Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----set, echo=FALSE--------------------------------------------------------------------------------------------------------------------------------
# Change the width of html file
options(width = 150)
## ----data-------------------------------------------------------------------------------------------------------------------------------------------
# the package can be downloaded from cran and github:
# install.packages("TipingPoint")
# devtools::install_github("XikunHan/TippingPoint")
library(TippingPoint)
# Load the dataset
data(tippingdata)
# Show the first 6 rows of the data
head(tippingdata)
## ----basic plot, fig.width=8,fig.height=6-----------------------------------------------------------------------------------------------------------
## for binary outcome
# Using `estimate`
TippingPoint(outcome=tippingdata$binary,
treat= tippingdata$treat,group.infor=TRUE,
plot.type = "estimate",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
# Using `p.value` with formula class
TippingPoint(binary~treat, data=tippingdata,
plot.type = "p.value",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
# Using `both`
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
plot.type = "both",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
# for continuous outcome
TippingPoint(continuous~treat, data=tippingdata,
group.infor=TRUE, plot.type = "estimate",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
plot.type = "p.value",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
plot.type = "both",ind.values = TRUE,
impValuesT = NA, impValuesC = NA,
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
## ----impute plot, fig.width=8,fig.height=6----------------------------------------------------------------------------------------------------------
# Load the imputed dataset
data(imputedata)
# Show the first 6 rows of the data
head(imputedata)
## for binary outcome
TippingPoint(outcome=tippingdata$binary,
treat= tippingdata$treat, group.infor=TRUE,
plot.type = "estimate",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T2","MCAR_T2")],
impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
# User-defined colors
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
plot.type = "p.value",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T2","MCAR_T2")],
impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
impValuesColor = RColorBrewer::brewer.pal(8,"Accent")[5:6],
summary.type = "credible.region", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
# Using `point.size` and `point.shape` to control the points.
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
plot.type = "both",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T2","MCAR_T2")],
impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
impValuesColor =c("red","blue"),
point.size=0.8,point.shape = 15,
summary.type = "convex.hull", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(0.38,0.4), HistMeanC = c(0.2,0.55))
## for continuous outcome
TippingPoint(outcome=tippingdata$continuous,
treat= tippingdata$treat, group.infor=TRUE,
plot.type = "p.value",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T1","MCAR_T1")],
impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
# Using `point.size` and `point.shape` to control the points.
TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
plot.type = "p.value",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T1","MCAR_T1")],
impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
point.size = 0.8, point.shape = 15,
summary.type = "credible.region", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
plot.type = "both",ind.values = TRUE,
impValuesT = imputedata[,c("MAR_T1","MCAR_T1")],
impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
summary.type = "convex.hull", alpha = 0.95, S=1.5, n.grid = 100,
HistMeanT = c(120), HistMeanC = c(131,137))
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