Version: 1.0.4
Priority: optional
Title: Gaussian Copula Marginal Regression
Author: Guido Masarotto [aut], Cristiano Varin ORCID iD [aut, cre]
Maintainer: Cristiano Varin <cristiano.varin@unive.it>
Description: Likelihood inference in Gaussian copula marginal regression models.
Depends: R (≥ 4.0.0)
Imports: graphics, grDevices, stats, utils, betareg, car, Formula, lmtest, nlme, sandwich, sp
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Repository: CRAN
Packaged: 2025-05-16 03:28:19 UTC; cristianovarin
Date/Publication: 2025-05-16 03:40:02 UTC

Gaussian Copula Marginal Regression

Description

Fits Gaussian copula marginal regression models described in Song (2000) and Masarotto and Varin (2012; 2017).

Details

Gaussian copula models are frequently used to extend univariate regression models to the multivariate case. The principal merit of the approach is that the specification of the regression model is conveniently separated from the dependence structure described in the familiar form of the correlation matrix of a multivariate Gaussian distribution (Song 2000). This form of flexibility has been successfully employed in several complex applications including longitudinal data analysis, spatial statistics, genetics and time series. Some useful references can be found in Masarotto and Varin (2012; 2017) and Song et al. (2013).

This package contains R functions that implement the methodology discussed in Masarotto and Varin (2012) and Guolo and Varin (2014). The main function is gcmr, which fits Gaussian copula marginal regression models. Inference is performed through a likelihood approach. Computation of the exact likelihood is possible only for continuous responses, otherwise the likelihood function is approximated by importance sampling. See Masarotto and Varin (2017) for details.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Guolo, A. and Varin, C. (2014). Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends. The Annals of Applied Statistics 8, 74–88.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

Song, P. X.-K. (2000). Multivariate dispersion models generated from Gaussian copula. Scandinavian Journal of Statistics 27, 305–320.

Song, P. X.-K., Li, M. and Zhang, P. (2013). Copulae in Mathematical and Quantitative Finance. In Vector Generalized Linear Models: A Gaussian Copula Approach, 251–276. Springer Berlin Heidelberg.

Hidden Unemployment in Sao Paulo

Description

Rate of hidden unemployment due to substandard work conditions in Sao Paulo, Brazil (Rocha and Cribari-Neto, 2009).

Usage

data(HUR)

Source

Institute of Applied Economic Research (Ipea), Brazil. Data obtained from the IPEAdata website http://www.ipeadata.gov.br.

References

Rocha, A.V. and Cribari-Neto, F. (2009). Beta autoregressive moving average models. Test 18, 529–545.

ARMA(p,q) Correlation

Description

Sets ARMA(p,q) correlation in Gaussian copula regression models.

Usage

arma.cormat(p, q)

Arguments

p

order of the autoregressive component.

q

order of the moving average component.

Value

An object of class cormat.gcmr representing a correlation matrix with ARMA(p,q) structure.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr.

Longitudinal/Clustered Data Correlation

Description

Sets longitudinal/clustered data correlation in Gaussian copula regression models.

Usage

cluster.cormat(id, type = c("independence", "ar1", "ma1", 
		   	    "exchangeable", "unstructured"))

Arguments

id

subject id. This is a vector of the same lenght of the number of observations. Please note that data must be sorted in way that observations from the same cluster are contiguous.

type

a character string specifying the correlation structure. At the moment, the following are implemented:

independence working independence.
ar1 autoregressive of order 1.
ma1 moving average of order 1.
exchangeable exchangeable.
unstructured unstructured.

Details

The correlation matrices are inherited from the nlme package (Pinheiro and Bates, 2000).

Value

An object of class cormat.gcmr representing a correlation matrix for longitudinal or clustered data.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.

See Also

gcmr, nlme.

Correlation Matrices for Gaussian Copula Regression Models

Description

Class of correlation matrices available in the gcmr package.

Value

At the moment, the following are implemented:

ind.cormat working independence.
arma.cormat ARMA(p,q).
cluster.cormat longitudinal/clustered data.
matern.cormat Matern spatial correlation.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr, ind.cormat, arma.cormat, cluster.cormat, matern.cormat.

Epilitic Seizures Data

Description

Longitudinal study on epilitic seizures (Thall and Vail, 1990; Diggle et al. 2002). The data consist into 59 individuals with five observations each: The baseline eight-week interval and measurements collected at subsequent visits every two-week.

