Merge pull request #142 from PLN-team/fix-note-cran

some fixes for CRAN

Author: jchiquet

DESCRIPTION

@@ -24,7 +24,7 @@ Description: The Poisson-lognormal model and variants (Chiquet,
 URL: https://pln-team.github.io/PLNmodels/
 BugReports: https://github.com/pln-team/PLNmodels/issues
 License: GPL (>= 3)
-Depends: R (>= 3.6)
+Depends: R (>= 4.1.0)
 Imports: 
     cli,
     corrplot,

PLNmodels.Rproj

@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: b13e0462-dac8-4d89-a484-68f8674d82d6
 
 RestoreWorkspace: No
 SaveWorkspace: No

R/PLNLDAfit-S3methods.R

@@ -16,11 +16,11 @@ isPLNLDAfit <- function(Robject) {inherits(Robject, "PLNLDAfit"       )}
 #' @param nb_axes scalar: the number of axes to be considered when map = "both". The default is min(3,rank).
 #' @param axes numeric, the axes to use for the plot when map = "individual" or "variable". Default it c(1,min(rank))
 #' @param var_cols a character or factor to define the color associated with the variables. By default, all variables receive the default color of the current palette.
-#' @param plot logical. Should the plot be displayed or sent back as [`ggplot2`] object
+#' @param plot logical. Should the plot be displayed or sent back as [`ggplot2::ggplot`] object
 #' @param main character. A title for the single plot (individual or variable factor map). If NULL (the default), an hopefully appropriate title will be used.
 #' @param ... Not used (S3 compatibility).
 #'
-#' @return displays an individual and/or variable factor maps for the corresponding axes, and/or sends back a [`ggplot2`] or gtable object
+#' @return displays an individual and/or variable factor maps for the corresponding axes, and/or sends back a [`ggplot2::ggplot`] or gtable object
 #' @examples
 #' data(trichoptera)
 #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)

R/PLNLDAfit-class.R

@@ -108,7 +108,7 @@ PLNLDAfit <- R6Class(
     ## Graphical methods -----------------
     #' @description Plot the factorial map of the LDA
     # @inheritParams plot.PLNLDAfit
-    #' @return a [`ggplot`] graphic
+    #' @return a [`ggplot2::ggplot`] graphic
     plot_individual_map = function(axes = 1:min(2,self$rank), main = "Individual Factor Map", plot = TRUE) {
 
       .scores <- data.frame(self$scores[,axes, drop = FALSE])
@@ -125,7 +125,7 @@ PLNLDAfit <- R6Class(
     #' @description Plot the correlation circle of a specified axis for a [`PLNLDAfit`] object
     # @inheritParams plot.PLNLDAfit
     #' @param cols a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.
-    #' @return a [`ggplot`] graphic
+    #' @return a [`ggplot2::ggplot`] graphic
     plot_correlation_map = function(axes=1:min(2,self$rank), main="Variable Factor Map", cols = "default", plot=TRUE) {
 
       ## data frame with correlations between variables and PCs

R/PLNPCAfamily-class.R

@@ -105,7 +105,7 @@ PLNPCAfamily <- R6Class(
     #' @param reverse A logical indicating whether to plot the value of the criteria in the "natural" direction
     #' (loglik - penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on
     #' the same scale as the log-likelihood.
-    #' @return A [`ggplot2`] object
+    #' @return A [`ggplot2::ggplot`] object
     plot = function(criteria = c("loglik", "BIC", "ICL"), reverse = FALSE) {
       vlines <- sapply(intersect(criteria, c("BIC", "ICL")) , function(crit) self$getBestModel(crit)$rank)
       p <- super$plot(criteria, reverse) + xlab("rank") + geom_vline(xintercept = vlines, linetype = "dashed", alpha = 0.25)

