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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