cumres {gof} | R Documentation |
Currently linear regression models (lm) and logistic and poisson regression models are supported.
## S3 method for class 'lm': cumres (model, ...) ## S3 method for class 'glm': cumres (model, variable=c("predicted",colnames(model.matrix(model))), data=data.frame(model.matrix(model)), R=500, b=0, plots=min(R,50), seed=round(runif(1,1,1e9)),...)
model |
Model object (lm or glm ) |
variable |
List of variable to order the residuals after |
data |
data.frame used to fit model (complete cases) |
R |
Number of samples used in simulation |
b |
Moving average bandwidth (0 corresponds to infinity = standard cumulated residuals) |
plots |
Number of realizations to save for use in the plot-routine |
seed |
Random seed |
... |
additional arguments |
Returns an object of class 'cumres'.
Klaus K. Holst
cox.aalen
in the timereg
-package
for similar GOF-methods for survival-data.
sim1 <- function(n=100, f=function(x1,x2) {10+x1+x2^2}, sd=1, seed=1) { if (!is.null(seed)) set.seed(seed) x1 <- rnorm(n); x2 <- rnorm(n) X <- cbind(1,x1,x2) y <- f(x1,x2) + rnorm(n,sd=sd) d <- data.frame(y,x1,x2) return(d) } d <- sim1(100); l <- lm(y ~ x1 + x2,d) system.time(g <- cumres(l, R=100, plots=50)) g ## Not run: plot(g) g1 <- cumres(l, c("y"), R=100, plots=50) g1 g2 <- cumres(l, c("y"), R=100, plots=50, b=0.5) g2