FKSUM (version 0.1.4)

fk_ppr: Projection pursuit regression with local linear kernel smoother

Description

Generates a projection pursuit regression model for covariates X and response y

Usage

fk_ppr(X, y, nterms=1, hmult=1, betas=NULL, loss=NULL,
    dloss=NULL, initialisation="lm", type = "loc-lin")

Arguments

X

a numeric matrix (num_data x num_dimensions) of covariates.

y

a numeric vector of responses.

nterms

The number of terms in the additive regression model. The default is a single term.

betas

The coefficients in the expression of the kernel. See Hofmeyr (2019) for details. The default is the smooth order one kernel described in the paper.

hmult

The bandwidth in the kernel smoother is set to eigen(cov(X))values[1]^.5/nrow(X)^.2*hmult during projection pursuit. The final value is based on leave-one-out cross validation on the optimal projection.

loss

The (additive) loss function to be used. Allows for generalised responses by specifying an appropriate likelihood/deviance function for the loss. Note: loss is to be minimised, so deviance or negative log-likelihood would be appropriate. If specified then must be a function of y and hy (the fitted values, yhat), returning a vector of "errors"" which are added as the total loss. The default is the squared error.

dloss

The derivative of the loss function. Also takes arguments y and hy, and returns the vector of partial derivatives of the loss w.r.t. hy.

initialisation

Method use to initialise the projection vectors. Must be one of "lm" and "random", or a function taking only arguments X and y, and returning a vector of length ncol(X). The default is "lm", which is a simple linear model with a small ridge to ensure a solution. "random" uses random initialisation.

type

The type of regression estimator. Must be one of "loc-lin" for local linear regression, or "NW" for the Nadaray Watson, or local constant regression. The default is local linear regression.

Value

A named list with class fk_ppr containing

$mu

The estimated (global) mean of the response.

$mu_X

The vector of estimated means of the covariates.

$y

The responses given as argument to the function.

$X

The covariates given as argument to the function.

$hs

A vector of bandwidths used for each term in the model.

$vs

A matrix whose rows are the projection vectors.

$fitted

The fitted values on each projection. Note that these are based on the residuals used to fit that component of the model, and not the original y values. $fitted is used for prediction on test data.

$beta

The beta coefficients in the kernel formulation.

References

Friedman, J., and Stuetzle, W. (1981) "Projection pursuit regression." Journal of the American statistical Association 76.376.

Hofmeyr, D.P. (2019) "Fast exact evaluation of univariate kernel sums", IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

Examples

# NOT RUN {
op <- par(no.readonly = TRUE)

set.seed(2)

### Generate a set of data

X = matrix(rnorm(10000), ncol = 10)

### Generate some "true" projection vectors

beta1 = (runif(10)>.5)*rnorm(10)
beta2 = (runif(10)>.5)*rnorm(10)

### Generate responses, dependent on X%*%beta1 and X%*%beta2

y = 1 + X%*%beta1 + ((X%*%beta2)>2)*(X%*%beta2-2)*10
y = y + (X%*%beta1)*(X%*%beta2)/5 + rnorm(1000)

### Fit a PPR model with 2 terms on a sample of the data

train_ids = sample(1:1000, 500)

model = fk_ppr(X[train_ids,], y[train_ids], nterms = 2)

### Predict on left out data, and compute
### estimated coefficient of determination

yhat = predict(model, X[-train_ids,])

MSE = mean((yhat-y[-train_ids])^2)
Var = mean((y[-train_ids]-mean(y[-train_ids]))^2)

1-MSE/Var


#################################################

### Add some "outliers" in the training data and fit
### the model again, as well as one with an absolute loss

y[train_ids] = y[train_ids] + (runif(500)<.05)*(rnorm(500)*100)

model1 <- fk_ppr(X[train_ids,], y[train_ids], nterms = 2)

model2 <- fk_ppr(X[train_ids,], y[train_ids], nterms = 2,
    loss = function(y, hy) abs(y-hy),
    dloss = function(y, hy) sign(hy-y))

### Plot the resulting components in the model on the test data

par(mar = c(2, 2, 2, 2))
par(mfrow = c(2, 2))

plot(X[-train_ids,]%*%model1$vs[1,], y[-train_ids])
plot(X[-train_ids,]%*%model1$vs[2,], y[-train_ids])
plot(X[-train_ids,]%*%model2$vs[1,], y[-train_ids])
plot(X[-train_ids,]%*%model2$vs[2,], y[-train_ids])

par(op)

### Estimate comparative estimated coefficients of determination

MSE1 = mean((predict(model1, X[-train_ids,])-y[-train_ids])^2)
MSE2 = mean((predict(model2, X[-train_ids,])-y[-train_ids])^2)
Var = mean((y[-train_ids]-mean(y[-train_ids]))^2)


1-MSE1/Var
1-MSE2/Var
# }