| meta.lm.cor.gen {vcmeta} | R Documentation |
Meta-regression analysis for correlations
Description
This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a Fisher-transformed correlation. The correlations can be of different types (e.g., Pearson, partial, Spearman). The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity. This function uses estimated correlations and their standard errors as input. 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.gen(alpha, cor, se, X)
Arguments
alpha |
alpha level for 1-alpha confidence |
cor |
vector of estimated correlations |
se |
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
cor <- c(.40, .65, .60, .45)
se <- c(.182, .114, .098, .132)
x1 <- c(18, 25, 23, 19)
X <- matrix(x1, 4, 1)
meta.lm.cor.gen(.05, cor, se, X)
# Should return:
# Estimate SE z p
# b0 -0.47832153 0.63427931 -0.7541181 0.451
# b1 0.05047154 0.02879859 1.7525699 0.080