# Visualize Contributions and cos2

Source:`R/AllGenerics.R`

, `R/viz_contributions.R`

, `R/viz_cos2.R`

`viz_contributions.Rd`

Plots contributions histogram and \(cos^2\) scatterplot.

## Usage

```
viz_contributions(x, ...)
viz_cos2(x, ...)
# S4 method for class 'MultivariateAnalysis'
viz_contributions(
x,
...,
margin = 2,
axes = 1,
sort = TRUE,
decreasing = TRUE,
limit = 10,
horiz = FALSE,
col = "grey90",
border = "grey10"
)
# S4 method for class 'MultivariateAnalysis'
viz_cos2(
x,
...,
margin = 2,
axes = c(1, 2),
active = TRUE,
sup = TRUE,
sort = TRUE,
decreasing = TRUE,
limit = 10,
horiz = FALSE,
col = "grey90",
border = "grey10"
)
```

## Arguments

- x
- ...
Extra parameters to be passed to

`graphics::barplot()`

.- margin
A length-one

`numeric`

vector giving the subscript which the data will be returned:`1`

indicates individuals/rows (the default),`2`

indicates variables/columns.- axes
A

`numeric`

vector giving the dimensions to be plotted.- sort
A

`logical`

scalar: should the data be sorted?- decreasing
A

`logical`

scalar: should the sort order be decreasing? Only used if`sort`

is`TRUE`

.- limit
An

`integer`

specifying the number of top elements to be displayed.- horiz
A

`logical`

scalar: should the bars be drawn horizontally with the first at the bottom?- col, border
A

`character`

string specifying the bars infilling and border colors.- active
A

`logical`

scalar: should the active observations be plotted?- sup
A

`logical`

scalar: should the supplementary observations be plotted?

## Value

`viz_contributions()`

and `viz_cos2()`

are called for their side-effects:
they result in a graphic being displayed. Invisibly return `x`

.

## Details

The red dashed line indicates the expected average contribution (variables with a contribution larger than this cutoff can be considered as important in contributing to the component).

## See also

Other plot methods:
`biplot()`

,
`plot()`

,
`screeplot()`

,
`viz_individuals()`

,
`viz_variables()`

,
`viz_wrap`

,
`wrap`

## Examples

```
## Load data
data("iris")
## Compute principal components analysis
X <- pca(iris, scale = TRUE)
#> 1 qualitative variable was removed: Species.
## Get row contributions
head(get_contributions(X, margin = 1))
#> F1 F2 F3
#> 1 1.1715796 0.16806554 0.074085470
#> 2 0.9891845 0.33146674 0.250034006
#> 3 1.2768164 0.08526419 0.008875320
#> 4 1.2077372 0.26029781 0.037858004
#> 5 1.3046313 0.30516562 0.001125175
#> 6 0.9841236 1.61748779 0.003303827
## Plot contributions
viz_contributions(X, axes = 1)
## Plot cos2
viz_cos2(X)
```