R: Fitted Values of a VLM object

fittedvlm {VGAM} | R Documentation

## Fitted Values of a VLM object

### Description

Extractor function for the fitted values of a model object that inherits from a vector linear model (VLM), e.g., a model of class `"vglm"`.

### Usage

    fittedvlm(object, drop = FALSE, type.fitted = NULL,
              percentiles = NULL, ...)

### Arguments

`object` | a model object that inherits from a VLM.
`drop` | Logical. If `FALSE` then the answer is a matrix. If `TRUE` then the answer is a vector.

`type.fitted` | Character. Some VGAM family functions have a `type.fitted` argument. If so then a different type of fitted value can be returned. It is recomputed from the model after convergence. Note: this is an experimental feature and not all VGAM family functions have this implemented yet. See `CommonVGAMffArguments` for more details.
`percentiles` | See `CommonVGAMffArguments` for details.
`...` | Currently unused.

### Details

The “fitted values” usually corresponds to the mean response, however, because the VGAM package fits so many models, this sometimes refers to quantities such as quantiles. The mean may even not exist, e.g., for a Cauchy distribution.

Note that the fitted value is output from the `@linkinv` slot of the VGAM family function, where the `eta` argument is the n x M matrix of linear predictors.

### Value

The fitted values evaluated at the final IRLS iteration.

### Note

This function is one of several extractor functions for the VGAM package. Others include `coef`, `deviance`, `weights` and `constraints` etc. This function is equivalent to the methods function for the generic function `fitted.values`.

If `fit` is a VLM or VGLM then `fitted(fit)` and `predict(fit, type = "response")` should be equivalent (see `predictvglm`). The latter has the advantage in that it handles a `newdata` argument so that the fitted values can be computed for a different data set.

### Author(s)

Thomas W. Yee

### References

Chambers, J. M. and T. J. Hastie (eds) (1992) Statistical Models in S. Wadsworth & Brooks/Cole.

### See Also

`fitted`, `predictvglm`, `vglmff-class`.

### Examples

    # Categorical regression example 1
    pneumo <- transform(pneumo, let = log(exposure.time))
    (fit1 <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo))
    fitted(fit1)

    # LMS quantile regression example 2
    fit2 <- vgam(BMI ~ s(age, df = c(4, 2)),
                 lms.bcn(zero = 1), data = bmi.nz, trace = TRUE)
    head(predict(fit2, type = "response"))  # Equal to the the following two:
    head(fitted(fit2))
    predict(fit2, type = "response", newdata = head(bmi.nz))

    # Zero-inflated example 3
    zdata <- data.frame(x2 = runif(nn <- 1000))
    zdata <- transform(zdata, pstr0.3  = logit(-0.5       , inverse = TRUE),
                              lambda.3 =  loge(-0.5 + 2*x2, inverse = TRUE))
    zdata <- transform(zdata, y1 = rzipois(nn, lambda = lambda.3, pstr0 = pstr0.3))
    fit3 <- vglm(y1 ~ x2, zipoisson(zero = NULL), data = zdata, trace = TRUE)
    head(fitted(fit3, type.fitted = "mean" ))      # E(Y), which is the default
    head(fitted(fit3, type.fitted = "pobs0"))      # P(Y = 0)
    head(fitted(fit3, type.fitted = "pstr0"))      #     Prob of a structural 0
    head(fitted(fit3, type.fitted = "onempstr0"))  # 1 - prob of a structural 0

[Package VGAM version 1.0-3 Index]