.092 683.238 0.691 0.490
## EDU_BCollege or higher 0.651 0.481 665.277 1.355 0.176
## MSTATUSMarried -0.523 2.431 698.466 -0.215 0.830
## SEFKORKorean 2.257 0.879 627.246 2.569 0.010
round(summary(SOC_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 104.670 12.153 685.060 8.613 0.000
## PDC24_c 0.350 0.422 667.835 0.831 0.406
## NCIIA24_c 0.947 0.551 654.145 1.721 0.086
## BBSEXMale -2.646 1.132 593.508 -2.338 0.020
## RANIntervention -0.350 1.202 678.535 -0.291 0.771
## INC_IMP_RC 1.053 0.427 681.110 2.467 0.014
## EDU_BCollege or higher 4.395 2.235 675.652 1.967 0.050
## MSTATUSMarried 0.724 11.227 687.300 0.065 0.949
## SEFKORKorean 3.747 4.110 664.157 0.912 0.362
round(summary(ADP_B)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 98.189 8.999 680.951 10.911 0.000
## PDC24_c -0.470 0.310 643.484 -1.514 0.131
## NCIIA24_c 0.094 0.405 625.047 0.232 0.817
## BBSEXMale -4.162 0.831 548.783 -5.008 0.000
## RANIntervention -0.032 0.890 674.292 -0.036 0.972
## INC_IMP_RC 1.269 0.316 676.732 4.011 0.000
## EDU_BCollege or higher -0.128 1.656 671.466 -0.077 0.939
## MSTATUSMarried -4.779 8.313 683.004 -0.575 0.566
## SEFKORKorean 7.513 3.046 660.181 2.466 0.014
COG<-lmer(BLYCOGC~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN<-lmer(BLYLANC~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN_R<-lmer(BLYLREB~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
LAN_E<-lmer(BLYLEXB~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT<-lmer(BLYMOTC~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT_F<-lmer(BLYMOTFB~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
MOT_G<-lmer(BLYMOTGB~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
SOC<-lmer(BLYSEC~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
ADP<-lmer(BLYADC~PDC24_c+NCIIA24_c+PDC24_c:NCIIA24_c+BBSEX+RAN+INC_IMP_RC+EDU_B+MSTATUS+SEFKOR+(1|SUBJNO),
data = data,REML = TRUE)
round(summary(COG)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 94.264 10.496 680.429 8.981 0.000
## PDC24_c -1.095 0.380 699.914 -2.881 0.004
## NCIIA24_c -0.378 0.486 701.073 -0.778 0.437
## BBSEXMale -3.858 1.009 675.546 -3.824 0.000
## RANIntervention -0.664 1.036 666.417 -0.641 0.522
## INC_IMP_RC 0.800 0.368 673.370 2.171 0.030
## EDU_BCollege or higher 3.259 1.925 659.574 1.693 0.091
## MSTATUSMarried -1.068 9.700 686.385 -0.110 0.912
## SEFKORKorean 9.198 3.531 631.112 2.605 0.009
## PDC24_c:NCIIA24_c 0.099 0.399 675.375 0.248 0.804
round(summary(LAN)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 68.597 13.159 677.563 5.213 0.000
## PDC24_c -1.270 0.462 608.459 -2.749 0.006
## NCIIA24_c 0.959 0.591 612.353 1.621 0.106
## BBSEXMale -7.024 1.216 536.721 -5.774 0.000
## RANIntervention -0.619 1.301 670.220 -0.475 0.635
## INC_IMP_RC 1.925 0.462 672.985 4.163 0.000
## EDU_BCollege or higher 6.754 2.421 667.358 2.790 0.005
## MSTATUSMarried 14.324 12.151 679.434 1.179 0.239
## SEFKORKorean 15.919 4.455 655.382 3.574 0.000
## PDC24_c:NCIIA24_c 1.077 0.481 536.152 2.242 0.025
round(summary(LAN_R)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.808 2.349 678.075 2.047 0.041
