Package 'marg' reference manual

Title:	Approximate Marginal Inference for Regression-Scale Models
Description:	Likelihood inference based on higher order approximations for linear nonnormal regression models.
Authors:	S original by Alessandra R. Brazzale <[email protected]>. R port by Alessandra R. Brazzale <[email protected]>, following earlier work by Douglas Bates.
Maintainer:	Alessandra R. Brazzale <[email protected]>
License:	GPL (>= 2) \| file LICENCE
Version:	1.2-2.1
Built:	2024-11-12 06:34:14 UTC
Source:	CRAN

Approximate marginal inference for regression-scale models

Description

Likelihood inference based on higher order approximations for linear nonnormal regression models

Details

Package:	marg
Version:	1.2-0
Date:	2009-10-03
Depends:	R (>= 2.6.0), statmod, survival
Suggests:	boot, cond, csampling, nlreg
License:	GPL (>= 2)
URL:	http://www.r-project.org, http://statwww.epfl.ch/AA/
LazyLoad:	yes
LazyData:	yes

Index:

Functions:
=========
cond                    Approximate Conditional Inference - Generic
                        Function
cond.rsm                Approximate Conditional Inference in
                        Regression-Scale Models
dHuber                  Huber's Least Favourable Distribution
family.rsm              Use family() on a "rsm" object
family.rsm.object       Family Object for Regression-Scale Models
logLik.rsm              Compute the Log Likelihood for
                        Regression-Scale Models
marg.object             Approximate Marginal Inference Object
plot.marg               Generate Plots for an Approximate Marginal
                        Inference Object
print.summary.marg      Use print() on a "summary.marg" object
residuals.rsm           Compute Residuals for Regression-Scale Models
rsm                     Fit a Regression-Scale Model
rsm.diag                Diagnostics for Regression-Scale Models
rsm.diag.plots          Diagnostic Plots for Regression-Scale Models
rsm.families            Generate a RSM Family Object
rsm.fit                 Fit a Regression-Scale Model Without Computing
                        the Model Matrix
rsm.null                Fit an Empty Regression-Scale Model
rsm.object              Regression-Scale Model Object
rsm.surv                Fit a Regression-Scale Model Without Computing
                        the Model Matrix
summary.marg            Summary Method for Objects of Class "marg"
summary.rsm             Summary Method for Regression-Scale Models
update.rsm              Update and Re-fit a RSM Model Call
vcov.rsm                Calculate Variance-Covariance Matrix for a
                        Fitted RSM Model


Datasets:
========
darwin                  Darwin's Data on Growth Rates of Plants
houses                  House Price Data
nuclear                 Nuclear Power Station Data
venice                  Sea Level Data

Further information is available in the following vignettes:

`Rnews-paper`	hoa: An R Package Bundle for Higher Order Likelihood Inference (source, pdf)

Author(s)

S original by Alessandra R. Brazzale <[email protected]>. R port by Alessandra R. Brazzale <[email protected]>, following earlier work by Douglas Bates.

Maintainer: Alessandra R. Brazzale <[email protected]>

Approximate Conditional Inference - Generic Function

Description

Performs approximate conditional inference.

Usage

cond(object, offset, ...)
cond(object, offset, ...)

Arguments

`object`	a fitted model object. Families supported are binomial and Poisson with canonical link function (class `glm`), and regression-scale models (class `rsm`).
`offset`	the covariate occurring in the model formula whose coefficient represents the parameter of interest. May be numerical or a two-level factor. In case of a two-level factor, it must be coded by contrasts and not appear as two dummy variables in the model. Can also be a call to a mathematical function (such as `exp`, `sin`, ...) or to a mathematical operator (`\^`, `/`, ...) applied to a numerical variable. The call must always agree with the label used to identify the corresponding parameter in the fitted model object passed through the `object` argument. Beware that the label includes the identity function `I()` if an arithmetic operator was used. Other function types (e.g. `factor`) and interactions are not admitted.
`...`	absorbs any additional arguments. See `cond.glm` and `cond.rsm` for details.

Details

This function is generic (see methods); method functions can be written to handle specific classes of data. Classes which already have methods for this function include: glm and rsm.

Value

The returned value is an approximate conditional inference object. Classes already supported are cond and marg depending on whether the fitted model object passed through the object argument has class glm or rsm. See cond.object or marg.object for more details.

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne. Chapter 6.

Examples

## Urine Data 
## Not run: 
data(urine)
urine.glm <- glm(r ~ gravity + ph + osmo + cond + urea + log(calc), 
                 family = binomial, data = urine)
##
## function call as offset variable 
labels(coef(urine.glm))
cond(urine.glm, log(calc))
##
## large estimate of regression coefficient
urine.glm <- glm(r ~ gravity + ph + osmo + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.glm <- glm(r ~ I(gravity * 100) + ph + osmo + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.cond <- cond(urine.glm, I(gravity * 100))
plot(urine.cond, which = 4)

