test.logis_re {pprof} | R Documentation |
Conduct hypothesis testing for provider effects from a fitted logis_re
object
Description
Conduct hypothesis tests on provider effects and identify outlying providers for a random effect logistic model.
Usage
## S3 method for class 'logis_re'
test(fit, parm, level = 0.95, null = 0, alternative = "two.sided", ...)
Arguments
fit |
a model fitted from |
parm |
specifies a subset of providers for which confidence intervals are to be given.
By default, all providers are included. The class of |
level |
the confidence level during the hypothesis test, meaning a significance level of |
null |
a number defining the null hypothesis for the provider effects. The default value is 0. |
alternative |
a character string specifying the alternative hypothesis, must be one of
|
... |
additional arguments that can be passed to the function. |
Details
The function identifies outlying providers based on hypothesis test results.
For two-sided tests, 1
indicates performance significantly higher than expected, -1
indicates lower,
For one-sided tests, 1
(right-tailed) or -1
(left-tailed) flags are used.
Providers whose performance falls within the central range are flagged as 0
.
Outlying providers are determined by the test statistic falling beyond the threshold based on the significance level 1 - \text{level}
.
Value
A data frame containing the results of the hypothesis test, with the following columns:
flag |
a flagging indicator where |
p-value |
the p-value of the hypothesis test. |
stat |
the test statistic. |
Std.Error |
the standard error of the provider effect estimate. |
Examples
data(ExampleDataBinary)
outcome <- ExampleDataBinary$Y
ProvID <- ExampleDataBinary$ProvID
covar <- ExampleDataBinary$Z
fit_re <- logis_re(Y = outcome, Z = covar, ProvID = ProvID)
test(fit_re)