confint.fe.prov {FEprovideR}

Compute confidence intervals for fitted model

Description

confint.fe.prov computes the (1-alpha)% confidence intervals for the fixed effect parameter estimates. Go to Github for a tutorial.

Usage

## S3 method for class 'fe.prov'
confint(object, parm = "all", level = 0.95, data,
  Y.char, Z.char, prov.char, ...)

Arguments

object

fitted model object (fit using fe.prov)

parm

provider IDs for which confidence intervals are desired. The default is "all". Specify a subset of privider effects with a numeric vector of provider IDs. For example, parm=c(1,20) for providers 1 and 20.

level

confidence level (default is 0.95)

data

prepared data.frame. Use fe.data.prep to prepare the raw data

Y.char

Y.char name of the response variable from data as a character string

Z.char

Z.char names of covariates from data as vector of character strings

prov.char

name of provider IDs variable as a character string

...

extra arguments to be passed to confint

Value

Returns a data.frame of gamma and SRR lower and upper CI bounds. Each row is a parameter, each column gives a different bound.

References

He, K., Kalbfleisch, J.D., Li, Y. and Li, Y., 2013. Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects. Lifetime data analysis, 19(4), pp.490-512.

See Also

fe.data.prep, fe.prov, test.fe.prov, funnel.SRR

Examples

# Name input variables and other parameters
# a small positive number specifying stopping
# criterion of Newton-Raphson algorithm
tol <- 1e-5
Y.char <- 'Y'
prov.char <- 'prov.ID'
Z.char <- paste0('z', 1:3)
data(hospital_prepared) # build in data set
fe.ls <- fe.prov(hospital_prepared, Y.char, Z.char, prov.char, tol) # model fitting

# confidence intervals
confint.fe.prov(fe.ls, parm = "all", level = 0.95,
                             hospital_prepared, Y.char, Z.char, prov.char)