Compute predictions from renewal objects.
Arguments
- object
Object of class inheriting from
"lm"- newdata
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
- type
type of prediction. If equal to
"response", give the mean probability associated with the individual covariates. If"prob", give the probability of the observed count.- se.fit
A switch indicating if standard errors are required.
- terms
If
type = "terms", which terms (default is all terms), acharactervector.- na.action
function determining what should be done with missing values in
newdata. The default is to predictNA.- time
TODO
- ...
further arguments passed to or from other methods.
Examples
fn <- system.file("extdata", "McShane_Wei_results_boot.RDS", package = "Countr")
object <- readRDS(fn)
data <- object$data
## old data
predOld.response <- predict(object, type = "response", se.fit = TRUE)
predOld.prob <- predict(object, type = "prob", se.fit = TRUE)
## newData (extracted from old Data)
newData <- head(data)
predNew.response <- predict(object, newdata = newData,
type = "response", se.fit = TRUE)
predNew.prob <- predict(object, newdata = newData,
type = "prob", se.fit = TRUE)
cbind(head(predOld.response$values),
head(predOld.response$se$scale),
head(predOld.response$se$shape),
predNew.response$values,
predNew.response$se$scale,
predNew.response$se$shape)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 2.635956 1.077712 1.356622 1.143575 1.077712 1.356622
#> 2 2.626821 1.078592 1.356622 1.141981 1.078592 1.356622
#> 3 2.386165 1.076344 1.356622 1.098238 1.076344 1.356622
#> 4 2.132807 1.081540 1.356622 1.047548 1.081540 1.356622
#> 5 2.505642 1.075564 1.356622 1.120402 1.075564 1.356622
#> 6 2.630692 1.082185 1.356622 1.142656 1.082185 1.356622
cbind(head(predOld.prob$values),
head(predOld.prob$se$scale),
head(predOld.prob$se$shape),
predNew.prob$values,
predNew.prob$se$scale,
predNew.prob$se$shape)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 1.725238e-01 1.077712 1.356622 1.725238e-01 1.077712 1.356622
#> 2 4.097677e-03 1.078592 1.356622 4.097677e-03 1.078592 1.356622
#> 3 1.455299e-01 1.076344 1.356622 1.455299e-01 1.076344 1.356622
#> 4 1.079834e-05 1.081540 1.356622 1.079834e-05 1.081540 1.356622
#> 5 1.583053e-01 1.075564 1.356622 1.583053e-01 1.075564 1.356622
#> 6 1.719442e-01 1.082185 1.356622 1.719442e-01 1.082185 1.356622