# associationsDiamondPlot

##### A diamondplot with confidence intervals for associations

This function produces is a diamondplot that plots the confidence intervals for associations between a number of covariates and a criterion. It currently only supports the Pearson's r effect size metric; other effect sizes are converted to Pearson's r.

associationsToDiamondPlotDf is a helper function that produces the required dataframe.

- Keywords
- hplot

##### Usage

```
associationsDiamondPlot(dat, covariates, criteria,
labels = NULL,
criteriaLabels = NULL,
decreasing=NULL,
sortBy=NULL,
conf.level=.95,
criteriaColors = brewer.pal(8, 'Set1'),
criterionColor = 'black',
returnLayerOnly = FALSE,
esMetric = 'r',
multiAlpha=.33,
singleAlpha = 1,
showLegend=TRUE,
xlab="Effect size estimates",
ylab="",
theme=theme_bw(),
lineSize = 1,
outputFile = NULL,
outputWidth = 10,
outputHeight = 10,
ggsaveParams = list(units='cm',
dpi=300,
type="cairo"),
...)
associationsToDiamondPlotDf(dat, covariates, criterion, labels = NULL,
decreasing = NULL, conf.level = 0.95,
esMetric = "r")
```

##### Arguments

- dat
The dataframe containing the relevant variables.

- covariates
The covariates: the list of variables to associate to the criterion or criteria, usually the predictors.

- criteria, criterion
The criteria, usually the dependent variables; one criterion (one dependent variable) can also be specified of course. The helper function

`associationsToDiamondPlotDf`

always accepts only one criterion.- labels
The labels for the covariates, for example the questions that were used (as a character vector).

- criteriaLabels
The labels for the criteria (in the legend).

- decreasing
Whether to sort the covariates by the point estimate of the effect size of their association with the criterion. Use

`NULL`

to not sort at all,`TRUE`

to sort in descending order, and`FALSE`

to sort in ascending order.- sortBy
When specifying multiple criteria, this can be used to indicate by which criterion the items should be sorted (if they should be sorted).

- conf.level
The confidence of the confidence intervals.

- criteriaColors, criterionColor
The colors to use for the different associations can be specified in

`criteriaColors`

. This should be a vector of valid colors with at least as many elements as criteria are specified in`criteria`

. If only one criterion is specified, the color in`criterionColor`

is used.- returnLayerOnly
Whether to return the entire object that is generated, or just the resulting ggplot2 layer.

- esMetric
The effect size metric to plot - currently, only 'r' is supported, and other values will return an error.

- multiAlpha, singleAlpha
The transparency (alpha channel) value of the diamonds for each association can be specified in

`multiAlpha`

, and if only one criterion is specified, the alpha level of the diamonds can be specified in`singleAlpha`

.- showLegend
Whether to show the legend.

- xlab, ylab
The label to use for the x and y axes (for

`duoComparisonDiamondPlot`

, must be vectors of two elements). Use`NULL`

to not use a label.- theme
The

`ggplot`

theme to use.- lineSize
The thickness of the lines (the diamonds' strokes).

- outputFile
A file to which to save the plot.

- outputWidth, outputHeight
Width and height of saved plot (specified in centimeters by default, see

`ggsaveParams`

).- ggsaveParams
Parameters to pass to ggsave when saving the plot.

- …
Any additional arguments are passed to

`diamondPlot`

and eventually to`ggDiamondLayer`

.

##### Details

This function can be used to quickly plot multiple confidence intervals.

##### Value

A plot.

##### See Also

##### Examples

```
# NOT RUN {
### Simple diamond plot with correlations
### and their confidence intervals
associationsDiamondPlot(mtcars,
covariates=c('cyl', 'hp', 'drat', 'wt',
'am', 'gear', 'vs', 'carb', 'qsec'),
criteria='mpg');
### Same diamond plot, but now with two criteria,
### and colouring the diamonds based on the
### correlation point estimates: a gradient
### is created where red is used for -1,
### green for 1 and blue for 0.
associationsDiamondPlot(mtcars,
covariates=c('cyl', 'hp', 'drat', 'wt',
'am', 'gear', 'vs', 'carb', 'qsec'),
criteria=c('mpg', 'disp'),
generateColors=c("red", "blue", "green"),
fullColorRange=c(-1, 1));
# }
```

*Documentation reproduced from package userfriendlyscience, version 0.7.2, License: GPL (>= 3)*