Calculate predicted CSMFs from a NBC model

Description

Obtains the predicted Cause Specific Mortality Fraction (CSMF) from a result nbc object.

Usage

csmf.nbc(object)

Arguments

Value

out A numeric vector of the predicted CSMFs in which the names are the corresponding causes.

See Also

Other wrapper functions: topCOD.nbc()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Obtain the predicted CSMFs
predCSMF <- csmf.nbc(results)

Check arguments for nbc()

Description

Performs checks to ensure that the arguments passed to internalNBC are correct. This function will also auto-clean when appropriate, and display warning messages of the cleaning tasks.

Usage

internalCheckNBC(train, test, known = TRUE, assume = FALSE, unknown = 99)

Arguments

Details

The following checks are applied to train and test to ensure they:

• are a dataframe

• have required number of rows and columns

• have required data types for each column

• have required symptom values

• are in the same format

• have unique ids

Value

out A list object containing the checked inputs:

• $train: dataframe of id, cause and symptoms

• $test: dataframe of id, cause and symptoms in the same format as train

• $known: TRUE if the test causes are known or FALSE if not

See Also

Other validation functions: internalCheckNBCSummary()

Examples

library(nbc4va)
data(nbc4vaData)

# Check train and test inputs, error if it does not pass check
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
checked <- nbc4va::internalCheckNBC(train, test)
train <- checked$train
test <- checked$test

Check arguments for summary.nbc()

Description

Performs checks to ensure that the arguments passed to summary.nbc are correct. This function will perform automatic data type conversions, and display warnings when appropriate.

Usage

internalCheckNBCSummary(object, top = 5, id = NULL, csmfa.obs = NULL, ...)

Arguments

Details

The following checks are applied:

• object is of class "nbc"

• top is a numeric value of length 1

• id is NULL or a character of length 1, and exists in the test data

• csmfa.obs is NULL or a character or vector of characters

Value

out A list object containing the checked inputs:

• $object: an nbc object

• $top: numeric value

• $id: NULL or character value

• $csmfa.obs: NULL or character vector

See Also

Other validation functions: internalCheckNBC()

Examples

library(nbc4va)
data(nbc4vaData)

# Create an nbc
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Check the inputs before passing on to summary
checked <- nbc4va::internalCheckNBCSummary(results, 5, "g85")
results <- checked$object
top <- checked$top
id <- checked$id
csmfa.obs <- checked$csmfa.obs

Calculate CSMF accuracy

Description

Calculates the overall CSMF accuracy given any number of predicted cases and any number of observed cases.

Usage

internalGetCSMFAcc(pred, obs)

Arguments

Value

csmfa Numeric value of the overall CSMF accuracy (see Methods documentation).

See Also

Other internal functions: internalGetCSMFMaxError(), internalGetCauseMetrics(), internalGetMetrics(), internalNBC()

Examples

library(nbc4va)
pred <- c("HIV", "Stroke", "HIV", "Stroke")
obs <- c("HIV", "HIV", "Stroke", "Stroke")
csmfa <- nbc4va::internalGetCSMFAcc(pred, obs)

Calculate CSMF maximum error

Description

Calculates the CSMF maximum error given a set of observed cases.

Usage

internalGetCSMFMaxError(obs)

Arguments

Value

csmfMaxError Numeric value of the CSMF maximum error (see Methods documentation).

See Also

Other internal functions: internalGetCSMFAcc(), internalGetCauseMetrics(), internalGetMetrics(), internalNBC()

Examples

library(nbc4va)
obs <- c("HIV", "HIV", "Stroke", "Stroke")
maxerror <- nbc4va::internalGetCSMFMaxError(obs)

Calculate performance metrics table per cause

Description

A table providing performance metrics per unique cause based on input predicted and observed cases.

Usage

internalGetCauseMetrics(pred, obs, causes = unique(c(pred, obs)))

Arguments

Details

This code is built on the original performance metrics code provided by Dr. Mireille Gomes.

