fe.prov {FEprovideR} | R Documentation |
Fit logistic fixed-effect model with high-dimensional predictors
Description
fe.prov
fits a fixed-effect logistic model using structured profile
likelihood algorithm. Standardized readmission ratios (SRRs) are also computed.
Go to Github for
a tutorial.
Usage
fe.prov(data, Y.char, Z.char, prov.char, tol = 1e-05, null = "median")
Arguments
data |
prepared |
Y.char |
name of the response variable from |
Z.char |
names of covariates from |
prov.char |
name of provider IDs variable as a character string |
tol |
tolerance level for convergence. Default is |
null |
use median for null comparison |
Value
An object of class fe.prov
, which is just a List
object with the following named elements:
-
beta:
a vector of fixed effect estimates -
Obs:
a vector of responses for included providers -
Exp:
a vector of expected probabilities of readmission within 30 days of discharge -
iter:
number of iterations needed for convergence -
beta.max.diff:
value of the stopping criterion -
df.prov:
df.prov
is a data.frame
of provider-level information with the following items:
-
Obs:
provider-level observed number of readmissions within 30 days -
Exp:
expected number of readmissions within 30 days -
SRR:
standardized readmission ratios for each hospital -
gamma:
a vector of provider effect estimates for included hospitals
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
, test.fe.prov
,
funnel.SRR
, confint.fe.prov
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