Package 'openVA' reference manual

Title:	Automated Method for Verbal Autopsy
Description:	Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, 'InterVA5' by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, 'InSilicoVA' by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, 'NBC' by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of 'Tariff' method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method.
Authors:	Zehang Richard Li, Jason Thomas, Tyler H. McCormick, Samuel J. Clark
Maintainer:	Zehang Richard Li <[email protected]>
License:	GPL-2
Version:	1.1.2
Built:	2024-11-25 06:40:51 UTC
Source:	CRAN

Running automated method on VA data

Description

Running automated method on VA data

Usage

codeVA(
  data,
  data.type = c("WHO2012", "WHO2016", "PHMRC", "customize")[2],
  data.train = NULL,
  causes.train = NULL,
  causes.table = NULL,
  model = c("InSilicoVA", "InterVA", "Tariff", "NBC")[1],
  Nchain = 1,
  Nsim = 10000,
  version = c("4.02", "4.03", "5")[2],
  HIV = "h",
  Malaria = "h",
  phmrc.type = c("adult", "child", "neonate")[1],
  convert.type = c("quantile", "fixed", "empirical")[1],
  ...
)
codeVA(
  data,
  data.type = c("WHO2012", "WHO2016", "PHMRC", "customize")[2],
  data.train = NULL,
  causes.train = NULL,
  causes.table = NULL,
  model = c("InSilicoVA", "InterVA", "Tariff", "NBC")[1],
  Nchain = 1,
  Nsim = 10000,
  version = c("4.02", "4.03", "5")[2],
  HIV = "h",
  Malaria = "h",
  phmrc.type = c("adult", "child", "neonate")[1],
  convert.type = c("quantile", "fixed", "empirical")[1],
  ...
)

Arguments

`data`	Input VA data, see `data.type` below for more information about the format.
`data.type`	There are four data input types currently supported by `codeVA` function as below. `WHO2012`: InterVA-4 input format using WHO 2012 questionnaire. For example see `data(RandomVA1)`. The first column should be death ID. `WHO2016`: InterVA-5 input format using WHO 2016 questionnaire. For example see `data(RandomVA5)`. The first column should be death ID. `PHMRC`: PHMRC data format. The raw PHMRC long format data will be processed internally following the steps described in McComirck et al. (2016). For example see `ConvertData.phmrc` `customized`: Any dichotomized dataset with “Y“ denote “presence”, “” denote “absence”, and “.” denote “missing”. The first column should be death ID.
`data.train`	Training data with the same columns as `data`, except for an additional column specifying cause-of-death label. It is not used if `data.type` is “WHO” and `model` is “InterVA” or “InSilicoVA”. The first column also has to be death ID for “WHO” and “customized” types.
`causes.train`	the column name of the cause-of-death assignment label in training data.
`causes.table`	list of causes to consider in the training data. Default to be NULL, which uses all the causes present in the training data.
`model`	Currently supports four models: “InSilicoVA”, “InterVA”, “Tariff”, and “NBC”.
`Nchain`	Parameter specific to “InSilicoVA” model. Currently not used.
`Nsim`	Parameter specific to “InSilicoVA” model. Number of iterations to run the sampler.
`version`	Parameter specific to “InterVA” model. Currently supports “4.02”, “4.03”, and “5”. For InterVA-4, “4.03” is strongly recommended as it fixes several major bugs in “4.02” version. “4.02” is only included for backward compatibility. “5” version implements the InterVA-5 model, which requires different data input format.
`HIV`	Parameter specific to “InterVA” model. HIV prevalence level, can take values “h” (high), “l” (low), and “v” (very low).
`Malaria`	HIV Parameter specific to “InterVA” model. Malaria prevalence level, can take values “h” (high), “l” (low), and “v” (very low).
`phmrc.type`	Which PHMRC data format is used. Currently supports only “adult” and “child”, “neonate” will be supported in the next release.
`convert.type`	type of data conversion when calculating conditional probability (probability of each symptom given each cause of death) for InterVA and InSilicoVA models. Both “quantile” and “fixed” usually give similar results empirically. `quantile`: the rankings of the P(S\|C) are obtained by matching the same quantile distributions in the default InterVA P(S\|C) `fixed`: P(S\|C) are matched to the closest values in the default InterVA P(S\|C) table. `empirical`: no ranking is calculated, but use the empirical conditional probabilities directly, which will force `updateCondProb` to be FALSE for InSilicoVA algorithm.
`...`	other arguments passed to `insilico`, `InterVA`, `interVA_train`, `tariff`, and nbc function in the nbc4va package. See respective package documents for details.

