weightedCovarRcppN: Estimate weighted covariance

In carSurv: Correlation-Adjusted Regression Survival (CARS) Scores

Description

Efficient C implementation of the sample covariance estimator. The denominator is defined as the sample size.

Usage

weightedCovarRcppN(x, y, w)

Arguments

x	Covariate without weighting (numeric vector).
y	Response. The mean of the response contains weights (numeric vector).
w	Weights for averaging (numeric vector).

Value

Weighted variance (numeric scalar).

Note

There are no safety checks of input arguments.

Author(s)

Thomas Welchowski

Examples

# Simulate two random vectors
set.seed(3975)
x <- rnorm(100)
set.seed(-3975)
y <- rnorm(100)
# Calculate variance with standard R function
# Rescaling ensures that both calculations use same denominator "n"
covarEst <- cov(x, y) * (100-1) / 100
# Calculate weighted variance with equal weights
equalW <- rep(1, 100)
weightCovarEst <- weightedCovarRcppN(x=x, y=y, w=equalW)
# Output comparison
all.equal(covarEst, weightCovarEst)
# Runtime comparison
library(microbenchmark)
microbenchmark(Default=cov(x, y), New=weightedCovarRcpp(x=x, y=y, w=equalW), times=25)
# -> New method is multiple times faster

carSurv documentation built on May 1, 2019, 8:44 p.m.