multilogit {VGAM} | R Documentation

## Multi-logit Link Function

### Description

Computes the multilogit transformation, including its inverse and the first two derivatives.

### Usage

    multilogit(theta, refLevel = "(Last)", M = NULL, whitespace = FALSE,
               bvalue = NULL, inverse = FALSE, deriv = 0,
               short = TRUE, tag = FALSE)

### Arguments

`theta` | Numeric or character. See below for further details.
`refLevel, M, whitespace` | See `multinomial`.
`bvalue` | See `Links`.
`inverse, deriv, short, tag` | Details at `Links`.

### Details

The `multilogit()` link function is a generalization of the `logit` link to M levels/classes. It forms the basis of the `multinomial` logit model. It is sometimes called the multi-logit link or the multinomial logit link. When its inverse function is computed it returns values which are positive and add to unity.

### Value

For `multilogit` with `deriv = 0`, the multilogit of `theta`, i.e., `log(theta[, j]/theta[, M+1])` when `inverse = FALSE`, and if `inverse = TRUE` then `exp(theta[, j])/(1+rowSums(exp(theta)))`.

For `deriv = 1`, then the function returns d `eta` / d `theta` as a function of `theta` if `inverse = FALSE`, else if `inverse = TRUE` then it returns the reciprocal.

Here, all logarithms are natural logarithms, i.e., to base e.

### Note

Numerical instability may occur when `theta` is close to 1 or 0 (for `multilogit`). One way of overcoming this is to use, e.g., `bvalue`. Currently `care.exp()` is used to avoid `NA`s being returned if the probability is too close to 1.

### Author(s)

Thomas W. Yee

### References

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd ed. London: Chapman & Hall.

### See Also

`Links`, `multinomial`, `logit`, `normal.vcm`, `CommonVGAMffArguments`.

### Examples

    pneumo <- transform(pneumo, let = log(exposure.time))
    fit <- vglm(cbind(normal, mild, severe) ~ let,
                multinomial, trace = TRUE, data = pneumo)  # For illustration only!
    fitted(fit)
    predict(fit)

    multilogit(fitted(fit))
    multilogit(fitted(fit)) - predict(fit)  # Should be all 0s

    multilogit(predict(fit), inverse = TRUE)  # rowSums() add to unity
    multilogit(predict(fit), inverse = TRUE, refLevel = 1)  # For illustration only
    multilogit(predict(fit), inverse = TRUE) - fitted(fit)  # Should be all 0s

    multilogit(fitted(fit), deriv = 1)
    multilogit(fitted(fit), deriv = 2)

[Package VGAM version 1.0-3 Index]