Refine the resultant plots (#25)

* CHG: refine the facet label by adding math expression

* FIX: correct grammar erros; correct the attribute name; update the manual

---------

Co-authored-by: joseph.wangwt <joseph.wangwt@shopee.com>

DESCRIPTION

@@ -16,7 +16,7 @@ Authors@R: c(person(
                 comment = c(ORCID = "0000-0002-5613-349X")
               )
             )
-Description: Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions
+Description: Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions
   by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The
   method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.
 License: GPL-3

R/SpatPCA.R

@@ -1,25 +1,25 @@
 # This file was generated by Rcpp::compileAttributes
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 #'
-#' Internal function: M-fold CV of SpatPCA with selecting K
+#' Internal function: M-fold CV of SpatPCA with selected K
 #'
 #' @keywords internal
 #' 
 #' @param x Location matrix
 #' @param Y Data matrix
 #' @param M The number of folds for cross validation; default is 5.
-#' @param tau1 Vector of a nonnegative smoothness parameter sequence. If NULL, 10 tau1 values in a range are used.
-#' @param tau2 Vector of a nonnegative sparseness parameter sequence. If NULL, none of tau2 is used.
-#' @param gamma Vector of a nonnegative hyper parameter sequence for tuning eigenvalues. If NULL, 10 values in a range are used.
+#' @param tau1 Vector of a non-negative smoothness parameter sequence. If NULL, 10 tau1 values in a range are used.
+#' @param tau2 Vector of a non-negative sparseness parameter sequence. If NULL, none of tau2 is used.
+#' @param gamma Vector of a non-negative hyper parameter sequence for tuning eigenvalues. If NULL, 10 values in a range are used.
 #' @param shuffle_split Vector of indices for random splitting Y into training and test sets 
 #' @param maxit Maximum number of iterations. Default value is 100.
 #' @param thr Threshold for convergence. Default value is \eqn{10^{-4}}.
-#' @param l2 Vector of a nonnegative tuning parameter sequence for ADMM use
+#' @param l2 Vector of a non-negative tuning parameter sequence for ADMM use
 #' @return A list of objects including
 #' \item{cv_result}{A list of resultant objects produced by `spatpcaCV`}
 #' \item{selected_K}{Selected K based on CV.}
 #'
-spatpcaCVWithSelectingK <- function(x, Y, M, tau1, tau2, gamma, shuffle_split, maxit, thr, l2) {
+spatpcaCVWithSelectedK <- function(x, Y, M, tau1, tau2, gamma, shuffle_split, maxit, thr, l2) {
   upper_bound <- fetchUpperBoundNumberEigenfunctions(Y, M)
   prev_cv_selection <- spatpcaCV(x, Y, M, 1, tau1, tau2, gamma, shuffle_split, maxit, thr, l2)
 
@@ -43,9 +43,9 @@ spatpcaCVWithSelectingK <- function(x, Y, M, tau1, tau2, gamma, shuffle_split, m
 #' @param Y Data matrix (\eqn{n \times p}) stores the values at \eqn{p} locations with sample size \eqn{n}.
 #' @param K Optional user-supplied number of eigenfunctions; default is NULL. If K is NULL or is_K_selected is TRUE, K is selected automatically.
 #' @param is_K_selected If TRUE, K is selected automatically; otherwise, is_K_selected is set to be user-supplied K. Default depends on user-supplied K.
-#' @param tau1 Optional user-supplied numeric vector of a nonnegative smoothness parameter sequence. If NULL, 10 tau1 values in a range are used.
-#' @param tau2 Optional user-supplied numeric vector of a nonnegative sparseness parameter sequence. If NULL, none of tau2 is used.
-#' @param gamma Optional user-supplied numeric vector of a nonnegative tuning parameter sequence. If NULL, 10 values in a range are used.
+#' @param tau1 Optional user-supplied numeric vector of a non-negative smoothness parameter sequence. If NULL, 10 tau1 values in a range are used.
+#' @param tau2 Optional user-supplied numeric vector of a non-negative sparseness parameter sequence. If NULL, none of tau2 is used.
+#' @param gamma Optional user-supplied numeric vector of a non-negative tuning parameter sequence. If NULL, 10 values in a range are used.
 #' @param M Optional number of folds for cross validation; default is 5.
 #' @param is_Y_detrended If TRUE, center the columns of Y. Default is FALSE.
 #' @param maxit Maximum number of iterations. Default value is 100.
@@ -174,7 +174,7 @@ spatpca <- function(x,
 
 
   if (is_K_selected) {
-    cv_with_selected_k <- spatpcaCVWithSelectingK(scaled_x, Y, M, tau1, tau2, gamma, shuffle_split, maxit, thr, l2)
+    cv_with_selected_k <- spatpcaCVWithSelectedK(scaled_x, Y, M, tau1, tau2, gamma, shuffle_split, maxit, thr, l2)
     cv_result <- cv_with_selected_k$cv_result
     selected_K <- cv_with_selected_k$selected_K
   }
@@ -246,7 +246,7 @@ predictEigenfunction <- function(spatpca_object, x_new) {
 
