gof.lmm.sim.orderbyoriginal.type2.mammen: Goodness-of fit test for LMM, only Pan and SF, faster,...

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

Goodness-of fit test for LMM, only Pan and SF, faster, ordering the residuals by the original fitted values also for the SF approach. In SF approach the same random number is applied to all subjects within the cluste (as in Pan and Lin approach).

Description

Goodness-of fit test based on cumulative sum stochastic process. Used for simulations. Returns only KS and CvM p-values for 2 methods (Pan, SF) and individual as well as cluster specific residuals. Fs not implemented here.

Usage

gof.lmm.sim.orderbyoriginal.type2.mammen(
  fit,
  std.type = c(1, 2),
  use.correction.for.imbalance = FALSE,
  M = 100,
  verbose = FALSE
)

Arguments

fit	The result of a call to "nlme". The model must be fitted with control=lmeControl( returnObject = TRUE) and keep.data=TRUE; ID variable must be numeric and ordered from 1:N !.
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.
M	Number of random simulations/sign-flipps/permutations. Defaults to 100.

Details

no colesky when creating new outcome. in Pan and sign-flip Mammen's 2 point distributrion is used instead of -1,1 or N as in other sim functions.

Value

KS and CvM pvalues for Pan, Simulation, sign-flip and permutations.

Author(s)

Rok Blagus, [e-mail]

See Also

gof.lmm.pan and gof.lmm

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" )
gof.lmm.sim.orderbyoriginal.type2.mammen(fit,std.type=2,use.correction.for.imbalance=FALSE,M=25,verbose=TRUE)

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