evaluate {cases} | R Documentation |
Evaluate the accuracy of multiple (candidate) classifiers in several subgroups
Description
Assess classification accuracy of multiple classifcation rules stratified by subgroups, e.g. in diseased (sensitivity) and healthy (specificity) individuals.
Usage
evaluate(
data,
contrast = define_contrast("raw"),
benchmark = 0.5,
alpha = 0.05,
alternative = c("two.sided", "greater", "less"),
adjustment = c("none", "bonferroni", "maxt", "bootstrap", "mbeta"),
transformation = c("none", "logit", "arcsin"),
analysis = c("co-primary", "full"),
regu = FALSE,
pars = list(),
...
)
Arguments
data |
(list) |
contrast |
( |
benchmark |
(numeric) |
alpha |
(numeric) |
alternative |
(character) |
adjustment |
(character) |
transformation |
(character) |
analysis |
(character) |
regu |
(numeric | logical) |
pars |
(list) |
... |
(any) |
Details
Adjustment methods (adjustment
) and additional parameters (pars
or ...
):
"none" (default): no adjustment for multiplicity
"bonferroni": Bonferroni adjustment
"maxt": maxT adjustment, based on a multivariate normal approximation of the vector of test statistics
"bootstrap": Bootstrap approach
nboot: number of bootstrap draws (default: 2000)
type: type of bootstrap, "pairs" (default) or "wild"
dist: residual distribution for wild bootstrap, "Normal" (default) or "Rademacher"
proj_est: should bootstrapped estimates for wild bootstrap be projected into unit interval? (default: TRUE)
res_tra: type of residual transformation for wild boostrap, 0,1,2 or 3 (default: 0 = no transformation) (for details on res_tra options, see this presentation by James G. MacKinnon (2012) and references therein)
"mbeta": A heuristic Bayesian approach which is based on a multivariate beta-binomial model.
nrep: number of posterior draws (default: 5000)
lfc_pr: prior probability of 'least-favorable parameter configuration' (default: 1 if analysis == "co-primary", 0 if analysis == "full").
Value
(cases_results
)
list of analysis results including (adjusted) confidence intervals and p-values
Examples
#
data <- draw_data_roc()
evaluate(data)