Fitted Values of a VLM object
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".
fittedvlm(object, drop = FALSE, type.fitted = NULL,
percentiles = NULL, ...)object |
a model object that inherits from a VLM. |
drop |
Logical.
If |
type.fitted |
Character.
Some VGAM family functions have a |
percentiles |
See |
... |
Currently unused. |
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.
The fitted values evaluated at the final IRLS iteration.
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.
Thomas W. Yee
Chambers, J. M. and T. J. Hastie (eds) (1992). Statistical Models in S. Wadsworth & Brooks/Cole.
# 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 0Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.