demo/example1linearModel.R

In gppm: Gaussian Process Panel Modeling

library(gppm)
#generate data according to the linear change model
b0 <- 3
b1 <- 2
sigma <- 1 #abbreviation for \sigma_\epsilon^2
nTime <- 3
trueModel <- function(t){b0+b1*t+rnorm(n=1,mean=0,sd=sqrt(sigma))}
tVector <- 1:nTime
yVector <- vapply(tVector, trueModel, 1)

#get results using lm
fittedLM <- lm(yVector ~ tVector)
parasLM <- coefficients(fittedLM)
parasLM[3] <-summary(fittedLM)$sigma
names(parasLM) <- c("LM.b0",'LM.b1','LM.sigma')
print(parasLM)

#get results using GPPM
myData <- data.frame(ID=rep(1,nTime),t=tVector,y=yVector)
gpModel <- gppm('b0+b1*t','(t==t#)*sigma',myData,ID='ID',DV='y')
gpModel <- fit(gpModel)

#compare results
coefSame <- all.equal(parasLM[1:2],coef(gpModel)[1:2],check.attributes = FALSE,tolerance = 0.0001)
message(sprintf('Coefficients of linear model are the same: %s',coefSame))
#residual variances, i.e. parasLM[3] and coef(gpModel) are not the same  because GPPM uses ML for parameter estimation and lm OLS