## 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

```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\$poi, scann = FALSE)
sco.distri(poi.coa\$l1[,1], doubs\$poi)
poi.nsc <- dudi.nsc(doubs\$poi, scann = FALSE)
sco.distri(poi.nsc\$l1[,1], doubs\$poi)
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))
```

### Worked out examples

```
> ### Name: sco.distri
> ### Title: Representation by mean- standard deviation of a set of weight
> ###   distributions on a numeric score
> ### Aliases: sco.distri
> ### Keywords: multivariate hplot
>
> ### ** Examples
>
> 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\$poi, scann = FALSE)
> sco.distri(poi.coa\$l1[,1], doubs\$poi)
```
```> poi.nsc <- dudi.nsc(doubs\$poi, scann = FALSE)
> sco.distri(poi.nsc\$l1[,1], doubs\$poi)
```
```> 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))
>
>
>
>
```