## End(Not run)

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
##
## parameter of interest: scale parameter
houses.marg <- cond(houses.rsm, scale)
plot(houses.marg, which = 2)
## Urine Data 
## Not run: 
data(urine)
urine.glm <- glm(r ~ gravity + ph + osmo + cond + urea + log(calc), 
                 family = binomial, data = urine)
##
## function call as offset variable 
labels(coef(urine.glm))
cond(urine.glm, log(calc))
##
## large estimate of regression coefficient
urine.glm <- glm(r ~ gravity + ph + osmo + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.glm <- glm(r ~ I(gravity * 100) + ph + osmo + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.cond <- cond(urine.glm, I(gravity * 100))
plot(urine.cond, which = 4)

## End(Not run)

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
##
## parameter of interest: scale parameter
houses.marg <- cond(houses.rsm, scale)
plot(houses.marg, which = 2)

Approximate Conditional Inference in Regression-Scale Models

Description

Performs approximate conditional inference on a scalar parameter of interest in regression-scale models. The output is stored in an object of class marg.

Usage

## S3 method for class 'rsm'
cond(object, offset, formula = NULL, family = NULL, 
     dispersion = NULL, data = sys.frame(sys.parent()), pts = 20, 
     n = max(100, 2*pts), tms = 0.6, from = NULL, to = NULL, 
     control = glm.control(...), trace = FALSE, ...)
## S3 method for class 'rsm'
cond(object, offset, formula = NULL, family = NULL, 
     dispersion = NULL, data = sys.frame(sys.parent()), pts = 20, 
     n = max(100, 2*pts), tms = 0.6, from = NULL, to = NULL, 
     control = glm.control(...), trace = FALSE, ...)

Arguments

`object`	a `rsm` object; for instance the result of a call to `rsm`.
`offset`	either the covariate occurring in the model formula whose coefficient represents the parameter of interest or `scale` if the parameter of interest is the scale parameter. In the first case, the variable may be numerical or a two-level factor. In case of a two-level factor, it must be coded by contrasts and not as two dummy variables. Can also be a call to a mathematical function (such as `exp`, `sin`, ...) or to a mathematical operator (`^`, `/`, ...) applied to a numerical variable. The call must always agree with the label used to identify the corresponding parameter in the `rsm` object passed through the `object` argument or defined by `formula` and `family`. Beware that the label includes the identity function `I()` if an arithmetic operator was used. Other function types (e.g. `factor`) and interactions are not admitted. If interest focuses on the scale parameter, it must not be fixed in `object` or when using the `dispersion` argument in case no `rsm` object is supplied.
`formula`	a formula expression (only if no `rsm` object is defined).
`family`	a `family.rsm` object defining the error distribution (only if no `rsm` object is defined). See `rsm.families` for the families supported.
`dispersion`	argument only to be used if no `rsm` object is defined. If `NULL`, the scale parameter is taken to be unknown. If known, the numerical value can be passed. The default is `NULL`. Huber's least favourable error distribution represents a special case. If `dispersion` is `NULL`, the maximum likelihood estimate is computed, while if `TRUE` the MAD estimate is calculated and the scale parameter fixed to this value in subsequent computations.
`data`	an optional data frame in which to interpret the variables occurring in the formula (only if no `rsm` object is defined).
`pts`	number of output points (minimum 10) that are calculated exactly; the default is 20.
`n`	approximate number of output points (minimum 50) produced by the spline interpolation. The default is the maximum between 100 and twice `pts`.
`tms`	defines the range MLE +/- `tms` * s.e. where cubic spline interpolation is replaced by polynomial interpolation. The default is 0.6.
`from`	starting value of the sequence that contains the values of the parameter of interest for which output points are calculated exactly. The default is MLE - 3.5 * s.e.
`to`	ending value of the sequence that contains the values of the parameter of interest for which output points are calculated exactly. The default is MLE + 3.5 * s.e.
`control`	a list of iteration and algorithmic constants that control the `rsm` fit. See `glm.control` for their names and default values.
`trace`	if `TRUE`, iteration numbers will be printed.
`...`	additional arguments, such as `weights`, `subset`, `control` etc. used by the `rsm` fitting routine if the `rsm` object is defined through `formula` and `family`. See `rsm` for their definition and use.

Details

This function is a method for the generic function cond for class rsm. It can be invoked by calling cond for an object of the appropriate class, or directly by calling cond.rsm regardless of the class of the object. cond.rsm has also to be used if the rsm object is not provided throught the object argument but specified by formula and family.

The function cond.rsm implements several small sample asymptotic methods for approximate conditional inference in regression-scale models. Approximations for both the modified/marginal log likelihood function and approximate conditional/marginal tail probabilities are available (see marg.object for details). Attention is restricted to a scalar parameter of interest, either a regression coefficient or the scale parameter. In the first case, the associated covariate may be either numerical or a two-level factor.

Approximate conditional (or equivalently marginal) inference is performed by either updating a fitted regression-scale model or defining the model formula and family. All approximations are calculated exactly for pts equally spaced points ranging from from to to. A spline interpolation is used to extend them over the whole interval of interest, except for the range of values defined by MLE +/- tms * s.e. where the spline interpolation is replaced by a higher order polynomial interpolation. This is done in order to avoid numerical instabilities which are likely to occur for values of the parameter of interest close to the MLE. Results are stored in an object of class marg. Method functions like print, summary and plot can be used to examine the output or represent it graphically. Components can be extracted using coef, formula and family.