Value

a fitted object

References

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. https://arxiv.org/abs/1411.3042, Journal of the American Statistical Association

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.

Zehang R. Li, Tyler H. McCormick, Samuel J. Clark (2014) InterVA4: An R package to analyze verbal autopsy data. Center for Statistics and the Social Sciences Working Paper, No.146

http://www.interva.net/

Miasnikof P, Giannakeas V, Gomes M, Aleksandrowicz L, Shestopaloff AY, Alam D, Tollman S, Samarikhalaj, Jha P. Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths. BMC Medicine. 2015;13:286.

Examples


data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause", write=FALSE,
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)



data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause", write=FALSE,
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

Converting Input data with different coding scheme to standard format

Description

Converting Input data with different coding scheme to standard format

Usage

ConvertData(
  input,
  yesLabel = NULL,
  noLabel = NULL,
  missLabel = NULL,
  data.type = c("WHO2012", "WHO2016")[1]
)
ConvertData(
  input,
  yesLabel = NULL,
  noLabel = NULL,
  missLabel = NULL,
  data.type = c("WHO2012", "WHO2016")[1]
)

Arguments

`input`	matrix input, the first column is ID, the rest of the columns each represent one symptom
`yesLabel`	The value(s) coding "Yes" in the input matrix.
`noLabel`	The value(s) coding "No" in the input matrix.
`missLabel`	The value(s) coding "Missing" in the input matrix.
`data.type`	The coding scheme of the output. This can be either "WHO2012" or "WHO2016".

Value

a data frame coded as follows. For WHO2012 scheme: "Y" for yes, "" for No, and "." for missing. For WHO2016 scheme: "y" for yes, "n" for No, and "-" for missing.

Examples


# make up a fake 2 by 3 dataset with 2 deaths and 3 symptoms
id <- c("d1", "d2")
x <- matrix(c("Yes", "No", "Don't know", 
			  "Yes", "Refused to answer", "No"), 
			byrow = TRUE, nrow = 2, ncol = 3)
x <- cbind(id, x)
colnames(x) <- c("ID", "S1", "S2", "S3")
# see possible raw data (or existing data created for other purpose)
x
new <- ConvertData(x, yesLabel = "Yes", noLabel = "No", 
			missLabel = c("Don't know", "Refused to answer"))
new

# make up a fake 2 by 3 dataset with 2 deaths and 3 symptoms
id <- c("d1", "d2")
x <- matrix(c("Yes", "No", "Don't know", 
			  "Yes", "Refused to answer", "No"), 
			byrow = TRUE, nrow = 2, ncol = 3)
x <- cbind(id, x)
colnames(x) <- c("ID", "S1", "S2", "S3")
# see possible raw data (or existing data created for other purpose)
x
new <- ConvertData(x, yesLabel = "Yes", noLabel = "No", 
			missLabel = c("Don't know", "Refused to answer"))
new

Convert standard PHMRC data into binary indicator format

Description

The PHMRC data and the description of the format could be found at https://ghdx.healthdata.org/record/ihme-data/population-health-metrics-research-consortium-gold-standard-verbal-autopsy-data-2005-2011. This function convert the symptoms into binary indicators of three levels: Yes, No, and Missing. The health care experience (HCE) and free-text columns, i.e., columns named "word_****", are not considered in the current version of data conversion.

