Description Value Methods Author(s) References See Also Examples
An object returned by the mixmeta
function, inheriting from class "mixmeta"
, and representing a fitted univariate or multivariate metaanalytical model.
Objects of class "mixmeta"
are lists with defined components. Dimensions of such components may refer to k outcome parameters, p fixedeffects and q randomeffects predictors, m groups and n units used for fitting the model (the latter can be different from those originally selected due to missing). Depending on the type of metaanalytical model, the following components can bu included in a legitimate mixmeta
object:

a kpdimensional vector of the fixedeffects coefficients. 
vcov 
estimated kp x kp (co)variance matrix of the fixedeffects coefficients. 
Psi 
the estimated kq x kq randomeffects (co)variance matrix, or a list of matrices for multilevel models. Only for randomeffects models. 
residuals 
a ndimensional vector (for univariate models) or n x k matrix (for multivariate models) of residuals, that is observed minus fitted values. 
fitted.values 
a ndimensional vector (for univariate models) or n x k matrix (for multivariate models) of fitted mean values. 
df.residual 
the residual degrees of freedom. 
rank 
the numeric rank of the fixedeffects part of the fitted model. 
logLik 
the (restricted) loglikelihood of the fitted model. Set to 
converged, niter 
for models with iterative estimation methods, logical scalar indicating if the algorithm eventually converged and number or iterations, respectively. 
par 
parameters estimated in the optimization process when using likelihoodbased estimators. These correspond to trasformations of entries of the randomeffects (co)variance matrix, dependent on chosen 
hessian 
Hessian matrix of the estimated parameters in 
dim 
list with the following components: 
df 
list with the following scalar components: 
lab 
list with the following label vectors: 
S 
a n x k(k+1)/2 matrix, where each row represents the vectorized entries of the lower triangle of the related withinunit (co)variance error matrix, taken by column. See 
call 
the function call. 
formula 
the formula for the fixedeffects part of the model. See 
model 
the model frame used for fitting. Reported if 
terms 
the 
contrasts 
(where relevant) the contrasts used. 
xlevels 
(where relevant) a record of the levels of the factors used in fitting. 
na.action 
(where relevant) information returned by 
method 
the estimation method. 
random 
the formula (or list of formulae for multilevel models) for the randomeffects part of the model. See 
bscov 
a string defining the randomeffects (co)variance structure in likelihood based models. See 
control 
a list with the values of the control arguments used, as returned by 
A number of methods functions are available for mixmeta
objects, most of them common to other regression functions.
Specificallywritten method functions are defined for predict
(standard predictions) and blup
(best linear unbiased predictions). The method function simulate
produces simulated outcomes from a fitted model, while qtest
performs the Cochran Q test for heterogeneity. Other methods have been produced for summary
, logLik
, coef
, and vcov
.
Specific methods are also available for model.frame
and model.matrix
. In particular, the former produces the model frame (a data frame with special attributes storing the variables used for fitting) with the additional class "data.frame.mixmeta"
. A method terms
is also available for extracting the terms object (only for fixedeffects or full). Methods na.omit
and na.exclude
for this class are useful for the handling of missing values in mixmeta
objects.
Printing functions for the objects of classes defined above are also provided. anova
methods for performing tests in mixmeta
objects are in development.
All the methods above are visible (exported from the namespace) and documented. In additions, several default method functions for regression are also applicable to objects of class "mixmeta"
, such as fitted
, residuals
, AIC
and BIC
, drop1
and add1
, or update
, among others.
Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk> and Francesco Sera <francesco.sera@lshtm.ac.uk>
Sera F, Armstrong B, Blangiardo M, Gasparrini A (2019). An extended mixedeffects framework for metaanalysis.Statistics in Medicine. 2019;38(29):54295444. [Freely available here].
See mixmeta
. See lm
or glm
for standard regression functions. See mixmetapackage
for an overview of this modelling framework.
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