# ggConfidenceCurve

##### Confidence Curves

Confidence curves are a way to show the confidence in an estimate computed from sample data. They are useful because they show all confidence levels simultaneously, thereby giving a good sense of the accuracy of the estimate, without forcing the researchers to make a more or less arbitrary choice for one confidence level.

- Keywords
- hplot

##### Usage

```
ggConfidenceCurve(metric = "d",
value = NULL,
n = NULL,
conf.level = NULL,
wRange = c(0.05, 0.8),
curveSize = 1,
curveColor = "black",
confRange = c(1e-04, 0.9999),
confLines = c(0.5, 0.8, 0.95, 0.99),
widthLines = c(min(wRange), 0.1, 0.2, 0.3, max(wRange)),
lineColor = brewer.pal(9, 'Set1'),
lineSize = 1,
lineAlpha = .5,
xlab = metric,
steps = 1000,
theme = theme_bw(),
gradient=NULL,
gradientWidth=.01,
outputFile = NULL,
outputWidth = 16,
outputHeight = 16,
ggsaveParams = list(units='cm',
dpi=300,
type="cairo"))
```

##### Arguments

- metric
The metric, currently only 'd' (Cohen's

*d*) and 'r' (Pearson's*r*) are implemented.- value
The value for which to create the confidence curve plot.

- n
The sample size for which to create the confidence curve plot. If

`n`

is specified, the y axis shows confidence levels (i.e. a conventional confidence curve is generated). If`n`

is set to`NULL`

, the y axis shows sample sizes. Either`n`

or`conf.level`

must be`NULL`

.- conf.level
The confidence level for which to create the confidence curve plot. If

`conf.level`

is specified, the y axis shows sample sizes. If`conf.level`

is set to`NULL`

, the y axis shows confidence levels (i.e. a conventional confidence curve is generated). Either`n`

or`conf.level`

must be`NULL`

.- wRange
The range of 'half-widths', or margins of error, to plot in the confidence curve plot if no sample size is specified (if

`n=NULL`

).- curveSize
The line size of the confidence curve line.

- curveColor
The color of the confidence curve line.

- confRange
The range of confidence levels to plot.

- confLines, widthLines
If a traditional confidence curve is generated, lines can be added to indicate the metric values corresponding to the lower and upper confidence interval bounds. For an inverse confidence curve, lines can be added to inficate the metric values and sample sizes corresponding to specific margins of error (or 'half-widths').

- lineColor
If confidence or 'interval width lines' lines are added (see

`confLines`

), this is the color in which they are drawn. Specify a vector (e.g.`brewer.pal(9, 'Set1')`

) to have the colors drawn in different colors for each confidence level or width.- lineSize
If confidence lines or 'interval width lines' are added (see

`confLines`

and`widthLines`

), these arguments specify the color and size in which they are drawn.- lineAlpha
The alpha value (transparency) of the confidence lines or 'interval width lines'.

- xlab
The label on the x axis.

- steps
The number of steps to use when generating the data for the confidence curves' more steps yield prettier, smoother curves, but take more time.

- theme
The

`ggplot`

theme to use.- gradient
Whether to use a gradient as background to make the confidence more explicit. This is experimental and pretty influential in terms of how the plot looks. The default gradient, used when passing

`TRUE`

, uses black as background color when the confidence is 0 percent, and white for 100 percent. If two colors are specified, these are used instead.- gradientWidth
If using a gradient, the width of the

`geom_tile`

geoms used to create the gradient.- 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.

##### Value

A `ggplot2`

plot.

##### References

Bender, R., Berg, G., & Zeeb, H. (2005). Tutorial: Using confidence curves in medical research. *Biometrical Journal*, 47(2), 237-247. http://doi.org/10.1002/bimj.200410104

Birnbaum, A. (1961). Confidence curves: An omnibus technique for estimation and testing statistical hypotheses. *Journal of the American Statistical Association*, 56(294), 246-249. http://doi.org/10.1080/01621459.1961.10482107

##### See Also

##### Examples

```
# NOT RUN {
ggConfidenceCurve(metric='d', value = .5, n = 128);
ggConfidenceCurve(metric='d', value = .5, conf.level = .95);
# }
```

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