| residuals {npreg} | R Documentation |
Extract Model Residuals
Description
Extracts the residuals from a fit smoothing spline ("ss"), smooth model ("sm"), or generalized smooth model ("gsm") object.
Usage
## S3 method for class 'ss'
residuals(object, type = c("working", "response", "deviance",
"pearson", "partial"), ...)
## S3 method for class 'sm'
residuals(object, type = c("working", "response", "deviance",
"pearson", "partial"), ...)
## S3 method for class 'gsm'
residuals(object, type = c("deviance", "pearson", "working",
"response", "partial"), ...)
Arguments
object |
an object of class "ss", "sm", or "gsm" |
type |
type of residuals |
... |
other arugments (currently ignored) |
Details
For objects of class ss and sm
* the working and response residuals are defined as 'observed - fitted'
* the deviance and Pearson residuals multiply the working residuals by sqrt(weights(object))
For objects of class gsm, the residual types are the same as those produced by the residuals.glm function
Value
Residuals from object
Author(s)
Nathaniel E. Helwig <helwig@umn.edu>
References
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. 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
See Also
Examples
# 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)
res.ss <- residuals(mod.ss)
# smooth model
mod.sm <- sm(y ~ x, knots = 10)
res.sm <- residuals(mod.sm)
# generalized smooth model (family = gaussian)
mod.gsm <- gsm(y ~ x, knots = 10)
res.gsm <- residuals(mod.gsm)
# y = fitted + residuals
mean((y - fitted(mod.ss) - res.ss)^2)
mean((y - fitted(mod.sm) - res.sm)^2)
mean((y - fitted(mod.gsm) - res.gsm)^2)