## PDC24_c -0.207 0.084 658.255 -2.476 0.014
## NCIIA24_c 0.205 0.107 661.428 1.909 0.057
## BBSEXMale -0.979 0.221 606.226 -4.425 0.000
## RANIntervention -0.224 0.232 668.813 -0.963 0.336
## INC_IMP_RC 0.333 0.083 672.842 4.034 0.000
## EDU_BCollege or higher 1.117 0.432 664.774 2.588 0.010
## MSTATUSMarried 1.621 2.170 681.323 0.747 0.455
## SEFKORKorean 3.100 0.794 648.096 3.906 0.000
## PDC24_c:NCIIA24_c 0.173 0.087 605.938 1.978 0.048
round(summary(LAN_E)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.565 2.625 679.305 1.739 0.082
## PDC24_c -0.218 0.093 627.206 -2.354 0.019
## NCIIA24_c 0.096 0.118 630.872 0.807 0.420
## BBSEXMale -1.376 0.244 562.648 -5.643 0.000
## RANIntervention 0.046 0.260 671.888 0.178 0.859
## INC_IMP_RC 0.321 0.092 674.806 3.477 0.001
## EDU_BCollege or higher 1.175 0.483 668.899 2.434 0.015
## MSTATUSMarried 3.223 2.424 681.404 1.330 0.184
## SEFKORKorean 2.209 0.888 656.440 2.487 0.013
## PDC24_c:NCIIA24_c 0.169 0.096 562.183 1.759 0.079
round(summary(MOT)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 97.914 11.307 683.741 8.659 0.000
## PDC24_c -1.027 0.407 690.815 -2.523 0.012
## NCIIA24_c 1.200 0.521 692.520 2.304 0.022
## BBSEXMale -2.991 1.079 660.633 -2.773 0.006
## RANIntervention -0.380 1.116 673.534 -0.340 0.734
## INC_IMP_RC 0.807 0.397 678.388 2.033 0.042
## EDU_BCollege or higher 1.996 2.076 668.725 0.962 0.337
## MSTATUSMarried -4.750 10.446 687.864 -0.455 0.649
## SEFKORKorean 10.554 3.811 648.734 2.769 0.006
## PDC24_c:NCIIA24_c 1.587 0.426 660.471 3.725 0.000
round(summary(MOT_F)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.244 1.956 683.839 5.747 0.000
## PDC24_c -0.054 0.071 697.322 -0.770 0.442
## NCIIA24_c 0.174 0.090 698.631 1.926 0.055
## BBSEXMale -1.068 0.187 672.144 -5.701 0.000
## RANIntervention -0.099 0.193 672.383 -0.514 0.607
## INC_IMP_RC 0.192 0.069 677.976 2.794 0.005
## EDU_BCollege or higher 0.012 0.359 666.858 0.032 0.974
## MSTATUSMarried -1.086 1.808 688.615 -0.601 0.548
## SEFKORKorean 1.371 0.659 643.814 2.082 0.038
## PDC24_c:NCIIA24_c 0.091 0.074 671.991 1.233 0.218
round(summary(MOT_G)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.055 2.609 686.992 3.087 0.002
## PDC24_c -0.277 0.095 708.275 -2.914 0.004
## NCIIA24_c 0.217 0.121 708.700 1.785 0.075
## BBSEXMale 0.021 0.252 695.084 0.081 0.935
## RANIntervention -0.022 0.257 672.716 -0.084 0.933
## INC_IMP_RC 0.075 0.092 680.058 0.822 0.411
## EDU_BCollege or higher 0.680 0.478 665.465 1.423 0.155
## MSTATUSMarried -0.504 2.412 693.107 -0.209 0.834
## SEFKORKorean 2.160 0.876 634.693 2.467 0.014
## PDC24_c:NCIIA24_c 0.416 0.100 694.938 4.170 0.000
round(summary(SOC)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 104.484 12.159 684.215 8.593 0.000
## PDC24_c 0.399 0.431 652.289 0.925 0.355
## NCIIA24_c 0.947 0.551 655.308 1.718 0.086
## BBSEXMale -2.696 1.136 600.591 -2.373 0.018
## RANIntervention -0.348 1.202 677.358 -0.290 0.772
## INC_IMP_RC 1.048 0.427 680.186 2.453 0.014
## EDU_BCollege or higher 4.373 2.235 674.486 1.956 0.051
## MSTATUSMarried 0.714 11.228 686.362 0.064 0.949
## SEFKORKorean 3.809 4.111 662.524 0.926 0.355
## PDC24_c:NCIIA24_c -0.249 0.449 600.256 -0.555 0.579
round(summary(ADP)$coefficients, 3)
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 98.413 9.010 679.767 10.922 0.000
