Summary

This repository contains concrete application examples for the micompr R package, which implements a procedure for comparing multivariate samples associated with different groups.

These examples are described in detail in the following reference:

• Fachada N, Rodrigues J, Lopes VV, Martins RC, Rosa AC. (2016) micompr: An R Package for Multivariate Independent Comparison of Observations. The R Journal 8(2):405–420. https://journal.r-project.org/archive/2016-2/fachada-rodrigues-lopes-etal.pdf

Examples

Simulation model with multiple outputs

The replication of a simulation model in a new context highlights differences between the conceptual and implemented models, as well as inconsistencies in the conceptual model specification, promoting model verification, model validation and model credibility

In this example, provided in the pphpc.R script, micompr is used for comparing the outputs of two implementations of the PPHPC agent-based model. The compared output data is available at https://zenodo.org/record/46848. Uncompress the data to a local folder, and specify the folder in the dir_data variable within the script.

Monthly sunspots

This example, provided in the sunspot.R script, uses the monthly sunspot data included with R, which contains the monthly numbers of sunspots from 1749 to the present day. The example aims to answer the following question: Were the solar cycles during the 1749–1859 interval significantly different from the more recent observations?

Saugeen river flow

This example, provided in the saugeen.R script, uses the Saugeen River daily flow data included in the deseasonalize R package. This data consists of a time series of the rivers’ daily flow (m³/s) from 1915 to 1979. The example aims to answer the following question: is there any statistical difference between the flow dynamics during the 1915–1944 and 1950–1979 periods (perhaps due to climate change or some other factor)?

PH² database of dermoscopic images

In this example we use the tools provided by the micompr package to study the PH² database of dermoscopic images. This image database contains a total of 200 dermoscopic images of melanocytic lesions, including, from benign to more serious, 80 common nevi, 80 atypical nevi, and 40 melanomas. The goal is to verify if images of the three types of lesions form statistically distinguishable samples.

This example is provided in the derma.R script. However, the following pre-processing of the images was performed (using ImageMagick command-line utilities under Linux) before comparing them with micompr:

• Since each image comes in its own separate folder, we first copy all the images to the same folder:

# List folders (within the "PH2 Dataset images" folder) containing images
DIRS=`ls PH2\ Dataset\ images/`

# Create "images" folder
mkdir -p images

# Copy all images to the "images" folder
for DIR in $DIRS
do
  cp PH2\ Dataset\ images/${DIR}/${DIR}_Dermoscopic_Image/${DIR}.* images
done

• These are 8-bit RGB color images, with a resolution of purportedly 768 × 560 pixels. The following command shows this is not the case, and that the image sizes vary between 761 × 570 and 769 × 577:

# List all PH2 images
IMGS=`ls images/`

# Check properties of all images
for IMG in $IMGS
do
  identify images/${IMG} | cut -d " " -f 3,5
done

• As such, we resize all images to 760 × 570 prior before comparing them with micompr:

# List all PH2 images
IMGS=`ls images/`

# Create a folder for the resized images
mkdir -p images_resize

# Resize all images to 760 × 570
for IMG in $IMGS
do
  convert images/${IMG} -resize 760x570\! images_resize/${IMG}
done

The folder containing the resized images is specified in the imgfolder variable within the derma.R script.

License

MIT License