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Meta-regression analysis for 1-group means

Source: R/meta_model.R
meta.lm.mean1.Rd

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a mean from one group. The estimates are OLS estimates with robust standard errors that accomodate residual heteroscedasticity.

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

Usage

meta.lm.mean1(alpha, m, sd, n, X)

Arguments

alpha

alpha level for 1-alpha confidence

m

vector of estimated means

sd

vector of estimated standard deviations

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

Examples

n <- c(25, 15, 30, 25, 40)
m <- c(20.1, 20.5, 19.3, 21.5, 19.4)
sd <- c(10.4, 10.2, 8.5, 10.3, 7.8)
x1 <- c(1, 1, 0, 0, 0)
x2 <- c( 12, 13, 11, 13, 15)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.mean1(.05, m, sd, n, X)
#>       Estimate        SE     t     p         LL        UL  df
#> b0 19.45490196 6.7873381 2.866 0.005  6.0288763 32.880928 132
#> b1  0.25686275 1.9834765 0.130 0.897 -3.6666499  4.180375 132
#> b2  0.04705882 0.5064693 0.093 0.926 -0.9547876  1.048905 132

# Should return: 
#       Estimate        SE     t     p         LL        UL  df
# b0 19.45490196 6.7873381 2.866 0.005  6.0288763 32.880928 132
# b1  0.25686275 1.9834765 0.130 0.897 -3.6666499  4.180375 132
# b2  0.04705882 0.5064693 0.093 0.926 -0.9547876  1.048905 132