gof.lmm.pan: Goodness-of fit test for LMM as proposed by Pan et al.

In rokblagus/gofLMM: Goodness-of-fit for linear mixed effects model (gofLMM)

Goodness-of fit test for LMM as proposed by Pan et al.

Description

Obsolete. Goodness-of fit test based on cumulative sum stochastic process using the simulation approach proposed by Pan et al. Note: could equivalently obtained from gof.lmm(...,order.by.original=TRUE,type="simulation",flip.cluster=TRUE,use.sigmoid = FALSE,transform=FALSE,...)

Usage

gof.lmm.pan(
  fit,
  residuals = c("individual", "cluster"),
  std.type = c(1, 2),
  use.correction.for.imbalance = FALSE,
  subset.fix = NULL,
  M = 100,
  verbose = FALSE,
  use.normal = FALSE,
  use.mammen = FALSE,
  use.sigmoid = FALSE,
  lambda = 0.5
)

Arguments

fit	The result of a call to "nlme". The model must be fitted with control=lmeControl( returnObject = TRUE) and keep.data=TRUE. An error message is returned otherwise. ID variable must be numeric and ordered from 1:N !
residuals	Residuals to be used when constructing the process. Possible values are "individual" and "cluster" for \textitindividual and \textitcluster-speciffic residuals, respectively.
std.type	Type of standardization to be used for the residuals when constructing the process. Currently implemeneted options are 1 and 2 for $S_i=\hat\sigma^-1/2I_n_i$ and $S_i=\hatV_i^-1/2$.
use.correction.for.imbalance	Logical. use $n_i^-1/2 S_i$ when standardizing the residuals. Defaults to FALSE.
subset.fix	Two-sided formula. If nonnull, the process $W^F^s$ will be constructed using the variables defined on the RHS of the formula. Deafults to NULL and the process $W^F^s$ is not constructed.
M	Number of random simulations/sign-flipps/permutations. Defaults to 100.
verbose	Logical. Print the current status of the test. Can slow down the algorithm, but it can make it feel faster. Defaults to FALSE.
use.normal	Lolgical. Use normal random variables instead of sign-flip. Defaultes to FALSE.
use.mammen	Logical. Use Mammen's 2 point distribution instead of sign-flip. Not in use when use.normal=TRUE. Defaults to FALSE.

Value

An object of class "gofLMM" for which plot and summary functions are available.

Author(s)

Rok Blagus, [e-mail]

See Also

gof.lmm, plot.gofLMM and summary.gofLMM

Examples

# simulate some data:
N=50
set.seed(1)
n<-floor(runif(N,min=1,max=15)) #imbalanced
betas<-c(1,1,1,15) #don't change! #the last one is only used whe omit.important.predictor=TRUE
norm.eps<-FALSE
shape=0.5
scale=1
norm.re.intercept<-FALSE
shape.re.intercept=0.5
scale.re.intercept=1
norm.re.slope<-FALSE
shape.re.slope=0.5
scale.re.slope=1
sim.re.slope=FALSE
over.parameterized.model=FALSE #i.e. fit a variable which is not used when generating the data
omit.important.predictor=FALSE
yy<-NA
x22<-NA
id<-NA
x1<-NA
for (gg in 1:N){

  id<-c(id,rep(gg,each=n[gg]))
  x11<-rep(rbinom(1,size=1,prob=0.4),each=n[gg])
  x1<-c(x1,x11)

  if (norm.re.intercept==TRUE) re.int<-rnorm(1,sd=sqrt(2)) else re.int<-rgamma(1,shape=shape.re.intercept,scale=scale.re.intercept)-shape.re.intercept*scale.re.intercept

  b<-rep(re.int,each=n[gg])

  if (norm.re.slope==TRUE) re.slope<-rnorm(1,sd=sqrt(1)) else re.slope<-rgamma(1,shape=shape.re.slope,scale=scale.re.slope)-shape.re.slope*scale.re.slope

  b2<-rep(re.slope,each=n[gg])
  x2<-1:n[gg]
  x4<-runif(n[gg])

  if (norm.eps==TRUE) eps<-rnorm(n[gg]) else eps<-rgamma(n[gg],shape=shape,scale=scale)-shape*scale

  if (sim.re.slope==TRUE) {
    if (omit.important.predictor==FALSE) y<-betas[1]+betas[2]*x2+betas[3]*(x11*x2)+b+b2*x2+eps else y<-betas[1]+betas[2]*x2+betas[3]*(x11*x2)+b+b2*x2+eps+betas[4]*x4
  } else {
    if (omit.important.predictor==FALSE) y<-betas[1]+betas[2]*x2+betas[3]*(x11*x2)+b+eps else y<-betas[1]+betas[2]*x2+betas[3]*(x11*x2)+b+eps+betas[4]*x4
  }
  yy<-c(yy,y)
 x22<-c(x22,x2)
}
yy<-yy[-1]
x22<-x22[-1]
x1<-x1[-1]
id<-id[-1]
x4<-runif(sum(n))
aids.art<-data.frame(ptnt=id,outcome=yy,x1=x1,x2=x22,x4=x4)
library(nlme)
fit<-lme(fixed=outcome~ x2+x1:x2, data=aids.art, random=~x2|ptnt,control=lmeControl( returnObject = TRUE),method="REML" )
fit.gof.pan<-gof.lmm.pan(fit,residuals= "individual" ,std.type=2,use.correction.for.imbalance=FALSE,subset.fix=outcome~x2,M=25,verbose=TRUE)
plot.gofLMM(fit.gof.pan,type=2,subset.M=NULL,xlab="",main="Example")
summary.gofLMM(fit.gof.pan)

rokblagus/gofLMM documentation built on April 4, 2022, 8:41 p.m.