MLQ (Minus number 9) Emily Griffith

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


data <- data[data$Time > 1, ]

# 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) 
##       5.393

confint(M0)

## Computing profile confidence intervals ...

##              2.5 % 97.5 %
## .sig01      0.3239 0.8979
## .sigma      0.4821 0.8015
## (Intercept) 5.1662 5.6221

M1 <- update(M0, . ~ . + Time, REML = FALSE)
fixef(M1)

## (Intercept)        Time 
##      5.0242      0.1458

confint(M1)

## Computing profile confidence intervals ...

##                2.5 % 97.5 %
## .sig01       0.38766 0.9228
## .sigma       0.46039 0.7630
## (Intercept)  4.49937 5.5570
## Time        -0.04675 0.3320

M2 <- update(M1, . ~ . + baseline)
fixef(M2)

## (Intercept)        Time    baseline 
##     5.41929    -0.03147     0.46563

confint(M2)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4318 0.9390
## .sigma       0.4427 0.7303
## (Intercept)  4.6915 6.1475
## Time        -0.3273 0.2631
## baseline    -0.1438 1.0639

M3 <- update(M2, . ~ . + I(Time^2))

## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

fixef(M3)

## (Intercept)        Time    baseline 
##     5.41929    -0.03147     0.46563

confint(M3)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4318 0.9390
## .sigma       0.4427 0.7303
## (Intercept)  4.6915 6.1475
## Time        -0.3273 0.2631
## baseline    -0.1438 1.0639

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)

## Warning: longer object length is not a multiple of shorter object length

## Error: replacement has 126 rows, data has 89

with(data, plot(TimeJIT, meanCPS, col = "grey", pch = "*"))

## Error: object 'TimeJIT' not found

lines(pdata, col = "red", lwd = 2)