piecewiseRegr: Piecewise regression analysis
In userfriendlyscience: Quantitative Analysis Made Accessible

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

This function conducts a piecewise regression analysis and shows a plot illustrating the results. The function enables easy customization of the main plot elements and easy saving of the plot with anti-aliasing.

piecewiseRegr(data,
              timeVar = 1,
              yVar = 2,
              phaseVar = NULL,
              baselineMeasurements = NULL,
              robust = FALSE,
              digits = 2,
              colors = list(pre = viridis(4)[1],
                            post = viridis(4)[4],
                            diff = viridis(4)[3],
                            intervention = viridis(4)[2],
                            points = "black"),
              theme = theme_minimal(),
              pointSize = 2,
              pointAlpha = 1,
              lineSize = 1,
              yRange=NULL,
              yBreaks = NULL,
              showPlot = TRUE,
              plotLabs = NULL,
              outputFile = NULL,
              outputWidth = 16,
              outputHeight = 16,
              ggsaveParams = list(units = "cm",
                                  dpi = 300,
                                  type = "cairo"))

`data`	The dataframe containing the variables for the analysis.
`timeVar`	The name of the variable containing the measurement moments (or an index of measurement moments). An index can also be specified, and assumed to be 1 if omitted.
`yVar`	The name of the dependent variable. An index can also be specified, and assumed to be 2 if omitted.
`phaseVar`	The variable containing the phase of each measurement. Note that this normally should only have two possible values.
`baselineMeasurements`	If no phaseVar is specified, `baselineMeasurements` can be used to specify the number of baseline measurements, which is then used to construct the `phaseVar` dummy variable.
`robust`	Whether to use normal or robust linear regression.
`digits`	The number of digits to show in the results.
`colors`	The colors to use for the different plot elements.
`theme`	The theme to use in the plot.
`pointSize,lineSize`	The sizes of points and lines in the plot.
`yRange`	This can be used to manually specify the possible values that the dependent variable can take. If not provided, the observed range of the dependent variable values is used instead.
`yBreaks`	If `NULL`, the `pretty` function is used to estimate the best breaks for the Y axis. If a value is supplied, this value is used as the size of intervals between the (floored) minimum and (ceilinged) maximum of `yRange` (e.g. if `yBreaks` is 1, a break point every integer; if 2 and the minimum is 1 and the maximum is 7, breaks at 1, 3, 5 and 7; etc).
`pointAlpha`	The alpha channel (transparency, or rather, 'opaqueness') of the points.
`showPlot`	Whether to show the plot or not.
`plotLabs`	A list with arguments to the `ggplot2` `labs` function, which can be used to conveniently set plot labels.
`outputFile`	If not `NULL`, the path and filename specifying where to save the plot.
`outputWidth, outputHeight`	The dimensions of the plot when saving it (in units specified in `ggsaveParams`).
`ggsaveParams`	The parameters to use when saving the plot, passed on to `ggsave`.

Mainly, this function prints its results, but it also returns them in an object containing three lists:

`input`	The arguments specified when calling the function
`intermediate`	Intermediat objects and values
`output`	The results such as the plot.

Peter Verboon & Gjalt-Jorn Peters (both at the Open University of the Netherlands)

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

Verboon, P. & Peters, G.-J. Y. (2018) Applying the generalised logistic model in single case designs: modelling treatment-induced shifts. PsyArXiv https://doi.org/10.17605/osf.io/ad5eh

genlog

### Load dataset
data(Singh);

### Extract Jason
dat <- Singh[Singh$tier==1, ];

### Conduct piecewise regression analysis
piecewiseRegr(dat,
              timeVar='time',
              yVar='score_physical',
              phaseVar='phase');

### Pretend treatment started between measurements
### 5 and 6
piecewiseRegr(dat,
              timeVar='time',
              yVar='score_physical',
              baselineMeasurements=5);