Main references for the methods considered are the papers by Barndorff-Nielsen (1991), DiCiccio, Field and Fraser (1990) and DiCiccio and Field (1991). The theory and statistics used are summarized in Brazzale (2000, Chapters 2 and 3). More details of the implementation are given in Brazzale (1999; 2000, Section 6.3.1).

Value

The returned value is an object of class marg; see marg.object for details.

Note

If the parameter of interest is the scale parameter, all calculations are performed on the logarithmic scale, though most results are reported on the original scale.

In rare occasions, cond.rsm dumps because of non-convergence of the function rsm which is used to refit the model for a fixed value of the parameter of interest. This happens for instance if this value is too extreme. The arguments from and to may then be used to limit the default range of MLE +/- 3.5 * s.e. A further possibility is to fine-tuning the constants (number of iterations, convergence threshold) that control the rsm fit through the control argument.

cond.rsm may also dump if the estimate of the parameter of interest is large (tipically > 400) in absolute value. This may be avoided by reparametrizing the model.

References

Barndorff-Nielsen, O. E. (1991) Modified signed log likelihood ratio. Biometrika, 78, 557–564.

Brazzale, A. R. (1999) Approximate conditional inference for logistic and loglinear models. J. Comput. Graph. Statist., 8, 653–661.

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

DiCiccio, T. J., Field, C. A. and Fraser, D. A. S. (1990) Approximations of marginal tail probabilities and inference for scalar parameters. Biometrika, 77, 77–95.

DiCiccio, T. J. and Field, C. A. (1991) An accurate method for approximate conditional and Bayesian inference about linear regression models from censored data. Biometrika, 78, 903–910.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
##
## quadratic model fitted to the sea level, includes 18.62-year 
## astronomical tidal cycle and 11-year sunspot cycle
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
names(coef(venice.rsm))
## "(Intercept)"  "Year"  "I(Year^2)"  "c11"  "s11"  "c19"  "s19"      
##
## variable of interest: quadratic term
venice.marg <- cond(venice.rsm, I(Year^2))
##
detach()

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
##
## parameter of interest: scale parameter
houses.marg <- cond(houses.rsm, scale)
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
##
## quadratic model fitted to the sea level, includes 18.62-year 
## astronomical tidal cycle and 11-year sunspot cycle
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
names(coef(venice.rsm))
## "(Intercept)"  "Year"  "I(Year^2)"  "c11"  "s11"  "c19"  "s19"      
##
## variable of interest: quadratic term
venice.marg <- cond(venice.rsm, I(Year^2))
##
detach()

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
##
## parameter of interest: scale parameter
houses.marg <- cond(houses.rsm, scale)

Darwin's Data on Growth Rates of Plants

Description

The darwin data frame has 15 rows and 3 columns.

Charles Darwin conducted an experiment to examine the superiority of cross-fertilized plants over self-fertilized plants. 15 pairs of plants were used. Each pair consisted of one cross-fertilized plant and one self-fertilized plant which germinated at the same time and grew in the same pot. The heights of the plants were measured at a fixed time after planting. Four different pots were used.

Usage

data(darwin)data(darwin)

Format

This data frame contains the following columns:

cross: the heights of the cross-fertilized plants (in inches);
self: the heights of the self-fertilized plants (in inches);
pot: a factor variable for the pot number.

Source

The data were obtained from

Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems From Many Fields for the Student and Research Worker (Chapter 2). New York: Springer-Verlag.

References

Darwin, C. (1878) The Effects of Cross and Self Fertilisation in the Vegetable Kingdom (2nd ed.). London: John Murray.

Examples

data(darwin)
plot(cross - self ~ pot, data = darwin)
data(darwin)
plot(cross - self ~ pot, data = darwin)

Use family() on a “rsm” object

Description

This is a method for the function family() for objects from which a family.rsm object can be extracted. Typically a fitted rsm model object. See family for the general behaviour of this function.

Usage

## S3 method for class 'rsm'
family(object, ...)
## S3 method for class 'rsm'
family(object, ...)

Arguments

`object`	any object from which a `family.rsm` object can be extracted.
`...`	absorbs any additional argument.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
family(venice.rsm)
detach()

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
family(houses.rsm)
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
family(venice.rsm)
detach()

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
family(houses.rsm)

Family Object for Regression-Scale Models

Description

Class of objects that characterize the error distribution of a regression-scale model.

Generation

This class of objects is returned by a call to a family.rsm generator function. See rsm.families for the distributions which are supported by the marg package. The object includes a list of functions and expressions that characterize the error distribution of a regression-scale model. These are used by the IRLS algorithm implemented in the rsm fitting routine. New families can be added to the ones already supported. See the demonstration file ‘margdemo.R’ that ships with the package. There is a print method for family.rsm objects which produces a simple summary without any detail; use unclass(family.rsm.object) to see the contents.

