Description Details Author(s) References Examples

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

`sample.quad`

Set up a regular grid of quadrature points

`env.var`

Interpolate environmental data to species presence locations

`ppmdat`

Calculate observation weights and set up design matrix for fitting

`point.interactions`

Calculate interpoint interactions for fitting area-interaction models

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

`ppmlasso`

Fit a regularisation path of point process models

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# 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)
# 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)
# 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)
``` |

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