VRATIO {boutliers} | R Documentation |
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 |
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
:alpha
th 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
:alpha
th 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.