Usage

ConvertData.phmrc(
  input,
  input.test = NULL,
  cause = NULL,
  phmrc.type = c("adult", "child", "neonate")[1],
  cutoff = c("default", "adapt")[1],
  ...
)
ConvertData.phmrc(
  input,
  input.test = NULL,
  cause = NULL,
  phmrc.type = c("adult", "child", "neonate")[1],
  cutoff = c("default", "adapt")[1],
  ...
)

Arguments

`input`	standard PHMRC data format
`input.test`	standard PHMRC data format to be transformed in the same way as `input`
`cause`	the column name for the cause-of-death variable to use. For example, "va34", "va46", or "va55". It is used if adaptive cut-offs are to be calculated for continuous variables. See below for details.
`phmrc.type`	which data input format it is. The three data formats currently available are "adult", "child", and "neonate".
`cutoff`	This determines how the cut-off values are to be set for continuous variables. "default" sets the cut-off values proposed in the original paper published with the dataset. "adapt" sets the cut-off values using the rules described in the original paper, which calculates the cut-off as being two median absolute deviations above the median of the mean durations across causes. However, we are not able to replicate the default cut-offs following this rule. So we suggest users to use this feature with caution.
`...`	not used

Value

converted dataset with only ID and binary symptoms. Notice that when applying this function to the raw PHMRC data, the returned ID variable corresponds to the row index of the raw PHMRC data (i.e., cleaned data with ID = 10 correspond to the 10th row of the raw dataset), and does not correspond to the "newid" column in the PHMRC data.

References

Examples

## Not run: 
# Starting from Jan 2024, PHMRC data requires registration at the GHDx website 
# to doload. The following commands assume the user has download the file for 
# PHMRC VA adult data from the website after logging in. 

# For more details on the download process, see ?getPHMRC_url.

raw <- read.csv("IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv", nrows = 100)
head(raw[, 1:20])
# default way of conversion
clean <- ConvertData.phmrc(raw, phmrc.type = "adult")
head(clean$output[, 1:20])
# using cut-offs calculated from the data (caution)
clean2 <- ConvertData.phmrc(raw, phmrc.type = "adult", 
						cause = "va55", cutoff = "adapt")
head(clean2$output[, 1:20])

# Now using the first 100 rows of data as training dataset
# And the next 100 as testing dataset
test <- read.csv("IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv", nrows = 200)
test <- test[-(1:100), ]

# For the default transformation it does matter
clean <- ConvertData.phmrc(raw, test, phmrc.type = "adult")
head(clean$output[, 1:20])
head(clean$output.test[, 1:20])
# For adaptive transformation, need to make sure both files use the same cutoff
clean2 <-ConvertData.phmrc(raw, test, phmrc.type = "adult", 
						cause = "va55", cutoff = "adapt")
head(clean2$output[, 1:20])
head(clean2$output.test[, 1:20])

## End(Not run)
## Not run: 
# Starting from Jan 2024, PHMRC data requires registration at the GHDx website 
# to doload. The following commands assume the user has download the file for 
# PHMRC VA adult data from the website after logging in. 

# For more details on the download process, see ?getPHMRC_url.

raw <- read.csv("IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv", nrows = 100)
head(raw[, 1:20])
# default way of conversion
clean <- ConvertData.phmrc(raw, phmrc.type = "adult")
head(clean$output[, 1:20])
# using cut-offs calculated from the data (caution)
clean2 <- ConvertData.phmrc(raw, phmrc.type = "adult", 
						cause = "va55", cutoff = "adapt")
head(clean2$output[, 1:20])

# Now using the first 100 rows of data as training dataset
# And the next 100 as testing dataset
test <- read.csv("IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv", nrows = 200)
test <- test[-(1:100), ]

