earth (version 1.2-2)

model.matrix.earth: Get the 'earth' basis matrix

Description

Get the basis matrix of an earth object.

Usage

## S3 method for class 'earth':
model.matrix(object, x, subset, which.terms, \dots)

Arguments

object

An earth object. This is the only required argument.

x

An input matrix with the same number of columns as the x matrix used to construct the original earth object. Default is NULL, meaning use the original x matrix after taki

subset

Which rows to use in x. Default is NULL, meaning use all of x.

which.terms

Which terms to use. Default is NULL, meaning use object$which.terms.

...

Unused, but provided for generic/method consistency.

Value

• A bx matrix of the same form returned by earth. If x, subset, and which.terms are all NULL, this function returns the object's bx. In this case, it is perhaps easier to simply use object$bx. The format of bx is described in earth. The basis matrix bx can be used, for example, as the input matrix to lm or glm as shown below.

See Also

earth, get.nterms.per.degree, get.nused.preds.per.subset

Examples

data(trees)
a <- earth(Volume ~ ., data = trees)
summary(a, decomp = "none") # "none" to print terms in same seq as a.lm below

# yields:
#    Call:
#    earth(formula = Volume ~ ., data = trees)
#    
#    Expression:
#      26.64062 
#      +  6.049141 * pmax(0,  Girth -   13.7) 
#      -  3.202820 * pmax(0,   13.7 -  Girth) 
#      + 0.6079078 * pmax(0, Height -     75) 
#    
#    Number of cases: 31
#    Selected 4 of 5 terms, and 2 of 2 predictors
#    Number of terms at each degree of interaction: 1 3 (additive model)
#    GCV: 10.56081     RSS: 196.2266     GRSq: 0.962176    RSq: 0.9757927

bx <- model.matrix(a)               # equivalent to bx <- a$bx
a.lm <- lm(trees$Volume ~ bx[,-1])  # -1 to drop intercept
summary(a.lm)                       # yields same coeffs as above summary
                                    # displayed p values are not meaningful
# yields:
#    Call:
#    lm(formula = trees$Volume ~ bx[, -1])
#    
#    Residuals:
#          Min        1Q    Median        3Q       Max 
#    -4.691469 -1.987719  0.008402  1.421682  4.820064 
#    
#    Coefficients:
#                          Estimate Std. Error t value Pr(>|t|)
#    (Intercept)            26.6406     1.0019  26.590  < 2e-16
#    bx[, -1]h(Girth-13.7)   6.0491     0.3344  18.087  < 2e-16
#    bx[, -1]h(13.7-Girth)  -3.2028     0.3502  -9.146  9.3e-10
#    bx[, -1]h(Height-75)    0.6079     0.1531   3.972 0.000477
#    
#    Residual standard error: 2.696 on 27 degrees of freedom
#    Multiple R-Squared: 0.9758,   Adjusted R-squared: 0.9731 
#    F-statistic: 362.8 on 3 and 27 DF,  p-value: < 2.2e-16