- This package calculates various measures of association and helps understand and visualize the link between causal models such as Marginal Structural Models and Standerdized measures.
library(devtools)
install_github("ehsanx/iptw")
require(iptw)
help(package="iptw")
r1 <- .9 # Risk of the treated subjects
r0 <- .5 # Risk of the untreated subjects
measures.calc(r1,r0)
# Calculates risk measures
form.table.object <- form.table(Y1A1L1=150,
Y1A0L1=45,
Y1A1L0=20,
Y1A0L0=5,
Y0A1L1=300,
Y0A0L1=10,
Y0A1L0=40,
Y0A0L0=55)
form.table.object
# Forms the crude, stratified tables suitable
# for using in other functions of this package.
ungrouped.data.object <- ungrouped.data(form.table.object)
# Creates ungrouped data suitable for performing regression on it
head(ungrouped.data.object)
associational(form.table.object)
# Calculates measures of associations
standardization(form.table.object)
# Calculates Standardized measures
iptw(form.table.object, type = "sw")
# Standardized measures are calculated by this function
# using inverse probability of treatment weights (IPTW).
iptw.regression(form.table.object, type = "sw")
# Calculates IPTW estimates from regression
# requires grid and survey package
graph(form.table.object, type = "sw")
# Maps probabilities and frequesncies
- Ehsan Karim Feel free to report any errors / update suggestions.
- Hernán MA, Robins JM (2016). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.