# For the default transformation it does matter
clean <- ConvertData.phmrc(raw, test, phmrc.type = "adult")
head(clean$output[, 1:20])
head(clean$output.test[, 1:20])
# For adaptive transformation, need to make sure both files use the same cutoff
clean2 <-ConvertData.phmrc(raw, test, phmrc.type = "adult", 
						cause = "va55", cutoff = "adapt")
head(clean2$output[, 1:20])
head(clean2$output.test[, 1:20])

## End(Not run)

Calculate Overall chance-corrected concordance (CCC)

Description

Denote the cause-specific accuracy for the j-th cause to be (# of deaths correctly assigned to cause j) / (# of death due to cause j). For causes 1, 2, ..., C, the cause-specific CCC is computed for the j-th cause is defined to be (j-th cause-specific accuracy - 1 / C) / (1 - 1 / C) and the overall CCC is the average of each cause-specific CCC.

Usage

getCCC(cod, truth, C = NULL)
getCCC(cod, truth, C = NULL)

Arguments

`cod`	a data frame of estimated cause of death. The first column is the ID and the second column is the estimated cause.
`truth`	a data frame of true causes of death. The first column is the ID and the second column is the estimated cause.
`C`	the number of possible causes to assign. If unspecified, the number of unique causes in cod and truth will be used.

Examples

est <- data.frame(ID = c(1, 2, 3), cod = c("C1", "C2", "C1"))
truth <- data.frame(ID = c(1, 2, 3), cod = c("C1", "C3", "C3"))
# If there are only three causes
getCCC(est, truth)
# If there are 20 causes that can be assigned
getCCC(est, truth, C = 20)

est <- data.frame(ID = c(1, 2, 3), cod = c("C1", "C2", "C1"))
truth <- data.frame(ID = c(1, 2, 3), cod = c("C1", "C3", "C3"))
# If there are only three causes
getCCC(est, truth)
# If there are 20 causes that can be assigned
getCCC(est, truth, C = 20)

Obtain CSMF from fitted model

Description

Obtain CSMF from fitted model

Usage

getCSMF(x, CI = 0.95, interVA.rule = TRUE)
getCSMF(x, CI = 0.95, interVA.rule = TRUE)

Arguments

`x`	a fitted object from `codeVA`.
`CI`	For `insilico` object only, specifying the credible interval to return. Default value to be 0.95.
`interVA.rule`	Logical indicator for `interVA` object only. If TRUE, it means only up to top 3 causes for each death are used to calculate CSMF and the rest are categorized as "undetermined"

Value

a vector or matrix of CSMF for all causes.

Examples

## Not run: 
library(InSilicoVA)
data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO2012", model = "InterVA", 
                  version = "4.03", HIV = "h", Malaria = "l", write=FALSE)
getCSMF(fit)
library(InterVA5)
data(RandomVA5)
fit <- codeVA(RandomVA5[1:100, ], data.type = "WHO2016", model = "InterVA", 
                  version = "5", HIV = "h", Malaria = "l", write=FALSE)
getCSMF(fit)

## End(Not run)

## Not run: 
library(InSilicoVA)
data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO2012", model = "InterVA", 
                  version = "4.03", HIV = "h", Malaria = "l", write=FALSE)
getCSMF(fit)
library(InterVA5)
data(RandomVA5)
fit <- codeVA(RandomVA5[1:100, ], data.type = "WHO2016", model = "InterVA", 
                  version = "5", HIV = "h", Malaria = "l", write=FALSE)
getCSMF(fit)

## End(Not run)

Calculate CSMF accuracy

Description

Calculate CSMF accuracy

Usage

getCSMF_accuracy(csmf, truth, undet = NULL)
getCSMF_accuracy(csmf, truth, undet = NULL)

Arguments

`csmf`	a CSMF vector from `getCSMF` or a InSilicoVA fitted object.
`truth`	a CSMF vector of the true CSMF.
`undet`	name of the category denoting undetermined causes. Default to be NULL. If undetermined cause is present, it will be removed and the rest of the CSMF will be re-normalized to sum to 1.

