# Computing marginal frequencies
n <- c(1:6) #example list of frequencies
var <- c("A","B")
marg <- list(c("A"),c("B"))
dim <- c(2,3)
at <- MarginalMatrix(var,marg,dim)
# list of marginal frequencies:
at
# identitymatrix: several ways of specifying:
marg <- c("A","B")
MarginalMatrix(var, marg,dim)
MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A", "B"), list("Identity", "Identity")))
MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A","B"), list(rbind(c(1,0),c(0,1)), rbind(c(1,0,0),c(0,1,0),c(0,0,1)))))
# omit second category of first variable
at <- MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A","B"), list(rbind(c(1,0)),"Identity")))
at
# Example of maximum augmented empirical likelihood (MAEL) estimation
data(acl)
dat <- acl[, 1:2] + 1 # select 2 items from ACL
var <- 1 : ncol(dat) # define the variables
marg <- Margins(var, c(0, 1)) # margins are total (0) and 1st order
dim <- rep(5, length(var))
n.obs <- RecordsToFrequencies(dat, var, dim, "obs") # frequency vector with observed cells
t(n.obs)
n.1k <- RecordsToFrequencies(dat, var, dim, "1k") # frequency vector with observed and
# some unobserved cells
t(n.1k)
at.obs <- MarginalMatrix(var, marg, dim, vec = n.obs) # marginal matrix based on n.obs
at.obs
at.1k <- MarginalMatrix(var, marg, dim, vec = n.1k) # marginal matrix based on n.1k
at.1k
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