boot.gllm: Bootstrap for generalized log-linear modelling

In gllm: Generalised log-Linear Model

Bootstrap for generalized log-linear modelling

Description

Fits log-linear models for incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Performs a bootstrap for the sampling distribution of the full unobserved table.

Usage

boot.gllm(y,s,X,method="hybrid",em.maxit=1,tol=0.00001,strata=NULL,R=200)

Arguments

y	is the observed contingency table.
s	is a vector of indices, one for each cell of the full (unobserved) contingency table, representing the appropriate cell of y
X	is the design matrix, or a formula.
method	chooses the EM, Fisher scoring or a hybrid (EM then scoring) method for fitting the model.
em.maxit	is the number of EM iterations.
tol	is the convergence criterion for the LR criterion.
strata	is a vector identifying the sampling strata.
R	is the number of bootstrap replicates.

Details

The generalized log-linear model allows for modelling of incomplete contingency tables, that is tables where one or more dimensions have been collapsed over. See gllm for details.

Often, functions of the full unobserved table are the main focus of the analysis. For example, in a double sampling design where there is a gold standard measure for one part of the data set and only an unreliable measure for another part, the expected value of the gold standard in the entire dataset is the outcome of interest. The standard error of this statistic may be a complex function of the observed counts and model parameters.

Bootstrapping is one way to estimate such standard errors from a complex sampling design. The bootstrap sampling may be stratified if the design implies this, e.g. product-multinomial.

Value

A matrix R+1 by ncol(X) containing the initial estimate of the full (unobserved) contingency table, and the R bootstrap replicates of the full table.

References

Hochberg Y (1977). On the use of double sampling schemes in analyzing categorical data with misclassification errors. J Am Statist Ass 72:914-921.

Examples

 
#
# Fit Hochberg 1977 double sampling data
# 2x2 table of imprecise measures and 2x2x2x2 reliability data
#
# 2x2 table of imprecise measures
#
y1 <-c(1196, 13562,
       7151, 58175)
a2<- 2-as.integer(gl(2,1,4))
b2<- 2-as.integer(gl(2,2,4))
set1<-data.frame(y1,a2,b2)
#
# 2x2x2x2 reliability data
#
y2<-c(17, 3,   10, 258,
       3, 4,    4,  25,
      16, 3,   25, 197,
     100, 13, 107, 1014)

a <- 2-as.integer(gl(2,1,16))
a2<- 2-as.integer(gl(2,2,16))
b <- 2-as.integer(gl(2,4,16))
b2<- 2-as.integer(gl(2,8,16))

set2<-data.frame(y2,a,a2,b,b2)
#
# Combined analysis
#
y<-c(y1,y2)
#
# Map observed table onto underlying 2x2x2x2x2 table
#
s <-c(1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4,
      5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
#
# Model combining the tables is A*A2*B*B2 + L (dummy study variable)
#
a <- 2-as.integer(gl(2,1,32))
a2<- 2-as.integer(gl(2,2,32))
b <- 2-as.integer(gl(2,4,32))
b2<- 2-as.integer(gl(2,8,32))
l <- 2-as.integer(gl(2,16,32))

X <- model.matrix( ~ a*a2*b*b2+l)

#
# Table 1 using unreliable measure
#
res1<-glm(y1 ~ a2*b2, family=poisson(),data=set1)
print(summary(res1))
#
# Table 2 using reliable measure
#
res2a<-glm(y2 ~ a*b, family=poisson(),data=set2)
print(summary(res2a))
#
# Table 2 demonstrating complex relationship between gold standard and
# unreliable measure
#
res2b<-glm(y2 ~ a*a2*b*b2, family=poisson(),data=set2)
print(summary(res2b))
#
# Combined analysis
#
require(gllm)
res12<-gllm(y,s,X)
print(summary.gllm(res12))
#
# Bootstrap the collapsed table to get estimated OR for reliable measures
#
# a and b are binary vectors the length of the *full* table
# and define the variables for which the odds ratio is to be
# estimated, here the reliable measure of injury and seatbelt usage.
#
boot.hochberg <- function (y,s,X,nrep,a,b) {
  z<-boot.gllm(y,s,X,R=nrep)
  boot.tab<-cbind(apply(z[,a & b],1,sum),
                  apply(z[,!a & b],1,sum),
                  apply(z[,a & !b],1,sum),
                  apply(z[,!a & !b],1,sum))
  oddsr<-boot.tab[,1]*boot.tab[,4]/boot.tab[,2]/boot.tab[,3] 
  hochberg.tab<-data.frame( c("yes","yes","no","no"),
                            c("yes","no","yes","no"),
                            boot.tab[1,],
                            apply(boot.tab[2:(1+nrep),],2,sd))
  colnames(hochberg.tab)<-c("Precise Injury","Precise seatbelt usage",
                            "Estimated Count","Bootstrap S.E.")
  print(hochberg.tab)
  cat("\nEstimated OR=",oddsr[1],"\n")
  cat("        Bias=",oddsr[1]-mean(oddsr[2:(1+nrep)]),"\n")
  cat("Bootstrap SE=",sd(oddsr[2:(1+nrep)]),"\n\nQuantiles\n\n")
  quantile(oddsr[2:(1+nrep)],c(0.025,0.50,0.975))
}
boot.hochberg(y,s,X,nrep=20,a,b)

gllm documentation built on Oct. 18, 2022, 9:06 a.m.