lava (version 1.4.1)

multinomial: Estimate probabilities in contingency table

Description

Estimate probabilities in contingency table

Usage

multinomial(x, marginal = FALSE, transform, ...)

Arguments

x

Matrix or data.frame with observations (1 or 2 columns)

marginal

If TRUE the marginals are estimated

transform

Optional transformation of parameters (e.g., logit)

...

Additional arguments to lower-level functions

Examples

set.seed(1)
breaks <- c(-Inf,-1,0,Inf)
m <- lvm(); covariance(m,pairwise=TRUE) <- ~y1+y2+y3+y4
d <- transform(sim(m,5e2),
              z1=cut(y1,breaks=breaks),
              z2=cut(y2,breaks=breaks),
              z3=cut(y3,breaks=breaks),
              z4=cut(y4,breaks=breaks))

multinomial(d[,5])
(a1 <- multinomial(d[,5:6]))
(K1 <- kappa(a1)) ## Cohen's kappa

K2 <- kappa(d[,7:8])
## Testing difference K1-K2:
estimate(merge(K1,K2,id=TRUE),diff)

estimate(merge(K1,K2,id=FALSE),diff) ## Wrong std.err ignoring dependence
sqrt(vcov(K1)+vcov(K2))
##'
## Average of the two kappas:
estimate(merge(K1,K2,id=TRUE),function(x) mean(x))
estimate(merge(K1,K2,id=FALSE),function(x) mean(x)) ## Independence

## Goodman-Kruskal's gamma
m2 <- lvm(); covariance(m2) <- y1~y2
breaks1 <- c(-Inf,-1,0,Inf)
breaks2 <- c(-Inf,0,Inf)
d2 <- transform(sim(m2,5e2),
              z1=cut(y1,breaks=breaks1),
              z2=cut(y2,breaks=breaks2))

(g1 <- gkgamma(d2[,3:4]))
## same as
gkgamma(table(d2[,3:4]))
gkgamma(multinomial(d2[,3:4]))