# predict.foba

From foba v0.1
0th

Percentile

##### Make predictions or extract coefficients from a fitted foba model

foba() returns a path of variable addition and deletion. predict.foba() allows one to extract a prediction, or coefficients at any desired sparsity level.

Keywords
methods, regression
##### Usage
predict.foba(object, newx, k, type=c("fit","coefficients"),...)
##### Arguments
object
A fitted foba object.
newx
If type="fit", then newx should be the x values at which the fit is required. If type="coefficients", then newx can be omitted.
k
The sparsity level. That is, the number of selected variables for the fitted model.
type
If type="fit", predict returns the fitted values. If type="coefficients", predict returns the coefficients. Abbreviations allowed.
...
further arguments passed to or from other methods.
##### Details

FoBa for least squares regression is described in [Tong Zhang (2008)]. This implementation supports ridge regression.

##### Value

Return either a "coefficients" object or a "fitted value" object, at the desired sparsity level.A coefficients object is a list containing the following components:
coefficients
coefficients of ridge regression solution using selected.variables
intercept
the intercept value
selected.variables
variables with non-zero coefficients
A "fitted value" object contains the following additional component:
fit
the predicted response for the data newx

##### References

Tong Zhang (2008) "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations", Rutgers Technical Report (long version).

Tong Zhang (2008) "Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models", NIPS'08 (short version).

print.foba and foba

• predict.foba
##### Examples
data(boston)

model <- foba(boston$x,boston$y,s=20,nu=0.9)

### make predictions at the values in x, at sparsity level 5

py <- predict(model, boston$x, k=5, type="fit") print(paste("mean squared error =", mean((py$fit-boston$y)^2))) ### extract the coefficient vector at sparsity level 5 coef <- predict(model, k=5, type="coef") print("top five variables:") coef$selected.variables


Documentation reproduced from package foba, version 0.1, License: GPL (>= 2)

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