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Meta-regression analysis for Pearson or partial correlations

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

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

Arguments

alpha

alpha level for 1-alpha confidence

n

vector of sample sizes

cor

vector of estimated Pearson or partial correlations

s

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

References

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

Examples


n <- c(55, 190, 65, 35)
cor <- c(.40, .65, .60, .45)
q <- 0
x1 <- c(18, 25, 23, 19)
X <- matrix(x1, 4, 1)
meta.lm.cor(.05, n, cor, q, X)
#>    Estimate      SE      z     p      LL     UL
#> b0  -0.4783 0.48632 -0.984 0.325 -1.4315 0.4748
#> b1   0.0505 0.02128  2.371 0.018  0.0088 0.0922

# Should return: 
#    Estimate      SE      z     p      LL     UL
# b0  -0.4783 0.48632 -0.984 0.325 -1.4315 0.4748
# b1   0.0505 0.02128  2.371 0.018  0.0088 0.0922