Structure

The following components, with the corresponding functionality, are required for a family.rsm object:

family: a character vector giving the family name.
g0: a function that yields minus the log density of the error distribution in the regression-scale model.
g1: a function that yields the first derivative of minus the log density.
g2: a function that yields the second derivative of minus the log density.
df: argument with NULL value; must be included to guarantee compatibility with the existing code.
k: argument with NULL value; must be included to guarantee compatibility with the existing code.

Note

For the sake of compatibility, the g0, g1 and g2 functions of a user-defined family can only take two arguments: y representing an observation and the ... argument which absorbes any additional arguments.

House Price Data

Description

The houses data frame has 26 rows and 5 columns.

Ms. Terry Tasch of Long-Kogan Realty, Chicago, provides data on the selling prices of houses and on different variables describing their status. This data frame contains the prices and a subset of the covariates.

Usage

data(houses)data(houses)

Format

This data frame contains the following columns:

price: selling price (in thousands of dollars);
bdroom: number of bedrooms;
floor: floor space (in square feet);
rooms: total number of rooms;
front: front footage of lot (in feet).

Source

The data were obtained from

Sen, A. and Srivastava, M. (1990) Regression Analysis: Theory, Methods and Applications (Exhibit 2.2, page 32). New York: Springer-Verlag.

Examples

data(houses)
summary(houses)
pairs(houses)
data(houses)
summary(houses)
pairs(houses)

Huber's Least Favourable Distribution

Description

Density, cumulative distribution, quantiles and random number generator for Huber's least favourable distribution.

Usage

dHuber(x, k = 1.345)
pHuber(q, k = 1.345)
qHuber(p, k = 1.345)
rHuber(n, k = 1.345)
dHuber(x, k = 1.345)
pHuber(q, k = 1.345)
qHuber(p, k = 1.345)
rHuber(n, k = 1.345)

Arguments

`x`	vector of quantiles. Missing values (`NA`s) are allowed.
`q`	vector of quantiles. Missing values (`NA`s) are allowed.
`p`	vector of probabilities. Missing values (`NA`s) are allowed.
`n`	sample size. If `length(n)` is larger than 1, then `length(n)` random values are returned.
`k`	tuning constant. Values should preferably lie between 1 and 1.5. The default is 1.345, which gives 95% efficiency at the Normal.

Details

Inversion of the cumulative distribution function is used to generate deviates from Huber's least favourable distribution.

Value

Density (dHuber), probability (pHuber), quantile (qHuber), or random sample (rHuber) for Huber's least favourable distribution with tuning constant k. If values are missing, NAs will be returned.

Side Effects

The function rHuber causes creation of the dataset .Random.seed if it does not already exist; otherwise its value is updated.

Background

Huber's least favourable distribution is a compound distribution with gaussian behaviour in the interval (-k,k) and double exponential tails. It is strongly related to Huber's M-estimator, which represents the maximum likelihood estimator of the location parameter.

References

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A. (1986) Robust Statistics: The Approach Based on Influence Functions. New York: Wiley.

Examples

pHuber(0.5)
## 0.680374
pHuber(0.5, k = 1.5)
## 0.6842623
pHuber(0.5)
## 0.680374
pHuber(0.5, k = 1.5)
## 0.6842623

Compute the Log Likelihood for Regression-Scale Models

Description

Computes the log likelihood for regression-scale models.

Usage

## S3 method for class 'rsm'
logLik(object, ...)
## S3 method for class 'rsm'
logLik(object, ...)

Arguments

`object`	an object inheriting from class `rsm` representing a fitted regression-scale model.
`...`	absorbs any additional argument.

Details

This is a method for the function logLik() for objects inheriting from class rsm.

Value

Returns an object class logLik which is a number with attributes, attr(r, "df") (degrees of freedom) giving the number of parameters (regression coefficients plus scale parameter, if not fixed) in the model.

Note

The default print method for logLik objects is used.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
logLik(venice.rsm)
detach()
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
logLik(venice.rsm)
detach()

Approximate Marginal Inference Object

Description

Class of objects returned when performing approximate conditional inference for regression-scale models.

Arguments

Objects of class marg are implemented as a list. The following components are included:

`workspace`	a list whose elements are the spline interpolations of several first order and higher order statistics. They are used to implement the following likelihood quantities: - the profile and modified profile/approximate marginal log likelihoods; - the Wald pivots from the unconditional and conditional/approximate marginal MLEs; - the profile and modified/approximate marginal likelihood roots; - the conditional/approximate marginal Lugannani-Rice tail area approximation; - the correction term used in the higher order statistics; - the conditional/marginal information and nuisance parameter aspects. Method functions work mainly on this part of the object. In order to avoid errors in the calculation of confidence intervals and tail probabilities, this part of the object should not be modified.
`coefficients`	a $2\times 2$ matrix containing the unconditional and approximate conditional/marginal MLEs and their standard errors.
`call`	the function call that created the `marg` object.
`formula`	the model formula.
`family`	the name of the error distribution.
`offset`	the covariate occurring in the model formula whose coefficient represents the parameter of interest or `scale` if the parameter of interest is the scale parameter.
`diagnostics`	diagnostics related to the decomposition of the higher order adjustments into an information and a nuisance parameters term.
`n.approx`	the number of output points for which the statistics have been calculated exactly.
`omitted.val`	the range of values omitted in the spline interpolation of some of the higher order statistics considered. The aim is to avoid numerical instabilities around the maximum likelihood estimate.
`is.scalar`	a logical value indicating whether there are any nuisance parameters. If `FALSE` there are none.

