Package 'Tariff'

Plot CSMF of the results obtained from Tariff algorithm

Description

This function plots the CSMF of the fitted results.

Usage

## S3 method for class 'tariff'
plot(x, top = NULL, min.prob = 0, ...)

Arguments

x	fitted object from tariff
top	maximum causes to plot
min.prob	minimum fraction for the causes plotted
...	Arguments to be passed to/from graphic function

Examples

data("RandomVA3")
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$cause)
fit <- tariff(causes.train = "cause", symps.train = train, 
				symps.test = test, causes.table = allcauses)
plot(fit, top = 10, main = "Top 5 population COD distribution")
plot(fit, min.prob = 0.05, main = "Ppulation COD distribution (at least 5%)")

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Print method for the summary of the results obtained from Tariff algorithm

Description

This function prints the summary message of the fitted results.

Usage

## S3 method for class 'tariff_summary'
print(x, ...)

Arguments

x	summary object for Tariff fit
...	not used

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400 records of Sample Input

Description

This is a dataset consisting of 400 arbitrary sample input deaths randomly sampled from cleaned PHMRC data.

Format

400 arbitrary input records.

Examples

data(RandomVA3)
head(RandomVA3$train)
head(RandomVA3$test)

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Grouping of causes in RandomVA3

Description

This is a matrix specifying a default grouping of the causes used in RandomVA3.

Format

17 by 2 matrix

Examples

data(SampleCategory3)
SampleCategory3

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Summary of the results obtained from Tariff algorithm

Description

This function prints the summary message of the fitted results.

Usage

## S3 method for class 'tariff'
summary(object, top = 5, id = NULL, ...)

Arguments

object	fitted object from tariff
top	number of top CSMF to show
id	the ID of a specific death to show
...	not used

Examples

data("RandomVA3")
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$cause)
fit <- tariff(causes.train = "cause", symps.train = train, 
			symps.test = test, causes.table = allcauses)
correct <- which(fit$causes.test[,2] == test$cause)
accuracy <- length(correct) / dim(test)[1]
summary(fit)
summary(fit, top = 10)
summary(fit, id = "p849", top = 3)

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Replicate Tariff methods

Description

This function implements Tariff method.

Usage

tariff(causes.train, symps.train, symps.test, causes.table = NULL,
  use.rank = TRUE, nboot.rank = 1, use.sig = TRUE, nboot.sig = 500,
  use.top = FALSE, ntop = 40, ...)

Arguments

causes.train	character vector of causes, or the column name of cause in the training data
symps.train	N.train by S matrix
symps.test	N.test by S matrix
causes.table	list of causes in the data
use.rank	logical indicator for whether using ranks instead of scores
nboot.rank	number of re-sampling for baseline rank comparison. Default to 1, which resamples training data to have a uniform cause distribution of the same size. Set this to 0 removes bootstrapping the training dataset.
use.sig	logical indicator for whether using significant Tariff only
nboot.sig	number of re-sampling for testing significance.
use.top	logical indicator for whether the tariff matrix should be cleaned to have only top symptoms
ntop	number of top tariff kept for each cause
...	not used

Value

score	matrix of score for each cause within each death
causes.train	vector of most likely causes in training data
causes.test	vector of most likely causes in testing data
csmf	vector of CSMF
causes.table	cause list used for output, i.e., list of existing causes in the training data
use.rank	logical indicator for whether using ranks instead of scores

Author(s)

Zehang Li, Tyler McCormick, Sam Clark

Maintainer: Zehang Li <[email protected]>

References

James, S. L., Flaxman, A. D., Murray, C. J., & Population Health Metrics Research Consortium. (2011). Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies. Population Health Metrics, 9(1), 1-16.

Serina, P., Riley, I., Stewart, A., James, S. L., Flaxman, A. D., Lozano, R., ... & Ahuja, R. (2015). Improving performance of the Tariff Method for assigning causes of death to verbal autopsies. BMC medicine, 13(1), 1.

Tyler H. McCormick, Zehang R. Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn and Samuel J. Clark(2016) Probabilistic cause-of-death assignment using verbal autopsies, http://arxiv.org/abs/1411.3042 To appear, Journal of the American Statistical Association

Examples

data("RandomVA3")
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$cause)
fit <- tariff(causes.train = "cause", symps.train = train, 
				symps.test = test, causes.table = allcauses)
correct <- which(fit$causes.test[,2] == test$cause)
accuracy <- length(correct) / dim(test)[1]

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