Value

a number (or vector if input is InSilicoVA fitted object) of CSMF accuracy as 1 - sum(abs(CSMF - CSMF_true)) / (2 * (1 - min(CSMF_true))).

Examples

csmf1 <- c(0.2, 0.3, 0.5)
csmf0 <- c(0.3, 0.3, 0.4)
names(csmf0) <- names(csmf1) <- c("c1", "c2", "c3")
getCSMF_accuracy(csmf1, csmf0)
getCSMF_accuracy(csmf1, rev(csmf0))



csmf1 <- c(0.2, 0.3, 0.5)
csmf0 <- c(0.3, 0.3, 0.4)
names(csmf0) <- names(csmf1) <- c("c1", "c2", "c3")
getCSMF_accuracy(csmf1, csmf0)
getCSMF_accuracy(csmf1, rev(csmf0))

Extract individual distribution of cause of death

Description

Extract individual distribution of cause of death

Usage

getIndivProb(x, CI = NULL, ...)
getIndivProb(x, CI = NULL, ...)

Arguments

`x`	a fitted object from `codeVA`.
`CI`	Credible interval for posterior estimates. If CI is set to TRUE, a list is returned instead of a data frame.
`...`	additional arguments that can be passed to `get.indiv` from InSilicoVA package.

Value

a data frame of COD distribution for each individual specified by row names.

Examples

data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO", model = "InterVA", 
                  version = "4.02", HIV = "h", Malaria = "l", write=FALSE)
probs <- getIndivProb(fit)

data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO", model = "InterVA", 
                  version = "4.02", HIV = "h", Malaria = "l", write=FALSE)
probs <- getIndivProb(fit)

Get the URL to the PHMRC dataset

Description

Get the URL to the PHMRC dataset

Usage

getPHMRC_url(type)
getPHMRC_url(type)

Arguments

type

adult, child, or neonate

Value

URL of the corresponding dataset

Examples

getPHMRC_url("adult")
 

getPHMRC_url("adult")

Extract the most likely cause(s) of death

Description

Extract the most likely cause(s) of death

Usage

getTopCOD(x, interVA.rule = TRUE, n = 1, include.prob = FALSE)
getTopCOD(x, interVA.rule = TRUE, n = 1, include.prob = FALSE)

Arguments

`x`	a fitted object from `codeVA`.
`interVA.rule`	Logical indicator for `interVA` object only. If TRUE and (the parameter) n <= 3, then the InterVA thresholds are used to determine the top causes.
`n`	Number of top causes to include (if n > 3, then the parameter interVA.rule is treated as FALSE).
`include.prob`	Logical indicator for including the probabilities (for `insilico`) or indicator of how likely the cause is (for `interVA`) in the results

Value

a data frame of ID, most likely cause assignment(s), and corresponding probability (for insilico) or indicator of how likely the cause is (for interVA)

Examples

data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO", model = "InterVA", 
                  version = "4.02", HIV = "h", Malaria = "l", write=FALSE)
getTopCOD(fit)
## Not run: 
library(openVA)

# InterVA4 Example
data(SampleInput)
fit_interva <- codeVA(SampleInput, data.type = "WHO2012", model = "InterVA",
                      version = "4.03", HIV = "l", Malaria = "l", write = FALSE)
getTopCOD(fit_interva, n = 1)
getTopCOD(fit_interva, n = 3)
getTopCOD(fit_interva, n = 3, include.prob = TRUE)
getTopCOD(fit_interva, interVA.rule = FALSE, n = 3)
getTopCOD(fit_interva, n = 5)
getTopCOD(fit_interva, n = 5, include.prob = TRUE)

