hnp.glm {asuR} | R Documentation |
In linear regression we inspect the normal quantile plot to check the normality of residuals. For generalized linear models we do not expect that the residuals are normally distributed. Nevertheless the half-normal plot can help us to find outliers. Outliers can be detected as points off the trend.
## S3 method for class 'glm': hnp(mymodel, id= c("all", "none"), ...)
mymodel |
an object of class glm , usually, a result of a
call to the function glm . |
id |
a character string or numeric value; in which panel should it be possible to interactively identify points |
... |
further arguments |
***expected pattern:
all points are scattered along a line
***question:
are there points of the trend? otherwise
1. Is the data point correct?
(keep attention that this is not a quantile–quantile plot where you check for normality!)
A plot and a vector with identified values (corresponding to the row nomber in the original data).
#