kw.lg: Calculate KW-LG pseudo-weights

In chkern/KWML: Boosted Kernel Weighting

Calculate KW-LG pseudo-weights

Description

This function computes KW pseudo-weights using logistic regression to predict propensity scores.

Usage

kw.lg(
  psa_dat,
  wt,
  rsp_name,
  formula,
  h = NULL,
  krn = "triang",
  large = F,
  rm.s = F
)

Arguments

psa_dat	Dataframe of the combined non-probability and probability sample
wt	Name of the weight variable in psa_dat (common weights of 1 for non-probability sample, and survey weights for probability sample)
rsp_name	Name of the non-probability sample membership indicator in psa_dat (1 for non-probability sample units, and 0 for probability sample units)
formula	Formula of the regression model
h	Bandwidth parameter (will be calculated corresponding to kernel function if not specified)
krn	Kernel function. "triang": triangular density on (-3, 3), "dnorm": standard normal density, "dnorm_t": truncated standard normal density on (-3, 3).
large	The cohort size is so large that it has to be divided into pieces. Default is FALSE.
rm.s	Remove unmatched survey units or not. Default is FALSE.

Value

A list
pswt: KW pseudo-weights
delt.svy: Number of unmatched survey sample units
h: Bandwidth

Examples

# KW-LG with example data
kwlg_w <- kw.lg(simu_dat, "wt", "trt", "trt_f ~ x1+x2+x3+x4+x5+x6+x7")$pswt
# Compute weighted mean of y in non-prob data
sum((simu_dat$y[simu_dat$trt == 1]*kwlg_w)/sum(kwlg_w))

chkern/KWML documentation built on Sept. 10, 2022, 9:49 p.m.