ggConfidenceCurve: Confidence Curves
In userfriendlyscience: Quantitative Analysis Made Accessible

Description Usage Arguments Value Author(s) References See Also Examples

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.

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"))

`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.

A ggplot2 plot.

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

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

cohensdCI, pwr.cohensdCI, confIntR, pwr.confIntR

1 2	ggConfidenceCurve(metric='d', value = .5, n = 128); ggConfidenceCurve(metric='d', value = .5, conf.level = .95);

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2
Registered S3 methods overwritten by 'lme4':
  method                          from
  cooks.distance.influence.merMod car 
  influence.merMod                car 
  dfbeta.influence.merMod         car 
  dfbetas.influence.merMod        car 
Registered S3 methods overwritten by 'ufs':
  method                     from               
  grid.draw.ggProportionPlot userfriendlyscience
  pander.associationMatrix   userfriendlyscience
  pander.dataShape           userfriendlyscience
  pander.descr               userfriendlyscience
  pander.normalityAssessment userfriendlyscience
  print.CramersV             userfriendlyscience
  print.associationMatrix    userfriendlyscience
  print.confIntOmegaSq       userfriendlyscience
  print.confIntV             userfriendlyscience
  print.dataShape            userfriendlyscience
  print.descr                userfriendlyscience
  print.ggProportionPlot     userfriendlyscience
  print.meanConfInt          userfriendlyscience
  print.multiVarFreq         userfriendlyscience
  print.normalityAssessment  userfriendlyscience
  print.regrInfluential      userfriendlyscience
  print.scaleDiagnosis       userfriendlyscience
  print.scaleStructure       userfriendlyscience
  print.scatterMatrix        userfriendlyscience