Generation

This class of objects is returned from calls to the function cond.rsm.

Methods

The class marg has methods for summary, plot, print, coef and family, among others.

Note

If the parameter of interest is the scale parameter, all calculations are performed on the logarithmic scale, though most results are reported on the original scale.

References

Barndorff-Nielsen, O. E. (1991) Modified signed log likelihood ratio. Biometrika, 78, 557–564.

Brazzale, A. R. (1999) Approximate conditional inference for logistic and loglinear models. J. Comput. Graph. Statist., 8, 653–661.

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

DiCiccio, T. J., Field, C. A. and Fraser, D. A. S. (1990) Approximations of marginal tail probabilities and inference for scalar parameters. Biometrika, 77, 77–95.

DiCiccio, T. J. and Field, C. A. (1991) An accurate method for approximate conditional and Bayesian inference about linear regression models from censored data. Biometrika, 78, 903–910.

Nuclear Power Station Data

Description

This data frame contains data on the construction of 32 light water reactors constructed in the USA.

Usage

data(nuclear)data(nuclear)

Format

A data frame with 32 observations on the following 11 variables.

cost: cost of construction (in billions of dollars adjusted to a 1976 base)
date: date at which the construction permit was issued
T1: time between application for and issue of permit
T2: time between issue of operating license and construction permit
cap: power plant capacity (in MWe)
PR: 1 if light water reactor already present on site
NE: 1 if constructed in north-east region of USA
CT: 1 if cooling tower used
BW: 1 if nuclear steam supply system manufactured by Babcock-Wilcox
N: cumulative number of power plants constructed by each architect-engineer
PT: 1 if partial turnkey plant

Source

The data were obtained from

Cox, D.R. and Snell, E.J. (1981). Applied Statistics (page 81, Example G). Chapman and Hall, London.

Examples

data(nuclear)
data(nuclear)

Generate Plots for an Approximate Marginal Inference Object

Description

Creates a set of plots for an object of class marg.

Usage

## S3 method for class 'marg'
plot(x = stop("nothing to plot"), from = x.axis[1], to = x.axis[n], 
     which = NULL, alpha = 0.05, add.leg = TRUE, loc.leg = FALSE, 
     add.labs = TRUE, cex = 0.7, cex.lab = 1, cex.axis = 1, 
     cex.main = 1, lwd1 = 1, lwd2 = 2, lty1 = "solid", 
     lty2 = "dashed", col1 = "black", col2 = "blue", tck = 0.02, 
     las = 1, adj = 0.5, lab = c(15, 15, 5), ...)           
## S3 method for class 'marg'
plot(x = stop("nothing to plot"), from = x.axis[1], to = x.axis[n], 
     which = NULL, alpha = 0.05, add.leg = TRUE, loc.leg = FALSE, 
     add.labs = TRUE, cex = 0.7, cex.lab = 1, cex.axis = 1, 
     cex.main = 1, lwd1 = 1, lwd2 = 2, lty1 = "solid", 
     lty2 = "dashed", col1 = "black", col2 = "blue", tck = 0.02, 
     las = 1, adj = 0.5, lab = c(15, 15, 5), ...)

Arguments

`x`	a `marg` object. This is assumed to be the result returned by the `cond.rsm` function.
`from`	the starting value for the x-axis range. The default value has been set by `cond.rsm`.
`to`	the ending value for the x-axis range. The default value has been set by `cond.rsm`.
`which`	which plot to print. Admissible values are `2` to `7` corresponding to the choices in the menu below.
`alpha`	the level used to read off confidence intervals; the default is 5%.
`add.leg`	if `TRUE`, a legend is added to each plot; default is `TRUE`.
`loc.leg`	if `TRUE`, the position of the legend can be located by hand; default is `FALSE`.
`add.labs`	if `TRUE`, labels are added; default is `TRUE`.
`cex`, `cex.lab`, `cex.axis`, `cex.main`	the character expansions relative to the standard size of the device to be used for printing text, labels, axes and main title. See `par` for details.
`lwd1`, `lwd2`	the line widths used to compare different curves in the same plot; default is `lwd2 = 2` for higher order solutions and `lwd1 = 1` for first order solutions.
`lty1`, `lty2`	line type used to compare different curves in the same plot; default is `lty2 = "dashed"` for the Wald statistic and `lty1 = "solid"` for the remaining first and higher order statistics.
`col1`, `col2`	colors used to compare different curves in the same plot; default is `col2 = "blue"` for higher order solutions, and `col1 = "black"` for the remaining first order statistics.
`tck`, `las`, `adj`, `lab`	further graphical parameters. See `par` for details.
`...`	optional graphical parameters; see `par` for details.

