View source: R/weightsjackknife.R
jackknifeWeights  R Documentation 
Computes 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 
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

maxit 
optional, the maximum number of iterations, passed to

optim.args 
optional list of other arguments passed to

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 optim
) to minimise
RMSE or loglikelihood 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 optim
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, informationtheoretic, and tactical approaches for predictive inference. Ecological Monographs 88, 485–504.
Weights
, model.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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