 #' @title  Spatial predictions on new locations
 #'
-#' @description Predict on new locations with the estimated spatial structures.
+#' @description Predict the response on new locations with the estimated spatial structures.
 #' 
 #' @param spatpca_object An `spatpca` class object 
 #' @param x_new New location matrix.
@@ -259,12 +259,12 @@ predictEigenfunction <- function(spatpca_object, x_new) {
 #' Phi_1D <- exp(-x_1D^2) / norm(exp(-x_1D^2), "F")
 #' set.seed(1234)
 #' Y_1D <- rnorm(n = 100, sd = 3) %*% t(Phi_1D) + matrix(rnorm(n = 100 * 10), 100, 10)
-#' rm_loc <- sample(1:10, 3)
-#' x_1Drm <- x_1D[-rm_loc]
-#' Y_1Drm <- Y_1D[, -rm_loc]
-#' x_1Dnew <- as.matrix(seq(-5, 5, length = 20))
-#' cv_1D <- spatpca(x = x_1Drm, Y = Y_1Drm, tau2 = 1:100, num_cores = 2)
-#' predictions <- predict(cv_1D, x_new = x_1Dnew)
+#' removed_location <- sample(1:10, 3)
+#' removed_x_1D <- x_1D[-removed_location]
+#' removed_Y_1D <- Y_1D[, -removed_location]
+#' new_x_1D <- as.matrix(seq(-5, 5, length = 20))
+#' cv_1D <- spatpca(x = removed_x_1D, Y = removed_Y_1D, tau2 = 1:100, num_cores = 2)
+#' predictions <- predict(cv_1D, x_new = new_x_1D)
 #' 
 predict <- function(spatpca_object, x_new, eigen_patterns_on_new_site = NULL) {
   checkNewLocationsForSpatpcaObject(spatpca_object, x_new)
@@ -279,7 +279,7 @@ predict <- function(spatpca_object, x_new, eigen_patterns_on_new_site = NULL) {
     spatpca_object$selected_gamma,
     eigen_patterns_on_new_site
   )
-  return(spatial_prediction$predict)
+  return(spatial_prediction$prediction)
 }
 
 #'
@@ -316,30 +316,55 @@ plot.spatpca <- function(x, ...) {
         plot.title = element_text(hjust = 0.5)
       )
   )
+  tau1_hat_string = paste(
+    c("hat(tau)[1]==", formatC(x$selected_tau1, format = "f", digits = 3)),
+    collapse = ""
+  )
+  tau2_hat_string = paste(
+    c("hat(tau)[2]==", formatC(x$selected_tau2, format = "f", digits = 3)),
+    collapse = ""
+  )
+
+  parameter_types = c(
+    "tau[1]~'|'~tau[2]==0",
+    paste(c("tau[2]~'|'~", tau1_hat_string), collapse=""),
+    paste(c("gamma~'|'~list(", tau1_hat_string, ",", tau2_hat_string, ")"),collapse="")
+  )
+
+
   cv_dataframe <- rbind(
     data.frame(
-      type = "tau1 given tau2 = 0",
+      type = parameter_types[1],
       parameter = array(x$tau1),
       cv = array(x$cv_score_tau1)
     ),
     data.frame(
-      type = "tau2 given selected tau1",
+      type = parameter_types[2],
       parameter = array(x$tau2),
       cv = array(x$cv_score_tau2)
     ),
     data.frame(
-      type = "gamma given selected tau1 and tau2",
+      type = parameter_types[3],
       parameter = array(x$gamma),
       cv = array(x$cv_score_gamma)
     )
+
   )
+  cv_dataframe$type = factor(cv_dataframe$type, levels=parameter_types)
+
   result <-
     ggplot(
       cv_dataframe,
       aes(x = parameter, y = cv, color = type)
     ) +
-    geom_line(linewidth = 1.5) +
-    facet_grid(scales = "free", . ~ type)
+    geom_line(linewidth = 1.5)+
+    facet_grid(
+      scales = "free",
+      . ~ type,
+      labeller = labeller(type=label_parsed)) +
+    ggtitle("Result of K-fold CV")
 
+
   return(suppressMessages(print(result)))
 }
+

tests/testthat/test-SpatPCA.R

@@ -172,7 +172,7 @@ tau2 <- setTau2(NULL, M)
 l2 <- setL2(tau2)
 setCores(2)
 cv_with_k_seleted <-
-  spatpcaCVWithSelectingK(x_1D, Y_1D, M, tau1, tau2, 1, shuffle_split, 10, 1e-04, l2)
+  spatpcaCVWithSelectedK(x_1D, Y_1D, M, tau1, tau2, 1, shuffle_split, 10, 1e-04, l2)
 
 test_that("auxiliary function for selecting K", {
   expect_equal(cv_with_k_seleted$selected_K, 1)