This function is analogous to the
diagnose.ppm function of the
## S3 method for class 'ppmlasso' diagnose(object, ...)
A fitted regularisation path of point process models. The diagnostic plots will be created for the model that optimises the given criterion.
Other arguments for producing diagnostic plots, as given by the
See the help file for
diagnose.ppm in the
spatstat package for further details of diagnostic plots.
Ian W. Renner
Baddeley, A.J. & Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12, 1-42.
envelope.ppmlasso, for other goodness-of-fit functions inherited from
data(BlueMountains) sub.env = BlueMountains$env[BlueMountains$env$Y > 6270 & BlueMountains$env$X > 300,] sub.euc = BlueMountains$eucalypt[BlueMountains$eucalypt$Y > 6270 & BlueMountains$eucalypt$X > 300,] ppm.form = ~poly(FC, TMP_MIN, TMP_MAX, RAIN_ANN, degree = 2) + poly(D_MAIN_RDS, D_URBAN, degree = 2) ppm.fit = ppmlasso(ppm.form, sp.xy = sub.euc, env.grid = sub.env, sp.scale = 1, n.fits = 20, writefile = FALSE) diagnose(ppm.fit, which = "smooth", type = "Pearson")
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