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.
meta.lm.semipart(alpha, n, cor, r2, X)alpha level for 1-alpha confidence
vector of sample sizes
vector of estimated semipartial correlations
vector of squared multiple correlations for a model that includes the IV and all control variables
matrix of predictor values
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
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)
#> 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
# 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