genipoisson {VGAM} | R Documentation

## Generally-Inflated Poisson Distribution Family Function

### Description

Fits a generally-inflated Poisson distribution based on a mixture model involving a multinomial distribution and a Poisson distribution.

### Usage

    genipoisson(inflate = 0, zero = NULL, llambda = "loglink",
               type.fitted = c("mean", "lambda", "pstr.i", "onempstr.i"),
               imethod = 1, mux.inflate = 0.5, ipstr0 = NULL,
               ilambda = NULL, ishrinkage = 0.95, probs.y = 0.35)

### Arguments

`inflate` | Vector of inflated values, i.e., nonnegative integers. Must have unique values only. May be a `NULL`, which stands for empty set (same as `poissonff`). The default is the 0-inflated Poisson. See below for recommendations of its use.
`llambda` | See `Links` for more choices and information.
`type.fitted` | See `CommonVGAMffArguments` for information. The choice `"pstr.i"` is the sum of the structural probabilities and `"onempstr.i"` is its complement.
`mux.inflate` | Numeric, between 0 and 1. For the initial values of the multinomial probabilities, the sample proportions are multiplied by this multiplication factor to estimate the amount of inflation. The default is to assume that half of all the inflated values are due to inflation, and the other half from the Poisson distribution. A value close to 1 is recommended for severe amounts of inflation.
`imethod, ipstr0, ilambda` | See `CommonVGAMffArguments` for information.
`probs.y, ishrinkage` | See `CommonVGAMffArguments` for information.
`zero` | See `CommonVGAMffArguments` for information. Setting `zero = "pstr"` will model the multinomial probabilities as simple as possible (intercept-only), hence should be more numerically stable than the default, and this is recommended for many analyses especially when there are many explanatory variables.

### Details

The generally-inflated Poisson distribution is a mixture of an ordinary Poisson distribution and the probability of certain values (`inflate` argument) being inflated by a multinomial distribution. The latter is modelled by a multinomial logit model. Thus each inflated value has two sources: either from the Poisson distribution or the multinomial distribution. The 0-inflated Poisson distribution is a special case and is the default.

This family function can be easily misused. The data should exhibit strong evidence of inflation in certain values before the `inflate` argument is assigned these values. Deflation or very little inflation will result in numerical problems. The `inflate` argument should not be assigned a vector having too many values. Probably `genapoisson` is recommended in general instead as it handles deflation and inflation.

This function implements Fisher scoring and currently does not handle multiple responses.

### 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 (default). The choice `type.fitted = "pstr.i"` returns the sum of all the inflated probabilities.

### Note

This family function does not yet have the robustness of `multinomial` when computing the working weight matrices. Several improvements are needed, e.g., better labelling and initial values and handling multiple responses. And `summaryvglm` does not work yet on these fitted models.

### Author(s)

T. W. Yee and Chenchen Ma

### See Also

`Genipois`, `genapoisson`, `gentpoisson`, `zipoisson`, `zipoissonff`, `multinomial`, `CommonVGAMffArguments`, `simulate.vlm`.

### Examples

    ivec <- c(0, 3)  # Inflate these values
    gdata <- data.frame(x2 = runif(nn <- 1000))
    gdata <- transform(gdata, x3 = runif(nn))
    gdata <- transform(gdata, lambda1   = exp(1 + 2 * x2 + 0.5 * x3),
                              lambda2   = exp(1 - 1 * x2 + 0.5 * x3),
                              lambda3   = exp(1))
    gdata <- transform(gdata,
      y1 = rgenipois(nn, lambda1, pstr.i = c(0.2, 0.3), byrow = TRUE,
                     inflate = ivec),
      y2 = rgenipois(nn, lambda2, pstr.i = c(0.2, 0.3), byrow = TRUE,
                     inflate = ivec),
      y3 = rgenipois(nn, lambda3, pstr.i = c(0.2, 0.3), byrow = TRUE,
                     inflate = ivec))
    genipoisson(inflate = ivec)
    with(gdata, table(y1))
    with(gdata, table(y2))
    with(gdata, table(y3))

    fit1 <- vglm(y1 ~ x2 + x3, genipoisson(inflate = ivec),
                 crit = "coef", trace = TRUE, data = gdata)
    head(fitted(fit1))
    head(predict(fit1))
    coef(fit1, matrix = TRUE)
    # summary(fit1)  # Currently does not work!!

    # Another example ------------------------------
    fit3 <- vglm(y3 ~ 1, genipoisson(inflate = ivec), gdata, trace = TRUE)
    coef(fit3, matrix = TRUE)

[Package VGAM version 1.1-1 Index]