Usage

data(epilepsy)

Format

id patient's id .
age patient's age.
trt indicator if the patient is treated with progabide (1) or with placebo (2).
counts number of epileptic seizures.
time observation period in weeks (8 for baseline and 2 for subsequent visits).
visit indicator if observation at baseline (0) or subsequent visit (1).

Source

Thall, P.F. and Vail S.C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671.

References

Diggle, P.J., Heagerty, P., Liang, K.Y. and Zeger, S.L. (2002). Analysis of Longitudinal Data. Oxford: Oxford University Press. Second edition.

Marginals in Gaussian Copula Marginal Regression Models

Description

These functions set the marginals in Gaussian copula marginal regression models.

Usage

beta.marg(link = "logit")
binomial.marg(link = "logit")
Gamma.marg(link = "inverse")
gaussian.marg(link = "identity")
negbin.marg(link = "log")
poisson.marg(link = "log")
weibull.marg(link = "log")

Arguments

link

a specification for the model link function. See family for the special case of generalized linear models.

Details

Beta marginals specified by beta.marg are parametrized in terms of mean and dispersion as in betareg. See Cribari-Neto and Zeileis (2010) and Ferrari and Cribari-Neto (2004).

For binomial marginals specified by binomial.marg, the response is specified as a factor when the first level denotes failure and all others success or as a two-column matrix with the columns giving the numbers of successes and failures.

Negative binomial marginals implemented in negbin.marg are parametrized such that var(Y)=E(Y)+k E(Y)^2.

For back-compatibility with previous versions of the gcmr package, short names for the marginals bn.marg, gs.marg, nb.marg, and ps.marg remain valid as an alternative to (preferred) longer versions binomial.marg, gaussian.marg, negbin.marg, and poisson.marg.

Value

An object of class marginal.gcmr representing the marginal component.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software 34, 1–24.

Ferrari, S.L.P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics 31 (7), 799–815.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr, betareg.

Fitting Gaussian Copula Marginal Regression Models by Maximum (Simulated) Likelihood.

Description

Fits Gaussian copula marginal regression models by maximum (simulated) likelihood.

Usage

gcmr(formula, data, subset, offset, marginal,
     cormat, start, fixed, options=gcmr.options(...), model=TRUE,...)

gcmr.fit(x=rep(1,NROW(y)), y, z=NULL, offset=NULL,
	 marginal, cormat, start, fixed, options=gcmr.options())

Arguments

formula

a symbolic description of the model to be fitted of type y ~ x or y ~ x | z, for details see below.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used in the fitting process.

offset

optional numeric vector with an a priori known component to be included in the linear predictor for the mean. When appropriate, offset may also be a list of two offsets for the mean and precision equation, respectively.

x

design matrix.

y

vector of observations.

z

optional design matrix for the dispersion/shape.

marginal

an object of class marginal.gcmr specifying the marginal part of the model.

cormat

an object of class cormat.gcmr representing the correlation matrix of the errors.

start

optional numeric vector with starting values for the model parameters.

fixed

optional numeric vector of the same length as the total number of parameters. If supplied, only NA entries in fixed will be varied.

options

list of options passed to function gcmr.options.

model

logical. If TRUE, then the model frame is returned.

...

arguments passed to gcmr.options.

Details

Function gcmr computes maximum likelihood estimation in Gaussian copula marginal regression models. Computation of the exact likelihood is possible only for continuous responses, otherwise the likelihood function is approximated by importance sampling. See Masarotto and Varin (2012; 2017) for details.

Standard formula y ~ x1 + x2 indicates that the mean response is modelled as a function of covariates x1 and x2 through an appropriate link function. Extended formula y ~ x1 + x2 | z1 + z2 indicates that the dispersion (or the shape) parameter of the marginal distribution is modelled as a function of covariates z1 and z2. Dispersion (or shape) parameters are always modelled on logarithm scale. The model specification is inspired by beta regression as implemented in betareg (Cribari-Neto and Zeileis, 2010) through extended Formula objects (Zeileis and Croissant, 2010).

For binomial marginals specified by binomial.marg the response is specified as a factor when the first level denotes failure and all others success or as a two-column matrix with the columns giving the numbers of successes and failures.

gcmr.fit is the workhorse function: it is not normally called directly but can be more efficient where the response vector and design matrix have already been calculated.

