context("or_gam")
library(mgcv)
test_that("or_gam works for continuous variable", {
data("data_gam")
library(mgcv)
fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
offset(x3) + x4, data = data_gam) # fit model
# Calculate OR for specific increment step of continuous variable
out <- or_gam(
data = data_gam, model = fit_gam, pred = "x2",
values = c(0.099, 0.198)
)
expect_length(out, 6)
})
test_that("or_gam works with indicator variables", {
data("data_gam")
library(mgcv)
fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
offset(x3) + x4, data = data_gam) # fit model
## Calculate OR for change of indicator variable
out <- or_gam(
data = data_gam, model = fit_gam, pred = "x4",
values = c("B", "D")
)
expect_length(out, 6)
## Calculate ORs for percentage increments of predictor distribution
## (here: 20%)
or_gam(
data = data_gam, model = fit_gam, pred = "x2",
percentage = 20, slice = TRUE
)
})
test_that("or_gam works on percentage increments", {
data("data_gam")
library(mgcv)
fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
offset(x3) + x4, data = data_gam) # fit model
## Calculate ORs for percentage increments of predictor distribution
## (here: 20%)
out <- or_gam(
data = data_gam, model = fit_gam, pred = "x2",
percentage = 20, slice = TRUE
)
expect_length(out, 8)
})
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