# plot.SVC_mle: Plotting Residuals of 'SVC_mle' model In varycoef: Modeling Spatially Varying Coefficients

 plot.SVC_mle R Documentation

## Plotting Residuals of `SVC_mle` model

### Description

Method to plot the residuals from an `SVC_mle` object. For this, `save.fitted` has to be `TRUE` in `SVC_mle_control`.

### Usage

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

### Arguments

 `x` (`SVC_mle`) `which` (`numeric`) A numeric vector and subset of `1:2` indicating which of the 2 plots should be plotted. `...` further arguments

### Value

a maximum 2 plots

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

• QQ-plot

### Author(s)

Jakob Dambon

`legend` SVC_mle

### Examples

```#' ## ---- 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)

```

varycoef documentation built on Sept. 18, 2022, 1:07 a.m.