residuals: Extract Model Residuals

In npreg: Nonparametric Regression via Smoothing Splines

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 <[e-mail]>

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

ss, sm, gsm

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)

npreg documentation built on July 21, 2022, 1:06 a.m.