stackplotVA: plot grouped CSMF from a "insilico" object

In openVA: Automated Method for Verbal Autopsy

plot grouped CSMF from a "insilico" object

Description

Produce bar plot of the CSMFs for a fitted object in broader groups. This function extends the stackplot() function in the InSilicoVA package to allow for the same visualization for results from InterVA, NBC, and Tariff algorithms.

Usage

stackplotVA(
  x,
  grouping = NULL,
  type = c("stack", "dodge")[1],
  group_order = NULL,
  err = TRUE,
  CI = 0.95,
  sample_size_print = FALSE,
  xlab = "",
  ylab = "CSMF",
  ylim = NULL,
  title = "CSMF by broader cause categories",
  horiz = FALSE,
  angle = 0,
  err_width = 0.4,
  err_size = 0.6,
  border = "black",
  bw = FALSE,
  filter_legend = FALSE,
  ...
)

Arguments

x	one or a list of fitted object from codeVA function
grouping	C by 2 matrix of grouping rule. If set to NULL, make it default.
type	type of the plot to make
group_order	list of grouped categories. If set to NULL, make it default.
err	indicator of inclusion of error bars
CI	Level of posterior credible intervals.
sample_size_print	Logical indicator for printing also the sample size for each sub-population labels.
xlab	Labels for the causes.
ylab	Labels for the CSMF values.
ylim	Range of y-axis.
title	Title of the plot.
horiz	Logical indicator indicating if the bars are plotted horizontally.
angle	Angle of rotation for the texts on x axis when horiz is set to FALSE
err_width	Size of the error bars.
err_size	Thickness of the error bar lines.
border	The color for the border of the bars.
bw	Logical indicator for setting the theme of the plots to be black and white.
filter_legend	Logical indicator for including all broad causes in the plot legend (default; FALSE) or filtering to only the broad causes in the data being plotted
...	Not used.

Author(s)

Zehang Li, Tyler McCormick, Sam Clark

Maintainer: Zehang Li <[e-mail]>

See Also

Other visualization: plotVA()

Examples

data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause", write=FALSE,
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

data(SampleCategory)
stackplotVA(fit1, grouping = SampleCategory, type ="dodge", 
            ylim = c(0, 1), title = "InSilicoVA")
stackplotVA(fit2, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "InterVA4.02")
stackplotVA(fit3, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "Tariff")

openVA documentation built on March 31, 2023, 9:31 p.m.