mbpcaiv: Multiblock principal component analysis with instrumental...

In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Multiblock principal component analysis with instrumental variables

Description

Function to perform a multiblock redundancy analysis of several explanatory blocks (X_1, …, X_k), defined as an object of class ktab, to explain a dependent dataset $Y$, defined as an object of class dudi

Usage

mbpcaiv(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)

Arguments

dudiY	an object of class dudi containing the dependent variables
ktabX	an object of class ktab containing the blocks of explanatory variables
scale	logical value indicating whether the explanatory variables should be standardized
option	an option for the block weighting. If uniform, the block weight is equal to $1/K$ for (X_1, …, X_K) and to $1$ for $X$ and $Y$. If none, the block weight is equal to the block inertia
scannf	logical value indicating whether the eigenvalues bar plot should be displayed
nf	integer indicating the number of kept dimensions

Value

A list containing the following components is returned:

call	the matching call
tabY	data frame of dependent variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform")
tabX	data frame of explanatory variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform")
TL, TC	data frame useful to manage graphical outputs
nf	numeric value indicating the number of kept dimensions
lw	numeric vector of row weights
X.cw	numeric vector of column weighs for the explanalatory dataset
blo	vector of the numbers of variables in each explanatory dataset
rank	maximum rank of the analysis
eig	numeric vector containing the eigenvalues
lX	matrix of the global components associated with the whole explanatory dataset (scores of the individuals)
lY	matrix of the components associated with the dependent dataset
Yc1	matrix of the variable loadings associated with the dependent dataset
Tli	matrix containing the partial components associated with each explanatory dataset
Tl1	matrix containing the normalized partial components associated with each explanatory dataset
Tfa	matrix containing the partial loadings associated with each explanatory dataset
cov2	squared covariance between lY and Tl1
Yco	matrix of the regression coefficients of the dependent dataset onto the global components
faX	matrix of the regression coefficients of the whole explanatory dataset onto the global components
XYcoef	list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset
bip	block importances for a given dimension
bipc	cumulated block importances for a given number of dimensions
vip	variable importances for a given dimension
vipc	cumulated variable importances for a given number of dimensions

Author(s)

Stéphanie Bougeard ([e-mail]) and Stéphane Dray ([e-mail])

References

Bougeard, S., Qannari, E.M. and Rose, N. (2011) Multiblock Redundancy Analysis: interpretation tools and application in epidemiology. Journal of Chemometrics, 23, 1-9

Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi: 10.18637/jss.v086.i01

See Also

mbpls, testdim.multiblock, randboot.multiblock

Examples

data(chickenk)
Mortality <- chickenk[[1]]
dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf =
FALSE)
ktabX.chick <- ktab.list.df(chickenk[2:5])
resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE,
option = "uniform", scannf = FALSE)
summary(resmbpcaiv.chick)
if(adegraphicsLoaded())
plot(resmbpcaiv.chick)

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.