Multilevel models in R using nmle package - r

Question

I am using the nlme package to learn multilevel models, and following examples from the textbook "Discovering Statistics Using R" when it happened.
Mixed Models Code
The data set is Honeymoon Period.dat, also downloadable under their companion website.
Data Set - Multilevel Models
require(nlme)
require(reshape2)
satisfactionData = read.delim("Honeymoon Period.dat", header = TRUE)
restructuredData<-melt(satisfactionData, id = c("Person", "Gender"), measured = c("Satisfaction_Base", "Satisfaction_6_Months", "Satisfaction_12_Months", "Satisfaction_18_Months"))
names(restructuredData)<-c("Person", "Gender", "Time", "Life_Satisfaction")
#print(restructuredData)
#restructuredData.sorted<-restructuredData[order(Person),]
intercept <-gls(Life_Satisfaction~1, data = restructuredData, method = "ML", na.action = na.exclude)
randomIntercept <-lme(Life_Satisfaction ~1, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt="optim"))
anova(intercept, randomIntercept)
timeRI<-update(randomIntercept, .~. + Time)
timeRS<-update(timeRI, random = ~Time|Person)
ARModel<-update(timeRS, correlation = corAR1(0, form = ~Time|Person))
The error occured at this moment, when I am trying to update "timeRS" model.
The error message is as follows:
Error in as.character.factor(X[[i]], ...) : malformed factor
Any stats people/programmers here who knows what this means?

Answer 1

I have looked at this book. It appears that the coding is wrong. The error you are getting is because your time variable is a factor and you need it to be numeric. In the author's first figure in the book he represents time as numeric (0 - 3) but his code for the models is incorrect. I've recoded it for you:
## First, Time needs to be recoded as a numeric
restructuredData$Time.Numeric <- with(restructuredData, ifelse(Time == "Satisfaction_Base", 0,
ifelse(Time == "Satisfaction_6_Months", 1,
ifelse(Time == "Satisfaction_12_Months", 2,
ifelse(Time == "Satisfaction_18_Months", 3, NA)))))
## Baseline Model
intercept <-gls(Life_Satisfaction~1, data = restructuredData, method = "ML", na.action = na.exclude)
summary(intercept)
## Model where intercept can vary for Individuals
randomIntercept <- lme(Life_Satisfaction ~ 1, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))
summary(randomIntercept)
## Add time as a fixed effect
timeRI <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))
summary(timeRI)
## Add a random slope to the model by nesting the Individual within the test time
timeRS <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~Time.Numeric|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))
summary(timeRS)
## Modeling the covariance structure structure of the errors with a first-order autoregressive covariance structure
ARModel <- update(timeRS, correlation = corAR1(0, form = ~Time.Numeric|Person))
summary(ARModel)
anova(intercept, randomIntercept, timeRI, timeRS, ARModel)
The anova read out for model comparisons is now exactly as shown in the book.