Value

An object of class "gcmr" with the following components:

estimate

the maximum likelihood estimate.

maximum

the maximum likelihood value.

hessian

(minus) the Hessian at the maximum likelihood estimate.

jac

the Jacobian at the maximum likelihood estimate.

fitted.values

the fitted values.

marginal

the marginal model used.

cormat

the correlation matrix used.

fixed

the numeric vector indicating which parameters are constants.

ibeta

the indices of marginal parameters.

igamma

the indices of dependence parameters.

nbeta

the number of marginal parameters.

ngamma

the number of dependence parameters.

options

the fitting options used, see gcmr.options.

call

the matched call.

formula

the model formula.

terms

the terms objects for the fitted model.

levels

the levels of the categorical regressors.

model

the model frame, returned only if model=TRUE.

contrasts

the contrasts corresponding to levels.

y

the y vector used.

x

the model matrix used for the mean response.

z

the (optional) model matrix used for the dispersion/shape.

offset

the offset used.

n

the number of observations.

not.na

the vector of binary indicators of the available observations (not missing).

Functions coefficients, logLik, fitted, vcov.gcmr and residuals.gcmr can be used to extract various useful features of the value returned by gcmr. Function plot.gcmr produces various diagnostic plots for fitted gcmr objects.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software 34, 1–24.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

Rocha, A.V. and Cribari-Neto, F. (2009). Beta autoregressive moving average models. Test 18, 529–545.

Zeileis, A. and Croissant, Y. (2010). Extended model formulas in R: Multiple parts and multiple responses. Journal of Statistical Software 34, 1–13.

See Also

cormat.gcmr, marginal.gcmr, gcmr.options, Formula, betareg.

Examples

## negative binomial model for longitudinal data
data(epilepsy)
gcmr(counts ~ offset(log(time)) + visit + trt + visit:trt, data = epilepsy, 
subset = (id != 49), marginal = negbin.marg, cormat = cluster.cormat(id, "ar1"), 
options=gcmr.options(seed=123, nrep=100 ))
## Hidden Unemployment Rate (HUR) data (Rocha and Cribari-Neto, 2009)
## beta regression with ARMA(1,3) errors
data(HUR)
trend <- scale(time(HUR))
gcmr(HUR ~ trend | trend, marginal = beta.marg, cormat = arma.cormat(1, 3))

Setting Options for Fitting Gaussian Copula Marginal Regression Models

Description

Sets options that affect the fitting of Gaussian copula marginal regression models.

Usage

gcmr.options(seed = round(runif(1, 1, 1e+05)), nrep = c(100, 1000), 
    no.se = FALSE, method = c("BFGS", "Nelder-Mead", "CG"), ...)

Arguments

seed

seed of the pseudorandom generator used in the importance sampling algorithm for likelihood approximation in case of discrete responses.

nrep

Monte Carlo size of the importance sampling algorithm for likelihood approximation in case of discrete responses. nrep can be a vector so that the model is fitted with a sequence of different Monte Carlo sizes. In this case, the starting values for optimization of the likelihood are taken from the previous fitting. A reasonable strategy is to fit the model with a small Monte Carlo size to obtain sensible starting values and then refit with a larger Monte Carlo size. The default value is 100 for the first optimization and 1000 for the second and definitive optimization.

no.se

logical. Should standard errors be computed and returned or not?

method

a character string specifying the method argument passed to optim. The default optimization routine is the quasi-Newton algorithm BFGS. See optim for details.

...

arguments passed to optim.

Value

A list containing the options.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr

Working Independence Correlation

Description

Sets working independence correlation in Gaussian copula marginal regression models.

Usage

ind.cormat()

Value

An object of class cormat.gcmr representing an identity correlation matrix.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr.

Gambia Malaria Data

Description

Malaria prevalence in children in Gambia. The data are constructed from the gambia dataframe in the geoR package (Diggle and Ribeiro, 2007) by village aggregation.

Usage

data(malaria)

Format

A data frame with the 65 observations with the following variables

x x-coordinate of the village (UTM).
y y-coordinate of the village (UTM).
cases number of sampled children with malaria in each village.
size number of sampled children in each village.
age mean age of the sampled children in each village.
netuse frequency of sampled children who regularly sleep under a bed-net in each village.
treated frequency of sampled children whose bed-net is treated.
green measure of vegetation green-ness in the immediate vicinity of the village.
phc indicator variable denoting the presence (1) or absence (0) of a health center in the village.
area indicator of the village area (Diggle et al., 2002).

Source

Diggle, P.J. and Ribeiro Jr, P.J. (2007). Model Based Geostatistics. New York: Springer.

