oalog {VGAM} | R Documentation

## One-Altered Logarithmic Distribution

### Description

Fits a one-altered logarithmic distribution based on a conditional model involving a Bernoulli distribution and a 1-truncated logarithmic distribution.

### Usage

    oalog(lpobs1 = "logit", lshape = "logit",
          type.fitted = c("mean", "shape", "pobs1", "onempobs1"),
          ipobs1 = NULL, gshape = ppoints(8), zero = NULL)

### Arguments

`lpobs1` | Link function for the parameter pobs1 or phi, called `pobs1` or `phi` here. See `Links` for more choices.
`lshape` | See `logff` for details.
`gshape, type.fitted` | See `CommonVGAMffArguments` and `fittedvlm` for information.

`ipobs1, zero` | See `CommonVGAMffArguments` for information.

### Details

The response Y is one with probability pobs1, or Y has a 1-truncated logarithmic distribution with probability 1-pobs1. Thus 0 < pobs1 < 1, which is modelled as a function of the covariates. The one-altered logarithmic distribution differs from the one-inflated logarithmic distribution in that the former has ones coming from one source, whereas the latter has ones coming from the logarithmic distribution too. The one-inflated logarithmic distribution is implemented in the VGAM package. Some people call the one-altered logarithmic a hurdle model.

The input can be a matrix (multiple responses). By default, the two linear/additive predictors of `oalog` are (logit(phi), logit(shape))^T.

### Value

An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm`, and `vgam`.

The `fitted.values` slot of the fitted object, which should be extracted by the generic function `fitted`, returns the mean mu (default) which is given by

mu = phi + (1- phi) A

where A is the mean of the one-truncated logarithmic distribution. If `type.fitted = "pobs1"` then pobs1 is returned.

### Note

This family function effectively combines `binomialff` and `otlog` into one family function.

### Author(s)

T. W. Yee

### See Also

`Oalog`, `logff`, `oilog`, `CommonVGAMffArguments`, `simulate.vlm`.

### Examples

    odata <- data.frame(x2 = runif(nn <- 1000))
    odata <- transform(odata, pobs1 = logit(-1 + 2*x2, inverse = TRUE),
                              shape  = logit(-2 + 3*x2, inverse = TRUE))
    odata <- transform(odata, y1 = roalog(nn, shape = shape, pobs1 = pobs1),
                              y2 = roalog(nn, shape = shape, pobs1 = pobs1))
    with(odata, table(y1))

    ofit <- vglm(cbind(y1, y2) ~ x2, oalog, data = odata, trace = TRUE)
    coef(ofit, matrix = TRUE)
    head(fitted(ofit))
    head(predict(ofit))
    summary(ofit)

[Package VGAM version 1.0-3 Index]