joint: Joint measures of predictive accuracy

Description Usage Arguments Details

Description

Calculates joint sensitivity, specificity, positive and negative predictive value, concordance and net benefit for a vector of predictors.

Usage

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joint(x, y, thresh = NULL, target = NULL, target2 = NULL, sample = 0,
  prev = NULL)

Arguments

x

Matrix of predicted risks. Each row corresponds to an individual, each column to a trait. Each entry should be a risk between 0 and 1.

y

Matrix of traits. Each row corresponds to an individual, each column to a trait. Must contain binary events coded as 0 and 1.

thresh

Vector of risk thresholds. For each row of x, an event is predicted for each trait that exceeds the corresponding element of thresh. These predictions are then compared to the elements of y.

target

Target trait vector for evaluating joint measures of accuracy. If NULL the marginal measures are calculated across the distribution of trait vectors.

target2

Second target vector for calculating joint concordance.

sample

Number of random samples to draw when estimating concordance. Defaults to 0, in which case all pairs of individuals in x are considered.

prev

Population prevalence of target.

Details

Joint measures consider the prediction of all events within a fixed target vector. If target is NULL, which is the default, then the marginal measures are calculated across the distribution of trait vectors. Unlike classical sensitivity, specificity and concordance, the measures then depend upon the distribution of traits in the input data, which may differ from that in the population.

If prev is not specified, it is estimated from the y matrix, ignoring ascertainment.


DudbridgeLab/multipred documentation built on May 28, 2019, 12:37 p.m.