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Untitled
Untitled
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11/29/2021
library(readr)
library(magrittr)
library(dplyr)
library(rmarkdown)
library(skimr)
library(readxl)
prop <- read_excel("data/prop.xlsx")
View(prop)
SMD for X1
library(designmatch)
Cox<-prop[,2:3]
indi<-prop$Z
absstddif(Cox,indi,0.05)
## [1] 0.04087738 0.04299143
library(survey)
prop$ids<-prop$...1
options(survey.lonely.psu = "adjust")
svdesgn<-svydesign(ids = ~ids,data = prop,nest = TRUE)
mod1<-svyglm(formula = Z~X1+X2,design = svdesgn,family = quasibinomial)
scores<-fitted(mod1)
prop$Zscore<-scores
plot(mod1)
Cox2<-prop[,c(7,9)]
indi<-prop$Z
absstddif(Cox2,indi,0.05)
## [1] 0.005706824 0.004661779
library(BayesTree)
set.seed(99)
bartFit = bart(Cox,indi,ndpost=1000)
## NOTE: assumming numeric response is binary
##
##
## Running BART with binary y
##
## number of trees: 200
## Prior:
## k: 2.000000
## binary offset is: 0.000000
## power and base for tree prior: 2.000000...