# sco.distri: Representation by mean- standard deviation of a set of weight... In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

 sco.distri R Documentation

## Representation by mean- standard deviation of a set of weight distributions on a numeric score

### Description

represents the mean- standard deviation of a set of weight distributions on a numeric score.

### Usage

```sco.distri(score, df, y.rank = TRUE, csize = 1, labels = names(df),
clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75,
include.origin = TRUE, origin = 0, sub = NULL, csub = 1)
```

### Arguments

 `score` a numeric vector `df` a data frame with only positive or null values `y.rank` a logical value indicating whether the means should be classified in ascending order `csize` an integer indicating the size segment `labels` a vector of strings of characters for the labels of the variables `clabel` if not NULL, a character size for the labels, used with `par("cex")*clabel` `xlim` the ranges to be encompassed by the x axis, if NULL they are computed `grid` a logical value indicating whether the scale vertical lines should be drawn `cgrid` a character size, parameter used with `par("cex")*cgrid` to indicate the mesh of the scale `include.origin` a logical value indicating whether the point "origin" should be belonged to the graph space `origin` the fixed point in the graph space, for example c(0,0) the origin axes `sub` a string of characters to be inserted as legend `csub` a character size for the legend, used with `par("cex")*csub`

### Value

returns an invisible data.frame with means and variances

Daniel Chessel

### Examples

```if(!adegraphicsLoaded()) {
w <- seq(-1, 1, le = 200)
distri <- data.frame(lapply(1:50,
function(x) sample((200:1)) * ((w >= (- x / 50)) & (w <= x / 50))))
names(distri) <- paste("w", 1:50, sep = "")
par(mfrow = c(1, 2))
sco.distri(w, distri, csi = 1.5)
sco.distri(w, distri, y.rank = FALSE, csi = 1.5)
par(mfrow = c(1, 1))

data(rpjdl)
coa2 <- dudi.coa(rpjdl\$fau, FALSE)
sco.distri(coa2\$li[, 1], rpjdl\$fau, lab = rpjdl\$frlab, clab = 0.8)

data(doubs)
par(mfrow = c(2, 2))
poi.coa <- dudi.coa(doubs\$fish, scann = FALSE)
sco.distri(poi.coa\$l1[, 1], doubs\$fish)
poi.nsc <- dudi.nsc(doubs\$fish, scann = FALSE)
sco.distri(poi.nsc\$l1[, 1], doubs\$fish)
s.label(poi.coa\$l1)
s.label(poi.nsc\$l1)

data(rpjdl)
fau.coa <- dudi.coa(rpjdl\$fau, scann = FALSE)
sco.distri(fau.coa\$l1[,1], rpjdl\$fau)
fau.nsc <- dudi.nsc(rpjdl\$fau, scann = FALSE)
sco.distri(fau.nsc\$l1[,1], rpjdl\$fau)
s.label(fau.coa\$l1)
s.label(fau.nsc\$l1)

par(mfrow = c(1, 1))
}
```

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