Produce a bar plot of the CSMFs for a fitted "insilico" object.
Usage
## S3 method for class 'insilico'
plot(x, type = c("errorbar", "bar", "compare")[1],
top = 10, causelist = NULL, which.sub = NULL, xlab = "Causes",
ylab = "CSMF", title = "Top CSMF Distribution", horiz = TRUE,
angle = 60, fill = "lightblue", err_width = 0.4, err_size = 0.6,
point_size = 2, border = "black", bw = FALSE, ...)
Arguments
x
fitted "insilico" object
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; "compare" for line charts on multiple
sub-populations.
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
causetext), or a vector of character string of the cause
names. The argument supports partial matching of the cause names. e.g.,
"HIV/AIDS related death" could be abbreviated into "HIV"; "Other and
unspecified infect dis" could be abbreviated into "Other and unspecified
infect".
which.sub
Specification of which sub-population to plot if there are
multiple and type is set to "bar".
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 horiz is
set to FALSE
fill
The color to fill the bars when type is set to "bar".
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 type is set
to "bar".
bw
Logical indicator for setting the theme of the plots to be black
and white.
...
Not used.
Details
To-do
Author(s)
Zehang Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Li <lizehang@uw.edu>
References
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, arXiv preprint arXiv:1411.3042http://arxiv.org/abs/1411.3042 (2014)
See Also
insilico, summary.insilico
Examples
## 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)
# basic line plot
plot(fit1)
# basic bar plot
plot(fit1, type = "bar")
# line plot with customized look
plot(fit1, top = 15, horiz = FALSE, fill = "gold",
bw = TRUE, title = "Top 15 CSMFs", angle = 70,
err_width = .2, err_size = .6, point_size = 2)
##
## 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)
summary(fit2)
# basic side-by-side line plot for all sub-populations
plot(fit2, type = "compare", main = "Top 5 causes comparison")
# basic line plot for specific sub-population
plot(fit2, which.sub = "Women", main = "Top 5 causes for women")
# customized plot with only specified causes
# the cause names need not be exact as InterVA cause list
# substrings in InterVA cause list is enough for specification
# e.g. the following two specifications are the same
some_causes_1 <- c("HIV/AIDS related death", "Pulmonary tuberculosis")
some_causes_2 <- c("HIV", "Pulmonary")
plot(fit2, type = "compare", horiz = FALSE, causelist = some_causes_1,
title = "HIV and TB fractions in two sub-populations",
angle = 20)
## End(Not run)