meta.lm.semipart: Meta-regression analysis for semipartial correlations

In dgbonett/vcmeta: Varying Coefficient Meta-Analysis

Meta-regression analysis for semipartial correlations

Description

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a Fisher-transformed semipartial 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.semipart(alpha, n, cor, r2, X)

Arguments

alpha	alpha level for 1-alpha confidence
n	vector of sample sizes
cor	vector of estimated semipartial correlations
r2	vector of squared multiple correlations for a model that includes the IV and all 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(128, 97, 210, 217)
cor <- c(.35, .41, .44, .39)
r2 <- c(.29, .33, .36, .39)
x1 <- c(18, 25, 23, 19)
X <- matrix(x1, 4, 1)
meta.lm.semipart(.05, n, cor, r2, X)

# Should return: 
#      Estimate        SE         z     p          LL         UL
# b0 0.19695988 0.3061757 0.6432905 0.520 -0.40313339 0.79705315
# b1 0.01055584 0.0145696 0.7245114 0.469 -0.01800004 0.03911172

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