geometric {VGAM} | R Documentation

## Geometric (Truncated and Untruncated) Distributions

### Description

Maximum likelihood estimation for the geometric and truncated geometric distributions.

### Usage

    geometric(link = "logitlink", expected = TRUE, imethod = 1,
              iprob = NULL, zero = NULL)
    truncgeometric(upper.limit = Inf,
                   link = "logitlink", expected = TRUE, imethod = 1,
                   iprob = NULL, zero = NULL)

### Arguments

`link` | Parameter link function applied to the probability parameter prob, which lies in the unit interval. See `Links` for more choices.
`expected` | Logical. Fisher scoring is used if `expected = TRUE`, else Newton-Raphson.
`iprob, imethod, zero` | See `CommonVGAMffArguments` for details.
`upper.limit` | Numeric. Upper values. As a vector, it is recycled across responses first. The default value means both family functions should give the same result.

### Details

A random variable Y has a 1-parameter geometric distribution if P(Y=y) = prob * (1-prob)^y for y=0,1,2,.... Here, prob is the probability of success, and Y is the number of (independent) trials that are fails until a success occurs. Thus the response Y should be a non-negative integer. The mean of Y is E(Y) = (1-prob)/prob and its variance is Var(Y) = (1-prob)/prob^2. The geometric distribution is a special case of the negative binomial distribution (see `negbinomial`). The geometric distribution is also a special case of the Borel distribution, which is a Lagrangian distribution. If Y has a geometric distribution with parameter prob then Y+1 has a positive-geometric distribution with the same parameter. Multiple responses are permitted.

For `truncgeometric()`, the (upper) truncated geometric distribution can have response integer values from 0 to `upper.limit`. It has density `prob * (1 - prob)^y / [1-(1-prob)^(1+upper.limit)]`.

For a generalized truncated geometric distribution with integer values L to U, say, subtract L from the response and feed in U-L as the upper limit.

### Value

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

### Author(s)

T. W. Yee. Help from Viet Hoang Quoc is gratefully acknowledged.

### References

Forbes, C., Evans, M., Hastings, N. and Peacock, B. (2011) Statistical Distributions, Hoboken, NJ, USA: John Wiley and Sons, Fourth edition.

### See Also

`negbinomial`, `Geometric`, `betageometric`, `expgeometric`, `zageometric`, `zigeometric`, `rbetageom`, `simulate.vlm`.

### Examples

    gdata <- data.frame(x2 = runif(nn <- 1000) - 0.5)
    gdata <- transform(gdata, x3 = runif(nn) - 0.5,
                              x4 = runif(nn) - 0.5)
    gdata <- transform(gdata, eta  = -1.0 - 1.0 * x2 + 2.0 * x3)
    gdata <- transform(gdata, prob = logitlink(eta, inverse = TRUE))
    gdata <- transform(gdata, y1 = rgeom(nn, prob))
    with(gdata, table(y1))
    fit1 <- vglm(y1 ~ x2 + x3 + x4, geometric, data = gdata, trace = TRUE)
    coef(fit1, matrix = TRUE)
    summary(fit1)

    # Truncated geometric (between 0 and upper.limit)
    upper.limit <- 5
    tdata <- subset(gdata, y1 <= upper.limit)
    nrow(tdata)  # Less than nn
    fit2 <- vglm(y1 ~ x2 + x3 + x4, truncgeometric(upper.limit),
                 data = tdata, trace = TRUE)
    coef(fit2, matrix = TRUE)

    # Generalized truncated geometric (between lower.limit and upper.limit)
    lower.limit <- 1
    upper.limit <- 8
    gtdata <- subset(gdata, lower.limit <= y1 & y1 <= upper.limit)
    with(gtdata, table(y1))
    nrow(gtdata)  # Less than nn
    fit3 <- vglm(y1 - lower.limit ~ x2 + x3 + x4,
                 truncgeometric(upper.limit - lower.limit),
                 data = gtdata, trace = TRUE)
    coef(fit3, matrix = TRUE)

[Package VGAM version 1.1-1 Index]