Details

Several plots are produced for an object of class marg. A menu lists all the plots that can be produced. They may be one or all of the following ones:

 Make a plot selection (or 0 to exit)

1: All
2: Profile and modified profile log likelihoods
3: Profile and modified profile likelihood ratios
4: Profile and modified likelihood root
5: Lugannani-Rice approximation
6: Confidence intervals
7: Diagnostics based on INF/NP decomposition

Selection:

If no nuisance parameters are presented, a subset of the above pictures is produced. A message is printed if this is the case. More details and examples are given in Brazzale (2000, Sections 6.5 and 5.3.2).

This function is a method for the generic function plot() for class marg. It can be invoked by calling plot or directly plot.marg for an object of the appropriate class.

Value

A plot is created on the current graphics device.

Side Effects

The current device is cleared. When add.leg is TRUE, a legend is added to each plot. Furthermore, if loc.leg is TRUE, the location of the legend can be set by the user. All screens are closed, but not cleared, on termination of the function.

Note

If the parameter of interest is the scale parameter, all calculations are performed on the log scale, though most results are reported on the original scale.

Four diagnostic plots are provided. The two panels on the right trace the information and nuisance correction terms, INF and NP, against the likelihood root statistic. These are generally smooth functions and used to approximate the information and nuisance parameter aspects as a function of the parameter of interest, as shown in the two panels on the left. This procedure has the advantage of largely eliminating the numerical instabilities that affect the statistics around the MLE. All four pictures are intended to give an idea of the order of magnitude of the two correction terms while trying to deal with the numerical problems that likely occur for these kinds of data.

More details can be found in Brazzale (2000, Appendix B.2).

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

Examples

# Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
#
# quadratic model fitted to the sea level, includes 18.62-year 
# astronomical tidal cycle and 11-year sunspot cycle
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
venice.marg <- cond(venice.rsm, I(Year^2))
plot(venice.marg, which = 4)
##
detach()
# Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
#
# quadratic model fitted to the sea level, includes 18.62-year 
# astronomical tidal cycle and 11-year sunspot cycle
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
venice.marg <- cond(venice.rsm, I(Year^2))
plot(venice.marg, which = 4)
##
detach()

Use print() on a “summary.marg” object

Description

This is a method for the function print() for objects of class summary.marg. See print and print.default for the general behaviour of this function and for the interpretation of digits.

Usage

## S3 method for class 'summary.marg'
print(x, all = x$all, Coef = x$cf, int = x$int, test = x$hyp,
      digits = if(!is.null(x$digits)) x$digits else max(3, getOption("digits")-3),
      ...)
## S3 method for class 'summary.marg'
print(x, all, Coef, int, test, digits, ...)
## S3 method for class 'summary.marg'
print(x, all = x$all, Coef = x$cf, int = x$int, test = x$hyp,
      digits = if(!is.null(x$digits)) x$digits else max(3, getOption("digits")-3),
      ...)
## S3 method for class 'summary.marg'
print(x, all, Coef, int, test, digits, ...)

Arguments

`x`	a `summary.marg` object. This is assumed to be the result returned by the `summary.marg` function.
`all`	if `TRUE` all the information stored in the `summary.marg` object is printed, else only a subset of it. The default is `FALSE`.
`Coef`	if `TRUE` all parameter estimates are printed. The default is `TRUE`.
`int`	if `TRUE` confidence intervals are printed. The default is `TRUE`.
`test`	if `TRUE` test statistics and tail probabilities are printed. The default is `FALSE`.
`digits`	the number of significant digits to be printed. The default depends on the value of `digits` set by `options`.
`...`	additional arguments.

Details

Changing the default values of all, Coef, int and test allows only a subset of the information in the summary.marg object to be printed. With all = FALSE, one-sided confidence intervals and the Lugannani-Rice tail area approximation are omitted. See summary.marg for more details.

Note

If the parameter of interest is the scale parameter, all calculations are performed on the log scale, though most results are reported on the original scale.

The amount of information printed may vary depending on whether there are any nuisance parameters. A message is printed if there are none.

Examples

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
houses.cond <- cond(houses.rsm, front)
print(summary(houses.cond), digits = 4)
print(summary(houses.cond), Coef = FALSE)
## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
houses.cond <- cond(houses.rsm, front)
print(summary(houses.cond), digits = 4)
print(summary(houses.cond), Coef = FALSE)

Compute Residuals for Regression-Scale Models

Description

Computes one of the six types of residuals available for regression-scale models.

Usage

## S3 method for class 'rsm'
residuals(object, type = c("deviance", "pearson", 
                           "response", "r.star", "prob", "deletion"),
          weighting = "observed", ...)
## S3 method for class 'rsm'
residuals(object, type = c("deviance", "pearson", 
                           "response", "r.star", "prob", "deletion"),
          weighting = "observed", ...)

Arguments

`object`	an object inheriting from class `rsm` representing a fitted regression-scale model.
`type`	character string; defines the type of residuals, with choices `"deviance"`, `"pearson"`, `"response"`, `"r.star"`, `"prob"` or `"deletion"`; the first is the default.
`weighting`	character string; defines the weight matrix that should be used in the calculation of the residuals and diagnostics. Possible choices are `"observed"`, `"score"`, `"deviance"` and `"max"`; see Jorgensen (1984) for their definition. The default is `"observed"`.
`...`	absorbs any additional argument.

