meta.lm.cor.gen: Meta-regression analysis for correlations

In dgbonett/vcmeta: Varying Coefficient Meta-Analysis

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

dgbonett/vcmeta documentation built on July 12, 2024, 3:12 p.m.