residuals | R Documentation |

Extracts the residuals from a fit smoothing spline ("ss"), smooth model ("sm"), or generalized smooth model ("gsm") object.

## 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"), ...)

`object` |
an object of class "ss", "sm", or "gsm" |

`type` |
type of residuals |

`...` |
other arugments (currently ignored) |

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

Residuals from `object`

Nathaniel E. Helwig <helwig@umn.edu>

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

`ss`

, `sm`

, `gsm`

# 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)

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