ppmlasso-package | R Documentation |

This package contains tools to fit point process models with sequences of LASSO penalties ("regularisation paths"). Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.

The key functions in ppmlasso are as follows:

**Useful pre-analysis functions:**

`findRes`

Determine the optimal spatial resolution at which to perform analysis

`getEnvVar`

Interpolate environmental data to species presence locations

`griddify`

Ensure a matrix of environmental data is on a rectangular grid

`ppmdat`

Calculate observation weights and set up design matrix for fitting

`pointInteractions`

Calculate interpoint interactions for fitting area-interaction models

`sampleQuad`

Set up a regular grid of quadrature points

**Creating regularisation paths of point process models:**

`ppmlasso`

Fit a regularisation path of point process models

`plotFit`

Plot the fitted intensity of a

`ppmlasso`

object`plotPath`

Plot the regularisation path of a

`ppmlasso`

object`print.ppmlasso`

Print output from a

`ppmlasso`

object`predict.ppmlasso`

Make predictions from a fitted point process model to new data

**Checking assumptions:**

`diagnose.ppmlasso`

Create diagnostic residual plots of

`ppmlasso`

object`envelope.ppmlasso`

Create simulation envelope for goodness-of-fit checks on a

`ppmlasso`

object

Ian W. Renner

Maintainer: Ian W. Renner <Ian.Renner@newcastle.edu.au>

Renner, I.W. & Warton, D.I. (2013). Equivalence of MAXENT and Poisson point process models for
species distribution modeling in ecology *Biometrics* **69**, 274-281.

Renner, I.W. et al (2015). Point process models for presence-only analysis. *Methods in Ecology and Evolution* **6**, 366-379.

Renner, I.W., Warton, D.I., & Hui, F.K.C. (2021). What is the effective sample size of a spatial point process? *Australian & New Zealand Journal of Statistics* **63**, 144-158.

# Fit a regularisation path of Poisson point process models 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, raw = TRUE) ppm.fit = ppmlasso(ppm.form, sp.xy = sub.euc, env.grid = sub.env, sp.scale = 1, n.fits = 20, writefile = FALSE) # Fit a regularisation path of area-interaction models data(BlueMountains) ai.form = ~ poly(FC, TMP_MIN, TMP_MAX, RAIN_ANN, degree = 2, raw = TRUE) ai.fit = ppmlasso(ai.form, sp.xy = sub.euc, env.grid = sub.env, sp.scale = 1, family = "area.inter", r = 2, availability = BlueMountains$availability, n.fits = 20, writefile = FALSE) # Print a ppmlasso object print(ppm.fit, out = "model") # Residual plot of a ppmlasso object diagnose(ppm.fit, which = "smooth", type = "Pearson") # Make predictions pred.mu = predict(ppm.fit, newdata = sub.env) # Plot the intensity from a fitted ppmlasso object plotFit(ppm.fit) # Plot the regularisation path from a fitted ppmlasso object plotPath(ppm.fit)

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