View source: R/psrwe_complikel_watt.R
psrwe_compl_watt | R Documentation |
Estimate the mean of the outcome based on PS-integrated composite likelihood approach with weights of ATT (WATT). Variance is estimated by Jack-Knife method. Applies to the case when there is only one external data source.
psrwe_compl_watt(
dta_psbor,
v_outcome = "Y",
outcome_type = c("continuous", "binary"),
stderr_method = c("jk", "sjk", "cjk", "sbs", "cbs", "none"),
n_bootstrap = 200,
...
)
dta_psbor |
A class |
v_outcome |
Column name corresponding to the outcome. |
outcome_type |
Type of outcomes: |
stderr_method |
Method for computing StdErr, see Details |
n_bootstrap |
Number of bootstrap samples (for bootstrap stderr) |
... |
Parameters for |
stderr_method
include jk
as default
using Jackknife method within each stratum,
sjk
for simple Jackknife method for combined estimates
such as point estimates in single arm or treatment effects in RCT, or
cjk
for complex Jackknife method including refitting PS model,
matching, trimming, calculating borrowing parameters, and
combining overall estimates.
Note that sjk
may take a while longer to finish and
cjk
will take even much longer to finish.
The sbs
and cbs
is for simple and complex Bootstrap
methods.
A data frame with class name PSRWE_RST
. It contains the
composite estimation of the mean for each stratum as well as the
jackknife estimation for each subject. The results can be further
summarized by its S3 method summary
.
The results can be also analyzed by psrwe_outana
for outcome
analysis and inference.
data(ex_dta)
dta_ps <- psrwe_est(ex_dta,
v_covs = paste("V", 1:7, sep = ""),
v_grp = "Group",
cur_grp_level = "current",
nstrata = 1)
ps_borrow <- psrwe_borrow(total_borrow = 30, dta_ps)
rst <- psrwe_compl_watt(ps_borrow, v_outcome = "Y_Bin")
rst
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