summary.insilico | R Documentation |
This function is the summary method for class insilico
.
## S3 method for class 'insilico' summary( object, CI.csmf = 0.95, CI.cond = 0.95, file = NULL, top = 10, id = NULL, ... )
object |
Fitted |
CI.csmf |
Confidence interval for CSMF estimates. |
CI.cond |
Confidence interval for conditional probability estimates |
file |
Optional .csv file to write to. If it is specified, individual cause of death distribution will be saved to the file. |
top |
Number of top causes to display on screen. |
id |
ID of specific death to display on screen. |
... |
Not used. |
summary.insilico
formats some basic information about the InSilicoVA
fitted object on screen and show the several top CSMFs of user's choice. See
below for more detail.
id.all |
all IDs of the deaths. |
indiv |
individual Cause of Death distribution matrix. |
csmf |
CSMF distribution and confidence interval for each cause. |
csmf.ordered |
CSMF distribution and confidence interval for each cause, ordered by mean. |
condprob |
Conditional probability matrix and confidence intervals. |
updateCondProb |
Component of |
keepProbbase.level |
Component of |
datacheck |
Component of |
Nsim |
Component of |
thin |
Component of |
burnin |
Component
of |
jump.scale |
Component of |
levels.prior |
Component of |
levels.strength |
Component of |
trunc.min |
Component of |
trunc.max |
Component of |
subpop_counts |
Component of |
showTop |
Component of |
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
, plot.insilico
## Not run: # load sample data together with sub-population list data(RandomVA1) # extract InterVA style input data data <- RandomVA1$data # extract sub-population information. # The groups are "HIV Positive", "HIV Negative" and "HIV status unknown". subpop <- RandomVA1$subpop # run without subpopulation fit1<- insilico( data, subpop = NULL, Nsim = 400, burnin = 200, thin = 10 , seed = 1, external.sep = TRUE, keepProbbase.level = TRUE) summary(fit1) summary(fit1, top = 10) # save individual COD distributions to files summary(fit1, file = "results.csv") ## End(Not run)
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