indivplot | R Documentation |
Produce a bar plot of the aggregated COD distribution as approximate CSMFs for a fitted "insilico"
object.
indivplot( x, type = c("errorbar", "bar")[1], top = 10, causelist = NULL, which.plot = NULL, xlab = "Causes", ylab = "COD distribution", title = "COD distributions for the top causes", horiz = TRUE, angle = 60, fill = "lightblue", err_width = 0.4, err_size = 0.6, point_size = 2, border = "black", bw = FALSE, ... )
x |
object from |
type |
An indicator of the type of chart to plot. "errorbar" for line plots of only the error bars on single population; "bar" for bar chart with error bars on single population. |
top |
The number of top causes to plot. If multiple sub-populations are to be plotted, it will plot the union of the top causes in all sub-populations. |
causelist |
The list of causes to plot. It could be a numeric vector
indicating the position of the causes in the InterVA cause list (see
|
which.plot |
Specification of which group to plot if there are multiple. |
xlab |
Labels for the causes. |
ylab |
Labels for the CSMF values. |
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 |
fill |
The color to fill the bars 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 to color the borders of bars when |
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: # Toy example with 1000 VA deaths data(RandomVA1) fit1<- insilico(RandomVA1, subpop = NULL, Nsim = 1000, burnin = 500, thin = 10 , seed = 1, auto.length = FALSE) summary(fit1, id = "d199") # update credible interval for individual probabilities to 90% indiv.new <- get.indiv(fit1, CI = 0.9) fit1$indiv.prob.lower <- indiv.new$lower fit1$indiv.prob.upper <- indiv.new$upper fit1$indiv.CI <- 0.9 summary(fit1, id = "d199") # get empirical aggregated COD distribution agg.csmf <- get.indiv(data = RandomVA2, fit1, CI = 0.95, is.aggregate = TRUE, by = NULL) head(agg.csmf) # aggregate individual COD distribution by sex and age # note the model was fitted assuming the same CSMF for all deaths # this aggregation provides an approximate CSMF for each sub-groups agg.by.sex.age <- get.indiv(data = RandomVA2, fit1, CI = 0.95, is.aggregate = TRUE, by = list("sex", "age")) head(agg.by.sex.age$mean) # plot of aggregated individual COD distribution # 0. plot for all data indivplot(agg.csmf, top = 10) # 1. plot for specific one group indivplot(agg.by.sex.age, which.plot = "Men 60-", top = 10) # 2. comparing multiple groups indivplot(agg.by.sex.age, which.plot = list("Men 60+", "Men 60-"), top = 5) # 3. comparing multiple groups on selected causes indivplot(agg.by.sex.age, which.plot = list("Men 60-", "Women 60-"), top = 0, causelist = c( "HIV/AIDS related death", "Pulmonary tuberculosis", "Other and unspecified infect dis", "Other and unspecified NCD")) ## End(Not run)
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