PHInfiniteEstimates (version 2.9.5)

network: This function enumerates conditional sample spaces associated with logistic regression,

Description

This function uses a network algorithm to enumerate conditional sample spaces associated with logistic regression, using a minimal version of the algorithm of mehtapatel;textualPHInfiniteEstimates.

Usage

network(
  dm,
  n = NULL,
  resp = NULL,
  conditionon = NULL,
  sst = NULL,
  addint = TRUE,
  verbose = FALSE,
  data.name = "Test data"
)

Value

For a successful run, a list with components:

• possible matrix with vectors of possible unconditioned values of the sufficient statistic.

• count count of entries in the conditional distribution.

• obsd Observed value of unconditioned sufficient statistics.

For an unsuccessful run (because of input inconsistencies) NA

Arguments

dm

matrix of covariates

n

Vector of number of trials. If null, make them all ones.

resp

vector of successes. Used only to calculate the sufficient statistics, unless sufficient statistics are entered directly. Either resp or sst must be provided.

conditionon

indices of covariate matrix indicating sufficient statistics to be conditioned on.

sst

sufficient statistic vector, if input directly. Otherwise, recomputed from resp.

addint

logical, true if a column of 1s must be added to the covariate matrix.

verbose

logical; if true, print intermediate results.

data.name

Name of the data set.

Details

Examples apply this to data from mehtapatel;textualPHInfiniteEstimates, citing goorinetal87;textualPHInfiniteEstimates.

References

mehtapatelPHInfiniteEstimates

goorinetal87PHInfiniteEstimates

Examples

#Columns in table are:
# Lymphocytic Infiltration (1=low, 0=high)
# Sex (1=male, 0=female)
# Any Ostioid Pathology (1=yes, 0=no)
# Number in LI-Sex-AOP group
# Number in LI-Sex-AOP group with disease free interval greater than 3 y
goorin<-data.frame(LI=c(0,0,0,0,1,1,1,1),Sex=c(0,0,1,1,0,0,1,1),
   AOP=c(0,1,0,1,0,1,0,1),N=c(3,2,4,1,5,5,9,17),Y=c(3,2,4,1,5,3,5,6))
# \donttest{
out<-network(goorin[,1:3],goorin[,4],conditionon=1:3,resp=goorin[,5])
inference(out)
# }