ANOPA v0.1.3

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: ANOPA
 Type: Package
 Title: Analyses of Proportions using Anscombe Transform
-Version: 0.1.2
-Date: 2024-03-18
+Version: 0.1.3
+Date: 2024-03-19
 Authors@R: c(
              person("Denis", "Cousineau", email = "[email protected]",
                     role = c("aut", "ctb", "cre")),

NEWS.md

@@ -1,3 +1,7 @@
+# ANOPA 0.1.3 (March 2023)
+
+* removed some cats and a few `
+
 # ANOPA 0.1.2 (March 2023)
 
 * added \donttest{} to speed tests;

R/ANOPA-anopa.R

@@ -3,8 +3,8 @@
 #'
 #' @md
 #'
-#' @description The function `anopa()` performs an ANOPA for designs with up to 4 factors
-#'      according to the `ANOPA` framework. See \insertCite{lc23;textual}{ANOPA} for more.
+#' @description The function 'anopa()' performs an ANOPA for designs with up to 4 factors
+#'      according to the 'ANOPA' framework. See \insertCite{lc23;textual}{ANOPA} for more.
 #' 
 #'
 #' @param formula A formula with the factors on the left-hand side. See below for writing the 
@@ -371,10 +371,9 @@ getAfterNested <- function(frm) {
 		v1 <- f[[3]]
 	} else {
 		if (f[[3]][[1]] == "*") {
-			# cat("case frm2: ", f[[3]][[2]], "\n")
 			v1 <- f[[3]][[2]]
 		} else {
-			cat("ANOPA::internal (-1): Case Inexistant: ", "\n")
+			stop("ANOPA::internal(-1): Case non-existant: That should never happen")
 		}
 	}
 	return( paste(v1)  )
@@ -402,7 +401,7 @@ getAroundNested <- function(frm) {
 				v2 <- f[[2]][[length(f[[2]])]]
 			}
 		} else {
-			cat("ANOPA::internal (-2): Case Inexistant: ", "\n")
+			stop("ANOPA::internal (-2): Case non-existant: That should never happen")
 		}
 	}
 	return( c( paste(v1), paste(v2)) )

R/ANOPA-contrastProportions.R

@@ -3,8 +3,8 @@
 #'
 #' @md
 #'
-#' @description The function `contrastProportions()` performs contrasts analyses 
-#'    on proportion data after an omnibus analysis has been obtained with `anopa()`
+#' @description The function 'contrastProportions()' performs contrasts analyses 
+#'    on proportion data after an omnibus analysis has been obtained with 'anopa()'
 #'    according to the ANOPA framework. See \insertCite{lc23;textual}{ANOPA} for more.
 #'      
 #' @param w An ANOPA object obtained from `anopa()` or `emProportions()`;
@@ -42,31 +42,31 @@
 #'          contrast1 = c(1,  1, -2)/2,
 #'          contrast2 = c(1, -1,  0) )
 #'       )
-#' #summary(cw)
+#' summary(cw)
 #'
-#' # Example using the Arrington et al. (2002) data, a 3 x 4 x 2 design involving 
-#' # Location (3 levels), Trophism (4 levels) and Diel (2 levels).
-#' ArringtonEtAl2002
-#'
-#' # performs the omnibus analysis first (mandatory):
-#' w <- anopa( {s;n} ~ Location * Trophism * Diel, ArringtonEtAl2002) 
-#' corrected(w)
-#'
-#' # execute the simple effect of Trophism for every levels of Diel and Location
-#' e <- emProportions(w, ~ Trophism | Diel * Location)
-#' #summary(e)
-#'
-#' # For each of these sub-cells, contrast the four tropisms, first
-#' # by comparing the first two levels to the third (contrast1), second
-#' # by comparing the first to the second level (contrast2), and finally by
-#' # by comparing the first three to the last (contrast3) :
-#' #f <- contrastProportions( e, list(
-#' #              contrast1 = c(1,  1, -2, 0)/2,
-#' #              contrast2 = c(1, -1,  0, 0),
-#' #              contrast3 = c(1,  1,  1, -3)/3
-#' #           )
-#' #      )
-#' #summary(f)
+# # Example using the Arrington et al. (2002) data, a 3 x 4 x 2 design involving 
+# # Location (3 levels), Trophism (4 levels) and Diel (2 levels).
+# ArringtonEtAl2002
+#
+# # performs the omnibus analysis first (mandatory):
+# w <- anopa( {s;n} ~ Location * Trophism * Diel, ArringtonEtAl2002) 
+# corrected(w)
+#
+# # execute the simple effect of Trophism for every levels of Diel and Location
+# e <- emProportions(w, ~ Trophism | Diel * Location)
+# summary(e)
+#
+# # For each of these sub-cells, contrast the four tropisms, first
+# # by comparing the first two levels to the third (contrast1), second
+# # by comparing the first to the second level (contrast2), and finally by
+# # by comparing the first three to the last (contrast3) :
+# f <- contrastProportions( e, list(
+#               contrast1 = c(1,  1, -2, 0)/2,
+#               contrast2 = c(1, -1,  0, 0),
+#               contrast3 = c(1,  1,  1, -3)/3
+#            )
+#       )
+# summary(f)
 #' 
 #' 
 ######################################################################################
@@ -95,6 +95,7 @@ contrastProportions <- function(
     } else {
         stop("ANOPA::oups(1): This part not yet done. Exiting...")
     } 
+print(relevantlevels)
     if (!(all(unlist(lapply(contrasts, length)) == relevantlevels )))
         stop("ANOPA::error(33): The contrats lengths does not match the number of levels. Exiting...")
 

