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Meta-regression analysis for semipartial correlations

Source: R/meta_model.R
meta.lm.semipart.Rd

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.

For more details, see Section 3.4 of Bonett (2021, Volume 5).

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

References

Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.

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)
#>    Estimate      SE     z     p      LL     UL
#> b0   0.1970 0.30618 0.643 0.520 -0.4031 0.7971
#> b1   0.0106 0.01457 0.725 0.468 -0.0180 0.0391

# Should return: 
#    Estimate      SE     z     p      LL     UL
# b0   0.1970 0.30618 0.643 0.520 -0.4031 0.7971
# b1   0.0106 0.01457 0.725 0.468 -0.0180 0.0391