View source: R/weights-jackknife.R
jackknifeWeights | R Documentation |
Compute model weights optimized for jackknifed model fits.
jackknifeWeights(
object, ..., data, type = c("loglik", "rmse"),
family = NULL, weights = NULL,
optim.method = "BFGS", maxit = 1000, optim.args = list(),
start = NULL, force.update = FALSE, py.matrix = FALSE
)
object , ... |
two or more fitted \lcodeglm objects, or a
|
data |
a data frame containing the variables in the model. It is
optional if all models are |
type |
a character string specifying the function to minimize. Either
|
family |
used only if |
weights |
an optional vector of ‘prior weights’
to be used in the model fitting process. Should be |
optim.method |
optional, optimisation method, passed to \lcodeoptim. |
maxit |
optional, the maximum number of iterations, passed to \lcodeoptim. |
optim.args |
optional list of other arguments passed to \lcodeoptim. |
start |
starting values for model weights. Numeric of length equal the number of models. |
force.update |
for |
py.matrix |
either a boolean value, then if |
Model weights are chosen (using \lcodeoptim) to minimise RMSE or log-likelihood of the prediction for data point i, of a model fitted omitting that data point i. The jackknife procedure is therefore run for all provided models and for all data points.
The function returns a numeric vector of model weights.
This procedure can give variable results depending on the
optimisation method and starting values. It is therefore
advisable to make several replicates using different optim.method
s.
See \lcodeoptim for possible values for this argument.
Kamil Bartoń. Carsten Dormann
Hansen, B. E. and Racine, J. S. 2012 Jackknife model averaging. Journal of Econometrics 979, 38–46
Dormann, C. et al. 2018 Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs 88, 485–504.
Weights, \lcodemodel.avg
Other model weights:
BGWeights()
,
bootWeights()
,
cos2Weights()
,
stackingWeights()
fm <- glm(Prop ~ mortality * dose, binomial(), Beetle, na.action = na.fail)
fits <- lapply(dredge(fm, eval = FALSE), eval)
amJk <- amAICc <- model.avg(fits)
set.seed(666)
Weights(amJk) <- jackknifeWeights(fits, data = Beetle)
coef(amJk)
coef(amAICc)
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