Automatic report

[Alessandro Cozzi-Lepri]

Hi

I no longer seem to be able to generate the automatic  report (either from GUI or using Investigate). Is this still a viable feature?

thanks

Alessandro

[Francois Husson]

Hi
Yes it is always possible to do automatic report. Check that you have the last version of FactoMineR, FactoInvestigate and Factoshiny.
Best
FH

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	François Husson Department Statistics & Computer science UMR 6625 IRMAR CNRS 65 rue de Saint-Brieuc, CS 84215, 35042 Rennes Cedex Tel: +33 2 23 48 58 86 https://husson.github.io
En 2022, Agrocampus Ouest devient l'Institut Agro Rennes-Angers.

[Alessandro Cozzi-Lepri]

I have successfully unpacked these

  Factorminer_2.6

  Factorshiny_2.4

  Factorinvestigate_1.8

It goes faint and produces this (text only)

--- title: "Principal Component Analysis" author: "Dataset pca_t6" output: html_document: default --- This dataset contains 64 individuals and 8 variables, 1 qualitative variable is considered as illustrative. - - - ```{r, echo = FALSE} library(FactoMineR) load('C:/Debate/Workspace.RData') ``` ### 1. Study of the outliers The analysis of the graphs does not detect any outlier. - - - ### 2. Inertia distribution The inertia of the first dimensions shows if there are strong relationships between variables and suggests the number of dimensions that should be studied. The first two dimensions of analyse express **54.73%** of the total dataset inertia ; that means that 54.73% of the individuals (or variables) cloud total variability is explained by the plane. This percentage is relatively high and thus the first plane well represents the data variability. This value is greater than the reference value that equals **43.13%**, the variability explained by this plane is thus significant (the reference value is the 0.95-quantile of the inertia percentages distribution obtained by simulating 8980 data tables of equivalent size on the basis of a normal distribution). From these observations, it should be better to also interpret the dimensions greater or equal to the third one. ```{r, echo = FALSE, fig.align = 'center', fig.height = 3.5, fig.width = 5.5} par(mar = c(2.6, 4.1, 1.1, 2.1)) ggplot2::ggplot(cbind.data.frame(x=1:nrow(res$eig),y=res$eig[,2])) + ggplot2::aes(x=x, y=y)+ ggplot2::geom_col(fill="blue") + ggplot2::xlab("Dimension") + ggplot2::ylab("Percentage of variance") + ggplot2::ggtitle("Decomposition of the total inertia") + ggplot2::theme_light() + ggplot2::theme(plot.title = ggplot2::element_text(hjust =0.5)) + ggplot2::scale_x_continuous(breaks=1:nrow(res$eig)) ``` **Figure 2 - Decomposition of the total inertia** An estimation of the right number of axis to interpret suggests to restrict the analysis to the description of the first 3 axis. These axis present an amount of inertia greater than those obtained by the 0.95-quantile of random distributions (72.8% against 59.22%). This observation suggests that only these axis are carrying a real information. As a consequence, the description will stand to these axis. - - - ### 3. Description of the plane 1:2 ```{r, echo = FALSE, fig.align = 'center', fig.height = 3.5, fig.width = 5.5} drawn <- c("53", "39", "3", "62", "14", "43", "59", "10", "49", "21", "64", "45", "47", "57", "35", "13", "40", "20", "52", "33") par(mar = c(4.1, 4.1, 1.1, 2.1)) plot.PCA(res, select = drawn, axes = c(1,2), choix = 'ind', invisible = 'quali', title = '', cex = cex) ``` **Figure 3.1 - Individuals factor map (PCA)** *The labeled individuals are those with the higher contribution to the plane construction.*