# predict.mave: Make predictions based on the dimension reduction space In MAVE: Methods for Dimension Reduction

## Description

This method make predictions based the reduced dimension of data using `mars` function.

## Usage

 ```1 2 3 4 5``` ```## S3 method for class 'mave' predict(object, newx, dim, ...) ## S3 method for class 'mave.dim' predict(object, newx, dim = "dim.min", ...) ```

## Arguments

 `object` the object of class 'mave' `newx` Matrix of the new data to be predicted `dim` the dimension of central space or central mean space. The matrix of the original data will be multiplied by the matrix of dimension reduction directions of given dimension. Then the prediction will be made based on the data of given dimensions. The value of dim should be given when the class of the argument dr is mave. When the class of the argument dr is mave.dim and dim is not given, the function will return the basis matrix of CS or CMS of dimension selected by `mave.dim` `...` further arguments passed to `mars` function such as degree.

## Value

the prediced response of the new data

`mave` for computing the dimension reduction space and `mave.dim` for estimating the dimension of the dimension reduction space
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```X = matrix(rnorm(10000),1000,10) beta1 = as.matrix(c(1,1,1,1,0,0,0,0,0,0)) beta2 = as.matrix(c(0,0,0,1,1,1,1,1,0,0)) err = as.matrix(rnorm(1000)) Y = X%*%beta1+X%*%beta2+err train = sample(1:1000)[1:500] x.train = X[train,] y.train = as.matrix(Y[train]) x.test = X[-train,] y.test = as.matrix(Y[-train]) dr = mave(y.train~x.train, method = 'meanopg') yp = predict(dr,x.test,dim=3,degree=2) #mean error mean((yp-y.test)^2) dr.dim = mave.dim(dr) yp = predict(dr.dim,x.test,degree=2) #mean error mean((yp-y.test)^2) ```