EG_LS

``` 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 /Volumes/TOSHIBA EXT/Dropbox/Schools Study Data/Emily Griffith Data and R Scripts
## 
## 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$meanLS <- apply(data[, c("LS_1",      "LS_2"   ,   "LS_3"   ,  
                                 "LS_4"  ,    "LS_5" )], 1, mean, na.rm = TRUE)
#Means or plotting
data$baseline <- ifelse(data$Time<4,0,1) 
pdata <- tapply(data[,"meanLS"], data[,3], mean, na.rm=TRUE)
plot(pdata, type="l")

M0 <- lmer(meanLS ~ 1 + (1|ID), data=data)
fixef(M0)

## (Intercept) 
##       3.641

confint(M0)

## Computing profile confidence intervals ...

## Warning: Last two rows have identical or NA .zeta values: using minstep
## Warning: non-monotonic profile

##             2.5 % 97.5 %
## .sig01      0.000    Inf
## .sigma      1.648  2.132
## (Intercept) 3.299  3.983

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

## (Intercept)        Time 
##       0.907       1.213

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 0.7447
## .sigma      1.2490 1.6799
## (Intercept) 0.2134 1.6030
## Time        0.9268 1.4938

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

## (Intercept)        Time    baseline 
##    -0.05849     1.76551    -2.36838

confint(M2)

## Computing profile confidence intervals ...

##               2.5 %  97.5 %
## .sig01       0.0000  0.6814
## .sigma       1.1546  1.5504
## (Intercept) -0.8287  0.7111
## Time         1.4077  2.1229
## baseline    -3.4140 -1.3327

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

## (Intercept)        Time    baseline   I(Time^2) 
##      -5.540       8.076       3.092      -1.571

confint(M3)

## Computing profile confidence intervals ...

##               2.5 %  97.5 %
## .sig01       0.2424  0.8972
## .sigma       0.8445  1.1874
## (Intercept) -7.0938 -3.9617
## Time         6.3893  9.7307
## baseline     1.4451  4.7021
## I(Time^2)   -1.9774 -1.1573

You can also embed plots, for example:

Intervention was given between 3 and 4 (Time 1 is not relevant because the data is not clean)

boxplot(data$meanLS~Time, data=data, notch=F, col=(c("red","blue", "green", "gold")), main="Life Satisfaction", xlab="Life Satisfaction", xlim = c(), ylim = c(2, 9 ), yaxs = "i")                                                                                                 

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.