metatest fits and tests a metaregression model. In addition to
the traditional z test on the estimated coefficients, metatest
also yields more reliable statistics: the t-test, log likelihood ratio
test, Bartlett corrected log likelihood ratio test, and the permutation
test. The Bartlett corrected log likelihood ratio test and the
permutation test are to be recommended since their type 1 errors are
adequate.
Usage
metatest(formula, variance, data, threshold = 1e-05, maxiter = 100, npermut = 1000, ...)
## S3 method for class 'metatest'
summary(object, digits = 4, ...)
## S3 method for class 'metatest'
print(x, ...)
Arguments
formula
formula specifying the meta regression model;
use y~x to specify a meta regression of effect sizes y
moderated by x; the moderators can be either continuous or
categorical variables; an intercept is included by default (use
y~x-1 to drop the intercept); use y~1 for an intercept
only model, i.e. a meta-analysis model.
variance
The variances of the effect sizes to be modelled (a
vector or a variable name interpreted in data).
data
A data.frame to interpet the variables in arguments
formula and variance.
threshold
The threshold used in estimating the model; the
threshold is the change in the value of the random effects variance
parameter.
maxiter
Maximum number of iterations allowed in estimating the model.
npermut
Number of permutations performed by the permutation test.
object, x
Object of class metatest.
digits
Determines the number of digits to use in printing the results.
...
Not currently used.
Details
The effect sizes to be analyzed can be of arbitrary type; some
transformations between different effect size measures
are provided. For many more see the package compute.es.
The print and summary methods are currently identical (this may change
in the future), and print the random effects variance, the
coefficients, and all the computed statistics and associated p-values.
Value
metatest returns an object of class metatest which is a named list
with the following elements:
convergence
Convergence info; 0 indicates convergence; -1
signals that the estimator of between study variance was set to zero
during estimation (with a warning).
iter
Number of iterations used in optimizing the parameters.
variance
Matrix with between study variance estimate, its associated log
likelihood ratio statistic, df and p-value.
coefficients
Estimated coefficients.
se
Standard errors of the coefficients.
tval
The t-ratios of the coefficients.
pZtest
The p-values associated with the z-test.
dfttest
The df's associated with the t-tests.
pttest
The p-values associated with the t-test.
LLR
The log likelihood ratio statistics.
pLLR
The p-values associated with the LLR statistics.
bartLLR
The Bartlett corrected LLR statistics.
bartscale
The Bartlett scaling factor used to compute the
corrected LLR statistics.
pBartlett
The p-values associated with the Bartlett corrected
LLR statistics.
ppermtest
The p-values of the permutation tests.
call
The function call that created the metatest object.
Hilde M. Huizenga, Ingmar Visser & Conor V. Dolan (2011). Hypothesis
testing in random effects meta-regression, British Journal of
Mathematical and Statistical Psychology, 64, 1-19.
Examples
data(metadata)
res <- metatest(y~x,yvar,data=metadata)
res