# InterVA5 & Example
data(RandomVA5)
fit_interva5 <- codeVA(RandomVA5[1:50,], data.type = "WHO2016", model = "InterVA",
                       version = "5", HIV = "l", Malaria = "l", write = FALSE)
getTopCOD(fit_interva5, n = 1)
getTopCOD(fit_interva5, n = 3)
getTopCOD(fit_interva5, n = 3, include.prob = TRUE)
getTopCOD(fit_interva5, interVA.rule = FALSE, n = 3)
getTopCOD(fit_interva5, n = 5)
getTopCOD(fit_interva5, n = 5, include.prob = TRUE)

# InSilicoVA Example
data(RandomVA5)
fit_insilico <- codeVA(RandomVA5[1:100,], data.type = "WHO2016", 
                       auto.length = FALSE)
getTopCOD(fit_insilico, n = 1)
getTopCOD(fit_insilico, n = 3)
getTopCOD(fit_insilico, n = 3, include.prob = TRUE)


# Tariff Example (only top cause is returned)
data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$cause)
fit_tariff <- tariff(causes.train = "cause", symps.train = train, 
                     symps.test = test, causes.table = allcauses)
getTopCOD(fit_tariff, n = 1)

# NBC Example
library(nbc4va)
data(nbc4vaData)
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
fit_nbc <- nbc(train, test, known=TRUE)
getTopCOD(fit_nbc, n = 1)
getTopCOD(fit_nbc, n = 3)
getTopCOD(fit_nbc, n = 3, include.prob = TRUE)

## End(Not run)

data(RandomVA1)
# for illustration, only use interVA on 100 deaths
fit <- codeVA(RandomVA1[1:100, ], data.type = "WHO", model = "InterVA", 
                  version = "4.02", HIV = "h", Malaria = "l", write=FALSE)
getTopCOD(fit)
## Not run: 
library(openVA)

# InterVA4 Example
data(SampleInput)
fit_interva <- codeVA(SampleInput, data.type = "WHO2012", model = "InterVA",
                      version = "4.03", HIV = "l", Malaria = "l", write = FALSE)
getTopCOD(fit_interva, n = 1)
getTopCOD(fit_interva, n = 3)
getTopCOD(fit_interva, n = 3, include.prob = TRUE)
getTopCOD(fit_interva, interVA.rule = FALSE, n = 3)
getTopCOD(fit_interva, n = 5)
getTopCOD(fit_interva, n = 5, include.prob = TRUE)

# InterVA5 & Example
data(RandomVA5)
fit_interva5 <- codeVA(RandomVA5[1:50,], data.type = "WHO2016", model = "InterVA",
                       version = "5", HIV = "l", Malaria = "l", write = FALSE)
getTopCOD(fit_interva5, n = 1)
getTopCOD(fit_interva5, n = 3)
getTopCOD(fit_interva5, n = 3, include.prob = TRUE)
getTopCOD(fit_interva5, interVA.rule = FALSE, n = 3)
getTopCOD(fit_interva5, n = 5)
getTopCOD(fit_interva5, n = 5, include.prob = TRUE)

# InSilicoVA Example
data(RandomVA5)
fit_insilico <- codeVA(RandomVA5[1:100,], data.type = "WHO2016", 
                       auto.length = FALSE)
getTopCOD(fit_insilico, n = 1)
getTopCOD(fit_insilico, n = 3)
getTopCOD(fit_insilico, n = 3, include.prob = TRUE)


# Tariff Example (only top cause is returned)
data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
allcauses <- unique(train$cause)
fit_tariff <- tariff(causes.train = "cause", symps.train = train, 
                     symps.test = test, causes.table = allcauses)
getTopCOD(fit_tariff, n = 1)

# NBC Example
library(nbc4va)
data(nbc4vaData)
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
fit_nbc <- nbc(train, test, known=TRUE)
getTopCOD(fit_nbc, n = 1)
getTopCOD(fit_nbc, n = 3)
getTopCOD(fit_nbc, n = 3, include.prob = TRUE)