References

Thomson, M., Connor, S., D Alessandro, U., Rowlingson, B., Diggle, P., Cresswell, M. and Greenwood, B. (1999). Predicting malaria infection in Gambian children from satellite data and bednet use surveys: the importance of spatial correlation in the interpretation of results. American Journal of Tropical Medicine and Hygiene 61, 2–8.

Diggle, P., Moyeed, R., Rowlingson, B. and Thomson, M. (2002). Childhood malaria in The Gambia: a case-study in model-based geostatistics, Applied Statistics 51, 493–506.

Examples

data(malaria)

Marginals for Gaussian Copula Marginal Regression

Description

Class of marginals available in the gcmr library.

Value

At the moment, the following are implemented:

beta.marg beta marginals.
binomial.marg binomial marginals.
Gamma.marg Gamma marginals.
gaussian.marg Gaussian marginals.
negbin.marg negative binomial marginals.
poisson.marg Poisson marginals.
weibull.marg Weibull marginals.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr, beta.marg, binomial.marg, gaussian.marg, Gamma.marg, negbin.marg, poisson.marg, weibull.marg.

Matern Spatial Correlation

Description

Sets a Matern spatial correlation matrix in Gaussian copula marginal regression models.

Usage

matern.cormat(D, alpha = 0.5)

Arguments

D

matrix with values of the distances between pairs of data locations.

alpha

value of the shape parameter of the Matern correlation class. The default alpha = 0.5 corresponds to an exponential correlation model.

Details

The Mat\'ern correlation function is inherited from the geoR package (Diggle and Ribeiro, 2007).

Value

An object of class cormat.gcmr representing a Matern correlation matrix.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Diggle, P. and Ribeiro, P.J. (2007). Model-based Geostatistics. Springer.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr.

Plot Diagnostics for Gaussian Copula Marginal Regression

Description

Various types of diagnostic plots for Gaussian copula regression.

Usage

## 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 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).

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.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr.

Examples

## 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)

Polio Time Series

Description

Time series of Polio incidences in U.S.A. from 1970 to 1983.

Usage

data(polio)

Format

A data frame with the 168 monthly observations (from January 1970 to December 1983) with the following variables

y time series of polio incidences.
t*10^( -3 ) linear trend multiplied by factor 10^{(-3)}.
cos( 2*pi*t/12 ) cosine annual seasonal component.
sin( 2*pi*t/12 ) sine annual seasonal component.
cos( 2*pi*t/6 ) cosine semi-annual seasonal component.
sin( 2*pi*t/6 ) sine semi-annual seasonal component.

Source

Zeger, S.L. (1988). A regression model for time series of counts. Biometrika 75, 822–835.

Examples

data(polio)

Profile Log-Likelihood for Gaussian Copula Marginal Regression Models

Description

Computes the profile log-likelihood for mean response parameters of a Gaussian copula marginal regression model.

Usage

## S3 method for class 'gcmr'
profile(fitted, which, low, up, npoints = 10, 
        display = TRUE, alpha = 0.05, progress.bar = TRUE, ...)

Arguments

fitted

a fitted Gaussian copula marginal regression model of class gcmr.

which

the index of the regression parameter which should be profiled.

low

the lower limit used in computation of the profile log-likelihood. If this is missing, then the lower limit is set equal to the estimate minus three times its standard error.

up

the upper limit used in computation of the profile log-likelihood. If this is missing, then the upper limit is set equal to the estimate plus three times its standard error.

npoints

number of points used in computation of the profile log-likelihood. Default is 10.

display

should the profile log-likelihood be displayed or not? default is TRUE.

alpha

the significance level, default is 0.05.

progress.bar

logical. If TRUE, a text progress bar is displayed.

...

further arguments passed to plot.

Details

If the display is requested, then the profile log-likelihood is smoothed by cubic spline interpolation.

Value

A list with the following components:

points

points at which the profile log-likelihood is evaluated.

profile

values of the profile log-likelihood.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr

Examples

## spatial binomial data
## Not run: 
data(malaria)
D <- sp::spDists(cbind(malaria$x, malaria$y))/1000
m <- gcmr(cbind(cases, size-cases) ~ netuse+I(green/100)+phc, data=malaria,
marginal=binomial.marg, cormat=matern.cormat(D), options=gcmr.options(seed=987))
prof <- profile(m, which = 2)
prof

## End(Not run)

Quantile Residuals for Gaussian Copula Marginal Regression

Description

Computes various type of quantile residuals for validation of a fitted Gaussian copula marginal regression model, as described in Masarotto and Varin (2012; 2017).