Value

out Dataframe of a performance metrics per cause (see Methods documentation):

• Columns: Cause, TruePositives, TrueNegatives, FalsePositives, FalseNegatives, PredictedFrequency, ObservedFrequency, Sensitivity, CSMFpredicted, CSMFobserved

• Cause (vectorof char): The unique causes from both the obs and pred inputs

• Sensitivity (vectorof double): the sensitivity for a cause

• CSMFpredicted (vectorof double): the cause specific mortality fraction for a cause given the predicted deaths

• CSMFobserved (vectorof double): the cause specific mortality fraction for a cause given the observed deaths

• TruePositives (vectorof double): The total number of true positives per cause

• TrueNegatives (vectorof double): The total number of true negatives per cause

• FalsePositives (vectorof double): The total number of false positives per cause

• FalseNegatives (vectorof double): The total number of false negatives per cause

• PredictedFrequency (vectorof double): The occurence of a cause in the pred input

• ObservedFrequency (vectorof double): The occurence of a cause in the obs input

Example:

See Also

Other internal functions: internalGetCSMFAcc(), internalGetCSMFMaxError(), internalGetMetrics(), internalNBC()

Examples

library(nbc4va)
pred <- c("HIV", "Stroke", "HIV", "Stroke")
obs <- c("HIV", "HIV", "Stroke", "Stroke")
cmetrics <- nbc4va::internalGetCauseMetrics(pred, obs)

Calculate overall performance metrics

Description

A vector providing overall performance metrics based on input predicted and observed cases.

Usage

internalGetMetrics(
  pred,
  obs,
  causes = unique(c(pred, obs)),
  csmfa.obs = NULL,
  causeMetrics = internalGetCauseMetrics(pred, obs, causes)
)

Arguments

Details

Developer Note: Depends on the internalGetCSMFAcc function to get the CSMF Accuracy.

Value

metrics Named numeric vector of performance metrics (see Methods documentation):

• Names: TruePositives, TrueNegatives, FalsePositives, FalseNegatives, Accuracy, Sensitivity, Specificity, PCCC, CSMFMaxError, CSMFaccuracy

• TruePositives (double): total number of true positives

• TrueNegatives (double): total number of true negatives

• FalsePositives (double): total number of false positives

• FalseNegatives (double): total number of false negatives

• Sensitivity (double): the overall sensitivity

• PCCC (double): the partial chance corrected concordance

• CSMFMaxError (double): the maximum Cause Specific Mortality Fraction Error

• CSMFaccuracy (double): the Cause Specific Mortaliy Fraction accuracy

See Also

Other internal functions: internalGetCSMFAcc(), internalGetCSMFMaxError(), internalGetCauseMetrics(), internalNBC()

Examples

library(nbc4va)
pred <- c("HIV", "Stroke", "HIV", "Stroke")
obs <- c("HIV", "HIV", "Stroke", "Stroke")
metrics <- nbc4va::internalGetMetrics(pred, obs)

NBC algorithm source code

Description

Performs Naive Bayes Classification given train and test (validation) datasets, as well as additional information for the train and test data.

Usage

internalNBC(train, test, known = TRUE)

Arguments

Details

This function was built on code provided by Miasnikof et al (2015). Edits to the code included the following improvements:

• Causes can be character type

• Matrix operations for speed

• Removal of order dependence for causes

• Refactoring of variable names for clarity

• Included list structure of model data and details

• Argument validation

Value

out The result list object containing:

• $prob.causes (vectorof double): the probabilities for each test case prediction by case id

• $pred.causes (vectorof char): the predictions for each test case by case id

• Additional values:

  • * indicates that the value is only available if test causes are known

  • $train (dataframe): the input train data

  • $train.ids (vectorof char): the ids of the train data

  • $train.causes (vectorof char): the causes of the train data by case id

  • $train.samples (double): the number of input train samples

  • $test (dataframe): the input test data

  • $test.ids (vectorof char): the ids of the test data

  • $test.causes* (vectorof char): the causes of the test data by case id

  • $test.samples (double): the number of input test samples

  • $test.known (logical): whether the test causes are known

  • $symptoms (vectorof char): all unique symptoms in order

  • $causes (vectorof char): all possible unique causes of death

  • $causes.train (vectorof char): all unique causes of death in the train data

  • $causes.test* (vectorof char): all unique causes of death in the test data

  • $causes.pred (vectorof char): all unique causes of death in the predicted cases

  • $causes.obs* (vectorof char): all unique causes of death in the observed cases

  • $pred (dataframe): a table of predictions for each test case, sorted by probability

    • Columns (in order): CaseID, TrueCause, Prediction-1 to Prediction-n..