## End(Not run)

Extended InterVA method for non-standard input

Description

Extended InterVA method for non-standard input

Usage

interVA_train(
  data,
  train,
  causes.train,
  causes.table = NULL,
  thre = 0.95,
  type = c("quantile", "fixed", "empirical")[1],
  prior = c("uniform", "train")[1],
  ...
)
interVA_train(
  data,
  train,
  causes.train,
  causes.table = NULL,
  thre = 0.95,
  type = c("quantile", "fixed", "empirical")[1],
  prior = c("uniform", "train")[1],
  ...
)

Arguments

`data`	A matrix input, or data read from csv files. Sample input is included as `data(RandomVA3)`.
`train`	A matrix input, or data read from csv files in the same format as `data`, but with an additional column specifying cause-of-death. Sample input is included as `data(RandomVA3)`.
`causes.train`	the column name of the cause-of-death assignment label in training data.
`causes.table`	list of causes to consider in the training data. Default to be NULL, which uses all the causes present in the training data.
`thre`	numerical number between 0 and 1. Symptoms with missing rate higher than `thre` in the training data will be dropped from both training and testing data.
`type`	type of data conversion when calculating conditional probability (probability of each symptom given each cause of death) for InterVA and InSilicoVA models. Both “quantile” and “fixed” usually give similar results empirically. `quantile`: the rankings of the P(S\|C) are obtained by matching the same quantile distributions in the default InterVA P(S\|C) `fixed`: P(S\|C) are matched to the closest values in the default InterVA P(S\|C) table. `empirical`: no ranking is calculated, but use the empirical conditional probabilities directly.
`prior`	The prior distribution of CSMF. “uniform” uses no prior information, i.e., 1/C for all C causes and “train” uses the CSMF in the training data as prior distribution of CSMF.
`...`	not used

Value

fitted interVA object

References

Zehang R. Li, Tyler H. McCormick, Samuel J. Clark (2014) InterVA4: An R package to analyze verbal autopsy data., Center for Statistics and the Social Sciences Working Paper, No.146

http://www.interva.net/

Examples


data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
out <- interVA_train(data = test, train = train, causes.train = "cause", 
                     prior = "train", type = "quantile")

data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
out <- interVA_train(data = test, train = train, causes.train = "cause", 
                     prior = "train", type = "quantile")

Check openVA packages status

Description

This will print the current versions of all openVA packages (and optionally, their dependencies) are up-to-date, and will install after an interactive confirmation.

Usage

openVA_status()
openVA_status()

Examples

## Not run: 
openVA_status()

## End(Not run)
## Not run: 
openVA_status()

## End(Not run)

Update openVA packages

Description

This will check to see if all openVA packages (and optionally, their dependencies) are up-to-date, and will install after an interactive confirmation.

Usage

openVA_update()
openVA_update()

Examples

## Not run: 
openVA_update()

## End(Not run)
## Not run: 
openVA_update()

## End(Not run)

Plot top CSMF for a fitted model

Description

Plot top CSMF for a fitted model

Usage

plotVA(object, top = 10, title = NULL, ...)
plotVA(object, top = 10, title = NULL, ...)

Arguments

`object`	a fitted object using `codeVA`
`top`	number of top causes to plot
`title`	title of the plot
`...`	additional arguments passed to `plot.insilico`, `plot.tariff`, `CSMF`, or plot.nbc function in the nbc4va package.

Examples


data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause",
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

plotVA(fit1)
plotVA(fit2)
plotVA(fit3)

data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause",
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

plotVA(fit1)
plotVA(fit2)
plotVA(fit3)

plot grouped CSMF from a "insilico" object

Description

Produce bar plot of the CSMFs for a fitted object in broader groups. This function extends the stackplot() function in the InSilicoVA package to allow for the same visualization for results from InterVA, NBC, and Tariff algorithms.

