crossvalidation {gosset} | R Documentation |
Cross-validation
Description
Methods for measuring the performance of a predictive model on sets of test data in Bradley-Terry model from psychotree, Generalized Linear and Generalized Nonlinear models from gnm, and Plackett-Luce model from PlackettLuce
Usage
crossvalidation(formula, data, k = 10, folds = NULL, seed = NULL, ...)
## S3 method for class 'bttree'
AIC(object, newdata = NULL, ...)
## S3 method for class 'bttree'
deviance(object, newdata = NULL, ...)
## S3 method for class 'pltree'
deviance(object, newdata = NULL, ...)
## S3 method for class 'gnm'
AIC(object, newdata = NULL, ...)
## S3 method for class 'gnm'
deviance(object, newdata = NULL, ...)
Arguments
formula |
an object of class "formula" (or one that can be
coerced to that class): a symbolic description of the model to be fitted,
of the form |
data |
a data frame (or object coercible by as.data.frame to a data frame) containing the variables in the model |
k |
an integer for the number of bins in the cross-validation |
folds |
an optional vector or list of vectors specifying the k-folds in the cross-validation |
seed |
integer, the seed for random number generation. If |
... |
additional arguments passed the methods of the chosen model |
object |
a model object |
newdata |
a data.frame with test data |
Value
an object of class gosset_cv
with the cross-validation
goodness-of-fit estimates, which are:
AIC |
Akaike Information Criterion |
deviance |
Model deviance |
logLik |
Log-Likelihood |
MaxLik |
Maximum likelihood pseudo R-squared |
CraggUhler |
Cragg and Uhler's pseudo R-squared |
McFadden |
McFadden pseudo R-squared |
kendallTau |
the Kendall correlation coefficient |
Author(s)
Kauê de Sousa, Jacob van Etten and David Brown
References
Elder J. F. (2003). Journal of Computational and Graphical Statistics, 12(4), 853–864. doi:10.1198/1061860032733
James G., et al. (2013). doi:10.1007/978-1-4614-7138-7
Whitlock M. C. (2005). Journal of Evolutionary Biology, 18(5), 1368–1373. doi:10.1111/j.1420-9101.2005.00917.x
See Also
Other model selection functions:
btpermute()
Examples
# Generalized Linear Models
if (require("gnm")) {
data("airquality")
cv = crossvalidation(Temp ~ Wind + Solar.R,
data = airquality,
k = 3,
seed = 999,
family = poisson())
}
# Plackett-Luce Model
if(require("PlackettLuce")) {
# beans data from PlackettLuce
data("beans", package = "PlackettLuce")
G = rank_tricot(data = beans,
items = c(1:3),
input = c(4:5),
additional.rank = beans[c(6:8)],
group = TRUE)
beans = cbind(G, beans)
# take seasons as bins
k = length(unique(beans$season))
folds = as.integer(as.factor(beans$season))
cv = crossvalidation(G ~ maxTN,
data = beans,
k = k,
folds = folds,
minsize = 100)
}