multinomial: Estimate probabilities in contingency table

View source: R/multinomial.R

multinomialR Documentation

Estimate probabilities in contingency table

Description

Estimate probabilities in contingency table

Usage

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

IC

Return ic decomposition (default TRUE)

...

Additional arguments to lower-level functions

Author(s)

Klaus K. Holst

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
## Not run: 
gkgamma(table(d2[,3:4]))
gkgamma(multinomial(d2[,3:4]))

## End(Not run)

##partial gamma
d2$x <- rbinom(nrow(d2),2,0.5)
gkgamma(z1~z2|x,data=d2)

lava documentation built on May 29, 2024, 3:10 a.m.