# predict.fk_ppr

From FKSUM v0.1.0
by David Hofmeyr

##### Predict method for class fk_ppr

Standard prediction method for regression models, specific to outputs from the fk_ppr() function. See help(fk_ppr) for more details on the model.

- Keywords
- file

##### Usage

```
# S3 method for fk_ppr
predict(object, Xtest = NULL, ...)
```

##### Arguments

- object
an object of class fk_ppr, output from the function of the same name.

- Xtest
(optional) matrix of test data on which predictions are to be made. If omitted then fitted values from training data are returned.

- ...
(optional) further arguments passed to or from other methods.

##### Value

A vector of predictions for Xtest.

##### 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
# }
```

*Documentation reproduced from package FKSUM, version 0.1.0, License: GPL*

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