Usage

stackplotVA(
  x,
  grouping = NULL,
  type = c("stack", "dodge")[1],
  group_order = NULL,
  err = TRUE,
  CI = 0.95,
  sample_size_print = FALSE,
  xlab = "",
  ylab = "CSMF",
  ylim = NULL,
  title = "CSMF by broader cause categories",
  horiz = FALSE,
  angle = 0,
  err_width = 0.4,
  err_size = 0.6,
  border = "black",
  bw = FALSE,
  filter_legend = FALSE,
  ...
)
stackplotVA(
  x,
  grouping = NULL,
  type = c("stack", "dodge")[1],
  group_order = NULL,
  err = TRUE,
  CI = 0.95,
  sample_size_print = FALSE,
  xlab = "",
  ylab = "CSMF",
  ylim = NULL,
  title = "CSMF by broader cause categories",
  horiz = FALSE,
  angle = 0,
  err_width = 0.4,
  err_size = 0.6,
  border = "black",
  bw = FALSE,
  filter_legend = FALSE,
  ...
)

Arguments

`x`	one or a list of fitted object from `codeVA` function
`grouping`	C by 2 matrix of grouping rule. If set to NULL, make it default.
`type`	type of the plot to make
`group_order`	list of grouped categories. If set to NULL, make it default.
`err`	indicator of inclusion of error bars
`CI`	Level of posterior credible intervals.
`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.
`border`	The color for the border of the bars.
`bw`	Logical indicator for setting the theme of the plots to be black and white.
`filter_legend`	Logical indicator for including all broad causes in the plot legend (default; FALSE) or filtering to only the broad causes in the data being plotted
`...`	Not used.

Author(s)

Zehang Li, Tyler McCormick, Sam Clark

Maintainer: Zehang Li <[email protected]>

Examples


data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause", write=FALSE,
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

data(SampleCategory)
stackplotVA(fit1, grouping = SampleCategory, type ="dodge", 
            ylim = c(0, 1), title = "InSilicoVA")
stackplotVA(fit2, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "InterVA4.02")
stackplotVA(fit3, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "Tariff")

data(RandomVA3)
test <- RandomVA3[1:200, ]
train <- RandomVA3[201:400, ]
fit1 <- codeVA(data = test, data.type = "customize", model = "InSilicoVA",
                    data.train = train, causes.train = "cause",
                    Nsim=1000, auto.length = FALSE)

fit2 <- codeVA(data = test, data.type = "customize", model = "InterVA",
               data.train = train, causes.train = "cause", write=FALSE,
               version = "4.02", HIV = "h", Malaria = "l")

fit3 <- codeVA(data = test, data.type = "customize", model = "Tariff",
               data.train = train, causes.train = "cause", 
               nboot.sig = 100)

data(SampleCategory)
stackplotVA(fit1, grouping = SampleCategory, type ="dodge", 
            ylim = c(0, 1), title = "InSilicoVA")
stackplotVA(fit2, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "InterVA4.02")
stackplotVA(fit3, grouping = SampleCategory, type = "dodge", 
            ylim = c(0, 1), title = "Tariff")

Package 'openVA'

Help Index

Running automated method on VA data

Description

Usage

Arguments

Value

References

See Also

Examples

Converting Input data with different coding scheme to standard format

Description

Usage

Arguments

Value

See Also

Examples

Convert standard PHMRC data into binary indicator format

Description

Usage

Arguments

Value

References

See Also

Examples

Calculate Overall chance-corrected concordance (CCC)

Description

Usage

Arguments

See Also

Examples

Obtain CSMF from fitted model

Description

Usage

Arguments

Value

See Also

Examples

Calculate CSMF accuracy

Description

Usage

Arguments

Value

See Also

Examples

Extract individual distribution of cause of death

Description

Usage

Arguments

Value

See Also

Examples

Get the URL to the PHMRC dataset

Description

Usage

Arguments

Value

Examples

Extract the most likely cause(s) of death

Description

Usage

Arguments

Value

See Also

Examples

Extended InterVA method for non-standard input

Description

Usage

Arguments

Value

References

Examples

Check openVA packages status

Description

Usage

See Also

Examples

Update openVA packages

Description

Usage