plot.PCAOS | R Documentation |

Visualisation of results from PCAOS method. See details for available plot.

## S3 method for class 'PCAOS' plot( x, choice = "ind", comp = c(1, 2), coloring.indiv = NULL, supp.var = FALSE, sub.var.quantif = NULL, ellipse = FALSE, level.conf = 0.95, size.label = 3.5, size.legend = 10, min.contribution = 0, label.size.freq = FALSE, ordinal.as.direction = FALSE, label.cat = "var+cat", ... )

`x` |
an object of class PCAOS |

`choice` |
the available graphs are "screeplot","quantif","ind","numeric","qualitative","all.var". See Details. |

`comp` |
a length 2 vector with the components to plot. |

`coloring.indiv` |
a vector of length N to color individuals. If NULL, no coloring is applied (individuals plot). |

`supp.var` |
TRUE or FALSE; if TRUE supplementary variables are added in factorial representation (individuals and variables plot). |

`sub.var.quantif` |
a vector with variable of interest (quantification plots). |

`ellipse` |
boolean (FALSE by default), if TRUE, draw ellipses around categories of the qualitative variable considered as supplementary (individuals plot). |

`level.conf` |
level of confidence ellipses (individuals plot). |

`size.label` |
size of label in graphs (all plots). |

`size.legend` |
size of label in graphs (all plots). |

`min.contribution` |
variables with a contribution (i.e loading) lower than this value will not be plotted in the 'all.var' graph (useful for dataset with a lot of variables) (all.var plot). |

`label.size.freq` |
boolean (FALSE by default); if TRUE size of categories are proportional to their citation frequencies (qualitative and all.var plot). |

`ordinal.as.direction` |
boolean (FALSE by default); if TRUE ordinal variables are represented as vectors, from the first categorie to the last one (qualitative and all.var plot). |

`label.cat` |
If == 'var+cat', the name of the variable is included in the labels of the categories; if == 'cat', only the name of the categorie is plotted (name of categories should be unique) (qualitative and all.var plot). |

`...` |
further arguments passed to or from other methods, such as cex, cex.main, ... |

screeplot: Representation of the percentage of inertia restituates (Y), for each component (X).

quantif: Reprensetation of the quantification of variables trought Optimal Scaling, with original variables (X) and quantified variables (Y).Possibility to select one or more variables of interest with the argument "var.sub".

ind: factorial representation of individuals

For numeric variables

numeric: factorial representation of numeric variables (also called loading plot) Each numeric variable is represented by it's weight/loadings

For qualitative (i.e nominal and ordinal) variables

qualitative: factorial representation of qualitatives variables trough the representation of it's categories. Coordinates of each category is calculted such as the single quantification of the category multiplied by the loading of the associated variable (rank.restriction = one). Or by averaging, per component, the principal component scores for all individuals in the same categories of a particular variable (rank.restriction = no.restriction).

For All variables

all.var : factorial representation of all variables (weight for numeric variables, and categories for qualitative variables)

All graph are ggplot object

A ggplot object

Martin PARIES (Maintainer: martin.paries@oniris-nantes.fr)

Evelyne Vigneau

Stephanie Bougeard

data (antibiotic) level.scale <- rep(NA,ncol(antibiotic)) #Setting level.scale argument level.scale[c(2,3,4)] <- "num" level.scale[c(1,5,6,7,8,9,10,11,12,13,14,15)] <- "nom" level.scale[c(1,15)] <- "ord" level.scale res.PCAOS <- PCA.OS::PCAOS( data = antibiotic, level.scale = level.scale, supp.var = c(1,2) ) #Individuals graph PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "ind",coloring = antibiotic$Atb.conso) PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "ind",supp.var = TRUE,ellipse = TRUE) #Quantifications PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "quantif",sub.var.quantif = c(4,8)) #Qualitative variables PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "qualitative",supp.var = TRUE) #Numeric variables PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "numeric",supp.var = TRUE) #All variables PCA.OS::plot.PCAOS(x = res.PCAOS,choice = "all.var",supp.var = TRUE)

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