Merge branch 'course-todo'

Author: jarioksa

R/constructGradient.R

@@ -4,21 +4,24 @@
 #'
 #' @param hM a fitted \code{Hmsc} model object
 #' @param focalVariable focal variable over which the gradient is constructed
-#' @param non.focalVariables list giving assumptions on how non-focal variables co-vary with the focal variable
+#' @param non.focalVariables list giving assumptions on how non-focal variables co-vary with the focal variable or a single number given the default type for all non-focal variables
 #' @param ngrid number of points along the gradient (for continuous focal variables)
 #'
 #' @return a named list with slots \code{XDataNew}, \code{studyDesignNew} and \code{rLNew}
 #'
 #' @details
-#' \code{non.focalVariables} is a list, of which each element is on the form variable=list(type,value),
-#' where variable is one of the non-focal variables, and the value is needed only if type = 3
-#' type = 1 sets the values of the non-focal variable
-#' to the most likely value (defined as expected value for covariates, mode for factors)
-#' type = 2 sets the values of the non-focal variable to most likely value, given the value of focal variable,
-#' based on a linear relationship
-#' type = 3 fixes to the value given.
-#' if a non.focalVariable is not listed, type=2 is used as default
-#' note that if the focal variable is continuous, selecting type 2 for a non-focal categorical variable can cause abrupt changes in response
+#' In basic form, \code{non.focalVariables} is a list, where each element is on the form variable=list(type,value),
+#' where \code{variable} is one of the non-focal variables, and the \code{value} is needed only if \code{type = 3}. Alternatives
+#' \code{type = 1} sets the values of the non-focal variable
+#' to the most likely value (defined as expected value for covariates, mode for factors),
+#' \code{type = 2} sets the values of the non-focal variable to most likely value, given the value of focal variable,
+#' based on a linear relationship, and
+#' \code{type = 3} fixes to the value given.
+#' If all non-focal variables are of the identical \code{type} \code{1} or \code{2},
+#' a single number (\code{1} or \code{2}) can be given as an argument to
+#' \code{non.focalVariables}.
+#' If a \code{non.focalVariable} is not listed, \code{type=2} is used as default.
+#' Note that if the focal variable is continuous, selecting type 2 for a non-focal categorical variable can cause abrupt changes in response.
 #'
 #' @seealso
 #' \code{\link{plotGradient}}, \code{\link{predict}}
@@ -38,9 +41,23 @@
 #'
 #' @export
 
-constructGradient = function(hM, focalVariable, non.focalVariables=list(), ngrid=20){
+constructGradient =
+   function(hM, focalVariable, non.focalVariables=list(), ngrid=20)
+{
+   ## default type 2 unless a single number is given as a non-focal variable
+   if (is.numeric(non.focalVariables) && length(non.focalVariables) == 1) {
+      defType <- non.focalVariables
+      if (!(defType %in% 1:2)) {
+         warning("only type 1 and 2 are allowed as a single number: setting to 2")
+         defType <- 2
+      }
+      non.focalVariables <- NULL
+   } else {
+      defType <- 2
+   }
 
    non.focalNames = names(non.focalVariables)
+
    Mode <- function(x, na.rm=FALSE) {
       if(na.rm){
          x = x[!is.na(x)]
@@ -75,7 +92,7 @@ constructGradient = function(hM, focalVariable, non.focalVariables=list(), ngrid
             }
          }
          if (!found) {
-            types = c(types,2)
+            types = c(types, defType)
             vals[[length(vals)+1]] = NA
          }
       }

R/plotGradient.R

@@ -17,6 +17,11 @@
 #' @param showData whether raw data are plotted as well
 #' @param jigger the amount by which the raw data are to be jiggered in x-direction (for factors) or
 #' y-direction (for continuous covariates)
+#'
+#' @param yshow scale y-axis so that these values are also
+#'     visible. This can used to scale y-axis so that it includes 0
+#'     and the expected maximum values.
+#'
 #' @param ... additional arguments for plot
 #'
 #' @return For the case of a continuous covariate, returns the posterior probability that the plotted
@@ -59,7 +64,12 @@
 #'
 #' @export
 
-plotGradient=function (hM, Gradient, predY, measure, xlabel = NULL, ylabel = NULL, index = 1, q = c(0.025, 0.5, 0.975), cicol = rgb(0,0,1,alpha=.5), pointcol = "lightgrey",  pointsize = 1, showData = FALSE, jigger = 0, ...){
+plotGradient =
+    function (hM, Gradient, predY, measure, xlabel = NULL, ylabel = NULL,
+              index = 1, q = c(0.025, 0.5, 0.975), cicol = rgb(0,0,1,alpha=.5),
+              pointcol = "lightgrey", pointsize = 1, showData = FALSE,
+              jigger = 0, yshow = NA,  ...)
+{
 
    Pr = NA
 
@@ -136,8 +146,8 @@ plotGradient=function (hM, Gradient, predY, measure, xlabel = NULL, ylabel = NUL
    hi = qpred[3, ]
    me = qpred[2, ]
 
-   lo1 = min(lo)
-   hi1 = max(hi)
+   lo1 = min(lo, yshow, na.rm = TRUE)
+   hi1 = max(hi, yshow, na.rm = TRUE)
 
    if(showData){
       switch(class(hM$X)[1L],
@@ -149,7 +159,7 @@ plotGradient=function (hM, Gradient, predY, measure, xlabel = NULL, ylabel = NUL
              }
       )
       if (measure == "S") {
-         pY = rowSums(hM$Y)
+         pY = rowSums(hM$Y, na.rm = TRUE)
       }
       if (measure == "Y") {
          pY = hM$Y[,index]
@@ -215,5 +225,5 @@ plotGradient=function (hM, Gradient, predY, measure, xlabel = NULL, ylabel = NUL
       lines(xx, qpred[2, ], lwd = 2)
 
    }
-   return(if(is.factor(xx)){pl} else {Pr})
+   return(if(is.factor(xx)) pl else Pr)
 }