Description Usage Arguments Details Value Note Author(s) See Also Examples
View source: R/model.frame.mvmeta.R
These method functions return the model frame and design matrix for univariate or multivariate meta-analytical models represented in objects of class "mvmeta"
.
1 2 3 4 5 | ## S3 method for class 'mvmeta'
model.frame(formula, ...)
## S3 method for class 'mvmeta'
model.matrix(object, ...)
|
object, formula |
an object of class |
... |
further arguments passed to or from other methods. |
The model frame is produced by mvmeta
when fitting the meta-analytical model, and stored in the mvmeta
object if argument model=TRUE
. Alternatively, the model frame is directly returned by mvmeta
with argument method="model.frame"
. The method function model.frame
simply extracts the saved model frame if available, or otherwise evaluates a call to mvmeta
when method="model.frame"
.
The method function model.matrix
extracts the design matrix from a fitted meta-analytical model. It first extract the model frame by calling model.frame
, and then passes the call to the default method.
These methods functions are similar to those provided for regression objects lm
and lm
.
For model.frame
, a data.frame with special attributes (see the default method model.frame
) and the additional class "data.frame.mvmeta"
.
For model.matrix
, the design matrix used to fit the model.
The reason why these specific method functions are made available for class mvmeta
, and in particular why a new class "data.frame.mvmeta"
has been defined for model frames, lies in the special handling of missing values in multivariate meta-analysis models fitted with mvmeta
. Methods na.omit
and na.exclude
for class "data.frame.mvmeta"
are useful for properly accounting for missing values when fitting these models.
Antonio Gasparrini, antonio.gasparrini@lshtm.ac.uk
See the default methods model.frame
and model.matrix
. See na.omit
and na.exclude
on the handling of missing values.
See mvmeta-package
for an overview of the package and modelling framework.
1 2 3 4 5 6 7 8 9 10 11 | # RUN THE MODEL AND SUMMARIZE THE RESULTS
model <- mvmeta(cbind(PD,AL)~pubyear,S=berkey98[5:7],data=berkey98,method="ml")
# MODEL FRAME
model$model
model.frame(model)
update(model,method="model.frame")
class(model.frame(model))
# MODEL MATRIX
model.matrix(model)
|
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