| Type: | Package |
| Title: | Replicate Tariff Method for Verbal Autopsy |
| Version: | 1.0.5 |
| Date: | 2018-10-23 |
| Author: | Zehang Li, Tyler McCormick, Sam Clark |
| Maintainer: | Zehang Li <lizehang@uw.edu> |
| Description: | Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <doi:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME. |
| License: | GPL-2 |
| RoxygenNote: | 6.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2018-10-23 21:47:24 UTC; zehangli |
| Repository: | CRAN |
| Date/Publication: | 2018-10-29 05:40:08 UTC |
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)
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
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 |
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$cause)
fit <- tariff(causes.train = "cause", symps.train = train,
symps.test = test, causes.table = allcauses)
plot(fit, top = 10, main = "Top 5 population COD distribution")
plot(fit, min.prob = 0.05, main = "Ppulation COD distribution (at least 5%)")
Print method for the summary of the results obtained from Tariff algorithm
Description
This function prints the summary message of the fitted results.
Usage
## S3 method for class 'tariff_summary'
print(x, ...)
Arguments
x |
summary object for Tariff fit |
... |
not used |
Summary of the results obtained from Tariff algorithm
Description
This function prints the summary message of the fitted results.
Usage
## S3 method for class 'tariff'
summary(object, top = 5, id = NULL, ...)
Arguments
object |
fitted object from |
top |
number of top CSMF to show |
id |
the ID of a specific death to show |
... |
not used |
Examples
data("RandomVA3")
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$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)
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 <lizehang@uw.edu>
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]