plot.SVC_mle | R Documentation |

`SVC_mle`

modelMethod to plot the residuals from an `SVC_mle`

object. For this, `save.fitted`

has to be `TRUE`

in
`SVC_mle_control`

.

## S3 method for class 'SVC_mle' plot(x, which = 1:2, ...)

`x` |
( |

`which` |
( |

`...` |
further arguments |

a maximum 2 plots

Tukey-Anscombe plot, i.e. residuals vs. fitted

QQ-plot

Jakob Dambon

`legend`

SVC_mle

#' ## ---- toy example ---- ## sample data # setting seed for reproducibility set.seed(123) m <- 7 # number of observations n <- m*m # number of SVC p <- 3 # sample data y <- rnorm(n) X <- matrix(rnorm(n*p), ncol = p) # locations on a regular m-by-m-grid locs <- expand.grid(seq(0, 1, length.out = m), seq(0, 1, length.out = m)) ## preparing for maximum likelihood estimation (MLE) # controls specific to MLE control <- SVC_mle_control( # initial values of optimization init = rep(0.1, 2*p+1), # using profile likelihood profileLik = TRUE ) # controls specific to optimization procedure, see help(optim) opt.control <- list( # number of iterations (set to one for demonstration sake) maxit = 1, # tracing information trace = 6 ) ## starting MLE fit <- SVC_mle(y = y, X = X, locs = locs, control = control, optim.control = opt.control) ## output: convergence code equal to 1, since maxit was only 1 summary(fit) ## plot residuals # only QQ-plot plot(fit, which = 2) # two plots next to each other oldpar <- par(mfrow = c(1, 2)) plot(fit) par(oldpar)

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