stackplot | R Documentation |
Produce bar plot of the CSMFs for a fitted "insilico"
object in broader groups.
stackplot( x, grouping = NULL, type = c("stack", "dodge")[1], order.group = NULL, order.sub = NULL, err = TRUE, CI = 0.95, sample.size.print = FALSE, xlab = "Group", ylab = "CSMF", ylim = NULL, title = "CSMF by broader cause categories", horiz = FALSE, angle = 60, err_width = 0.4, err_size = 0.6, point_size = 2, border = "black", bw = FALSE, ... )
x |
fitted |
grouping |
C by 2 matrix of grouping rule. If set to NULL, make it default. |
type |
type of the plot to make |
order.group |
list of grouped categories. If set to NULL, make it default. |
order.sub |
Specification of the order of sub-populations to plot |
err |
indicator of inclusion of error bars |
CI |
confidence interval for error bars. |
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 |
err_width |
Size of the error bars. |
err_size |
Thickness of the error bar lines. |
point_size |
Size of the points. |
border |
The color for the border of the bars. |
bw |
Logical indicator for setting the theme of the plots to be black and white. |
... |
Not used. |
Zehang Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Li <lizehang@uw.edu>
Tyler H. McCormick, Zehang R. Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn and Samuel J. Clark Probabilistic cause-of-death assignment using verbal autopsies, Journal of the American Statistical Association (2016), 111(515):1036-1049.
insilico
, summary.insilico
## Not run: data(RandomVA1) ## ## Scenario 1: without sub-population specification ## fit1<- insilico(RandomVA1, subpop = NULL, Nsim = 1000, burnin = 500, thin = 10 , seed = 1, auto.length = FALSE) # stack bar plot for grouped causes # the default grouping could be seen from data(SampleCategory) stackplot(fit1, type = "dodge", xlab = "") ## ## Scenario 2: with sub-population specification ## data(RandomVA2) fit2<- insilico(RandomVA2, subpop = list("sex"), Nsim = 1000, burnin = 500, thin = 10 , seed = 1, auto.length = FALSE) stackplot(fit2, type = "stack", angle = 0) stackplot(fit2, type = "dodge", angle = 0) # Change the default grouping by separating TB from HIV data(SampleCategory) SampleCategory[c(3, 9), ] SampleCategory[3, 2] <- "HIV/AIDS" SampleCategory[9, 2] <- "TB" stackplot(fit2, type = "stack", grouping = SampleCategory, sample.size.print = TRUE, angle = 0) stackplot(fit2, type = "dodge", grouping = SampleCategory, sample.size.print = TRUE, angle = 0) # change the order of display for sub-population and cause groups groups <- c("HIV/AIDS", "TB", "Communicable", "NCD", "External", "Maternal", "causes specific to infancy") subpops <- c("Women", "Men") stackplot(fit2, type = "stack", grouping = SampleCategory, order.group = groups, order.sub = subpops, sample.size.print = TRUE, angle = 0) ## End(Not run)
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