zipoisson {VGAM} | R Documentation

## Zero-Inflated Poisson Distribution Family Function

### Description

Fits a zero-inflated or zero-deflated Poisson distribution by full maximum likelihood estimation.

### Usage

    zipoisson(lpstr0 = "logit", llambda = "loge", type.fitted =
              c("mean", "lambda", "pobs0", "pstr0", "onempstr0"), ipstr0 =
              NULL, ilambda = NULL, gpstr0 = NULL, imethod = 1,
              ishrinkage = 0.95, probs.y = 0.35, zero = NULL)
    zipoissonff(llambda = "loge", lonempstr0 = "logit", type.fitted =
                c("mean", "lambda", "pobs0", "pstr0", "onempstr0"),
                ilambda = NULL, ionempstr0 = NULL, gonempstr0 = NULL,
                imethod = 1, ishrinkage = 0.95, probs.y = 0.35, zero =
                "onempstr0")

### Arguments

`lpstr0, llambda` | Link function for the parameter phi and the usual lambda parameter. See `Links` for more choices; see `CommonVGAMffArguments` for more information. For the zero-deflated model see below.
`ipstr0, ilambda` | Optional initial values for phi, whose values must lie between 0 and 1. Optional initial values for lambda, whose values must be positive. The defaults are to compute an initial value internally for each. If a vector then recycling is used.
`lonempstr0, ionempstr0` | Corresponding arguments for the other parameterization. See details below.
`type.fitted` | Character. The type of fitted value to be returned. The first choice (the expected value) is the default. The estimated probability of an observed 0 is an alternative, else the estimated probability of a structural 0, or one minus the estimated probability of a structural 0. See `CommonVGAMffArguments` and `fittedvlm` for more information.
`imethod` | An integer with value `1` or `2` which specifies the initialization method for lambda. If failure to converge occurs try another value and/or else specify a value for `ishrinkage` and/or else specify a value for `ipstr0`. See `CommonVGAMffArguments` for more information.
`ishrinkage` | How much shrinkage is used when initializing lambda. The value must be between 0 and 1 inclusive, and a value of 0 means the individual response values are used, and a value of 1 means the median or mean is used. This argument is used in conjunction with `imethod`. See `CommonVGAMffArguments` for more information.
`zero` | Specifies which linear/additive predictors are to be modelled as intercept-only. If given, the value can be either 1 or 2, and the default is none of them. Setting `zero = 1` makes phi a single parameter. See `CommonVGAMffArguments` for more information.
`gpstr0, gonempstr0, probs.y` | Details at `CommonVGAMffArguments`.

### Details

These models are a mixture of a Poisson distribution and the value 0; it has value 0 with probability phi else is Poisson(lambda) distributed. Thus there are two sources for zero values, and phi is the probability of a structural zero. The model for `zipoisson()` can be written

P(Y = 0) = phi + (1-phi) * exp(-lambda),

and for y=1,2,…,

P(Y = y) = (1-phi) * exp(-lambda) * lambda^y / y!.

Here, the parameter phi satisfies 0 < phi < 1. The mean of Y is (1-phi)*lambda and these are returned as the fitted values, by default. The variance of Y is (1-phi)*lambda*(1 + phi lambda). By default, the two linear/additive predictors of `zipoisson()` are (logit(phi), log(lambda))^T.

The VGAM family function `zipoissonff()` has a few changes compared to `zipoisson()`. These are: (i) the order of the linear/additive predictors is switched so the Poisson mean comes first; (ii) `onempstr0` is now 1 minus the probability of a structural 0, i.e., the probability of the parent (Poisson) component, i.e., `onempstr0` is `1-pstr0`; (iii) argument `zero` has a new default so that the `onempstr0` is intercept-only by default. Now `zipoissonff()` is generally recommended over `zipoisson()` (and definitely recommended over `yip88`). Both functions implement Fisher scoring and can handle multiple responses.

### Value

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

### Warning

Numerical problems can occur, e.g., when the probability of zero is actually less than, not more than, the nominal probability of zero. For example, in the Angers and Biswas (2003) data below, replacing 182 by 1 results in nonconvergence. Half-stepping is not uncommon. If failure to converge occurs, try using combinations of `imethod`, `ishrinkage`, `ipstr0`, and/or `zipoisson(zero = 1)` if there are explanatory variables. The default for `zipoissonff()` is to model the structural zero probability as an intercept-only.

### Note

This family function can be used to estimate the 0-deflated model, hence `pstr0` is not to be interpreted as a probability. One should set, e.g., `lpstr0 = "identitylink"`. Likewise, the functions in `Zipois` can handle the zero-deflated Poisson distribution too. Although the iterations might fall outside the parameter space, the `validparams` slot should keep them inside. A (somewhat) similar alternative for zero-deflation is to try the zero-altered Poisson model (see `zapoisson`).

The use of this VGAM family function with `rrvglm` can result in a so-called COZIGAM or COZIGLM. That is, a reduced-rank zero-inflated Poisson model (RR-ZIP) is a constrained zero-inflated generalized linear model. See COZIGAM. A RR-ZINB model can also be fitted easily; see `zinegbinomial`. Jargon-wise, a COZIGLM might be better described as a COZIVGLM-ZIP.

