ade4: s.multinom – R documentation

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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