binred_plot | R Documentation |

`binred_plot()`

provides a diagnostic of the fit of
the generalized linear model by "binning" the fitted and residual values
from the model and showing where they may fall outside 95% error bounds.

binred_plot(model, nbins, plot = TRUE)

`model` |
a fitted GLM model, assuming link is "logit" |

`nbins` |
number of "bins" for the calculation. Defaults to the rounded square root of the number of observations in the model in the absence of a user-specified override here. |

`plot` |
logical, defaults to TRUE. If TRUE, the function plots the binned residuals. If FALSE, the function returns a data frame of the binned residuals. |

The number of bins the user wants is arbitrary. Gelman and Hill (2007) say that, for larger data sets (n >= 100), the number of bins should be the rounded-down square root of the number of observations from the model. For models with a number of observations between 10 and 100, the number of bins should be 10. For models with fewer than 10 observations, the number of bins should be the rounded-down number of observations (divided by 2). The default is the rounded square root of the number of observations in the model. Be smart about what you want here.

`bindred_plot()`

returns a plot as a ggplot2 object, as
a default. The *y*-axis is the mean residuals of the particular bin. The
*x*-axis is the mean fitted values from the bin. Error bounds are 95%.
A LOESS smoother is overlaid as a solid blue line.

If `plot = FALSE`

, the function returns a data frame of the binned residuals
and a summary about whether the residuals are in the error bounds.

Steven V. Miller

M1 <- glm(vs ~ mpg + cyl + drat, data=mtcars, family=binomial(link="logit")) binred_plot(M1)

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