ppmlasso-package: PPM-LASSO: Point process models with LASSO penalties

Description Details Author(s) References Examples

Description

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.

Details

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

Author(s)

Ian W. Renner

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

References

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.

Examples

 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)

ppmlasso documentation built on May 2, 2019, 8:20 a.m.