### Author(s)

T. W. Yee

### References

Thas, O. and Rayner, J. C. W. (2005) Smooth tests for the zero-inflated Poisson distribution. Biometrics, 61, 808–815.

Data: Angers, J-F. and Biswas, A. (2003) A Bayesian analysis of zero-inflated generalized Poisson model. Computational Statistics & Data Analysis, 42, 37–46.

Cameron, A. C. and Trivedi, P. K. (1998) Regression Analysis of Count Data. Cambridge University Press: Cambridge.

Yee, T. W. (2014) Reduced-rank vector generalized linear models with two linear predictors. Computational Statistics and Data Analysis, 71, 889–902.

### See Also

`zapoisson`, `Zipois`, `yip88`, `rrvglm`, `zipebcom`, `rpois`, `simulate.vlm`, `hdeff.vglm`.

### Examples

    # Example 1: simulated ZIP data
    zdata <- data.frame(x2 = runif(nn <- 1000))
    zdata <- transform(zdata, pstr01  = logit(-0.5 + 1*x2, inverse = TRUE),
                              pstr02  = logit( 0.5 - 1*x2, inverse = TRUE),
                              Ps01    = logit(-0.5       , inverse = TRUE),
                              Ps02    = logit( 0.5       , inverse = TRUE),
                              lambda1 =  loge(-0.5 + 2*x2, inverse = TRUE),
                              lambda2 =  loge( 0.5 + 2*x2, inverse = TRUE))
    zdata <- transform(zdata, y1 = rzipois(nn, lambda = lambda1, pstr0 = Ps01),
                              y2 = rzipois(nn, lambda = lambda2, pstr0 = Ps02))

    with(zdata, table(y1))  # Eyeball the data
    with(zdata, table(y2))
    fit1 <- vglm(y1 ~ x2, zipoisson(zero = 1), data = zdata, crit = "coef")
    fit2 <- vglm(y2 ~ x2, zipoisson(zero = 1), data = zdata, crit = "coef")
    coef(fit1, matrix = TRUE)  # These should agree with the above values
    coef(fit2, matrix = TRUE)  # These should agree with the above values

    # Fit all two simultaneously, using a different parameterization:
    fit12 <- vglm(cbind(y1, y2) ~ x2, zipoissonff, data = zdata, crit = "coef")
    coef(fit12, matrix = TRUE)  # These should agree with the above values

    # For the first observation compute the probability that y1 is
    # due to a structural zero.
    (fitted(fit1, type = "pstr0") / fitted(fit1, type = "pobs0"))[1]

    # Example 2: McKendrick (1926). Data from 223 Indian village households
    cholera <- data.frame(ncases = 0:4,  # Number of cholera cases,
                          wfreq  = c(168, 32, 16, 6, 1))  # Frequencies
    fit <- vglm(ncases ~ 1, zipoisson, wei = wfreq, cholera, trace = TRUE)
    coef(fit, matrix = TRUE)
    with(cholera, cbind(actual = wfreq,
                        fitted = round(dzipois(ncases, lambda = Coef(fit)[2],
                                               pstr0 = Coef(fit)[1]) *
                                       sum(wfreq), digits = 2)))

    # Example 3: data from Angers and Biswas (2003)
    abdata <- data.frame(y = 0:7, w = c(182, 41, 12, 2, 2, 0, 0, 1))
    abdata <- subset(abdata, w > 0)
    fit <- vglm(y ~ 1, zipoisson(lpstr0 = probit, ipstr0 = 0.8),
                data = abdata, weight = w, trace = TRUE)
    fitted(fit, type = "pobs0")  # Estimate of P(Y = 0)
    coef(fit, matrix = TRUE)
    Coef(fit)  # Estimate of pstr0 and lambda
    fitted(fit)
    with(abdata, weighted.mean(y, w))  # Compare this with fitted(fit)
    summary(fit)

    # Example 4: zero-deflated model for intercept-only data
    zdata <- transform(zdata, lambda3 = loge(0.0, inverse = TRUE))
    zdata <- transform(zdata, deflat.limit = -1 / expm1(lambda3))  # Boundary
    # The 'pstr0' parameter is negative and in parameter space:
    zdata <- transform(zdata, usepstr0 = deflat.limit / 2)  # Not too near the boundary
    zdata <- transform(zdata, y3 = rzipois(nn, lambda3, pstr0 = usepstr0))
    head(zdata)
    with(zdata, table(y3))  # A lot of deflation
    fit3 <- vglm(y3 ~ 1, zipoisson(zero = -1, lpstr0 = "identitylink"),
                 data = zdata, trace = TRUE, crit = "coef")
    coef(fit3, matrix = TRUE)
    # Check how accurate it was:
    zdata[1, "usepstr0"]  # Answer
    coef(fit3)[1]         # Estimate
    Coef(fit3)
    vcov(fit3)  # Is positive-definite

    # Example 5: This RR-ZIP is known as a COZIGAM or COZIVGLM-ZIP
    set.seed(123)
    rrzip <- rrvglm(Alopacce ~ sm.bs(WaterCon, df = 3), zipoisson(zero = NULL),
                    data = hspider, trace = TRUE, Index.corner = 2)
    coef(rrzip, matrix = TRUE)
    Coef(rrzip)
    summary(rrzip)
    ## Not run: plotvgam(rrzip, lcol = "blue")

[Package VGAM version 1.0-4 Index]