Usage

## S3 method for class 'gcmr'
residuals(object, type=c("conditional","marginal"),
          method=c("random","mid"),...)

Arguments

object

an object of class gcmr, typically the result of a call to gcmr.

type

the type of quantile residuals which should be returned. The alternatives are: "conditional" (default) and "marginal".

method

different methods available for quantile residuals in case of discrete responses: "random" for randomized quantile residuals (default), and "mid" for mid interval quantile residuals as defined in Zucchini and MacDonald (2009).

...

further arguments passed to or from other methods.

Details

Quantile residuals are defined in Dunn and Smyth (1996). Two different types are available:

conditional quantile residuals that account for the dependence.
marginal quantile residuals that do not account for the dependence.

Conditional quantile residuals are normal quantiles of Rosenblatt (1952) transformations and they are appropriate for validation of the marginal regression models discussed in Masarotto and Varin (2012; 2017). If the responses are discrete, then the conditional quantile residuals are not well defined. This difficulty is overcame by randomized quantile residuals available through option method="random". Alternatively, Zucchini and MacDonald (2009) suggest the use of mid interval quantile residuals (method="mid").

Note

Differently from randomized quantile residuals, mid quantile residuals are not realizations of incorrelated standard normal variables under model conditions.

It is appropriate to inspect several sets of randomized quantile residuals before to take a decision about the model.

See Masarotto and Varin (2012; 2017) for more details.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Dunn, P.K. and Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236–244.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

Rosenblatt, M. (1952). Remarks on a multivariate transformation. The Annals of Mathematical Statistics 23, 470–472.

Zucchini, W. and MacDonald, I.L. (2009). Hidden Markov Models for Time Series. Chapman and Hall/CRC.

See Also

gcmr

Examples

## spatial binomial data
## Not run: 
data(malaria)
D <- sp::spDists(cbind(malaria$x, malaria$y))/1000
m <- gcmr(cbind(cases, size-cases) ~ netuse+I(green/100)+phc, data=malaria, 
marginal=binomial.marg, cormat=matern.cormat(D))
res <- residuals(m)
## normal probability plot
qqnorm(res)
qqline(res)
## or better via plot.gcmr
plot(m, which = 3)

## End(Not run)

Scotland Lip Cancer Data

Description

Male lip cancer in Scotland counties between 1975-1980.

Usage

data(scotland)

Format

A data frame with the 56 observations with the following variables

observed observed cases in each county.
expected expected cases in each county.
AFF proportion of the population employed in agriculture, fishing, or forestry.
latitude county latitude.
longitude county longitude.

Source

Waller, L.A. and Gotway, C.A. (2004). Applied Spatial Statistics for Public Health Data. New York: John Wiley and Sons.

References

Clayton D. and Kaldor J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43, 671–681.

Examples

data(scotland)

Methods for gcmr Objects

Description

Methods for extracting information from fitted beta regression model objects of class "gcmr".

Usage

## S3 method for class 'gcmr'
summary(object, ...)

## S3 method for class 'gcmr'
coef(object, ...)
## S3 method for class 'gcmr'
vcov(object, ...)
## S3 method for class 'gcmr'
bread(x, ...)
## S3 method for class 'gcmr'
estfun(x, ...)

Arguments

object, x

a fitted marginal regression model of class gcmr.

...

additional arguments, but currently not used.

Value

The function summary.gcmr returns an object of class "summary.glm", a list with some components of the gcmr object, plus

coefficients

a list with components marginal and copula containing the maximum likelihood estimates of the marginal and Gaussian copula parameters, respectively.

aic

Akaike Information Criterion.

Function coef returns the estimated coefficients and vcov their variance-covariance matrix. Functions bread and estfun extract the components of the robust sandwich variance matrix that can be computed with the sandwich package (Zeileis, 2004; 2006).

Author(s)

Guido Masarotto and Cristiano Varin.

References

Zeileis, A. (2004). Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software 11, issue 10.

Zeileis, A. (2006). Object-oriented computation of sandwich estimators. Journal of Statistical Software 16, issue 9.

See Also

bread, estfun, gcmr, sandwich.

Examples

data(epilepsy)
fit <- gcmr(counts ~ offset(log(time)) + visit + trt + visit:trt, data = epilepsy, 
subset = (id != 49), marginal = negbin.marg, cormat = cluster.cormat(id, "ar1"), 
options=gcmr.options(seed=123, nrep=c(25,100) ))
summary(fit)