    • CaseID (vectorof char): case identifiers

    • TrueCause* (vectorof char): the observed causes of death

    • Prediction-n.. (vectorsof char): the predicted causes of death, where Prediction1 is the most probable cause, and Prediction-n is the least probable cause

    Example:

    CaseID	Prediction1	Prediction2
    "a1"	"HIV"	"Stroke"
    "b2"	"Stroke"	"HIV"
    "c3"	"HIV"	"Stroke"

  • $obs* (dataframe): a table of observed causes matching $pred for each test case

    • Columns (in order): CaseID, TrueCause

    • CaseID (vectorof char): case identifiers

    • TrueCause (vectorof char): the actual cause of death if applicable

    Example:

    CaseID	TrueCause
    "a1"	"HIV"
    "b2"	"Stroke"
    "c3"	"HIV"

  • $obs.causes* (vectorof char): all observed causes of death by case id

  • $prob (dataframe): a table of probabilities of each cause for each test case

    • Columns (in order): CaseID, Cause-1 to Cause-n..

    • CaseID (vectorof char): case identifiers

    • Cause-n.. (vectorsof double): probabilies for each cause of death

    Example:

    CaseID	HIV	Stroke
    "a1"	0.5	0.5
    "b2"	0.3	0.7
    "c3"	0.9	0.1

Author(s)

Pierre Miasnikof (Original), Vasily Giannakeas (Original), Richard Wen (Edits) <wenr@smh.ca>

References

• 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. doi:10.1186/s12916-015-0521-2.

See Also

Other internal functions: internalGetCSMFAcc(), internalGetCSMFMaxError(), internalGetCauseMetrics(), internalGetMetrics()

Examples

library(nbc4va)
data(nbc4vaData)

# Create naive bayes classifier on random train and test data
# Set "known" to indicate whether or not "test" causes are known
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc4va::internalNBC(train, test, known=TRUE)

# Obtain the probabilities and predictions
prob <- results$prob.causes
pred <- results$pred.causes

Round values to whole numbers while preserving the sum

Description

Rounds a vector of values to whole numbers while preserving the sum (rounded if it is not a whole number) using the largest remainder method (Gallagher, 1991).

Usage

internalRoundFixedSum(v, roundSum = round)

Arguments

Value

out A vector of v with the values rounded to whole numbers but with the whole number sum preserved.

References

• Gallagher M. Proportionality, disproportionality and electoral systems. Electoral Studies. 1991;10(1)33-51. doi:10.1016/0261-3794(91)90004-C.

See Also

Other data functions: internalSubAsRest()

Examples

library(nbc4va)
dec <- c(rep(50/2, 2), rep(50/3, 3))
whole <- nbc4va::internalRoundFixedSum(dec)

Substitute values in a dataframe proportionally to all other values

Description

Substitute a target value proportionally to the distribution of the rest of the values in a column, given the following conditions:

• If a column contains only the target value, the column is removed

• If there are not enough target values to be distributed, then each target value will be randomly sampled from the rest of the column values with replacement

Usage

internalSubAsRest(
  dataset,
  x,
  cols = 1:ncol(dataset),
  ignore = c(NA, NaN),
  removal = FALSE
)

Arguments

Details

Pseudocode of algorithm:

  SET dataset = table of values with columns and rows
  SET x = target value for substitution

  IF x in dataset:
    FOR EACH column y in a dataset:
      SET xv = all x values in y
      SET rest = all values not equal to x in y
      IF xv == values in y:
        REMOVE y in dataset
      IF number of unique values of rest == 1:
        MODIFY xv = rest
      IF number of xv values < number of unique values of rest:
        SET xn = number of xv values
        MODIFY xv = random sample of rest with size xn
      ELSE:
        SET xn = number of xv values
        SET p = proportions of rest
        SET xnp = xn * p
        IF xnp has decimals:
          MODIFY xnp = round xnp such that sum(xnp) == xn via largest remainder method
        MODIFY xv = rest values with distribution of xnp
  RETURN dataset

