check ok

.Rbuildignore

@@ -0,0 +1 @@
+(^\.github/.+$)|(^README.+$)

R/anova_flipscores.R

@@ -51,8 +51,8 @@ anova.flipscores <- function(object, model1=NULL,
       score1=scores[,id_col,drop=FALSE]
       attributes(score1)$scale_objects=attributes(scores)$scale_objects[[id_col]]
       attributes(score1)$score_type=attributes(scores)$score_type
-      as.matrix(.flip_test_no_pval(score1, ftail=I,precompute_flips = FALSE,
-                           .score_fun = flipscores:::.score_std,n_flips = n_flips))
+      as.matrix(.flip_test_no_pval(score1, precompute_flips = FALSE,
+                           .score_fun = .score_std,n_flips = n_flips))
       })
     dst=mahalanobis_npc(Tspace)
 

R/compute_flips.R

@@ -44,32 +44,6 @@ compute_flips<- function(scores,alternative="two.sided",
                       ...){
 
 
-  ############### only for internal experiments
-  #######START
-  if(attributes(scores)$score_type=="my_lab") {
-    .score_fun <- function(flp,Y,Xt) Xt%*%flp%*%Y
-    n_obs=nrow(scores)
-    Xt=attributes(scores)$scale_objects$Xt
-
-    Tobs=  .score_fun(diag(n_obs),scores,Xt=Xt)
-    #        set.seed(seed)
-    Tspace=as.vector(c(Tobs,replicate(n_flips-1,{
-      flp<-flip::rom(n_obs)
-      .score_fun(flp,scores,Xt)
-    })))
-    #       set.seed(NULL)
-    # Tspace=.sum2t(Tspace,
-    #                sumY2 = sum(Y^2,na.rm = TRUE),
-    #               n=sum(!is.na(Y)))
-
-    p.values=.t2p(ftail(unlist(Tspace)))
-    # named vector?
-
-    out=list(Tspace=Tspace,p.values=p.values)
-    names(out$p.values)=names(scores)
-    return(out)  
-  }
-  ######### END
   ##########################################
 
   #      browser()

R/flipscores.R

@@ -1,34 +1,31 @@
 #' @title Robust testing in GLMs, by sign-flipping score contributions
 #'
 #' @description Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are often robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables.
-#' @param score_type The type of score that is computed. It can be "orthogonalized", "effective" or "basic". 
+#' @param formula see \code{glm} function. It can also be a model (usually generated by a call to \code{glm}); in this case, any other glm-related parameter (e.g. \code{family, data, etc.}) are discarded, the function will make use of the ones used to generate the model. 
+#' (i.e. \code{formula}, \code{family}, \code{data}, etc) are not considered.  It is \code{NULL} by default (i.e. not used). 
+#' @param family see \code{glm} function.
+#' @param data see \code{glm} function.
+#' @param score_type The type of score that is computed. It can be "standardized" "orthogonalized", "effective" or "basic". 
 #' Both "orthogonalized" and "effective" take into account the nuisance estimation and they provide the same
 #' test statistic. In case of small samples "effective score" might have a slight anti-conservative behaviour. 
-#' "orthogonalized effective score" gives a solution for this issue.
-#' Note that in case of a big model matrix, the "orthogonalized" may take a long time.
-#'
+#' "standardized effective score" gives a solution for this issue.
+#' "orthogonalized" has a similar intent, note however that in case of a big model matrix, it may be slow.
 #' @param n_flips The number of random flips of the score contributions. Overwritten with the \code{nrow(flips)} when \code{flips} is not \code{NULL} (see parameter \code{flips} for more details).
 #' When \code{n_flips} is equal or larger than the maximum number of possible flips (i.e. n^2), all possible flips are performed.
-#'
-#' @param id a \code{vector} identifying the clustered observations. If \code{NULL} (default) observations are assumed to be independent. If \code{id} is not \code{NULL}, only \code{score_type=="effective"} is allowed, yet.
 #' @param alternative It can be "greater", "less" or "two.sided" (default)
-# @param seed \code{NULL} by default. 
-#' @param formula see \code{glm} function. It can also be a model (usually generated by a call to \code{glm}); in this case, any other glm-related parameter (e.g. \code{family, data, etc.}) are discarded, the function will make use of the ones used to generate the model.  
-#' @param family see \code{glm} function.
-#' @param data see \code{glm} function.
-#' @param model can be a model (e.g. \code{lm} or \code{glm}). In this case other parameters used to fit a \code{glm} 
-#' (i.e. \code{formula}, \code{family}, \code{data}, etc) are not considered.  It is \code{NULL} by default (i.e. not used). 
+#' @param id a \code{vector} identifying the clustered observations. If \code{NULL} (default) observations are assumed to be independent. If \code{id} is not \code{NULL}, only \code{score_type=="effective"} is allowed, yet.
+#' @param seed \code{NULL} by default. 
 #' @param to_be_tested vector of indices or names of coefficients of the glm model to be tested (it is faster than computing every scores and p-values of course).
-#' @param precompute_flips \code{TRUE} by default. Overwritten if \code{flips} is not \code{NULL}. If \code{FALSE} the matrix of flips is not computed and the flips are made 'on-the-fly' before computing the test statistics; it may be usefull when \code{flips} is very large (see parameter \code{flips} for more details).
 #' @param flips matrix fo +1 or -1, the matrix has \code{n_flips} rows and n (number of observations) columns
+#' @param precompute_flips \code{TRUE} by default. Overwritten if \code{flips} is not \code{NULL}. If \code{FALSE} the matrix of flips is not computed and the flips are made 'on-the-fly' before computing the test statistics; it may be usefull when \code{flips} is very large (see parameter \code{flips} for more details).
 #' @param output_flips \code{FALSE} by default. If \code{TRUE} the \code{flips} matrix is returned. Useful when the same flips are needed for more glms, for example in the case of multivariate glms where the joint distribution of test statistis if used for multivariate inference. 
 #' @param ... see \code{glm} function.
 #' 
 #'
-#' @usage flipscores(formula, family, data, score_type, 
-#' n_flips=5000, alternative ="two.sided",
-#' output_flips=FALSE, 
-#' id = NULL, ...)
+#' @usage flipscores(formula, family, data, score_type = "standardized",
+#' n_flips = 5000, alternative = "two.sided", id = NULL,
+#' seed = NULL, to_be_tested = NULL, flips = NULL,
+#' precompute_flips = TRUE, output_flips = FALSE, ...)
 #'
 #' @return an object of class \code{flipscores}.
 #' See also its methods (\code{summary.flipscores}, \code{anova.flipscores}, \code{print.flipscores}). 
@@ -71,6 +68,7 @@ flipscores<-function(formula, family, data,
                      to_be_tested=NULL,
                      flips=NULL,
                      precompute_flips=TRUE,
+                     output_flips=FALSE,
                      ...){
   # if(FALSE) flip() #just a trick to avoid warnings in package building
   # temp=is(formula) #just a trick to avoid warnings in package building
@@ -88,7 +86,7 @@ flipscores<-function(formula, family, data,
   flip_param_call= mf[c(1L,m)]
 
   # rinomino la funzione da chiamare:
-  flip_param_call[[1L]]=.flip_test#quote(flipscores:::.flip_test) #
+  flip_param_call[[1L]]=.flip_test
 
   flip_param_call$id=eval(flip_param_call$id, parent.frame())
   flip_param_call$n_flips <- eval(flip_param_call$n_flips, parent.frame())