removing ipwrisk testing script for package creation

.Rbuildignore

 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^data-raw$
-^actg_ipwrisk$
+^R$

R/actg_ipwrisk.R

-#####################################
-## ipwrisk testing: ACTG example
-## Sara Levintow
-## August 7, 2017
-#####################################
-## Load packages
-library("devtools")
-## for re-installation of ipwrisk
-##devtools::install_bitbucket("novisci/ipwrisk",
-                            ##auth_user = "salevintow@gmail.com",
-                            ##dependencies = TRUE,
-                            ##password = .rs.askForPassword("Bitbucket Password"))
-library(ipwrisk)
-library(data.table)
-library(pryr) # had to add this, was not loaded as a dependency
-library(ggplot2)
-library(dplyr)
-library(survival)
-library(splines)
-library(rms)
-library(foreach)
-
-## Load data
-load("data/actg.Rda")
-
-## Created event time and censoring time variables in R data set (don't need to recreate):
-#actg$Y <- ifelse(actg$delta==1,actg$days,NA) #Y is time of event (AIDS or death)
-#actg$C <- ifelse(actg$delta==0,actg$days,NA) #C is time of censoring
-#actg$C1 <- ifelse(actg$drop==1,actg$days,NA) #C1 is time of censoring due to drop-out
-#actg$C2 <- ifelse(actg$delta==0 & actg$drop==0,actg$days,NA) #C2 is time of censoring not due to drop-out
-
-## Looking at distribution of covariates by treatment (ART)
-vars_by_art <- actg %>%
-  group_by(art) %>%
-  summarize(n(),mean(male),mean(black),mean(hispanic),mean(idu),mean(r),mean(age),mean(karnof),mean(cd4))
-
-## Crude analysis of treatment outcome
-outcome_by_art <- actg %>%
-  group_by(art) %>%
-  summarize(n(),outcome=sum(delta),risk=mean(delta),time=mean(na.omit(Y)),dropout=sum(drop))
-plot(survfit(Surv(days,delta)~art,data=actg),fun="event",xlim=c(0,365))
-
-## Load into a data.table
-a=data.table(actg)
-
-## Specifying the data structure and models
-ds.struct.1 = build_models(treat("art", formula = art~male+black+hispanic+idu+age+karnof+cd4),
-                         cens("C", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
-                         outcome("Y"))
-
-actg1 = est.ipwrisk(a,ds.struct.1, times = seq(0,365,10),
-                      label = c("actg","Main Analysis"))
-plot(actg1)
-plot(actg1,rd = TRUE) + theme_bw() + ggtitle("Cumulative RD in ACTG data")
-hist(actg1) + theme_bw() + ggtitle("Propensity Score Distributions")
-## covariates do not predict treatment due to randomization
-
-## now using multiple censoring models (1 for drop-out, 1 for admin censoring)
-ds.struct.2 = build_models(treat("art", formula = art~male+black+hispanic+idu+age+karnof+cd4),
-             cens("C1", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
-             cens("C2", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
-             outcome("Y"))
-
-actg2 = est.ipwrisk(a,ds.struct.2, times = seq(0,365,10),
-                    label = c("actg","Main Analysis"))
-summary(actg2)
-plot(actg2)
-plot(actg2,rd = TRUE) + theme_bw() + ggtitle("Cumulative RD in ACTG data")
-
-# comparing results (1 censoring model vs. 2 censoring models)
-library(knitr)
-kable(make_table2(actg1, actg2, index = 365), format="markdown")
-# risks and RDs not calculated? table just shows NA