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s.multinom

Graph of frequency profiles (useful for instance in genetic)


Description

The main purpose of this function is to draw categories using scores and profiles by their gravity center. Confidence intervals of the average position (issued from a multinomial distribution) can be superimposed.

Usage

s.multinom(dfxy, dfrowprof, translate = FALSE, xax = 1, yax = 2, 
   labelcat = row.names(dfxy), clabelcat = 1, cpointcat = if (clabelcat == 0) 2 else 0,
   labelrowprof = row.names(dfrowprof), clabelrowprof = 0.75, 
   cpointrowprof = if (clabelrowprof == 0) 2 else 0, pchrowprof = 20, 
   coulrowprof = grey(0.8), proba = 0.95, n.sample = apply(dfrowprof, 1, sum),
   axesell = TRUE, ...)

Arguments

dfxy

dfxy is a data frame containing at least two numerical variables. The rows of dfxy are categories such as 1,2 and 3 in the triangular plot.

dfrowprof

dfrowprof is a data frame whose the columns are the rows of dfxy. The rows of dfxy are profiles or frequency distributions on the categories. The column number of dfrowprof must be equal to the row number of dfxy. row.names(dfxy) and names(dfrowprof) must be identical.

translate

a logical value indicating whether the plot should be translated(TRUE) or not. The origin becomes the gravity center weighted by profiles.

xax

the column number of dfxy for the x-axis

yax

the column number of dfxy for the y-axis

labelcat

a vector of strings of characters for the labels of categories

clabelcat

an integer specifying the character size for the labels of categories, used with par("cex")*clabelcat

cpointcat

an integer specifying the character size for the points showing the categories, used with par("cex")*cpointcat

labelrowprof

a vector of strings of characters for the labels of profiles (rows of dfrowprof)

clabelrowprof

an integer specifying the character size for the labels of profiles used with par("cex")*clabelrowprof

cpointrowprof

an integer specifying the character size for the points representative of the profiles used with par("cex")*cpointrowprof

pchrowprof

either an integer specifying a symbol or a single character to be used for the profile labels

coulrowprof

a vector of colors used for ellipses, possibly recycled

proba

a value lying between 0.500 and 0.999 to draw a confidence interval

n.sample

a vector containing the sample size, possibly recycled. Used n.sample = 0 if the profiles are not issued from a multinomial distribution and that confidence intervals have no sense.

axesell

a logical value indicating whether the ellipse axes should be drawn

...

further arguments passed from the s.label for the initial scatter plot.

Value

Returns in a hidden way a list of three components :

tra

a vector with two values giving the done original translation.

ell

a matrix, with 5 columns and for rows the number of profiles, giving the means, the variances and the covariance of the profile for the used numerical codes (column of dfxy)

call

the matched call

Author(s)

Daniel Chessel

Examples

par(mfrow = c(2,2))
par(mar = c(0.1,0.1,0.1,0.1))
proba <- matrix(c(0.49,0.47,0.04,0.4,0.3,0.3,0.05,0.05,0.9,0.05,0.7,0.25), ncol = 3, byrow = TRUE)
proba.df <- as.data.frame (proba)
names(proba.df) <- c("A","B","C") ; row.names(proba.df) <- c("P1","P2","P3","P4")
w.proba <- triangle.plot(proba.df, clab = 2, show = FALSE)
box()

w.tri = data.frame(x = c(-sqrt(1/2),sqrt(1/2),0), y = c(-1/sqrt(6),-1/sqrt(6),2/sqrt(6)))
L3 <- c("A","B","C")
row.names(w.tri) <- L3
s.multinom(w.tri, proba.df, n.sample = 0, coulrowprof = "black", clabelrowprof = 1.5)
s.multinom(w.tri, proba.df, n.sample = 30, coul = palette()[5])
s.multinom(w.tri, proba.df, n.sample = 60, coul = palette()[6], add.p = TRUE)
s.multinom(w.tri, proba.df, n.sample = 120, coul = grey(0.8), add.p = TRUE)

print(s.multinom(w.tri, proba.df[-3,], n.sample = 0, translate = TRUE)$tra)

ade4

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

v1.7-16
GPL (>= 2)
Authors
Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Borcard, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.
Initial release

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