Repeated Measures for MLQ
#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
#Creating a new variable that is the mean of all positive purpose MLQ questions
data.test4$MLQP <- apply(data.test4[, c("MLQ1" ,"MLQ4", "MLQ5", "MLQ6")], 1, mean, na.rm = TRUE)For lme to work GROUP and ID need to be seen as factors
setwd## function (dir)
## .Internal(setwd(dir))
## <bytecode: 0x102db0f78>
## <environment: namespace:base>data.test4$GROUP <-as.factor(data.test4$GROUP)
data.test4$ID <-as.factor(data.test4$ID)
# Load the psych package
library(psych)Describe the MLQ variable by the GROUP variable
describeBy(data.test4$MLQP, group = data.test4$GROUP)## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 103 4.89 1.18 5 4.92 1.48 1.5 7 5.5 -0.31 -0.23 0.12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 91 5.15 1.39 5.5 5.26 1.48 2 7 5 -0.49 -0.65 0.15
## --------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 7 4.82 1.3 5 4.82 1.85 3.25 6.25 3 -0.08 -2.09 0.49Create a plot that visualizes MLQ variable by the GROUP variable
library(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%qplot(GROUP, MLQP, data=data.test4, geom="boxplot")
# Load the nlme package
library(nlme)Two way repeated measures
with(data.test4, boxplot(MLQP ~ wave + GROUP))
Graphing the Two-Way Interaction.
# Load the nlme package
library(nlme)I am not sure if I am doing this right
baseline <- lme(MLQP ~ 1, random = ~1 | ID/GROUP/wave, data = data.test4, method = "ML")
MLQPModel <- lme(MLQP ~ GROUP, random = ~1 | ID/GROUP/wave, data = data.test4, method = "ML")
MLQP2Model <- lme(MLQP ~ GROUP + wave, random = ~1 | ID/GROUP/wave, data = data.test4, method = "ML")
fullModel <- lme(MLQP ~ GROUP * wave, random = ~1 | ID/GROUP/wave, data = data.test4, method = "ML")We again the significance of our models by comparing them from the baseline model using the anova() function.
anova(baseline, MLQPModel, MLQP2Model, fullModel)## Model df AIC BIC logLik Test L.Ratio p-value
## baseline 1 5 608.5 625.1 -299.3
## MLQPModel 2 7 611.1 634.2 -298.6 1 vs 2 1.446 0.4852
## MLQP2Model 3 8 590.5 616.9 -287.2 2 vs 3 22.620 <.0001
## fullModel 4 10 583.4 616.4 -281.7 3 vs 4 11.099 0.0039summary(fullModel)## Warning: NaNs produced## Linear mixed-effects model fit by maximum likelihood
## Data: data.test4
## AIC BIC logLik
## 583.4 616.4 -281.7
##
## Random effects:
## Formula: ~1 | ID
## (Intercept)
## StdDev: 1.091
##
## Formula: ~1 | GROUP %in% ID
## (Intercept)
## StdDev: 0.0003546
##
## Formula: ~1 | wave %in% GROUP %in% ID
## (Intercept) Residual
## StdDev: 0.5363 0.3463
##
## Fixed effects: MLQP ~ GROUP * wave
## Value Std.Error DF t-value p-value
## (Intercept) 4.495 0.2312 108 19.446 0.0000
## GROUP1 -0.353 0.3299 0 -1.070 NaN
## GROUP2 -0.475 0.8558 86 -0.555 0.5801
## wave 0.116 0.0862 108 1.340 0.1829
## GROUP1:wave 0.426 0.1274 108 3.347 0.0011
## GROUP2:wave 0.329 0.3760 108 0.876 0.3832
## Correlation:
## (Intr) GROUP1 GROUP2 wave GROUP1:
## GROUP1 -0.680
## GROUP2 -0.270 0.184
## wave -0.645 0.449 0.174
## GROUP1:wave 0.454 -0.675 -0.123 -0.679
## GROUP2:wave 0.148 -0.103 -0.671 -0.229 0.156
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.46596 -0.23039 0.01945 0.29265 1.25978
##
## Number of Observations: 201
## Number of Groups:
## ID GROUP %in% ID wave %in% GROUP %in% ID
## 88 89 200```