logRegr: Userfriendly wrapper to do logistic regression in R
In userfriendlyscience: Quantitative Analysis Made Accessible

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

This function is meant as a userfriendly wrapper to approximate the way logistic regression is done in SPSS.

logRegr(formula,
        data = NULL,
        conf.level = 0.95,
        digits = 2,
        pvalueDigits = 3,
        crossTabs = TRUE,
        plot = FALSE,
        collinearity = FALSE,
        env = parent.frame(),
        predictionColor = viridis(3)[3],
        predictionAlpha = 0.5,
        predictionSize = 2,
        dataColor = viridis(3)[1],
        dataAlpha = 0.33,
        dataSize = 2,
        observedMeansColor = viridis(3)[2],
        binObservedMeans = 7,
        observedMeansSize = 2,
        observedMeansWidth = NULL,
        observedMeansAlpha = 0.5,
        theme = theme_bw())

`formula`	The formula, specified in the same way as for `glm` (which is used for the actual analysis).
`data`	Optionally, a dataset containing the variables in the formula (if not specified, the variables must exist in the environment specified in `env`.
`conf.level`	The confidence level for the confidence intervals.
`digits`	The number of digits used when printing the results.
`pvalueDigits`	The number of digits used when printing the p-values.
`crossTabs`	Whether to show cross tabulations of the correct predictions for the null model and the tested model, as well as the percentage of correct predictions.
`plot`	Whether to display the plot.
`collinearity`	Whether to show collinearity diagnostics.
`env`	If no dataframe is specified in `data`, use this argument to specify the environment holding the variables in the formula.
`predictionColor, dataColor, observedMeansColor`	The color of, respectively, the line and confidence interval showing the prediction; the points representing the observed data points; and the means based on the observed data.
`predictionAlpha, dataAlpha, observedMeansAlpha`	The alpha of, respectively, the confidence interval of the prediction; the points representing the observed data points; and the means based on the observed data (set to 0 to hide an element).
`predictionSize, dataSize, observedMeansSize`	The size of, respectively, the line of the prediction; the points representing the observed data points; and the means based on the observed data (set to 0 to hide an element).
`binObservedMeans`	Whether to bin the observed means; either FALSE or a single numeric value specifying the number of bins.
`observedMeansWidth`	The width of the lines of the observed means. If not specified (i.e. `NULL`), this is computed automatically and set to the length of the shortest interval between two successive points in the predictor data series (found using `findShortestInterval`.
`theme`	The theme used to display the plot.

This function

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, the cross tables, and the coefficients.

Ron Pat-El & Gjalt-Jorn Peters (both while at the Open University of the Netherlands)

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

regr and fanova for similar functions for linear regression and analysis of variance and glm for the regular interface for logistic regression.

### Simplest way to call logRegr
logRegr(data=mtcars, formula = vs ~ mpg);

### Also ordering a plot
logRegr(data=mtcars, formula = vs ~ mpg, plot=TRUE);

### Only use five bins
logRegr(data=mtcars, formula = vs ~ mpg, plot=TRUE, binObservedMeans=5);