catmiss {repeated}

Marginal Probabilities for Categorical Repeated Measurements with Missing Data

Description

catmiss calculates the marginal probabilities of repeated responses. If there are missing values, it gives both the complete data estimates and the estimates using all data. It is useful, for example, when a log linear model is fitted; the resulting fitted values can be supplied to catmiss to obtain the estimates of the marginal probabilities for the model. (Note however that the standard errors do not take into account the fitting of the model.)

Usage

catmiss(response, frequency, ccov = NULL)

Arguments

response

A matrix with one column for each of the repeated measures and one row for each possible combination of responses, including the missing values, indicated by NAs.

frequency

A vector containing the frequencies. Its length must be a multiple of the number of rows of response. Responses are arranged in blocks corresponding to the various possible combinations of values of the explanatory variables.

ccov

An optional matrix containing the explanatory variables (time-constant covariates) as columns, with one line per block of responses in frequency. Thus, the number of rows of response times the number of rows of ccov equals the length of frequency.

Value

A matrix with the probabilities and their standard errors is returned.

Author(s)

J.K. Lindsey

Examples

y <- rpois(27,15)
r1 <- gl(3,1,27)
r2 <- gl(3,3,27)
r3 <- gl(3,9)
# r1, r2, and r3 are factor variables with 3 indicating missing
# independence model with three binary repeated measures
# with missing values
print(z <- glm(y~r1+r2+r3, family=poisson))
# obtain marginal estimates (no observations with 3 missing values)
resp <- cbind(as.integer(r1), as.integer(r2), as.integer(r3))[1:26,]
resp <- ifelse(resp==3, NA, resp)
catmiss(resp, y[1:26])