Adjustr v0.5.0.0

The goal of Adjustr is to easily fit a given random variable in order to find it’s distribution.

Installation

The development version from GitHub with:

# install.packages("devtools")
devtools::install_github("moutikabdessabour/adjustr")

Motivation

I actually wrote these functions for a homework in order to simplify the process, now here we are 😃.

Using it

The package is made up of two main functions.

getfitdistr

To fit continuous random variables

getfitdistr(x, 
            short=TRUE, 
            showplots=TRUE,
            plots.as.vars=FALSE, 
            xlab="Claim amounts",
            color=c(gamma = "steelblue", lognormal="firebrick",exponential= "green"))

The available arguments are:

Argument	Description
short	when used the function will return only the best fitted distribution if it exists
x	The vector that contains the values of the continuous random variable
showplots	Whether To show the plots or not
plots.as.vars	Whether you want to add the plots to the result
xlab	what do you want to name the X axis?
color	The color you want to use in the plots

This code is all that’s needed to fit your random variable, the returned value is a list. let distribution be the name of a distribution we have:

File	Description
distributions	The distribution that the code looped through.
acceptedDistr	A data.frame that contains the distributions that verify the null hypothesis order from the lowest AIC to the biggest.
distribution	A list containing the $estimate for said distribution, the $p.value for the Kolmogorov-Smirnov test, $accepthyp when the Null Hypothesis is accepted or not and $aic containing the Akaike information criterion for this distribution.
bestdistr	the best fitted distribution

getgoodfit

To fit discrete random variables:

getgoodfit(x, showplots=F, short=TRUE, plots.as.vars=F, xlab="Number of claims")

The available arguments are:

Argument	Description
short	when used the function will return only the best fitted distribution if it exists
x	The vector that contains the values of the discrete random variable
showplots	Whether To show the plots or not
plots.as.vars	Whether you want to add the plots to the result
xlab	what do you want to name the X axis?

This code is all that’s needed to fit your random variable, the returned value is a list. let distribution be the name of a distribution we have:

File	Description
distributions	The distribution that the code looped through.
acceptedDistr	A data.frame that contains the distributions that verify the null hypothesis order from the lowest Chi-squared to the biggest.
distribution	A list containing the $estimate for said distribution, the $observed and $fitted values, the $count and the $prob, the $p.value, $Xsquared and the $df degrees of freedom for the Chi-squared test, $accepthyp when the Null Hypothesis is accepted or not.
bestdistr	the best fitted distribution

rmd.continuous & rmd.discrete

To create a PDF or HTML file analyzing a continuous/discrete random variable use the following functions:

rmd.continuous(output_file, 
                  author, 
                  path, 
                  title="Ajustement d'une V.A", 
                  date=Sys.Date(),
                  output_dir=getwd(),
                  output_format, 
                  verbose=F, 
                  ...)
rmd.discrete(output_file,
             author,
             path,
             title="Ajustement d'une V.A",
             date=Sys.Date(),
             output_dir=getwd(),
             output_format,
             verbose=F,
             ...)

The available arguments are:

Argument	Description
output_file	The name of the output file. If using NULL then the output filename will be based on filename for the input file. If a filename is provided, a path to the output file can also be provided. Note that the output_dir option allows for specifying the output file path as well, however, if also specifying the path, the directory must exist. If output_file is specified but does not have a file extension, an extension will be automatically added according to the output format. To avoid the automatic file extension, put the output_file value in I(), e.g., I('my-output').
author	The author to use in the template
path	The path to the csv file. note that the column that contains the random variable values must be the second column
title	The title to be used in the template
date	The date to be used in the template
output_dir	The output directory for the rendered output_file. This allows for a choice of an alternate directory to which the output file should be written (the default output directory of that of the input file). If a path is provided with a filename in output_file the directory specified here will take precedence. Please note that any directory path provided will create any necessary directories if they do not exist.
output_format	The R Markdown output format to convert to. The option “all” will render all formats defined within the file. The option can be the name of a format (e.g. “html_document”) and that will render the document to that single format. One can also use a vector of format names to render to multiple formats. Alternatively, you can pass an output format object (e.g. html_document()). If using NULL then the output format is the first one defined in the YAML frontmatter in the input file (this defaults to HTML if no format is specified there).
verbose	An option to suppress printing of the pandoc command line.
...	Additional arguments to be passed to rmarkdown::render()

When run_pandoc = TRUE, the compiled document is written into the output file, and the path of the output file is returned. When run_pandoc = FALSE, the path of the Markdown output file, with attributes knit_meta (the knitr meta data collected from code chunks) and intermediates (the intermediate files/directories generated by render()

NB: The Rmd templates are only in french 😢 To see the results check the following links

• continuous format html,
• continuous format pdf,
• discrete format html,
• discrete format pdf

Questions?

Confused by anything? Open an issue on github and let me know.