Details

This is a method for the function residuals() for objects inheriting from class rsm. As several types of residuals are available for rsm objects, there is an additional optional argument type. The "deviance", "pearson", "r.star", "prob" and "deletion" residuals are derived from the final IRLS fit. The "response" residuals are standardized residuals on the scale of the response, the "prob" residuals are on the $U(0,1)$ scale, whereas the remaining ones follow approximately the standard normal distribution.

The default weighting scheme used is "observed". The weights used are the values stored in the q2 component of the rsm object. Some of the IRLS weights returned by rsm may be negative if the error distribution is Student's t or user-defined. In order to avoid missing values in the residuals, the default weighting scheme used is then "score" unless otherwise specified. The "score" weights are also used by default if Huber's least favourable error distribution is used.

More details, in particular of the use of these residuals, are given in Brazzale (2000, Section 6.3.1).

Value

A numeric vector of residuals. See Davison and Snell (1991) for detailed definitions of each type of residual.

Note

The summary method for rsm objects produces response residuals. The residuals component of a rsm object contains the response residuals.

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

Davison, A. C. and Snell, E. J. (1991) Residuals and diagnostics. In Statistical Theory and Modelling: In Honour of Sir David Cox (eds. D.V. Hinkley, N. Reid, and E.J. Snell), 83–106. London: Chapman \& Hall.

Davison, A. C. and Tsai, C.-L. (1992) Regression model diagnostics. Int. Stat. Rev., 60, 337–353.

Jorgensen, B. (1984). The delta algorithm and GLIM. Int. Stat. Rev., 52, 283–300.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
residuals(venice.rsm)
## deviance residuals with observed weights
residuals(venice.rsm, type = "r.star", weighting = "score")
## r* residuals with score weights
detach()
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
residuals(venice.rsm)
## deviance residuals with observed weights
residuals(venice.rsm, type = "r.star", weighting = "score")
## r* residuals with score weights
detach()

Fit a Regression-Scale Model

Description

Produces an object of class rsm which is a regression-scale model fit of the data.

Usage

rsm(formula = formula(data), family = gaussian, 
    data = sys.frame(sys.parent()), dispersion = NULL, 
    weights = NULL, subset = NULL, na.action = na.fail, 
    offset = NULL, method = "rsm.surv", 
    control = glm.control(maxit=100, trace=FALSE), 
    model = FALSE, x = FALSE, y = TRUE, contrasts = NULL, ...)
rsm(formula = formula(data), family = gaussian, 
    data = sys.frame(sys.parent()), dispersion = NULL, 
    weights = NULL, subset = NULL, na.action = na.fail, 
    offset = NULL, method = "rsm.surv", 
    control = glm.control(maxit=100, trace=FALSE), 
    model = FALSE, x = FALSE, y = TRUE, contrasts = NULL, ...)

Arguments

`formula`	a formula expression as for other linear regression models, of the form `response ~ predictors` where the predictors are separated by suitable operators. See the documentation of `lm` and `formula` for details.
`family`	a `family.rsm` object, i.e. a list of functions and expressions characterizing the error distribution. Families supported are `gaussian`, `student` (Student's t), `extreme` (Gumbel or extreme value), `logistic`, `logWeibull`, `logExponential`, `logRayleigh` and `Huber` (Huber's least favourable). These represent calls to the corresponding generator functions. The calls to `gaussian`, `extreme`, `logistic`, `logWeibull`, `logExponential` and `logRayleigh` can be given without parentheses. The functions `student` and `Huber` may take as argument respectively the degrees of freedom (`df`) and the tuning constant (`k`). Users can construct their own families, as long as they have components compatible with those given in `rsm.distributions`. The demonstration file ‘margdemo.R’ that ships with the package shows how to create a new generator function. The default is `gaussian`.
`data`	an optional data frame in which to interpret the variables occurring in the model formula, or in the `subset` and the `weights` arguments. If this is missing, then the variables in the formula should be on the search list.
`dispersion`	if `NULL`, the scale parameter is taken to be unknown. If known, the numerical value can be passed. The default is `NULL`. Huber's least favourable distribution represents a special case. If `dispersion` is `NULL`, the maximum likelihood estimate is computed, while if `TRUE` the MAD estimate is calculated and the scale parameter fixed to this value in subsequent computations.
`weights`	the optional weights for the fitting criterion. If supplied, the response variable and the covariates are multiplied by the weights in the IRLS algorithm. The length of the `weights` argument must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the `subset` argument.
`subset`	expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
`na.action`	a function to filter missing data. This is applied to the model frame after any `subset` argument has been used. The default (with `na.fail`) is to create an error if any missing value is found. A possible alternative is `na.omit`, which deletes observations that contain one or more missing values.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. An `offset` term can be included in the formula instead or as well, and if both are specified their sum is used. Defaults to `NULL`
`method`	the fitting method to be used; the default is `rsm.fit`. The method `model.frame` simply returns the model frame.
`control`	a list of iteration and algorithmic constants. See `glm.control` for their names and default values.
`model`	if `TRUE`, the model frame is returned; default is `FALSE`.
`x`	if `TRUE`, the model matrix is returned; default is `FALSE`.
`y`	if `TRUE`, the response variable is returned; default is `TRUE`.
`contrasts`	a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. The names of the list should be the names of the corresponding variables, and the elements should either be contrast-type matrices (matrices with as many rows as levels of the factor and with columns linearly independent of each other and of a column of one's), or else they should be functions that compute such contrast matrices.
`...`	absorbs any additional argument.

