Nothing
## ----setup, include = FALSE----------------------------------------------
library(confoundr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
data("example_sml")
## ------------------------------------------------------------------------
drops <- c("h_0", "h_1", "h_2")
mydata <- example_sml[ , !(names(example_sml) %in% drops)]
mydata.history <- makehistory.one(input=mydata,
id="id",
exposure="a",
name.history="h",
times=c(0,1,2))
## ------------------------------------------------------------------------
mydata.tidy <- lengthen(
input=example_sml, #mydata.history,
id="id",
diagnostic=3,
censoring="no",
times.exposure=c(0,1,2),
times.covariate=c(0,1,2),
exposure="a",
temporal.covariate=c("l","m","o"),
static.covariate=c("n","p"),
history="h",
weight.exposure="wax"
)
## ------------------------------------------------------------------------
mydata.tidy.omit <- omit.history(
input=mydata.tidy,
omission="relative",
covariate.name=c("l","m","o"),
distance=1
)
## ------------------------------------------------------------------------
mytable <- balance (
input=mydata.tidy.omit,
diagnostic=3,
approach="weight",
censoring="no",
scope="all",
times.exposure=c(0,1,2),
times.covariate=c(0,1,2),
exposure="a",
history="h",
weight.exposure="wax",
ignore.missing.metric="no",
sort.order= c("l","m","o","n","p")
)
## ------------------------------------------------------------------------
myplot <- makeplot (
input=mytable,
diagnostic =3,
approach="weight",
scope="all",
metric="SMD"
)
## ------------------------------------------------------------------------
myplot
## ------------------------------------------------------------------------
mydata.tidy <- lengthen(
input=mydata.history,
diagnostic=1,
censoring="no",
id="id",
times.exposure=c(0,1,2),
times.covariate=c(0,1,2),
exposure="a",
temporal.covariate=c("l","m","n","o","p"),
history="h"
)
head(mydata.tidy)
## ------------------------------------------------------------------------
library(dplyr)
library(broom)
mydata.tidy.reg <- mutate(mydata.tidy,
time=time.exposure,
distance=time.exposure-time.covariate,
history=h)
output <- mydata.tidy.reg %>%
group_by(name.cov) %>% #note, you can include other stratifying variables here or in the model
filter(time.exposure>=time.covariate) %>% #lengthen actually arealdy took care of this, provided here for clarity
do(tidy(lm(formula=value.cov~a+time+distance+history,.))) %>% #same model form used for every covariate
filter(term=="a") %>% ungroup()
table.reg <- output %>%
select(name.cov,estimate) %>%
rename(D=estimate)
print(table.reg)
## ------------------------------------------------------------------------
table.std <- balance(input=mydata.tidy,
diagnostic=1,
approach="none",
censoring="no",
scope="average",
average.over="distance",
ignore.missing.metric="no",
times.exposure=c(0,1,2),
times.covariate=c(0,1,2),
exposure="a",
history="h"
)
print(table.std)
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