R/model_get_data.R
construct_model_points.Rd
construct_model_points()
is used to construct model points from generalized linear models, and must
be preceded by model_data()
. construct_model_points()
can also be used
in combination with a data.frame.
construct_model_points(
x,
exposure = NULL,
exposure_by = NULL,
agg_cols = NULL,
drop_na = FALSE
)
Object of class model_data or of class data.frame
column with exposure
split column exposure by (e.g. year)
list of columns to aggregate (sum) by, e.g. number of claims
drop na values (default to FALSE)
data.frame
if (FALSE) {
# With data.frame
library(dplyr)
mtcars %>%
select(cyl, vs) %>%
construct_model_points()
mtcars %>%
select(cyl, vs, disp) %>%
construct_model_points(exposure = disp)
mtcars %>%
select(cyl, vs, disp, gear) %>%
construct_model_points(exposure = disp, exposure_by = gear)
mtcars %>%
select(cyl, vs, disp, gear, mpg) %>%
construct_model_points(exposure = disp, exposure_by = gear,
agg_cols = list(mpg))
# With glm
library(datasets)
data1 <- warpbreaks %>%
mutate(jaar = c(rep(2000, 10), rep(2010, 44))) %>%
mutate(exposure = 1) %>%
mutate(nclaims = 2)
pmodel <- glm(breaks ~ wool + tension, data1, offset = log(exposure),
family = poisson(link = "log"))
model_data(pmodel) %>%
construct_model_points()
model_data(pmodel) %>%
construct_model_points(agg_cols = list(nclaims))
model_data(pmodel) %>%
construct_model_points(exposure = exposure, exposure_by = jaar) %>%
add_prediction(., pmodel)
}