Dependency changes:
carData
package (formerly car
)Other minor changes to namespace calls to pass CRAN checks
predVals()
has been rewritten to compute fitted values according to the
"observed value" approach advocated in the following article:Hanmer, M. J. and Ozan Kalkan, K. (2013), Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models. American Journal of Political Science, 57: 263--277. doi: 10.1111/j.1540-5907.2012.00602.x
For more details, see ?predVals
.
k-fold cross-validation can now be performed in parallel for adaptive LASSO
models, controlled via the .parallel
argument of polywog()
. Parallel
computation of bootstrap iterations is now handled via the .parallel
argument of bootPolywog()
or control.bp()
.
Adds model.matrix
and model.frame
methods for objects of class
"polywog"
Polynomial expansions of the design matrix are now handled in C++, and
obsolete functions polym2()
and rawpoly()
have been removed
New arguments of polywog()
:
lambda
, nlambda
, and lambda.min.ratio
for finer control of the
sequence of penalization factor values examinedfoldid
for direct specification of cross-validation folds (only
available when fitting via the adaptive LASSO)thresh
and maxit
for finer control of the convergence criterion,
replacing old argument scad.maxit
Dependency changes:
miscTools
which must be attached to provide the margEff
genericglmnet
1.9-5 required (for parallel cross-validation)ncvreg
2.4-0 required (for bug fix in cv.ncvreg
)iterators
and Rcpp
requiredcar
no longer required (but still suggested)matrixStats
and games
no longer requiredpolywog()
now has argument unpenalized
to exclude some terms from the
adaptive LASSO penalty
bootPolywog()
now has argument maxtries
to control failure when a
non-collinear bootstrap model matrix cannot be found
bootPolywog()
now has argument min.prop
to ensure a minimum amount of
variation in the bootstrapped response variable in binary models
The fitted.values
element of "polywog"
objects is now on the response
scale instead of the link scale (i.e., transformed to probabilities when
family = "binomial"
)
Fixed bug where the polywog.fit
element of cv.polywog()
output would not
contain fitted values
Fixed bug that sometimes caused predVals()
to fail unexpectedly
New function cv.polywog()
to select both the polynomial degree and the
penalization parameter by cross-validation
New method margEff.polywog()
to compute observation-wise and average
marginal effects from a fitted model
varNames
element of a "polywog"
object is now a character vector rather
than a list (and is generated more safely)
"polyTerms"
attribute of matrix returned by polym2()
is now a matrix
rather than a data frame
predict.polywog()
now works correctly when newdata
is a model frame
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