multinomial: Estimate probabilities in contingency table

In kkholst/lava: Latent Variable Models

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)

kkholst/lava documentation built on Feb. 22, 2024, 4:07 p.m.