gppr: Generalised projection pursuit regression

In gsg: Calculation of selection coefficients

Description

Performs generalized projection pursuit regression for binary and Poisson response variables. Wraps ppr from package stats in an iterative re-weighting algorithm. Included in package gsg to support inference of the form of natural selection, and for inference of selection gradients from projection pursuit regression-based inference of fitness functions.

Usage

gppr(y, xterms, data, nterms = 1, tol = 0.001, gcvpen = 1, 
      maxit = 50, family = 'binomial', max.terms = 2)

Arguments

y	response variable, e.g., absolute fitness
xterms	names of predictor variables in data
data	a data frame containing columns with names matching xterms
nterms	number of terms to include in the final model
tol	maximum sum of relative changes in linear predictors between iterations
gcvpen	penalty used for each degree of freedom in GCV selection for spline ridge functions
maxit	maximum number if iterations of the iterative re-weighting procedure
family	distribution of the response variable, currently 'binomial' and 'Poisson' are supported
max.terms	the maximum number of terms to choose from when building the model
...	other parameters passed to ppr

Value

A gppr object, which contains a ppr object, plus information information pertaining to the iterative re-weighting procedure. Most of these repeat arguments passed to gppr(), also included are:

iterations	the number of iterations of the iterative re-weighting procedure that were performed
f	the formula passed to ppr, constructed from xterms

Author(s)

Michael Morrissey [e-mail]

References

M.B. Morrissey. in prep.

See Also

predict.gppr, ppr,gppr.gradients

Examples

# simulated data (two traits, stabilizing selection on trait 1)
n<-250
z<-cbind(rnorm(n,0,1),rnorm(n,0,1))
W<-rpois(n,exp(2-0.6*z[,1]^2))
d<-as.data.frame(cbind(W,z))
names(d)<-c("W","z1","z2")

fit.func<-gppr(y="W",xterms=c("z1","z2"),data=d,family="poisson",
   nterms=2,max.terms=2)

# direction of axes, and their loadings
fit.func$alpha
fit.func$beta

## not run - plot ridge functions (linear predictor scale)
# par(mfrow=c(1,2))
# plot(fit.func)

Example output

NULL
NULL

gsg documentation built on May 2, 2019, 9:37 a.m.