plot.insilico function

plot CSMF from a "insilico" object

Produce a bar plot of the CSMFs for a fitted "insilico" object.

## 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 = TRUE,
  ...
)

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

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)

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, Journal of the American Statistical Association (2016), 111(515):1036-1049.

See Also

insilico, summary.insilico

Author(s)

Zehang Li, Tyler McCormick, Sam Clark

Maintainer: Zehang Li lizehang@uw.edu