witwit.model: Within Correspondence Analysis using divers Models and... In pamctdp: Principal Axes Methods for Contingency Tables with Partition Structures on Rows and Columns

Description

`witwit.model` performs an Double Within Tables Correspondence Analysis. Modification of witwit.coa of ade4 to allow Intra Block Model and divers weights

Usage

 ```1 2 3 4 5 6``` ```witwit.model(dudi, row.blocks, col.blocks, pfil = dudi\$lw, pcol = dudi\$cw, model = "C", weight = "coa", scannf = TRUE, nf = 2,eps=1e-15,iter=100) ## S3 method for class 'wwmodel' summary(object, ...) ## S3 method for class 'wwmodel' print(x, ...) ```

Arguments

 `dudi` an object of class `coa` `row.blocks` a numeric vector indicating the row numbers for each block of rows `col.blocks` a numeric vector indicating the column numbers for each block of columns `scannf` a logical value indicating whether the eigenvalues bar plot should be displayed `nf` if scannf FALSE, an integer indicating the number of kept axes `pfil` a numeric vector indicating the row weights `pcol` a numeric vector indicating the column weights `model` "C": the same model of ICA,"B": intra blocks independence model `weight` c("coa": the same row and columns weights than CA, "mfa": MFA-like weights in rows and columns, "mafc": MFA-like weights in columns, "mfar": MFA-like weights in rows `eps` convergence error if weight="mfa" `iter` maximum itection number if if weight="mfa"

 `object` an object of class `wwmodel`

 `x` an object of class `wwmodel` `...` further arguments passed to or from other methods

Details

This function is build up with `witwit.coa` of ade4, in order to allow diferents weights and models in a contingency table with double structure of partition. If model="C" and weight="coa" the results are the same of witwit.coa. If model="B" and weight="coa" a Intra-Blocks Correspondence Analysis (IBCA) is buld up If model="B" and weight="mfa" a Weighted Intra-Blocks Correspondence Analysis (WIBCA) is build up

Value

Returns a list of class `wwmodel` containing:

 `tab` a data frame with I rows and K columns `cw` column weights, a vector with K components `lw` row weights, a vector with I components `eig` eigenvalues, a vector with min(I,K) components `nf` integer, number of kept axes `c1` principal axes, data frame with I rows and nf columns `l1` principal components, data frame with I rows and nf columns `co` column coordinates, data frame with K rows and nf columns `li` row coordinates, data frame with I rows and nf columns `call` original call `rbvar` a data frame with the within variances of the rows of the factorial coordinates `lbw` a data frame with the marginal weighting of the row bands `cvar` a data frame with the within variances of the columns of the factorial coordinates `cbw` a data frame with the marginal weighting of the column bands `hom` homotecia to read some aids as in MFA `rbl` number of rows in each row-band `cbl` number of columns in each column-band `sepeig.col` band-column separate firt eigenvalues if weight="mfa" `sepeig.row` band-row separate firt eigenvalues if weight="mfa"

Author(s)

Campo Elías PARDO cepardot@unal.edu.co

References

Becue M., Pages J. and Pardo C.E. (2005). Contingency table with a double partition on rows and columns. Visualization and comparison of the partial and global structures. In: Proceedings ASMDA, Brest, France. May,17-20, 2005. Eds: Jacques Janssen and Philippe Lenca. ENST Bretagne. pages 355–364. http://conferences.telecom-bretagne.eu/asmda2005/IMG/pdf/proceedings/355.pdf

Cazes, P., Chessel, D. and Doledec, S. (1988) L'analyse des correspondances internes d'un tableau partitionne : son usage en hydrobiologie. Revue de Statistique Appliquee, 36, 39–54. http://archive.numdam.org/ARCHIVE/RSA/RSA_1988__36_1/RSA_1988__36_1_39_0/RSA_1988__36_1_39_0.pdf

Pardo, Campo Elías, Mónica Bécue-Bertaut, and Jorge Eduardo Ortiz. (2013). Correspondence Analysis of Contingency Tables with Subpartitions on Rows and Columns. Revista Colombiana de Estadística 36.1: 115–144.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```data(ardeche) # change column names names(ardeche\$tab) <- paste(ardeche\$sta.fac,ardeche\$dat.fac,sep="") rownames(ardeche\$tab) <- # change row names paste(strtrim(rownames(ardeche\$tab),1),substr(rownames(ardeche\$tab),4, length(rownames(ardeche\$tab))),sep="") coa1 <- dudi.coa(ardeche\$tab, scannf = FALSE, nf = 4) ww <- witwit.model(coa1, ardeche\$row.blocks, ardeche\$col.blocks, scann = FALSE) ww plot(ww) summary(ww) ```

pamctdp documentation built on May 1, 2019, 10:19 p.m.