EG_MLQP

MLQ Emily Griffith with Time1

``` Load Packages

library(arm); library(lmerTest); library(psych)

## 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 /private/var/folders/9c/88_yf73x0ys02zvn5hkvvq6r0000gn/T/RtmpmxWTU5
## 
## 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
## 
## 
## Attaching package: 'psych'
## 
## The following objects are masked from 'package:arm':
## 
##     logit, rescale, sim

Loading Data

setwd("/Volumes/TOSHIBA EXT/Dropbox/Schools Study Data/Emily Griffith Data and R Scripts")

data <- read.csv("EmilyGriffith_all.csv")

Creating School ID as ID

data$ID <- data$Q1
#Create scale scores
data$meanMLQ <- apply(data[, c("MLQ_1" ,"MLQ_4", "MLQ_5", "MLQ_6")], 1, mean, na.rm = TRUE)
#Means or plotting
data$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) 
##       5.403

confint(M0)

## Computing profile confidence intervals ...

##              2.5 % 97.5 %
## .sig01      0.4126 0.9868
## .sigma      0.8318 1.1634
## (Intercept) 5.1368 5.6746

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

## (Intercept)        Time 
##     5.23198     0.07806

confint(M1)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4154 0.9871
## .sigma       0.8287 1.1593
## (Intercept)  4.7149 5.7481
## Time        -0.1246 0.2807

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

## (Intercept)        Time    baseline 
##    5.357977    0.006649    0.310006

confint(M2)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4205 0.9898
## .sigma       0.8248 1.1542
## (Intercept)  4.7477 5.9663
## Time        -0.2668 0.2807
## baseline    -0.4944 1.1119

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

## (Intercept)        Time    baseline   I(Time^2) 
##      6.3378     -1.1199     -0.6554      0.2814

confint(M3)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4517 1.0105
## .sigma       0.8073 1.1316
## (Intercept)  4.8332 7.8177
## Time        -2.7016 0.4884
## baseline    -2.2084 0.9169
## I(Time^2)   -0.1146 0.6710

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=-.1, max=.1)
with(data, plot(TimeJIT, meanMLQ, col="grey", pch="*"))
lines(pdata, col="red", lwd=2)

boxplot(data$meanMLQ~Time, data=data, notch=F, col=(c("red","blue", "green", "gold")), main="MLQ All", xlab="Purpose in Life", xlim = c(), ylim = c(2, 8 ), yaxs = "i")