earth (version 2.3-0)

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 = stop("no 'object' arg"),
    x = NULL, subset = NULL, which.terms = NULL,
    ...,
    env = parent.frame(),
    trace = 0,
    Callers.name = "model.matrix.earth")

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$selected.terms.

...

Unused, but provided for generic/method consistency.

env

For internal use.

trace

Default 0. Set to non-zero to see which data model.matrix.earth is using.

Callers.name

For internal use (used by earth in trace messages).

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. In fact, that is what earth does internally after the pruning pass --- it calls lm.fit, and additionally glm if earth's glm argument is used.

See Also

earth

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)
#
#                Volume
#  (Intercept)   23.208
#  h(Girth-12.9)  5.746
#  h(12.9-Girth) -2.866
#  h(Height-76)   0.718
#
#  Selected 4 of 5 terms, and 2 of 2 predictors
#  Estimated importance: Girth Height
#  Number of terms at each degree of interaction: 1 3 (additive model)
#  GCV 11.48697   RSS 213.4354   GRSq 0.958859   RSq 0.9736697

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 t values are not meaningful
# yields:
#  Call:
#  lm(formula = trees$Volume ~ bx[, -1])
#
#  Residuals:
#      Min      1Q  Median      3Q     Max
#  -5.2900 -1.9782  0.0712  1.9209  4.1267
#
#  Coefficients:
#                        Estimate Std. Error t value Pr(>|t|)
#  (Intercept)             23.208      0.997   23.28  < 2e-16
#  bx[, -1]h(Girth-12.9)    5.746      0.297   19.35  < 2e-16
#  bx[, -1]h(12.9-Girth)   -2.866      0.444   -6.45  6.5e-07
#  bx[, -1]h(Height-76)     0.718      0.175    4.10  0.00034
#
#  Residual standard error: 2.81 on 27 degrees of freedom
#  Multiple R-squared: 0.974,   Adjusted R-squared: 0.971
#  F-statistic:  333 on 3 and 27 DF,  p-value: <2e-16