model.matrix | R Documentation |
model.matrix
returns the design (or model) matrix used by the input object
to produce the fitted values (for objects of class ss
or sm
) or the linear predictors (for objects of class gsm
).
## S3 method for class 'ss' model.matrix(object, ...) ## S3 method for class 'sm' model.matrix(object, ...) ## S3 method for class 'gsm' model.matrix(object, ...)
object |
an object of class |
... |
additional arguments (currently ignored) |
For ss
objects, the basis.poly
function is used to construct the design matrix.
For sm
objects, the predict.sm
function with option design = TRUE
is used to construct the design matrix.
For gsm
objects, the predict.gsm
function with option design = TRUE
is used to construct the design matrix.
The design matrix that is post-multiplied by the coefficients to produce the fitted values (or linear predictors).
Nathaniel E. Helwig <helwig@umn.edu>
Chambers, J. M. (1992) Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Helwig, N. E. (2020). Multiple and Generalized Nonparametric Regression. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), SAGE Research Methods Foundations. doi: 10.4135/9781526421036885885
basis.poly
for the smoothing spline basis
predict.sm
for predicting from smooth models
predict.gsm
for predicting from generalized smooth models
# generate data set.seed(1) n <- 100 x <- seq(0, 1, length.out = n) fx <- 2 + 3 * x + sin(2 * pi * x) y <- fx + rnorm(n, sd = 0.5) # smoothing spline mod.ss <- ss(x, y, nknots = 10) X.ss <- model.matrix(mod.ss) mean((mod.ss$y - X.ss %*% mod.ss$fit$coef)^2) # smooth model mod.sm <- sm(y ~ x, knots = 10) X.sm <- model.matrix(mod.sm) mean((mod.sm$fitted.values - X.sm %*% mod.sm$coefficients)^2) # generalized smooth model (family = gaussian) mod.gsm <- gsm(y ~ x, knots = 10) X.gsm <- model.matrix(mod.gsm) mean((mod.gsm$linear.predictors - X.gsm %*% mod.gsm$coefficients)^2)
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