# plot.gcmr

##### Plot Diagnostics for Gaussian Copula Marginal Regression

Various types of diagnostic plots for Gaussian copula regression.

##### Usage

```
# S3 method for gcmr
plot(x, which = if (!time.series) 1:4 else c(1, 3, 5, 6),
caption = c("Residuals vs indices of obs.", "Residuals vs linear predictor",
"Normal plot of residuals", "Predicted vs observed values",
"Autocorrelation plot of residuals", "Partial ACF plot of residuals"),
main = "", ask = prod(par("mfcol")) < length(which) && dev.interactive(),
level = 0.95, col.lines = "gray",
time.series = inherits(x$cormat, "arma.gcmr"), …)
```

##### Arguments

- x
a fitted model object of class

`gcmr`

.- which
select one, or more, of the six available plots. The default choice adapts to the correlation structure and selects four plots depending on the fact that the data are a regular time series or not.

- caption
captions to appear above the plots.

- main
title to each plot in addition to the above caption.

- ask
if

`TRUE`

, then the user is asked before each plot.- level
confidence level in the normal probability plot. The default is

`0.95`

.- col.lines
color for lines. The default is

`"gray"`

.- time.series
if

`TRUE`

, four plots suitable for time series data are displayed. The default is`TRUE`

when the correlation matrix corresponds to that of ARMA(p,q) process and`FALSE`

otherwise.- …
other parameters to be passed through to plotting functions.

##### Details

The plot method for `gcmr`

objects produces six types of diagnostic plots selectable through the `which`

argument. Available choices are: Quantile residuals vs indices of the observations (`which=1`

); Quantile residuals vs linear predictor (`which=2`

); Normal probability plot of quantile residuals (`which=3`

); Fitted vs observed values (`which=4`

); Autocorrelation plot of quantile residuals (`which=5`

); Partial autocorrelation plot of quantile residuals (`which=6`

). The latter two plots make sense for regular time series data only.

The normal probability plot is computed via function `qqPlot`

from the package `car`

(Fox and Weisberg, 2011).

##### References

Fox, J. and Weisberg, S. (2011). *An R Companion to Applied Regression*. Second Edition. Thousand Oaks CA: Sage. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. *Electronic Journal of Statistics* **6**, 1517--1549. http://projecteuclid.org/euclid.ejs/1346421603.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. *Journal of Statistical Software*, **77**(8), 1--26. 10.18637/jss.v077.i08.

##### See Also

`gcmr`

.

##### Examples

```
# NOT RUN {
## beta regression with ARMA(1,3) errors
data(HUR)
trend <- scale(time(HUR))
m <- gcmr(HUR ~ trend | trend, marginal = beta.marg, cormat = arma.cormat(1, 3))
## normal probability plot
plot(m, 3)
## autocorrelation function of residuals
plot(m, 5)
# }
```

*Documentation reproduced from package gcmr, version 1.0.2, License: GPL (>= 2)*