Merge branch 'develop'

DESCRIPTION

@@ -1,11 +1,12 @@
 Package: mdatools
 Title: Multivariate Data Analysis for Chemometrics
-Version: 0.11.1
-Date: 2020-07-23
+Version: 0.11.2
+Date: 2020-10-22
 Author: Sergey Kucheryavskiy
 Maintainer: Sergey Kucheryavskiy <[email protected]>
 Description: Projection based methods for preprocessing,
     exploring and analysis of multivariate data used in chemometrics.
+    S. Kucheryavskiy (2020) <doi: 10.1016/j.chemolab.2020.103937>.
 Encoding: UTF-8
 License: MIT + file LICENSE
 Imports: methods, graphics, grDevices, stats, Matrix

NEWS.md

@@ -1,3 +1,10 @@
+v.0.11.2
+========
+
+* fixed an issue, which lead to a bug in `simcam.getPerformanceStats`, returning implausible and asymmetrical results (thanks to @svonallmen).
+
+* fixed a small issue sometimes giving warning when running tests on CRAN (did not influence the user experience though).
+
 v.0.11.1
 ========
 
@@ -66,7 +73,7 @@ Finally, all model results (calibration, cross-validation and test set validatio
 into a single list, `model$res`. This makes a lot of things easier. However, the old way of
 accessing the result objects (e.g. `model$calres` or `model$cvres`) still works, you can access e.g. calibration results both using `model$res$cal` and `model$calres`, so this change will not break the compatibility.
 
-Below is more detailed list of changes. The [tutorial](http://mdatools.com/docs/) has been updated accordingly.
+Below is more detailed list of changes. The [tutorial](https://mdatools.com/docs/) has been updated accordingly.
 
 ## Breaking changes
 

R/classres.R

@@ -534,10 +534,8 @@ plotPerformance.classres <- function(obj, nc = 1, type = "b",
    # prepare plot data
    plot_data <- do.call(rbind, lapply(obj[param], function(x) x[nc, , drop = FALSE]))
 
-   attr(plot_data, "name") <- sprintf(
-      if (length(param) == 1) capitalize(param) else "Classification performance (%s)",
-      obj$classnames[[nc]]
-   )
+   attr(plot_data, "name") <- if (length(param) == 1) capitalize(param) else sprintf(
+      "Classification performance (%s)", obj$classnames[[nc]])
 
    attr(plot_data, "xaxis.name") <- "Components"
    rownames(plot_data) <- param

R/simcam.R

@@ -324,7 +324,7 @@ simcam.getPerformanceStats <- function(models, classnames) {
 
       # calculate variance for the residuals
       s11 <- colSums(e11^2) / (nrow(d1) - m1$ncomp.selected - 1)
-      s22 <- colSums(e12^2) / (nrow(d2) - m2$ncomp.selected - 1)
+      s22 <- colSums(e22^2) / (nrow(d2) - m2$ncomp.selected - 1)
       s12 <- colSums(e12^2) / (nrow(d1))
       s21 <- colSums(e21^2) / (nrow(d2))
 
@@ -386,18 +386,20 @@ simcam.getPerformanceStats <- function(models, classnames) {
 #' using calibration sets X1 and X2 with number of rows n1 and n2.
 #' Then we do the following:
 #'
-#' 1. Project X2 to model m1 and compute residuals, E12
-#' 2. Compute variance of the residuals as s12 = sum(E12^2) / n1
-#' 3. Project X1 to model m2 and compute residuals, E21
-#' 4. Compute variance of the residuals as s21 = sum(E21^2) / n2
-#' 5. Compute variance of residuals for m1 as s1 = sum(E1^2) / (n1 - A1 - 1)
-#' 6. Compute variance of residuals for m2 as s2 = sum(E2^2) / (n2 - A2 - 1)
+#' \enumerate{
+#' \item Project X2 to model m1 and compute residuals, E12
+#' \item Compute variance of the residuals as s12 = sum(E12^2) / n1
+#' \item Project X1 to model m2 and compute residuals, E21
+#' \item Compute variance of the residuals as s21 = sum(E21^2) / n2
+#' \item Compute variance of residuals for m1 as s1 = sum(E1^2) / (n1 - A1 - 1)
+#' \item Compute variance of residuals for m2 as s2 = sum(E2^2) / (n2 - A2 - 1)
+#' }
 #'
 #' The model distance then can be computed as: d = sqrt((s12 + s21) / (s1 + s2))
 #'
 #' As one can see, if the two models and corresponding calibration sets are identical, then the
 #' distance will be sqrt((n - A - 1) / n). For example, if n = 25 and A = 2, then the distance
-#' between the model and itself is sqrt(25/22) = sqrt(0.88) = 0.938. This case is demonstrated
+#' between the model and itself is sqrt(22/25) = sqrt(0.88) = 0.938. This case is demonstrated
 #' in the example section.
 #'
 #' In general, if distance between models is below one classes are overlapping. If it is above 3