R/ANOPA-convert.R

@@ -7,7 +7,7 @@
 #'
 #' @md
 #'
-#' @description The functions `toWide()`, `toLong()`, and `toCompiled()` 
+#' @description The functions 'toWide()', 'toLong()', and 'toCompiled()' 
 #'      converts the data into various formats.
 #' 
 #' @usage toWide(w)

R/ANOPA-emProportions.R

@@ -3,8 +3,8 @@
 #'
 #' @md
 #'
-#' @description The function `emProportions()` performs a _simple effect_ analyses 
-#'    of proportions after an omnibus analysis has been obtained with `anopa()`
+#' @description The function 'emProportions()' performs a _simple effect_ analyses 
+#'    of proportions after an omnibus analysis has been obtained with 'anopa()'
 #'    according to the ANOPA framework. Alternatively, it is also called an
 #'    _expected marginal_ analysis of proportions. See \insertCite{lc23b;textual}{ANOPA} for more.
 #'
@@ -52,12 +52,8 @@
 #'
 #' # Let's execute the simple effect of Difficulty for every levels of Class
 #' e <- emProportions(w, ~ Difficulty | Class )
-#' #summary(e)
+#' summary(e)
 #'
-#' # As a check, you can verify that the _F_s are decomposed additively
-#' #sum(e$results[,1])
-#' #w$results[3,1]+w$results[4,1] 
-#' 
 #' 
 #' # -- SECOND EXAMPLE --
 #' # Example using the Arrington et al. (2002) data, a 3 x 4 x 2 design involving 
@@ -84,9 +80,6 @@
 #' e <- emProportions(w, ~ Trophism | Diel )
 #' summary(e)
 #'
-#' # Again, as a check, you can verify that the $F$s are decomposed additively
-#' #w$results[4,1]+w$results[7,1] # B + B:C
-#' #sum(e$results[,1])
 #' 
 #' # You can ask easier outputs with
 #' corrected(w) # or summary(w) for the ANOPA table only

R/ANOPA-functions.R

@@ -5,10 +5,10 @@
 #'
 #' @md
 #'
-#' @description The transformation functions `A()` performs the
+#' @description The transformation functions 'A()' performs the
 #'      Anscombe transformation on a pair \{number of success; number
 #'      of trials\} = \{s; n\} (where the symbol ";" is to be read "over".
-#'      The function `varA()` returns the theoretical variance from
+#'      The function 'varA()' returns the theoretical variance from
 #'      the pair \{s; n\}. Both functions are central to the ANOPA
 #'	    \insertCite{lc23}{ANOPA}. It was originally proposed by 
 #'      \insertCite{z35}{ANOPA} and formalized by \insertCite{a48}{ANOPA}.
@@ -102,7 +102,6 @@ SE.Atrans <- function(v) {
 
