MLQ Emily Griffith with Time1

library(arm)
## Loading required package: MASS
## Loading required package: Matrix
## Loading required package: lme4
## Loading required package: Rcpp
## 
## arm (Version 1.7-03, built: 2014-4-27)
## 
## Working directory is /Users/levibrackman/Google Drive/R/Emily Griffith
library(lmerTest)
## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
## 
## Attaching package: 'lmerTest'
## 
## The following object is masked from 'package:lme4':
## 
##     lmer
## 
## The following object is masked from 'package:stats':
## 
##     step
library(psych)
## 
## Attaching package: 'psych'
## 
## The following objects are masked from 'package:arm':
## 
##     logit, rescale, sim

data <- read.csv("EmilyGriffith_all.csv")
data$ID <- data$Q1
# Create scale scores
data$meanMLQ <- apply(data[, c("MLQ_1", "MLQ_2", "MLQ_3", "MLQ_4", "MLQ_5", 
    "MLQ_6", "MLQ_7", "MLQ_8", "MLQ_10")], 1, mean, na.rm = TRUE)
# Means or plotting
data$baseline <- ifelse(data$Time < 4, 0, 1)
pdata <- tapply(data[, "meanMLQ"], data[, 3], mean, na.rm = TRUE)
plot(pdata, type = "l")
M0 <- lmer(meanMLQ ~ 1 + (1 | ID), data = data)
fixef(M0)
## (Intercept) 
##       5.449
confint(M0)
## Computing profile confidence intervals ...
##              2.5 % 97.5 %
## .sig01      0.0000 0.6251
## .sigma      0.6769 0.9606
## (Intercept) 5.2685 5.6303
M1 <- update(M0, . ~ . + Time, REML = FALSE)
fixef(M1)
## (Intercept)        Time 
##     5.55073    -0.04638
confint(M1)
## Computing profile confidence intervals ...
## Warning: convergence code 3 from bobyqa: bobyqa -- a trust region step
## failed to reduce q
##               2.5 % 97.5 %
## .sig01       0.0000 0.6226
## .sigma       0.6766 0.9607
## (Intercept)  5.1543 5.9479
## Time        -0.2078 0.1140
M2 <- update(M1, . ~ . + baseline)
fixef(M2)
## (Intercept)        Time    baseline 
##      5.7865     -0.1816      0.5937
confint(M2)
## Computing profile confidence intervals ...
##                2.5 %  97.5 %
## .sig01       0.00000 0.63926
## .sigma       0.65742 0.93697
## (Intercept)  5.32268 6.25018
## Time        -0.39509 0.03179
## baseline    -0.03808 1.22146
M3 <- update(M2, . ~ . + I(Time^2))
fixef(M3)
## (Intercept)        Time    baseline   I(Time^2) 
##     6.30867    -0.78572     0.07464     0.15105
confint(M3)
## Computing profile confidence intervals ...
## Warning: convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
## Warning: convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
##               2.5 % 97.5 %
## .sig01       0.0000 0.6421
## .sigma       0.6527 0.9299
## (Intercept)  5.1327 7.4805
## Time        -2.0511 0.4841
## baseline    -1.1697 1.3179
## I(Time^2)   -0.1620 0.4629

Pdata <- tapply(data[, "meanMLQ"], data[, 3], mean, na.rm = TRUE)
# Add random noise to time to better see the points of interest
data$TimeJIT <- data$Time + runif(126, min = -0.1, max = 0.1)
with(data, plot(TimeJIT, meanCPS, col = "grey", pch = "*"))
## Error: object 'meanCPS' not found
lines(pdata, col = "red", lwd = 2)

plot of chunk unnamed-chunk-1