Summarizes the results from a
nbc object. The summary
can be either for a particular case or for the entirety of cases.
A number that produces top causes depending on id:
A character representing a case id in the test data.
A character vector of the true causes for calculating the CSMF accuracy.
Additional arguments to be passed if applicable
See Methods documentation for details on calculations and metrics.
out A summary object built from a
nbc object with modifications/additions:
If (id is char):
Additions to a
$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
* 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
1 2 3 4 5 6 7 8 9 10 11
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"
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