lava (version 1.6.4)

multinomial: Estimate probabilities in contingency table

Description

Estimate probabilities in contingency table

Usage

multinomial(x, data = parent.frame(), marginal = FALSE, transform,
  vcov = TRUE, iid = TRUE, ...)

Arguments

x

Formula (or matrix or data.frame with observations, 1 or 2 columns)

data

Optional data.frame

marginal

If TRUE the marginals are estimated

transform

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

vcov

Calculate asymptotic variance (default TRUE)

iid

Return iid decomposition (default TRUE)

...

Additional arguments to lower-level functions

Examples

# NOT RUN {
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
# }
# NOT RUN {
gkgamma(table(d2[,3:4]))
gkgamma(multinomial(d2[,3:4]))
# }
# NOT RUN {
##partial gamma
d2$x <- rbinom(nrow(d2),2,0.5)
gkgamma(z1~z2|x,data=d2)
# }