confint.logis_fe {pprof} | R Documentation |
Get confidence intervals for provider effects or standardized measures from a fitted logis_fe
object
Description
Provide confidence intervals for provider effects or standardized measures from a fixed effect logistic model.
Usage
## S3 method for class 'logis_fe'
confint(
object,
parm,
level = 0.95,
test = "exact",
option = "SM",
stdz = "indirect",
null = "median",
measure = c("rate", "ratio"),
alternative = "two.sided",
...
)
Arguments
object |
a model fitted from |
parm |
specify a subset of providers for which confidence intervals are given.
By default, all providers are included. The class of |
level |
the confidence level. The default value is 0.95. |
test |
a character string specifying the type of testing method. The default is "exact".
|
option |
a character string specifying whether the confidence intervals should be provided for provider effects or standardized measures:
|
stdz |
a character string or a vector specifying the standardization method
if |
null |
a character string or a number defining the population norm if |
measure |
a character string or a vector indicating whether the output measure is "ratio" or "rate" if
|
alternative |
a character string specifying the alternative hypothesis, must be one of
|
... |
additional arguments that can be passed to the function. |
Details
The wald test is invalid for extreme providers (i.e. when provider effect goes to infinity). We suggest using score or exact test to generate confidence intervals.
Value
A dataframe (option = "gamma"
) or a list of data frames (option = "SM"
) containing the point estimate, and lower and upper bounds of the estimate.
Examples
data(ExampleDataBinary)
outcome = ExampleDataBinary$Y
covar = ExampleDataBinary$Z
ProvID = ExampleDataBinary$ProvID
fit_fe <- logis_fe(Y = outcome, Z = covar, ProvID = ProvID, message = FALSE)
confint(fit_fe, option = "gamma")
confint(fit_fe, option = "SM")