 # its variance...
 var.Atrans <- function(v) {
-print(length(v))
     1 / (4*(length(v)+1/2))
 }
 
@@ -126,8 +125,8 @@ CI.prop <- function(v, gamma = 0.95) {
     n     <- length(v)
     cilen <- CI.Atrans(v, gamma)
     # the difference adjustment is done herein.
-    ylo   <- y - sqrt(2) * cilen
-    yhi   <- y + sqrt(2) * cilen
+    ylo   <- max( y - sqrt(2) * cilen, 0)
+    yhi   <- min( y + sqrt(2) * cilen, pi/2)
     # reverse arc-sin transformation
     xlo <- (n+3/4)*(sin(ylo)^2) - 3/8
     xhi <- (n+3/4)*(sin(yhi)^2) - 3/8

R/ANOPA-logicals.R

@@ -7,9 +7,9 @@
 ##
 ## @md
 ##
-## @description The functions `is.formula()`, `is.one.sided()`,
-##      `has.nested.terms()`, 
-##      `has.cbind.terms()`, `in.formula()` and `sub.formulas()`
+## @description The functions 'is.formula()', 'is.one.sided()',
+##      'has.nested.terms()', 
+##      'has.cbind.terms()', 'in.formula()' and 'sub.formulas()'
 ##      performs checks or extract sub-formulas from a given formula.
 ##s
 ## @usage is.formula(frm)

R/ANOPA-methods.R

@@ -26,7 +26,7 @@
 #' @md
 #'
 #' @description
-#' `explain()` provides a human-readable, exhaustive, description of
+#' 'explain()' provides a human-readable, exhaustive, description of
 #' the results. It also provides references to the key results.
 #' 
 #' @usage explain(object, ...)
@@ -47,8 +47,8 @@ explain.default <- function(object, ...) { print(object) }
 #'
 #' @md
 #'
-#' @description `summarize()` provides the statistics table an ANOPAobject. 
-#' It is synonym of `summary()` (but as actions are verbs, I used a verb).
+#' @description 'summarize()' provides the statistics table an ANOPAobject. 
+#' It is synonym of 'summary()' (but as actions are verbs, I used a verb).
 #' 
 #' @param object   an object to summarize
 #' @param ...      ignored
@@ -75,8 +75,7 @@ summary.ANOPAobject <- function(object, ...) {
 #' @method print ANOPAobject
 #' @export 
 print.ANOPAobject <- function(x, ...) {
-    cat("ANOPA completed. My first advise is to use anopaPlot() now. \n")
-    cat("Use summary() or summarize() to obtain the ANOPA table.\n")
+    ANOPAmessage("ANOPA completed! My first advise is to use anopaPlot() now. \nUse summary() or summarize() to obtain the ANOPA table.")
     y <- unclass(x)
     class(y) <- "list"
     print(y, digits = 5)
@@ -98,7 +97,7 @@ print.ANOPAtable <- function(x, ...) {
 #' @export 
 summarize.ANOPAobject <-  function(object, ...) {
     if (length(object$omnibus) == 1) 
-        cat(object$omnibus)
+        print(object$omnibus)
     else {
         u <- object$omnibus
         class(u) <- c("ANOPAtable", class(u))
@@ -123,7 +122,7 @@ explain.ANOPAobject <- function(object, ...) {
 #' @md
 #'
 #' @description
-#' `corrected()` provides an ANOPA table with only the corrected 
+#' 'corrected()' provides an ANOPA table with only the corrected 
 #' statistics.
 #' 
 #' @usage corrected(object, ...)
@@ -140,7 +139,7 @@ corrected.default <- function(object, ...) { print(object) }
 #' @export 
 corrected.ANOPAobject <- function(object, ...) {
     if (length(object$omnibus) == 1) 
-        cat(object$omnibus)
+        print(object$omnibus)
     else {
         u <- object$omnibus[,c(1,2,3,5,6,7)]
         class(u) <- c("ANOPAtable", class(u))
@@ -154,7 +153,7 @@ corrected.ANOPAobject <- function(object, ...) {
 #' @md
 #'
 #' @description
-#' `uncorrected()` provides an ANOPA table with only the uncorrected  
+#' 'uncorrected()' provides an ANOPA table with only the uncorrected  
 #' statistics.
 #' 
 #' @usage uncorrected(object, ...)
@@ -172,7 +171,7 @@ uncorrected.default <- function(object, ...) { print(object) }
 #' @export 
 uncorrected.ANOPAobject <- function(object, ...) {
     if (length(object$omnibus) == 1) 
-        cat(object$omnibus)
+        print(object$omnibus)
     else {
         u <- object$omnibus[,c(1,2,3,4)]
         class(u) <- c("ANOPAtable", class(u))

