This function performs classical frequentist statistical inference to a discrete multivariate canonical exponential family. It produces the maximum likelihood estimator, one- and two-sided p-values for the test that model parameters are zero, and providing confidence intervals for the parameters. The discrete probability model is given by a set of possible values of the random vectors, and null weights for these vectors. Such a discrete probability model arises in logistic regression, and this function is envisioned to be applied to the results of a network algorithm for conditional logistic regression. Examples apply this to data from \insertCitemehtapatel;textualPHInfiniteEstimates, citing \insertCitegoorinetal87;textualPHInfiniteEstimates.
List of the sort provided by network.
Test level, or 1- confidence level.
Range of possible parameter values.
List with components:
ospv Observed one-sided p values
tspv Observed two-sided p value.
ci confidence interval.
mle Maximum likelihood estimator.
1 2 3 4 5 6 7 8 9 10 11
#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)) out<-network(goorin[,1:3],goorin[,4],conditionon=1:3,resp=goorin[,5]) inference(out)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.