yardstick (version 0.0.1)

sens: Calculate sensitivity, specificity and predictive values

Description

These functions calculate the sensitivity, specificity or predictive values of a measurement system compared to a reference results (the truth or a gold standard). The measurement and "truth" data must have the same two possible outcomes and one of the outcomes must be thought of as a "positive" results or the "event".

Usage

sens(data, ...)
# S3 method for data.frame
sens(data, truth, estimate, na.rm = TRUE, ...)
# S3 method for table
sens(data, ...)
# S3 method for matrix
sens(data, ...)
# S3 method for data.frame
spec(data, truth, estimate, na.rm = TRUE, ...)
ppv(data, ...)
# S3 method for table
ppv(data, prevalence = NULL, ...)
# S3 method for matrix
ppv(data, prevalence = NULL, ...)
npv(data, ...)
# S3 method for table
npv(data, prevalence = NULL, ...)
# S3 method for matrix
npv(data, prevalence = NULL, ...)

Arguments

data

For the default functions, a factor containing the discrete measurements. For the table or matrix functions, a table or matrix object, respectively, where the true class results should be in the columns of the table.

...

Not currently used.

truth

The column identifier for the true class results (that is a factor). This should an unquoted column name although this argument is passed by expression and support quasiquotation (you can unquote column names or column positions).

estimate

The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name.

na.rm

A logical value indicating whether NA values should be stripped before the computation proceeds

prevalence

A numeric value for the rate of the "positive" class of the data.

Value

A number between 0 and 1 (or NA).

Details

The sensitivity is defined as the proportion of positive results out of the number of samples which were actually positive. When there are no positive results, sensitivity is not defined and a value of NA is returned. Similarly, when there are no negative results, specificity is not defined and a value of NA is returned. Similar statements are true for predictive values.

The positive predictive value is defined as the percent of predicted positives that are actually positive while the negative predictive value is defined as the percent of negative positives that are actually negative.

There is no common convention on which factor level should automatically be considered the "event" or "positive" results. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest.

Suppose a 2x2 table with notation

	Reference
Predicted	Event	No Event
Event	A	B
No Event	C	D

The formulas used here are: $$Sensitivity=A/(A+C)$$ $$Specificity=D/(B+D)$$ $$Prevalence=(A+C)/(A+B+C+D)$$ $$PPV=(sensitivity\ast Prevalence)/((sensitivity\ast Prevalence)+((1-specificity)\ast (1-Prevalence)))$$ $$NPV=(specificity\ast (1-Prevalence))/(((1-sensitivity)\ast Prevalence)+((specificity)\ast (1-Prevalence)))$$

See the references for discussions of the statistics.

If more than one statistic is required, it is more computationally efficient to create the confusion matrix using conf_mat() and applying the corresponding summary method (summary.conf_mat()) to get the values at once.

References

Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 1: sensitivity and specificity,'' British Medical Journal, vol 308, 1552.

Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 2: predictive values,'' British Medical Journal, vol 309, 102.

See Also

conf_mat(), summary.conf_mat(), recall(), mcc()

Examples

# NOT RUN {
data("two_class_example")

# Given that a sample is Class 1, 
#   what is the probability that is predicted as Class 1? 
sens(two_class_example, truth = truth, estimate = predicted)

# Given that a sample is predicted to be Class 1, 
#  what is the probability that it truly is Class 1? 
ppv(two_class_example, truth = truth, estimate = predicted)

# But what if we think that Class 1 only occurs 40% of the time?
ppv(two_class_example, truth, predicted, prevalence = 0.40) 
# }