Meta-regression analysis for paired-samples mean differences
Source:R/meta_model.R
meta.lm.mean.ps.RdThis function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a paired-samples mean difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.
For more details, see Section 3.4 of Bonett (2021, Volume 5).
Arguments
- alpha
alpha level for 1-alpha confidence
- m1
vector of estimated means for measurement 1
- m2
vector of estimated means for measurement 2
- sd1
vector of estimated SDs for measurement 1
- sd2
vector of estimated SDs for measurement 2
- cor
vector of estimated correlations for paired measurements
- n
vector of sample sizes
- 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
t - t-value
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
df - degrees of freedom
References
Bonett DG (2009). “Meta-analytic interval estimation for standardized and unstandardized mean differences.” Psychological Methods, 14(3), 225–238. ISSN 1939-1463, doi:10.1037/a0016619 .
Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.
Examples
n <- c(65, 30, 29, 45, 50)
cor <- c(.87, .92, .85, .90, .88)
m1 <- c(20.1, 20.5, 19.3, 21.5, 19.4)
m2 <- c(10.4, 10.2, 8.5, 10.3, 7.8)
sd1 <- c(9.3, 9.9, 10.1, 10.5, 9.8)
sd2 <- c(7.8, 8.0, 8.4, 8.1, 8.7)
x1 <- c(2, 3, 3, 4, 4)
X <- matrix(x1, 5, 1)
meta.lm.mean.ps(.05, m1, m2, sd1, sd2, cor, n, X)
#> Estimate SE t p LL UL df
#> b0 8.00 1.2491990 6.404 0.000 5.5378833 10.462117 217
#> b1 0.85 0.3796019 2.239 0.026 0.1018213 1.598179 217
# Should return:
# Estimate SE t p LL UL df
# b0 8.00 1.2491990 6.404 0.000 5.5378833 10.462117 217
# b1 0.85 0.3796019 2.239 0.026 0.1018213 1.598179 217