metansue (version 1.2)

metalm.meta.nsue: Fitting Meta-Analytic Linear Models for “meta.nsue” Objects

Description

Fits meta-analytic linear models. Along with linearHypothesis, it can be used to carry out meta-regression, meta-comparisons, analyses of variance and covariance, and etcetera.

Usage

# S3 method for meta.nsue
metalm(x, formula, maxiter = 100, tol = 1e-06, ...)

Arguments

x

an object of class "meta.nsue".

formula

an object of class "formula": a symbolic description of the model to be fitted.

maxiter

maximum number of iterations in the REML estimation of \(\tau^2\).

tol

tolerance in the REML estimation of \(\tau^2\).

...

other arguments (currently ignored).

Value

metalm.meta.nsue returns an object of class "meta.nsue".

The functions print and summary may be used to print the details or a summary of the results. The generic accessor functions coefficients, fitted.values and residuals extract various useful features of the value returned by metalm.meta.nsue.

Details

Models for meta.nsue, leave1out.nsue and metalm.meta.nsue are specified symbolically. The formula is a series of terms which specify a linear predictor for x. A formula specification of the form first + second indicates a multiple regression by first and second. A specification of the form first:second indicates the interaction of first with second. The specification first*second is the same as first + second + first:second.

References

Radua, J., Schmidt, A., Borgwardt, S., Heinz, A., Schlagenhauf, F., McGuire, P., Fusar-Poli, P. (2015) Ventral striatal activation during reward processing in psychosis. A neurofunctional meta-analysis. JAMA Psychiatry, 72, 1243--1251.

See Also

meta for conducting a meta-analysis.

linearHypothesis for testing linear hypotheses.

metabias for testing for funnel plot asymmetry.

Examples

# NOT RUN {
t <- c(3.4, NA, NA, NA, NA, 2.8, 2.1, 3.1, 2.0, 3.4)
n <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22)
m <- meta(smc_from_t(t, n))
mean.age <- rnorm(10, 30, 5)
metalm(m, ~ mean.age)
# }