| meta.lm.cor {vcmeta} | R Documentation |
Meta-regression analysis for Pearson or partial correlations
Description
This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a Fisher-transformed Pearson or partial correlation. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity. The correlations are Fisher-transformed and hence the parameter estimates do not have a simple interpretation. However, the hypothesis test results can be used to decide if a population slope is either positive or negative.
Usage
meta.lm.cor(alpha, n, cor, s, X)
Arguments
alpha |
alpha level for 1-alpha confidence |
n |
vector of sample sizes |
cor |
vector of estimated Pearson or partial correlations |
s |
number of control variables |
X |
matrix of predictor values |
Value
Returns a matrix. The first row is for the intercept with one additional row per predictor. The matrix has the following columns:
Estimate - OLS estimate
SE - Standard error
z - z-value
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Examples
n <- c(55, 190, 65, 35)
cor <- c(.40, .65, .60, .45)
q <- 0
x1 <- c(18, 25, 23, 19)
X <- matrix(x1, 4, 1)
meta.lm.cor(.05, n, cor, q, X)
# Should return:
# Estimate SE z p LL UL
# b0 -0.47832153 0.48631509 -0.983563 0.325 -1.431481595 0.47483852
# b1 0.05047154 0.02128496 2.371231 0.018 0.008753794 0.09218929