Repeated Measures MLQ (all Purpose Questions)
#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
# Load the psych package
library(psych)Creating a new variable that is the mean of all purpose meanmlq questions
data.test4$meanmlq <- apply(data.test4[, c("MLQ1" ,"MLQ4", "MLQ5", "MLQ6", "MLQ9")], 1, mean, na.rm = TRUE)
library(reshape2); library(car)## Warning: package 'car' was built under R version 3.1.2##
## Attaching package: 'car'
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## The following object is masked from 'package:psych':
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## logitdata <- data.test4[,c("ID", "GROUP", "wave", "meanmlq")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanmlq")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )Create new data set with ID Group baseline meanmlq and wave so that we have Baseline, time 1 and 2 to compare to
data2 <- as.data.frame(mapply(c,data[,1:4], data[,c(1:3,5)]))
data2$wave <- rep(1:2, each=89)
names(data2) <- c("ID", "GROUP", "BASELINE", "meanmlq", "WAVE")Drop the cases where participants did not complete the intervention completely
#data2 <- data2[-c(which(data2$GROUP ==2)),]Intention to treat model (ITT) where we keep the cases who dropped out and did not complete the study (http://en.wikipedia.org/wiki/Intention-to-treat_analysis).
data2[which(data2$GROUP ==2), "GROUP"] <- 1For lme to work GROUP and ID need to be seen as factors
data2$GROUP <-as.factor(data2$GROUP)
data2$ID <-as.factor(data2$ID)Describe the meanmlq variable by the GROUP variable
describeBy(data2[,3:4], group = data2$GROUP)## group: 0
## vars n mean sd median trimmed mad min max range skew
## BASELINE 1 86 4.32 0.72 4.4 4.34 0.89 2.6 5.8 3.2 -0.21
## meanmlq 2 59 4.51 0.72 4.6 4.51 0.59 3.0 5.8 2.8 -0.07
## kurtosis se
## BASELINE -0.28 0.08
## meanmlq -0.51 0.09
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## BASELINE 1 88 4.25 0.93 4.2 4.26 0.89 2.2 5.8 3.6 -0.04
## meanmlq 2 54 4.91 0.69 5.0 4.95 0.59 3.0 6.0 3.0 -0.50
## kurtosis se
## BASELINE -0.69 0.10
## meanmlq -0.34 0.09Create a plot that visualizes meanmlq variable by the GROUP variable
library(ggplot2)##
## Attaching package: 'ggplot2'
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## The following object is masked from 'package:psych':
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## %+%Take a look at the residuals
residual <- lm(meanmlq ~ BASELINE, data=data2)$residualPlot the residuals to see that they are random
plot(density(residual))# A density plot
qqnorm(residual) # A quantile normal plot to checking normality
qqline(residual)
Checking the different between intervention and control groups residuals. This allows us to control for individual unsystematic differences.
data2$residual <- NA
sel1 <- which(!is.na(data2$meanmlq))
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, meanmlq, data=data2, geom="boxplot")## Warning: Removed 65 rows containing non-finite values (stat_boxplot).
Plot of the difference between intervention and control groups.
qplot(GROUP, residual, data=data2, geom="boxplot")## Warning: Removed 69 rows containing non-finite values (stat_boxplot).
Two way repeated measures ======================================================== Graphing the Two-Way Interaction. Both meanmlq and the Residuals
# Load the nlme package
library(nlme)
with(data2, boxplot(meanmlq ~ WAVE + GROUP))
with(data2, boxplot(residual ~ WAVE + GROUP))
Comparing Basline to Wave 2 and 3 by Group.
fullModel <- lme(meanmlq ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = data2, method = "ML", na.action = "na.omit")Explanation of significance:
We asses the significance of our models by comparing them from the baseline model using the anova() function.
(Intercept): Where everything is 0
GROUP1: Is there a difference between group. If it is significant than there is a difference and the treatment had an effect.
WAVE: Asseses whether the effects gets bigger beteen time 2 and 3 (does not have to be significant)
BASELINE: Should not be significant. If it is then it shows that there is a difference between groups before the treatment.
GROUP1:WAVE: If this is significant then it means that the effect was either fleeting or it happened after the treatment i.e. between time 2 and 3.
summary(fullModel)## Linear mixed-effects model fit by maximum likelihood
## Data: data2
## AIC BIC logLik
## 176.4 195.3 -81.21
##
## Random effects:
## Formula: ~1 | ID
## (Intercept) Residual
## StdDev: 0.3895 0.3742
##
## Fixed effects: meanmlq ~ GROUP * WAVE + BASELINE
## Value Std.Error DF t-value p-value
## (Intercept) 1.7817 0.3768 66 4.728 0.0000
## GROUP1 0.7796 0.2538 66 3.072 0.0031
## WAVE 0.1315 0.1097 38 1.198 0.2382
## BASELINE 0.5646 0.0779 66 7.246 0.0000
## GROUP1:WAVE -0.2377 0.1605 38 -1.481 0.1469
## Correlation:
## (Intr) GROUP1 WAVE BASELI
## GROUP1 -0.289
## WAVE -0.363 0.595
## BASELINE -0.890 -0.024 -0.042
## GROUP1:WAVE 0.225 -0.875 -0.685 0.054
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.18865 -0.39792 0.06977 0.43677 1.90553
##
## Number of Observations: 109
## Number of Groups: 69Table with P-values
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| (Intercept) | 1.7817 | 0.3768 | 66.0000 | 4.7282 | 0.0000 |
| GROUP1 | 0.7796 | 0.2538 | 66.0000 | 3.0716 | 0.0031 |
| WAVE | 0.1315 | 0.1097 | 38.0000 | 1.1984 | 0.2382 |
| BASELINE | 0.5646 | 0.0779 | 66.0000 | 7.2456 | 0.0000 |
| GROUP1:WAVE | -0.2377 | 0.1605 | 38.0000 | -1.4809 | 0.1469 |
``` Table with confidence intervals
| est. | lower | upper | |
|---|---|---|---|
| (Intercept) | 1.7817 | 1.0468 | 2.5165 |
| GROUP1 | 0.7796 | 0.2846 | 1.2745 |
| WAVE | 0.1315 | -0.0855 | 0.3484 |
| BASELINE | 0.5646 | 0.4126 | 0.7165 |
| GROUP1:WAVE | -0.2377 | -0.5551 | 0.0797 |