README.md

@@ -7,26 +7,26 @@ Multivariate Data Analysis Tools
 
 <img src="https://mdatools.com//images/logo.svg" align="left" style="top: 5px 10px 5px 0;" />
 
-*mdatools* is an R package for preprocessing, exploring and analysis of multivariate data. The package provides methods mostly common for [Chemometrics](http://en.wikipedia.org/wiki/Chemometrics). It was created for an introductory PhD course on Chemometrics given at Section of Chemical Engineering, Aalborg University. The general idea of the package is to collect most widespread chemometric methods and give a similar "user interface" (or rather API) for using them. So if a user knows how to make a model and visualize results for one method, he or she can easily do this for the others.
+*mdatools* is an R package for preprocessing, exploring and analysis of multivariate data. The package provides methods mostly common for [Chemometrics](https://en.wikipedia.org/wiki/Chemometrics). It was created for an introductory PhD course on Chemometrics given at Section of Chemical Engineering, Aalborg University. The general idea of the package is to collect most widespread chemometric methods and give a similar "user interface" (or rather API) for using them. So if a user knows how to make a model and visualize results for one method, he or she can easily do this for the others.
 
-For more details and examples read a [Bookdown tutorial](http://mdatools.com/docs/). The project website, [mdatools.com](https://mdatools.com), contains additional information about supplementary materials and tools.
+For more details and examples read a [Bookdown tutorial](https://mdatools.com/docs/). The project website, [mdatools.com](https://mdatools.com), contains additional information about supplementary materials and tools.
 
 If you want to cite the package, please use the following: Sergey Kucheryavskiy, *mdatools – R package for chemometrics*, Chemometrics and Intelligent Laboratory Systems, Volume 198,
 2020 (DOI: [10.1016/j.chemolab.2020.103937](https://doi.org/10.1016/j.chemolab.2020.103937)).
 
 What is new
 -----------
 
-Latest release (0.11.1) is available from GitHub. It will be available on CRAN soon (release is expected for *25.07.2020*). You can see the full list of changes [here](NEWS.md). The Bookdown tutorial has been also updated and contains the description of new methods added in the last release.
+Latest release (0.11.2) is available both from GitHub and CRAN. You can see the full list of changes [here](NEWS.md). The Bookdown tutorial has been also updated and contains the description of new methods added in the last release.
 
 
 How to install
 --------------
 
-The package is available from CRAN by usual installing procedure. However, due to restrictions in CRAN politics regarding number of submissions (one in 3-4 month), mostly major releases will be published there (with 2-3 weeks delay after GitHub release as more thorough testing is needed). You can [download](https://github.com/svkucheryavski/mdatools/releases) a zip-file with source package and install it using the `install.packages` command, e.g. if the downloaded file is `mdatools_0.11.1.tar.gz` and it is located in a current working directory, just run the following:
+The package is available from CRAN by usual installing procedure. However, due to restrictions in CRAN politics regarding number of submissions (one in 3-4 month), mostly major releases will be published there (with 2-3 weeks delay after GitHub release as more thorough testing is needed). You can [download](https://github.com/svkucheryavski/mdatools/releases) a zip-file with source package and install it using the `install.packages` command, e.g. if the downloaded file is `mdatools_0.11.2.tar.gz` and it is located in a current working directory, just run the following:
 
 ```
-install.packages("mdatools_0.11.1.tar.gz")
+install.packages("mdatools_0.11.2.tar.gz")
 ```
 
 If you have `devtools` package installed, the following command will install the current developer version from the master branch of GitHub repository (do not forget to load the `devtools` package first):