VRATIO: Variance ratio influential statistics

In boutliers: Outlier Detection and Influence Diagnostics for Meta-Analysis

Variance ratio influential statistics

Description

Calculating the variance ratio influential statistics by leave-one-out analysis and the percentiles of their bootstrap distributions.

Usage

VRATIO(y, v, B=2000, alpha=0.05)

Arguments

y	A vector of the outcome measure estimates (e.g., MD, SMD, log OR, log RR, RD)
v	A vector of the variance estimate of y
B	The number of bootstrap resampling (default: 2000)
alpha	The bootstrap percentile to be outputted (default: 0.05)

Value

The variance ratio influential statistics by leave-one-out analysis and their bootstrap percentiles. The outputs are ordered by the sizes of the variance ratio statistics.

• id: ID of the study.

• VR: The VRATIO statistic (relative change of the variance of the overall estimator) by leave-one-out analysis.

• Q1: alphath percentile for the bootstrap distribution of the VRATIO statistic.

• TR: The TAU2RATIO statistic (relative change of the heterogeneity variance) by leave-one-out analysis.

• Q2: alphath percentile for the bootstrap distribution of the TAU2RATIO statistic.

Examples

require(metafor)
data(finasteride)

edat3 <- escalc(m1i=m1,sd1i=s1,n1i=n1,m2i=m0,sd2i=s0,n2i=n0,measure="MD",data=finasteride)

VRATIO(edat3$yi, edat3$vi, B=10)
# This is an example command for illustration. B should be >= 1000.

boutliers documentation built on May 31, 2023, 7:38 p.m.