## PDC24_c -0.531 0.316 611.053 -1.680 0.093
## NCIIA24_c 0.104 0.405 614.762 0.257 0.798
## BBSEXMale -4.090 0.832 541.243 -4.917 0.000
## RANIntervention -0.034 0.891 672.957 -0.038 0.970
## INC_IMP_RC 1.276 0.317 675.496 4.029 0.000
## EDU_BCollege or higher -0.104 1.658 670.322 -0.063 0.950
## MSTATUSMarried -4.758 8.320 681.455 -0.572 0.568
## SEFKORKorean 7.431 3.050 659.263 2.436 0.015
## PDC24_c:NCIIA24_c 0.307 0.329 540.673 0.935 0.350
#MODEL COMPARSION
anova(COG_B,COG)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## COG_B: BLYCOGC ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## COG: BLYCOGC ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## COG_B 11 5805.7 5856.1 -2891.8 5783.7
## COG 12 5807.6 5862.6 -2891.8 5783.6 0.0624 1 0.8028
anova(LAN_B,LAN)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## LAN_B: BLYLANC ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## LAN: BLYLANC ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## LAN_B 11 6116.7 6167.1 -3047.3 6094.7
## LAN 12 6113.8 6168.8 -3044.9 6089.8 4.8805 1 0.02716 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LAN_R_B,LAN_R)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## LAN_R_B: BLYLREB ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## LAN_R: BLYLREB ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## LAN_R_B 11 3635.8 3686.2 -1806.9 3613.8
## LAN_R 12 3634.0 3688.9 -1805.0 3610.0 3.8765 1 0.04897 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LAN_E_B,LAN_E)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## LAN_E_B: BLYLEXB ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## LAN_E: BLYLEXB ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## LAN_E_B 11 3789.2 3839.6 -1883.6 3767.2
## LAN_E 12 3788.1 3843.1 -1882.1 3764.1 3.0448 1 0.08099 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(MOT_B,MOT)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## MOT_B: BLYMOTC ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## MOT: BLYMOTC ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## MOT_B 11 5921.8 5972.2 -2949.9 5899.8
## MOT 12 5910.2 5965.2 -2943.1 5886.2 13.518 1 0.0002363 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(MOT_F_B,MOT_F)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## MOT_F_B: BLYMOTFB ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## MOT_F: BLYMOTFB ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## MOT_F_B 11 3379.2 3429.6 -1678.6 3357.2
## MOT_F 12 3379.7 3434.7 -1677.8 3355.7 1.54 1 0.2146
anova(MOT_G_B,MOT_G)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## MOT_G_B: BLYMOTGB ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## MOT_G: BLYMOTGB ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## MOT_G_B 11 3816.9 3867.3 -1897.4 3794.9
## MOT_G 12 3802.1 3857.1 -1889.0 3778.1 16.769 1 4.222e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SOC_B,SOC)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## SOC_B: BLYSEC ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## SOC: BLYSEC ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## SOC_B 11 6002.7 6053.1 -2990.3 5980.7
## SOC 12 6004.4 6059.4 -2990.2 5980.4 0.307 1 0.5795
anova(ADP_B,ADP)
