Calculate equivalent vectors sensu Escoufier, that is, most significant variables from a multivariate data frame according to a principal component analysis (variables that are most correlated with the principal axes). This method is useful mainly for physical or chemical data where simply summarizing them with a PCA does not always gives easily interpretable principal axes.
escouf(x, level=1, verbose=TRUE)
# S3 method for escouf
print(x, ...)
# S3 method for escouf
summary(object, ...)
# S3 method for summary.escouf
print(x, ...)
# S3 method for escouf
plot(x, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
lcol=2, llty=2, diff=TRUE, dlab="RV' (units not shown)", dcol=4,
dlty=par("lty"), dpos=0.8, type="s", xlab="variables", ylab="RV",
main=paste("Escoufier's equivalent vectors for:",x$data), ...)
# S3 method for escouf
lines(x, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
col=2, lty=2, ...)
# S3 method for escouf
identify(x, lhorz=TRUE, lvert=TRUE, lvars=TRUE, col=2,
lty=2, ...)
# S3 method for escouf
extract(e, n, level=e$level, ...)An object of type 'escouf' is returned. It has methods print(), summary(), plot(), lines(), identify(), extract().
For escouf(), a data frame containing the variables to sort according to the Escoufier's method. For the other functions, an 'escouf' object
The level of correlation at which to stop calculation. By default level=1, the highest value, and all variables are sorted. Specify a value lower than one to speed up calculation. If you specify a too low values you will not be able to extract all significant variables (extraction level must be lower than calculation level). We advise you keep 0.95 < level < 1
Print calculation steps. This allows to control the percentage of calculation already achieved when computation takes a long time (that is, with many variables to sort)
An 'escouf' object returned by escouf
An 'escouf' object returned by escouf
If TRUE then an horizontal line indicating the extraction level is drawn
If TRUE then a vertical line separate the n extracted variables at left from the rest
If TRUE then the x-axis labels of the n extracted variables at left are printed in a different color to emphasize them
The color to use to draw the lines (lhorz=TRUE and lvert=TRUE) and the variables labels (lvars=TRUE) of the n extracted variables. By default, color 2 is used
The style used to draw the lines (lhorz=TRUE and lvert=TRUE). By default, lines are dashed
If TRUE then the RV' curve is also plotted (by default)
The label to use for the RV' curve. By default: "RV' (units not shown)"
The color to use for the RV' curve (by default, color 4 is used)
The type of graph to plot
the label of the x-axis
the label of the y-axis
the main title of the graph
The style for the RV' curve
The color to use to draw the lines (lhorz=TRUE and lvert=TRUE) and the variables labels (lvars=TRUE) of the n extracted variables. By default, color 2 is used
The style used to draw the lines (lhorz=TRUE and lvert=TRUE). By default, lines are dashed
The relative horizontal position of the label for the RV' curve. The default value of 0.8 means that the label is placed at 80% of the horizontal axis.Vertical position of the label is automatically determined
The number of variables to extract. If a value is given, it has the priority on level
additional parameters
Frédéric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean (phgrosjean@sciviews.org), Benjamin Planque (Benjamin.Planque@ifremer.fr), Jean-Marc Fromentin (Jean.Marc.Fromentin@ifremer.fr)
Since a large number of iterations is done, this function is slow with a large number of variables (more than 25-30)!
Cambon, J., 1974. Vecteur équivalent à un autre au sens des composantes principales. Application hydrologique. DEA de Mathématiques Appliquées, Université de Montpellier.
Escoufier, Y., 1970. Echantillonnage dans une population de variables aléatoires réelles. Pub. Inst. Stat. Univ. Paris, 19:1-47.
Jabaud, A., 1996. Cadre climatique et hydrobiologique du lac Léman. DEA d'Océanologie Biologique Paris.
abund
data(marbio)
marbio.esc <- escouf(marbio)
summary(marbio.esc)
plot(marbio.esc)
# The x-axis has short labels. For more info., enter:
marbio.esc$vr
# Define a level at which to extract most significant variables
marbio.esc$level <- 0.90
# Show it on the graph
lines(marbio.esc)
# This can also be done interactively on the plot using:
# marbio.esc$level <- identify(marbio.esc)
# Finally, extract most significant variables
marbio2 <- extract(marbio.esc)
names(marbio2)
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