R/PLNPCAfit-S3methods.R

@@ -17,11 +17,11 @@ isPLNPCAfit <- function(Robject) {inherits(Robject, "PLNPCAfit"       )}
 #' @param axes numeric, the axes to use for the plot when `map = "individual"` or `map = "variable"`. Default it `c(1,min(rank))`
 #' @param ind_cols a character, factor or numeric to define the color associated with the individuals. By default, all variables receive the default color of the current palette.
 #' @param var_cols a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.
-#' @param plot logical. Should the plot be displayed or sent back as [`ggplot`] object
+#' @param plot logical. Should the plot be displayed or sent back as [`ggplot2::ggplot`] object
 #' @param main character. A title for the single plot (individual or variable factor map). If NULL (the default), an hopefully appropriate title will be used.
 #' @param ... Not used (S3 compatibility).
 #'
-#' @return displays an individual and/or variable factor maps for the corresponding axes, and/or sends back a [`ggplot`] or gtable object
+#' @return displays an individual and/or variable factor maps for the corresponding axes, and/or sends back a [`ggplot2::ggplot`] or gtable object
 #' @examples
 #' data(trichoptera)
 #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)

R/PLNPCAfit-class.R

@@ -184,7 +184,7 @@ PLNPCAfit <- R6Class(
       #' @description Plot the factorial map of the PCA
       # @inheritParams plot.PLNPCAfit
       #' @param cols a character, factor or numeric to define the color associated with the individuals. By default, all individuals receive the default color of the current palette.
-      #' @return a [`ggplot`] graphic
+      #' @return a [`ggplot2::ggplot`] graphic
       plot_individual_map = function(axes=1:min(2,self$rank), main = "Individual Factor Map", plot = TRUE, cols = "default") {
 
         .scores <- data.frame(self$scores[,axes, drop = FALSE])
@@ -200,7 +200,7 @@ PLNPCAfit <- R6Class(
       #' @description Plot the correlation circle of a specified axis for a [`PLNLDAfit`] object
       # @inheritParams plot.PLNPCAfit
       #' @param cols a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.
-      #' @return a [`ggplot`] graphic
+      #' @return a [`ggplot2::ggplot`] graphic
       plot_correlation_circle = function(axes=1:min(2,self$rank), main="Variable Factor Map", cols = "default", plot=TRUE) {
 
         ## data frame with correlations between variables and PCs

R/PLNfamily-class.R

@@ -120,7 +120,7 @@ PLNfamily <-
       #' @param reverse A logical indicating whether to plot the value of the criteria in the "natural" direction
       #' (loglik - penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on
       #' the same scale as the log-likelihood.
-      #' @return A [`ggplot2`] object
+      #' @return A [`ggplot2::ggplot`] object
       plot = function(criteria, reverse) {
         stopifnot(!anyNA(self$criteria[criteria]))
         dplot <- self$criteria %>%

R/PLNmixturefamily-class.R

@@ -222,14 +222,14 @@ PLNmixturefamily <-
       #' @param reverse A logical indicating whether to plot the value of the criteria in the "natural" direction
       #' (loglik - 0.5 penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the
       #' natural direction, on the same scale as the log-likelihood..
-      #' @return A [`ggplot2`] object
+      #' @return A [`ggplot2::ggplot`] object
       plot = function(criteria = c("loglik", "BIC", "ICL"), reverse = FALSE) {
         vlines <- map_int(intersect(criteria, c("BIC", "ICL")), function(crit) self$getBestModel(crit)$k)
         p <- super$plot(criteria, reverse) + xlab("# of clusters") + geom_vline(xintercept = vlines, linetype = "dashed", alpha = 0.25)
         p
        },
       #' @description Plot objective value of the optimization problem along the penalty path
-      #' @return a [`ggplot`] graph
+      #' @return a [`ggplot2::ggplot`] graph
       plot_objective = function() {
         objective <- self$models %>% map('optim_par') %>% map('objective') %>% unlist
         changes   <- self$models %>% map('optim_par') %>% map('outer_iterations') %>% unlist %>% cumsum

R/PLNmixturefit-S3methods.R

@@ -17,10 +17,10 @@ isPLNmixturefit <- function(Robject) {inherits(Robject, "PLNmixturefit")}
 #' @param x an R6 object with class [`PLNmixturefit`]
 #' @param type character for the type of plot, either "pca", for or "matrix". Default is `"pca"`.
 #' @param main character. A title for the  plot. If NULL (the default), an hopefully appropriate title will be used.
-#' @param plot logical. Should the plot be displayed or sent back as [`ggplot`] object
+#' @param plot logical. Should the plot be displayed or sent back as [`ggplot2::ggplot`] object
 #' @param ... Not used (S3 compatibility).
 #'
-#' @return a [`ggplot`] graphic
+#' @return a [`ggplot2::ggplot`] graphic
 #' @examples
 #' data(trichoptera)
 #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)