Value

out A dataframe or list depending on removal:

• if (removal is FALSE) return the dataset with values of x substituted by the rest of the values per column

• if (removal is TRUE) return a list with the following:

  • $removed (vectorof numeric): the removed column indices if the column(s) consists only of x values

  • $dataset (dataframe): the dataset with values of x substituted by the rest of the values per column

See Also

Other data functions: internalRoundFixedSum()

Examples

library(nbc4va)
data(nbc4vaDataRaw)
unclean <- nbc4vaDataRaw
clean <- nbc4va::internalSubAsRest(unclean, 99)

Train a NBC model

Description

Performs supervised Naive Bayes Classification on verbal autopsy data.

Usage

nbc(train, test, known = TRUE)

Arguments

Value

out The result nbc list object containing:

• $prob.causes (vectorof double): the probabilities for each test case prediction by case id

• $pred.causes (vectorof char): the predictions for each test case by case id

• Additional values:

  • * indicates that the value is only available if test causes are known

  • $train (dataframe): the input train data

  • $train.ids (vectorof char): the ids of the train data

  • $train.causes (vectorof char): the causes of the train data by case id

  • $train.samples (double): the number of input train samples

  • $test (dataframe): the input test data

  • $test.ids (vectorof char): the ids of the test data

  • $test.causes* (vectorof char): the causes of the test data by case id

  • $test.samples (double): the number of input test samples

  • $test.known (logical): whether the test causes are known

  • $symptoms (vectorof char): all unique symptoms in order

  • $causes (vectorof char): all possible unique causes of death

  • $causes.train (vectorof char): all unique causes of death in the train data

  • $causes.test* (vectorof char): all unique causes of death in the test data

  • $causes.pred (vectorof char): all unique causes of death in the predicted cases

  • $causes.obs* (vectorof char): all unique causes of death in the observed cases

  • $pred (dataframe): a table of predictions for each test case, sorted by probability

    • Columns (in order): CaseID, TrueCause, Prediction-1 to Prediction-n..

    • CaseID (vectorof char): case identifiers

    • TrueCause* (vectorof char): the observed causes of death

    • Prediction-n.. (vectorsof char): the predicted causes of death, where Prediction1 is the most probable cause, and Prediction-n is the least probable cause

    Example:

    CaseID	Prediction1	Prediction2
    "a1"	"HIV"	"Stroke"
    "b2"	"Stroke"	"HIV"
    "c3"	"HIV"	"Stroke"

  • $obs* (dataframe): a table of observed causes matching $pred for each test case

    • Columns (in order): CaseID, TrueCause

    • CaseID (vectorof char): case identifiers

    • TrueCause (vectorof char): the actual cause of death if applicable

    Example:

    CaseID	TrueCause
    "a1"	"HIV"
    "b2"	"Stroke"
    "c3"	"HIV"

  • $obs.causes* (vectorof char): all observed causes of death by case id

  • $prob (dataframe): a table of probabilities of each cause for each test case

    • Columns (in order): CaseID, Cause-1 to Cause-n..

    • CaseID (vectorof char): case identifiers

    • Cause-n.. (vectorsof double): probabilies for each cause of death

    Example:

    CaseID	HIV	Stroke
    "a1"	0.5	0.5
    "b2"	0.3	0.7
    "c3"	0.9	0.1

References

• 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. doi:10.1186/s12916-015-0521-2.

See Also

Other main functions: plot.nbc(), print.nbc_summary(), summary.nbc()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
# Set "known" to indicate whether or not "test" causes are known
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test, known=TRUE)

# Obtain the probabilities and predictions
prob <- results$prob.causes
pred <- results$pred.causes

nbc4va: Bayes Classifier for Verbal Autopsy Data

Description

An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.

