| meta.lm.mean2 {vcmeta} | R Documentation |
Meta-regression analysis for 2-group mean differences
Description
This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group mean difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.
Usage
meta.lm.mean2(alpha, m1, m2, sd1, sd2, n1, n2, X)
Arguments
alpha |
alpha level for 1-alpha confidence |
m1 |
vector of estimated means for group 1 |
m2 |
vector of estimated means for group 2 |
sd1 |
vector of estimated SDs for group 1 |
sd2 |
vector of estimated SDs for group 2 |
n1 |
vector of group 1 sample sizes |
n2 |
vector of group 2 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.
Examples
n1 <- c(65, 30, 29, 45, 50)
n2 <- c(67, 32, 31, 20, 52)
m1 <- c(31.1, 32.3, 31.9, 29.7, 33.0)
m2 <- c(34.1, 33.2, 30.6, 28.7, 26.5)
sd1 <- c(7.1, 8.1, 7.8, 6.8, 7.6)
sd2 <- c(7.8, 7.3, 7.5, 7.2, 6.8)
x1 <- c(4, 6, 7, 7, 8)
x2 <- c(1, 0, 0, 0, 1)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.mean2(.05, m1, m2, sd1, sd2, n1, n2, X)
# Should return:
# Estimate SE t p LL UL df
# b0 -15.20 3.4097610 -4.457791 0.000 -21.902415 -8.497585 418
# b1 2.35 0.4821523 4.873979 0.000 1.402255 3.297745 418
# b2 2.85 1.5358109 1.855697 0.064 -0.168875 5.868875 418