catmiss {repeated} R Documentation

## 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

`glm`, `nordr`

### 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])
```

[Package repeated version 1.0 Index]