For documentation and help, please see:

https://rrwen.github.io/nbc4va/

Acknowledgements

This package was developed at the Centre for Global Health Research (CGHR) in Toronto, Ontario, Canada. The original NBC algorithm code was developed by Pierre Miaskinof and Vasily Giannakeas. The original performance metrics code was provided by Dr. Mireille Gomes whom also offered guidance in metrics implementation and user testing. Special thanks to Richard Zehang Li for providing a standard structure for the package and Patrycja Kolpak for user testing of the GUI.

Author(s)

Richard Wen <rrwen.dev@gmail.com>

References

Use citation("nbc4va") to view citation information for the nbc4va package.

• 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. doi:10.1186/s12916-015-0521-2.

Examples

## Not run: 
library(nbc4va)

# Quick start
# Follow the instructions in the web interface
nbc4vaGUI()

# View user guides for the nbc4va package
browseVignettes("nbc4va")

## End(Not run)

Example of clean data in nbc4va

Description

A random generation of clean verbal autopsy synthetic data for use in demonstrating the nbc4va package.

Usage

nbc4vaData

Format

A dataframe with 100 rows and 102 columns:

• id (vectorof char): the case identifiers

• cause (vectorof char): the cause of death for each case

• symptom1..100 (vectorsof (1 OR 0)): whether the symptom is recorded as present (1) or not (0) for each case (row)

• Example:

  id	cause	symptom1	symptom2	symptom3
  "a27"	"cause10"	1	0	0
  "k37"	"cause2"	0	0	1
  "e57"	"cause8"	1	0	0

Source

Random generation using the sample function with set.seed set to 1.

Examples

library(nbc4va)
data(nbc4vaData)

Example of unclean data in nbc4va

Description

A random generation of unclean verbal autopsy synthetic data for use in demonstrating the nbc4va package.

Usage

nbc4vaDataRaw

Format

A dataframe with 100 rows and 102 columns:

• id (vectorof char): the case identifiers

• cause (vectorof char): the cause of death for each case

• symptom1..100 (vectorsof (1 OR 0 OR 99)): whether the symptom is recorded as present (1), absent (0), or unknown (99) for each case (row)

• Example:

  id	cause	symptom1	symptom2	symptom3
  "a27"	"cause10"	99	0	1
  "k37"	"cause2"	0	99	1
  "e57"	"cause8"	1	0	99

Details

Warning: This data may produce errors depending on how you use it in the package.

Source

Random generation using the sample function with set.seed set to 1.

Examples

library(nbc4va)
data(nbc4vaDataRaw)

Web-based graphical user interface in nbc4va

Description

A Graphical User Interface (GUI) for the nbc4va package using shiny.

Usage

nbc4vaGUI()

Details

This function requires the shiny package, which can be installed via:

install.packages("shiny")

Use esc in the R console to stop the GUI.

Please use a modern browser (e.g. latest firefox, chrome) for the best experience.

Value

Creates a GUI for running nbc4va in a web browser.

See Also

Other utility functions: nbc4vaIO()

Examples

## Not run: 
library(nbc4va)
nbc4vaGUI()

## End(Not run)

Run nbc4va using file input and output

Description

Runs nbc and uses summary.nbc on input data files or dataframes to output result files or dataframes with data on predictions, probabilities, causes, and performance metrics in an easily accessible way.

Usage

nbc4vaIO(
  trainFile,
  testFile,
  known = TRUE,
  csmfaFile = NULL,
  saveFiles = TRUE,
  outDir = dirname(testFile),
  fileHeader = strsplit(basename(testFile), "\\.")[[1]][[1]],
  fileReader = read.csv,
  fileReaderIn = "file",
  fileReaderArgs = list(as.is = TRUE),
  fileWriter = write.csv,
  fileWriterIn = "x",
  fileWriterOut = "file",
  fileWriterArgs = list(row.names = FALSE),
  outExt = "csv"
)

Arguments

Details

See Methods documentation for details on the methodology and implementation of the Naive Bayes Classifier algorithm. This function may also act as a wrapper for the main nbc4va package functions.

