fpca
Focused Principal Components Analysis
Graphical representation similar to a principal components analysis but adapted to data structured with dependent/independent variables
 Keywords
 multivariate
Usage
fpca(formula=NULL,y=NULL, x=NULL, data, cx=0.75, pvalues="No", partial="Yes", input="data", contraction="No", sample.size=1)
Arguments
 formula
 "model" formula, of the form y ~ x
 y
 column number of the dependent variable
 x
 column numbers of the independent (explanatory) variables
 data
 name of datafile
 cx
 size of the lettering (0.75 by default, 1 for bigger letters, 0.5 for smaller)
 pvalues
 vector of prespecified pvalues (pvalues="No" by default) (see below)
 partial
 partial="Yes" by default, corresponds to the original method (see below)
 input
 input="Cor" for a correlation matrix (input="data" by default)
 contraction
 change the aspect of the diagram, contraction="Yes" is convenient for large data set (contraction="No" by default)
 sample.size
 to be specified if input="Cor"
Details
This representation is close to a Principal Components Analysis (PCA). Contrary to PCA, correlations between the dependent variable and the other variables are represented faithfully. The relationships between non dependent variables are interpreted like in a PCA: correlated variables are close or diametrically opposite (for negative correlations), independent variables make a right angle with the origin. The focus on the dependent variable leads formally to a partialisation of the correlations between the non dependent variables by the dependent variable (see reference). To avoid this partialisation, the option partial="No" can be used. It may be interesting to represent graphically the strength of association between the dependent variable and the other variables using p values coming from a model. A vector of pvalue may be specified in this case.
Value

A plot (q plots in fact).
References
Falissard B, Focused Principal Components Analysis: looking at a correlation matrix with a particular interest in a given variable. Journal of Computational and Graphical Statistics (1999), 8(4): 906912.
Examples
data(sleep)
fpca(Paradoxical.sleep~Body.weight+Brain.weight+Slow.wave.sleep+Maximum.life.span+Gestation.time+Predation+Sleep.exposure+Danger,data=sleep)
fpca(y="Paradoxical.sleep",x=c("Body.weight","Brain.weight","Slow.wave.sleep","Maximum.life.span","Gestation.time","Predation","Sleep.exposure","Danger"),data=sleep)
## focused PCA of the duration of paradoxical sleep (dreams, 5th column)
## against constitutional variables in mammals (columns 2, 3, 4, 7, 8, 9, 10, 11).
## Variables inside the red cercle are significantly correlated
## to the dependent variable with p<0.05.
## Green variables are positively correlated to the dependent variable,
## yellow variables are negatively correlated.
## There are three clear clusters of independent variables.
corsleep < as.data.frame(cor(sleep[,2:11],use="pairwise.complete.obs"))
fpca(Paradoxical.sleep~Body.weight+Brain.weight+Slow.wave.sleep+Maximum.life.span+Gestation.time+Predation+Sleep.exposure+Danger,
data=corsleep,input="Cor",sample.size=60)
## when missing data are numerous, the representation of a pairwise correlation
## matrix may be preferred (even if mathematical properties are not so good...)
numer < c(2:4,7:11)
l < length(numer)
resu < vector(length=l)
for(i in 1:l)
{
int < sleep[,numer[i]]
mod < lm(sleep$Paradoxical.sleep~int)
resu[i] < summary(mod)[[4]][2,4]*sign(summary(mod)[[4]][2,1])
}
fpca(Paradoxical.sleep~Body.weight+Brain.weight+Slow.wave.sleep+Maximum.life.span+Gestation.time+Predation+Sleep.exposure+Danger,
data=sleep,pvalues=resu)
## A representation with p values
## When input="Cor" or pvalues="Yes" partial is turned to "No"
mod < lm(sleep$Paradoxical.sleep~sleep$Body.weight+sleep$Brain.weight+
sleep$Slow.wave.sleep+sleep$Maximum.life.span+sleep$Gestation.time+
sleep$Predation+sleep$Sleep.exposure+sleep$Danger)
resu < summary(mod)[[4]][2:9,4]*sign(summary(mod)[[4]][2:9,1])
fpca(Paradoxical.sleep~Body.weight+Brain.weight+Slow.wave.sleep+Maximum.life.span+Gestation.time+Predation+Sleep.exposure+Danger,
data=sleep,pvalues=resu)
## A representation with p values which come from a multiple linear model
## (here results are difficult to interpret)