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Last data update: 2014.03.03

R: plot grouped CSMF from a "insilico" object

stackplot

R Documentation

plot grouped CSMF from a "insilico" object

Description

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

Usage

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, ...)

Arguments

`x`	fitted `"insilico"` object
`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 `horiz` is set to FALSE
`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.

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:1411.3042 http://arxiv.org/abs/1411.3042 (2014)

Examples


  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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(InSilicoVA)
Loading required package: rJava
Loading required package: coda
Loading required package: ggplot2

Please cite the 'InSilicoVA' package as:
Tyler H. McCormick, Zehang R. Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn and Samuel J. Clark (2014). Probabilistic cause-of-death assignment using verbal autopsies, Journal of the American Statistical Association, to appear

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/InSilicoVA/stackplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: stackplot
> ### Title: plot grouped CSMF from a "insilico" object
> ### Aliases: stackplot
> ### Keywords: InSilicoVA
> 
> ### ** Examples
> 
> ## No test: 
>   data(RandomVA1)
>   ##
>   ## Scenario 1: without sub-population specification
>   ##
>   fit1<- insilico(RandomVA1, subpop = NULL,
+                 Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
+                 auto.length = FALSE)
Performing data consistency check...
Data check finished.
Warning: 58 symptom missing completely and added to missing list 
List of missing symptoms: 
 died_d1, died_d23, died_d36, died_w1, whoop, chest_in, eye_sunk, born_early, born_3437, born_38, ab_size, born_small, born_big, twin, comdel, cord, waters, move_lb, cyanosis, baby_br, born_nobr, cried, no_life, mushy, fed_d1, st_suck, ab_posit, conv_d1, conv_d2, arch_b, font_hi, font_lo, unw_d1, unw_d2, cold, umbinf, b_yellow, devel, born_malf, mlf_bk, mlf_lh, mlf_sh, mttv, b_norm, b_assist, b_caes, b_first, b_more4, b_mbpr, b_msmds, b_mcon, b_mbvi, b_mvbl, b_bfac, b_bhome, b_bway, b_bprof, vaccin
InSilico Sampler initiated, 1000 iterations to sample
...........
Iteration: 100 
Sub-population 0 acceptance ratio: 0.44 
0.06min elapsed, 0.55min remaining 
..........
Iteration: 200 
Sub-population 0 acceptance ratio: 0.38 
0.12min elapsed, 0.47min remaining 
..........
Iteration: 300 
Sub-population 0 acceptance ratio: 0.35 
0.18min elapsed, 0.42min remaining 
..........
Iteration: 400 
Sub-population 0 acceptance ratio: 0.35 
0.23min elapsed, 0.34min remaining 
..........
Iteration: 500 
Sub-population 0 acceptance ratio: 0.32 
0.27min elapsed, 0.27min remaining 
..........
Iteration: 600 
Sub-population 0 acceptance ratio: 0.31 
0.33min elapsed, 0.22min remaining 
..........
Iteration: 700 
Sub-population 0 acceptance ratio: 0.29 
0.38min elapsed, 0.16min remaining 
..........
Iteration: 800 
Sub-population 0 acceptance ratio: 0.28 
0.41min elapsed, 0.10min remaining 
..........
Iteration: 900 
Sub-population 0 acceptance ratio: 0.28 
0.46min elapsed, 0.05min remaining 
.........
Overall acceptance ratio
Sub-population 0 : 0.2670 
Organizing output, might take a moment...
Not all causes with CSMF > 0.02 are convergent.
 Please check using csmf.diag() for more information.
>   # 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)
Performing data consistency check...
Data check finished.
Warning: 58 symptom missing completely and added to missing list 
List of missing symptoms: 
 died_d1, died_d23, died_d36, died_w1, whoop, chest_in, eye_sunk, born_early, born_3437, born_38, ab_size, born_small, born_big, twin, comdel, cord, waters, move_lb, cyanosis, baby_br, born_nobr, cried, no_life, mushy, fed_d1, st_suck, ab_posit, conv_d1, conv_d2, arch_b, font_hi, font_lo, unw_d1, unw_d2, cold, umbinf, b_yellow, devel, born_malf, mlf_bk, mlf_lh, mlf_sh, mttv, b_norm, b_assist, b_caes, b_first, b_more4, b_mbpr, b_msmds, b_mcon, b_mbvi, b_mvbl, b_bfac, b_bhome, b_bway, b_bprof, vaccin
InSilico Sampler initiated, 1000 iterations to sample
...........
Iteration: 100 
Sub-population 0 acceptance ratio: 0.41 
Sub-population 1 acceptance ratio: 0.45 
0.05min elapsed, 0.41min remaining 
..........
Iteration: 200 
Sub-population 0 acceptance ratio: 0.47 
Sub-population 1 acceptance ratio: 0.45 
0.10min elapsed, 0.39min remaining 
..........
Iteration: 300 
Sub-population 0 acceptance ratio: 0.43 
Sub-population 1 acceptance ratio: 0.46 
0.16min elapsed, 0.37min remaining 
..........
Iteration: 400 
Sub-population 0 acceptance ratio: 0.42 
Sub-population 1 acceptance ratio: 0.41 
0.22min elapsed, 0.33min remaining 
..........
Iteration: 500 
Sub-population 0 acceptance ratio: 0.42 
Sub-population 1 acceptance ratio: 0.40 
0.27min elapsed, 0.27min remaining 
..........
Iteration: 600 
Sub-population 0 acceptance ratio: 0.40 
Sub-population 1 acceptance ratio: 0.38 
0.32min elapsed, 0.21min remaining 
..........
Iteration: 700 
Sub-population 0 acceptance ratio: 0.38 
Sub-population 1 acceptance ratio: 0.36 
0.37min elapsed, 0.16min remaining 
..........
Iteration: 800 
Sub-population 0 acceptance ratio: 0.37 
Sub-population 1 acceptance ratio: 0.36 
0.43min elapsed, 0.11min remaining 
..........
Iteration: 900 
Sub-population 0 acceptance ratio: 0.37 
Sub-population 1 acceptance ratio: 0.34 
0.48min elapsed, 0.05min remaining 
.........
Overall acceptance ratio
Sub-population 0 : 0.3730 
Sub-population 1 : 0.3350 
Organizing output, might take a moment...
Not all causes with CSMF > 0.02 are convergent.
 Please check using csmf.diag() for more information.
>   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), ]
                 InterVA Physician
3 HIV/AIDS related death   TB/AIDS
9 Pulmonary tuberculosis   TB/AIDS
>   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(No test)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>