Value

out Vector or list of respective paths or data from the naive bayes classifier:

• If (saveFiles is TRUE) return a named character vector of the following:

  • Names: dir, pred, prob, causes, summary

  • dir (char): the path to the directory of the output files

  • pred (char): the path to the prediction table file, where the columns of Pred1..PredN are ordered by the prediction probability with Pred1 being the most probable cause

  • prob (char): the path to the probability table file, where the columns excluding the CaseID are the cause and each cell has a probability value

  • causes (char): the path to the cause performance metrics table file, where each column is a metric and each row is a cause

  • metrics (char): the path to the overall performance metrics table file, where each column is a metric

• If (saveFiles is FALSE) return a list of the following:

  • Names: pred, prob, causes, summary

  • pred (dataframe): the prediction table, where the columns of Pred1..PredN are ordered by the prediction probability with Pred1 being the most probable cause

  • prob (dataframe): the probability table, where the columns excluding the CaseID are the cause and each cell has a probability value

  • causes (dataframe): the cause performance metrics table, where each column is a metric and each row is a cause

  • metrics (dataframe): the summary table, where each column is a performance metric

  • nbc (object): the returned nbc object

  • nbc_summary (object): the returned summary.nbc object

See Also

Other utility functions: nbc4vaGUI()

Examples

library(nbc4va)
data(nbc4vaData)

# Split data into train and test sets
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]

# Save train and test data as csv in temp location
trainFile <- tempfile(fileext=".csv")
testFile <- tempfile(fileext=".csv")
write.csv(train, trainFile, row.names=FALSE)
write.csv(test, testFile, row.names=FALSE)

# Use nbc4vaIO via file input and output
# Set "known" to indicate whether test causes are known
outFiles <- nbc4vaIO(trainFile, testFile, known=TRUE)

# Use nbc4vaIO as a wrapper
out <- nbc4vaIO(train, test, known=TRUE, saveFiles=FALSE)

Translate open verbal autopsy arguments to train a NBC model

Description

A wrapper function for creating an nbc object with the parameters specified by the openVA package.

Usage

ova2nbc(symps.train, symps.test, causes.train, causes.table = NULL, ...)

Arguments

Value

nbc An nbc object with the following modifications:

• $id (vectorof char): set to test data ids

• $prob (matrixof numeric): set to a matrix of likelihood for each cause of death for the test cases

• $CSMF (vectorof char): set to the predicted CSMFs with names for the corresponding causes

References

• Li Z, McCormick T, Clark S. openVA: Automated Method for Verbal Autopsy [Internet]. 2016. [cited 2016 Apr 29]. Available from: https://cran.r-project.org/package=openVA

Examples

## Not run: 
library(openVA)  # install.packages("openVA")
library(nbc4va)

# Obtain some openVA formatted data
data(RandomVA3) # cols: deathId, cause, symptoms..
train <- RandomVA3[1:100, ]
test <- RandomVA3[101:200, ]

# Run naive bayes classifier on openVA data
results <- ova2nbc(train, test, "cause")

# Obtain the probabilities and predictions
prob <- results$prob.causes
pred <- results$pred.causes

## End(Not run)

Bar plot of top predicted causes from a NBC model

Description

Plots the results from a nbc object as a barplot for a number of causes based on predicted Cause Specific Mortality Fraction (CSMF).

Usage

## S3 method for class 'nbc'
plot(
  x,
  top.plot = length(x$causes.pred),
  min.csmf = 0,
  csmfa.obs = NULL,
  footnote = TRUE,
  footnote.color = "gray48",
  footnote.size = 0.7,
  main = paste("Naive Bayes Classifier: Top ", top.plot, " Causes by Predicted CSMF",
    sep = ""),
  xlab = "Predicted CSMF",
  col = "dimgray",
  horiz = TRUE,
  border = NA,
  las = 1,
  ...
)

Arguments

Details

See Methods documentation for details on CSMF and CSMF accuracy.

Value

Generates a bar plot the top predicted causes from the NBC model

See Also

barplot

Other main functions: nbc(), print.nbc_summary(), summary.nbc()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Plot the top 3 causes by CSMF
plot(results, top.plot=3)

Print top predicted causes from a NBC model

Description

Prints a summary message from a summary.nbc object of the top causes by probability or predicted Cause Specific Mortality Fraction (CSMF).

