# Plot Diagnostics for Gaussian Copula Marginal Regression

### Description

Various types of diagnostic plots for Gaussian copula regression.

### Usage

1 2 3 4 5 6 7 8 | ```
## S3 method for class '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 |

`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 |

`level` |
confidence level in the normal probability plot. The default is |

`col.lines` |
color for lines. The default is |

`time.series` |
if |

`...` |
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).

### Author(s)

Guido Masarotto and Cristiano Varin.

### 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.

### See Also

`gcmr`

.

### Examples

1 2 3 4 5 6 7 8 |