## refitting model(s) with ML (instead of REML)
## Data: data
## Models:
## ADP_B: BLYADC ~ PDC24_c + NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## ADP: BLYADC ~ PDC24_c + NCIIA24_c + PDC24_c:NCIIA24_c + BBSEX + RAN + INC_IMP_RC + EDU_B + MSTATUS + SEFKOR + (1 | SUBJNO)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## ADP_B 11 5565.2 5615.6 -2771.6 5543.2
## ADP 12 5566.4 5621.3 -2771.2 5542.4 0.8673 1 0.3517
#PICK A POINT PLOT
sd_NCIIA24_c <- sd(data$NCIIA24_c, na.rm = TRUE)
sd_NCIIA12_c <- sd(data$NCIIA12_c, na.rm = TRUE)
mean_NCIIA24_c <- mean(data$NCIIA24_c, na.rm = TRUE) # ≈ 0
sd_NCIIA24_c <- sd(data$NCIIA24_c, na.rm = TRUE) # 1.14
PLAN <- plot_model(LAN,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
PLAN_R <- plot_model(LAN_R,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
PLAN_E <- plot_model(LAN_E,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
PMOT <- plot_model(MOT,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
PMOT_F <- plot_model(MOT_F,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
PMOT_G <- plot_model(MOT_G,type = "pred",terms=c("PDC24_c",paste0("NCIIA24_c [",round(-sd_NCIIA24_c, 2), ", 0, ", round(sd_NCIIA24_c, 2), "]")))
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:sjPlot':
##
## set_theme
#DETAIL
PLAN2<-PLAN+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Language Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
PLAN_R2<-PLAN_R+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Receptive Language Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
PLAN_E2<-PLAN_E+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Expresive Language Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
PMOT2<-PMOT+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Motor Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
PMOT_F2<-PMOT_F+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Fine Motor Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
PMOT_G2<-PMOT_G+ theme_classic(base_size = 12,base_family = "Serif")+labs(x = "Number of PDC", y = "Bayley Gross Motor Development",
color = "Caregiver RTC", title = " ") +scale_color_discrete(labels = c("−1 SD","Mean", "+1 SD"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
##JN
MOT_JP<-johnson_neyman(MOT, pred=PDC24_c, modx = NCIIA24_c, vmat = NULL, alpha = 0.05,
plot = TRUE, control.fdr = FALSE, line.thickness = 0.5,
df = "residual", digits = getOption("jtools-digits", 2),
critical.t = NULL, sig.color = "#00BFC4", insig.color = "#F8766D",
mod.range = NULL, title = "")
MOT_JP_F<-MOT_JP$plot + xlab("Responsiveness to Distress") + ylab("PDC effect on Child Motor Development")
MOT_G_JP<-johnson_neyman(MOT_G, pred=PDC24_c, modx = NCIIA24_c, vmat = NULL, alpha = 0.05,
plot = TRUE, control.fdr = FALSE, line.thickness = 0.5,
df = "residual", digits = getOption("jtools-digits", 2),
critical.t = NULL, sig.color = "#00BFC4", insig.color = "#F8766D",
mod.range = NULL, title = "")
MOT_G_JP_F<-MOT_G_JP$plot + xlab("Responsiveness to Distress") + ylab("PDC effect on Gross Motor Development")
LAN_R_JP<-johnson_neyman(LAN_R, pred=PDC24_c, modx = NCIIA24_c, vmat = NULL, alpha = 0.05,
plot = TRUE, control.fdr = FALSE, line.thickness = 0.5,
df = "residual", digits = getOption("jtools-digits", 2),
critical.t = NULL, sig.color = "#00BFC4", insig.color = "#F8766D",
mod.range = NULL, title = "")
LAN_R_JP_F<-LAN_R_JP$plot + xlab("Responsiveness to Distress") + ylab("PDC effect on Receptive Language Development")