Usage

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

Arguments

Details

See Methods documentation for details on CSMF and probability from the Naive Bayes Classifier.

Value

Prints a summary of the top causes of death by probability for the NBC model.

See Also

Other main functions: nbc(), plot.nbc(), summary.nbc()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Print a summary of all the test data for the top 3 causes by predicted CSMF
brief <- summary(results, top=3)
print(brief)

Summarize a NBC model with metrics

Description

Summarizes the results from a nbc object. The summary can be either for a particular case or for the entirety of cases.

Usage

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

Arguments

Details

See Methods documentation for details on calculations and metrics.

Value

out A summary object built from a nbc object with modifications/additions:

• If (id is char):

  • Additions to a nbc object:

    • $id (char): the case id chosen by the user

    • $top (numeric): the input number of top causes for id

    • $top.prob (vectorof double): the top probabilities for id

  • The following are modified from a nbc object to be id specific:
    $test, $test.ids, $test.causes, $obs.causes, $prob, $prob.causes, $pred, $pred.causes

• If (id is NULL):

  • Additions to the nbc object:

    • * indicates that the item is only available if test causes are known

    • ** indicates that the item ignores * if csmfa.obs is given

    • $top.csmf.pred (vectorof double): the top predicted CSMFs by cause

    • $top.csmf.obs* (vectorof double): the top observed CSMFs by cause

    • $metrics.all** (vectorof double): a numeric vector of overall metrics.

      • Names: TruePositives, TrueNegatives, FalsePositives, FalseNegatives, Accuracy, Sensitivity, PCCC, CSMFMaxError, CSMFaccuracy

      • TruePositives* (double): total number of true positives

      • TrueNegatives* (double): total number of true negatives

      • FalsePositives* (double): total number of false positives

      • FalseNegatives* (double): total number of false negatives

      • Sensitivity* (double): the overall sensitivity

      • PCCC* (double): the partial chance corrected concordance

      • CSMFMaxError** (double): the maximum Cause Specific Mortality Fraction Error

      • CSMFaccuracy** (double): the Cause Specific Mortaliy Fraction accuracy

    • $metrics.causes (dataframe): a perfomance table of metrics by cause.

      • Columns: Cause, Sensitivity, CSMFpredicted, CSMFobserved

      • Cause (vectorof char): The unique causes from both the obs and pred inputs

      • Sensitivity* (vectorof double): the sensitivity for a cause

      • CSMFpredicted (vectorof double): the cause specific mortality fraction for a cause given the predicted deaths

      • CSMFobserved* (vectorof double): the cause specific mortality fraction for a cause given the observed deaths

      • TruePositives (vectorof double): The total number of true positives per cause

      • TrueNegatives (vectorof double): The total number of true negatives per cause

      • FalsePositives (vectorof double): The total number of false positives per cause

      • FalseNegatives (vectorof double): The total number of false negatives per cause

      • PredictedFrequency (vectorof double): The occurence of a cause in the pred input

      • ObservedFrequency (vectorof double): The occurence of a cause in the obs input

      • Example:

        Cause	Sensitivity	Metric-n..
        HIV	0.5	#..
        Stroke	0.5	#..

See Also

Other main functions: nbc(), plot.nbc(), print.nbc_summary()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Obtain a summary for the results
brief <- summary(results, top=2)  # top 2 causes by CSMF for all test data
briefID <- summary(results, id="v48")  # top 5 causes by probability for case "v48"

Cause of death predictions from a NBC model

Description

Obtains the top causes of deaths for each testing case from a result nbc object.

Usage

topCOD.nbc(object)

Arguments

Value

out A dataframe of the top CODs:

• Columns: ID, COD

• ID (vectorof char): The ids for each testing case

• COD (vectorof char): The top prediction for each testing case

See Also

Other wrapper functions: csmf.nbc()

Examples

library(nbc4va)
data(nbc4vaData)

# Run naive bayes classifier on random train and test data
train <- nbc4vaData[1:50, ]
test <- nbc4vaData[51:100, ]
results <- nbc(train, test)

# Obtain the top cause of death predictions for the test data
topPreds <- topCOD.nbc(results)