R/ANOPA-package.R

@@ -3,7 +3,7 @@
 #' 
 #' @md
 #' 
-#' @description `ANOPA` is a library to perform proportion analyses.
+#' @description 'ANOPA' is a library to perform proportion analyses.
 #' It is based on the F statistics (first developed by Fisher).
 #' This statistics is fully additive and can be decomposed in 
 #' main effects and interaction effects, in simple effects in the
@@ -16,8 +16,8 @@
 #' 
 #' The data supplied to an ANOPA can be in three formats: (i) long format,
 #' (ii) wide format, (iii) compiled format, or (iv) raw format. Check 
-#' the `anopa` commands for more precision (in what follow, we assume 
-#' the compiled format where the proportions are given in a column name `Freq`)
+#' the 'anopa' commands for more precision (in what follow, we assume 
+#' the compiled format where the proportions are given in a column name 'Freq')
 #' 
 #' The main function is
 #' 
@@ -56,7 +56,7 @@
 #' }
 #' 
 #' The functions uses the following options: \itemize{
-#'     \item{\code{ANOPA.feedback}} ((currently unused));
+#'     \item{\code{ANOPA.feedback}} 'design', 'warnings', 'summary', 'all' or 'none';
 #'     \item{\code{ANOPA.zeros}}    how are handled the zero trials to avoid 0 divided by 0 error;
 #'     \item{\code{ANOPA.digits}}   for the number of digits displayed in the summary table.
 #' }
@@ -96,7 +96,7 @@ ANOPA.env <- new.env(parent = emptyenv())
 #  summary  is not used so far...
 ANOPAwarning <- function( txt ) {
     if ( ("all" %in% getOption("ANOPA.feedback"))|("warnings" %in% getOption("ANOPA.feedback"))) {
-      warning(txt)
+      warning(txt, call. = FALSE)
     }
 }
 ANOPAmessage <- function( txt ) {

R/ANOPA-plot.R

@@ -5,11 +5,11 @@
 #'
 #' @md
 #'
-#' @description The function `anopaPlot()` performs a plot of proportions for designs 
+#' @description The function 'anopaPlot()' performs a plot of proportions for designs 
 #'      with up to 4 factors according to the
-#'      `ANOPA` framework. See \insertCite{lc23;textual}{ANOPA} for more. The plot is 
-#'      realized using the `suberb` library; see \insertCite{cgh21;textual}{ANOPA}.
-#'      It uses the arc-sine transformation `A()`.
+#'      'ANOPA' framework. See \insertCite{lc23;textual}{ANOPA} for more. The plot is 
+#'      realized using the 'suberb' library; see \insertCite{cgh21;textual}{ANOPA}.
+#'      It uses the arc-sine transformation 'A()'.
 #'
 #'   
 #' @usage anopaPlot(w, formula, confidenceLevel = .95, allowImputing = FALSE,
@@ -134,11 +134,9 @@ anopa_asn_trans2 <- function(x) {
 anopa_asn_trans <- function () {
     scales::trans_new("asn2", 
 		function(x) {
-			#cat("1:",x," = ", sapply(x, anopa_asn_trans1),"\n"); 
 			sapply(x, anopa_asn_trans1)
 		}, 
 		function(x) {
-			#cat("2:",x," = ", sapply(x, anopa_asn_trans2),"\n"); 
 			sapply(x, anopa_asn_trans2)
 		},
 		domain = c(0, 1)