ko.ordinal: Statistics of the knockoffs procedure for ordinalNet...
In kosel: Variable Selection by Revisited Knockoffs Procedures

Description Usage Arguments Value Note See Also Examples

View source: R/ko.ordinal.R

Returns the vector of statistics W of the revisited knockoffs procedure for regressions available in the R package ordinalNet. Most of the parameters come from ordinalNet(). See ordinalNet documentation for more details.

ko.ordinal(x, y, family = "cumulative", reverse = FALSE,
  link = "logit", alpha = 1, parallelTerms = TRUE,
  nonparallelTerms = FALSE, nVal = 100, warn = FALSE,
  random = FALSE)

`x`	Covariate matrix, of dimension nobs x nvars; each row is an observation vector. It is recommended that categorical covariates are converted to a set of indicator variables with a variable for each category (i.e. no baseline category); otherwise the choice of baseline category will affect the model fit.
`y`	Response variable. Can be a factor, ordered factor, or a matrix where each row is a multinomial vector of counts. A weighted fit can be obtained using the matrix option, since the row sums are essentially observation weights. Non-integer matrix entries are allowed.
`family`	Specifies the type of model family. Options are "cumulative" for cumulative probability, "sratio" for stopping ratio, "cratio" for continuation ratio, and "acat" for adjacent category.
`reverse`	Logical. If TRUE, then the "backward" form of the model is fit, i.e. the model is defined with response categories in reverse order. For example, the reverse cumulative model with K+1 response categories applies the link function to the cumulative probabilities P(Y >= 2), …, P(Y >= K+1), rather then P(Y <= 1), …, P(Y <= K).
`link`	Specifies the link function. The options supported are logit, probit, complementary log-log, and cauchit.
`alpha`	The elastic net mixing parameter, with `0 <= alpha <= 1`. `alpha=1` corresponds to the lasso penalty, and `alpha=0` corresponds to the ridge penalty.
`parallelTerms`	Logical. If `TRUE`, then parallel coefficient terms will be included in the model. `parallelTerms` and `nonparallelTerms` cannot both be `FALSE`.
`nonparallelTerms`	Logical. if `TRUE`, then nonparallel coefficient terms will be included in the model. `parallelTerms` and `nonparallelTerms` cannot both be `FALSE`. Default is `FALSE`. `nonparallelTerms = TRUE` is highly discouraged.
`nVal`	Length of lambda sequence - default is 100.
`warn`	Logical. If `TRUE`, the following warning message is displayed when fitting a cumulative probability model with `nonparallelTerms=TRUE` (i.e. nonparallel or semi-parallel model). "Warning message: For out-of-sample data, the cumulative probability model with `nonparallelTerms=TRUE` may predict cumulative probabilities that are not monotone increasing." The warning is displayed by default, but the user may wish to disable it.
`random`	If `TRUE`, the matrix of knockoffs is different for every run. If `FALSE`, a seed is used so that the knockoffs are the same. The default is `FALSE`.