VGAM (version 1.1-6)

fittedvlm: 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.

Value

The fitted values evaluated at the final IRLS iteration.

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 \(n \times M\) matrix of linear predictors.

References

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

See Also

fitted, predictvglm, vglmff-class.

Examples

Run this code
# NOT RUN {
# 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  = logitlink(-0.5       , inverse = TRUE),
                          lambda.3 =   loglink(-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
# }

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