Compute all the package functions : detection of outliers, evaluation of inertia distribution, dimensions description, classification and realisation of graphical views. All the results are written as Word, html or PDF documents.
Investigate(res, file = "Investigate.Rmd", document = c("html_document"),
Iselec = "contrib", Vselec = "cos2", Rselec = "contrib",
Cselec = "cos2", Mselec = "cos2", Icoef = 1, Vcoef = 1, Rcoef = 1,
Ccoef = 1, Mcoef = 1, ncp = NULL, time = "10s", nclust = -1,
mmax = 10, nmax = 10, hab = NULL, ellipse = TRUE, display.HCPC = TRUE,
out.selec = TRUE, remove.temp = TRUE, parallel = TRUE, cex = 0.7,
openFile = TRUE, keepRmd = FALSE, codeGraphInd = NULL,
codeGraphVar=NULL, codeGraphCA = NULL, options = NULL,
language = "auto")the function creates and opens a Word, html or PDF document that contains all the descriptions of analysis.
a PCA, CA or MCA object.
the file path where to write the description in Rmarkdown language. If the file already exists, its content is overwritten. If not specified, the description is written in the console.
a character vector giving the document format desired between "word_document", "pdf_document" and "html_document".
the individuals to select ; see the details section.
the variables to select ; see the details section.
the rows to select (for a CA res object) ; see the details section.
the columns to select (for a CA res object) ; see the details section.
the supplementary variables to select ; see the details section.
a numerical coefficient to adjust the individuals selection rule ; see the details section.
a numerical coefficient to adjust the variables selection rule ; see the details section.
a numerical coefficient to adjust the rows selection rule (for a CA res object) ; see the details section.
a numerical coefficient to adjust the columns selection rule (for a CA res object) ; see the details section.
a numerical coefficient to adjust the supplementary variables selection rule ; see the details section.
an integer to force the number of dimension to analyse.
a character indicating the loop condition. This string is made of a number and a letter coupled. The number X with letter L means to compute X datasets exactly. The number X with letter s means to compute as many datasets as possible during approximativley X seconds.
an integer to force the number of cluster for the classification.
an integer giving the maximum number of individuals (or rows) to illustrate each group (by defaut 10).
an integer giving the maximum number of variables (or columns) to illustrate each group of individuals (by defaut 10).
a variable name or index to use to color the individuals (or rows) among the variable categories.
a boolean : if TRUE, ellipses are plotted with the coloration of individuals (or rows).
a boolean : if TRUE, the function performs the classification.
a boolean : if TRUE, the function performs the detection of outliers.
a boolean : if TRUE, the temporary files created are deleted after the function execution.
a boolean : if TRUE, the computation uses map reduce on the processor cores to increase the performance. Useful for huge datasets.
an optional argument for the generic plot functions, used to adjust the size of the elements plotted.
Open the file with the appropriate application; TRUE by default
Keep the Rmd file; FALSE by default
a character string corresponding to the code to use for the individuals graph.
a character string corresponding to the code to use for the variables graph.
a character string corresponding to the code to use for the CA graph.
a character string that gives the output options fir the figures.
If NULL, options="r, echo = FALSE, fig.align = 'center', fig.height = 3.5, fig.width = 5.5" for linux and Mac
and options="r, echo = FALSE, fig.height = 3.5, fig.width = 5.5" for Windows
possible values "auto", "en", or "fr": by default, "auto" detects the language (English or French), "en" for English and "fr" for "French"
Simon Thuleau and Francois Husson
The Iselec argument (respectively Vselec, Rselec or Cselec) is used in order to select a part of the elements that are drawn and described. For example, you can use either :
- Iselec = 1:5 then the individuals (respectively the variables, the rows or the columns) numbered 1 to 5 are drawn.
- Iselec = c("name1","name5") then the individuals (respectively the variables, the rows or the columns) named name1 and name5 are drawn.
- Iselec = "contrib 10" then the 10 active or illustrative individuals (respectively the variables, the rows or the columns) that have the highest contribution on the 2 dimensions of the plane are drawn.
- Iselec = "contrib" then the optimal number of active or illustrative individuals (respectively the variables, the rows or the columns) that have the highest contribution on the 2 dimensions of the plane are drawn.
- Iselec = "cos2 5" then the 5 active or illustrative individuals (respectively the variables, the rows or the columns) that have the highest cos2 on the 2 dimensions of the plane are drawn.
- Iselec = "cos2 0.8" then the active or illustrative individuals (respectively the variables, the rows or the columns) that have a cos2 higher to 0.8 on the plane are drawn.
- Iselec = "cos2" then the optimal number of active or illustrative individuals (respectively the variables, the rows or the columns) that have the highest cos2 on the 2 dimensions of the plane are drawn.
The Icoef argument (respectively Vcoef, Rcoef or Ccoef) is used in order to adjust the selection of the elements when based on Iselec = "contrib" or Iselec = "cos2". For example :
- if Icoef = 2, the threshold is 2 times higher, and thus 2 times more restrictive.
- if Icoef = 0.5, the threshold is 2 times lower, and thus 2 times less restrictive.
require(FactoMineR)
data(decathlon)
if (FALSE) {
res.pca = PCA(decathlon, quanti.sup = c(11:12), quali.sup = c(13), graph = FALSE)
Investigate(res.pca, file = "PCA.Rmd", document = "html_document", time = "1000L",
parallel = FALSE)
data(children)
res.ca = CA(children, row.sup = 15:18, col.sup = 6:8, graph = FALSE)
Investigate(res.ca, file = "CA.Rmd", document = "pdf_document")
data(tea)
res.mca = MCA(tea, quanti.sup = 19,quali.sup = 20:36, graph = FALSE)
Investigate(res.mca, file = "MCA.Rmd", document = c("word_document", "pdf_document"))
}
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