Details

The model is fitted using Iteratively Reweighted Least Squares, IRLS for short (Green, 1984, Jorgensen, 1984). The working response and iterative weights are computed using the functions contained in the family.rsm object.

The two workhorses of rsm are rsm.fit and rsm.surv, which expect an X and Y argument rather then a formula. The first function is used for the families student with df $<$ 3 and Huber; the second one, based on the survreg.fit routine for fitting parametric survival models, is used in case of extreme, logistic, logWeibull, logExponential, logRayleigh and student (with df > 2) error distributions. In the presence of a user-defined error distribution the rsm.fit routine is used. The rsm.null function is invoked to fit an empty (null) model.

The details are given in Brazzale (2000, Section 6.3.1).

Value

an object of class rsm is returned which inherits from glm and lm. See rsm.object for details.

The output can be examined by print, summary, rsm.diag.plots and anova. Components can be extracted using fitted, residuals, formula and family. It can be modified using update. It has most of the components of a glm object, with a few more. Use rsm.object for further details.

Note

In case of extreme, logistic, logWeibull, logExponential, logRayleigh and student (with df > 2) error distributions, both methods, rsm.fit (default choice) and rsm.surv, can be used to fit the model. There are, however, examples where one of the two algorithms (most likely the one invoked by rsm.surv) breaks down. If this is the case, try and refit the model with the alternative choice.

The message "negative iterative weights returned!" is returned if some of the iterative weights (q2 component of the fitted rsm object) are negative. These would be used by default by the rsm.diag routine for the definition of residuals and regression diagnostics. In order to avoid missing values (NAs), the default weighting scheme "observed" automatically switches to "score" unless otherwise specified.

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

Green, P. J. (1984) Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives (with Discussion). J. R. Statist. Soc. B, 46, 149–192.

Jorgensen, B. (1984) The delta algorithm and GLIM. Int. Stat. Rev., 52, 283–300.

Examples

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
## model fit including all covariates
houses.rsm <- rsm(price ~ ., family = student(5), data = houses, 
                  method = "rsm.fit", control = glm.control(trace = TRUE))
## prints information about the iterative procedure at each iteration
update(houses.rsm, ~ . - bdroom + offset(7 * bdroom))
## "bdroom" is included as offset variable with fixed (= 7) coefficient

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
venice.2.rsm <- rsm(sea ~ Year + I(Year^2), family = extreme)
## quadratic model fitted to sea level data
venice.1.rsm <- update(venice.2.rsm, ~. - I(Year^2))
## linear model fit
##
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
## includes 18.62-year astronomical tidal cycle and 11-year sunspot cycle
venice.11.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11, family = extreme)
venice.19.rsm <- rsm(sea ~ Year + I(Year^2) + c19 + s19, family = extreme)
## includes either astronomical cycle
##
## comparison of linear, quadratic and periodic (11-year, 19-year) models 
plot(year, sea, ylab = "sea level") 
lines(year, fitted(venice.1.rsm))
lines(year, fitted(venice.2.rsm), col="red")
lines(year, fitted(venice.11.rsm), col="blue")
lines(year, fitted(venice.19.rsm), col="green")
##
detach()

## Darwin's Data on Growth Rates of Plants
data(darwin)
darwin.rsm <- rsm(cross - self ~ pot - 1, family  =  student(3), 
                  data = darwin)
## Maximum likelihood estimates
darwin.rsm <- rsm(cross - self ~ pot - 1, family = Huber, data = darwin)
## M-estimates
## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
## model fit including all covariates
houses.rsm <- rsm(price ~ ., family = student(5), data = houses, 
                  method = "rsm.fit", control = glm.control(trace = TRUE))
## prints information about the iterative procedure at each iteration
update(houses.rsm, ~ . - bdroom + offset(7 * bdroom))
## "bdroom" is included as offset variable with fixed (= 7) coefficient

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
venice.2.rsm <- rsm(sea ~ Year + I(Year^2), family = extreme)
## quadratic model fitted to sea level data
venice.1.rsm <- update(venice.2.rsm, ~. - I(Year^2))
## linear model fit
##
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
## includes 18.62-year astronomical tidal cycle and 11-year sunspot cycle
venice.11.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11, family = extreme)
venice.19.rsm <- rsm(sea ~ Year + I(Year^2) + c19 + s19, family = extreme)
## includes either astronomical cycle
##
## comparison of linear, quadratic and periodic (11-year, 19-year) models 
plot(year, sea, ylab = "sea level") 
lines(year, fitted(venice.1.rsm))
lines(year, fitted(venice.2.rsm), col="red")
lines(year, fitted(venice.11.rsm), col="blue")
lines(year, fitted(venice.19.rsm), col="green")
##
detach()

## Darwin's Data on Growth Rates of Plants
data(darwin)
darwin.rsm <- rsm(cross - self ~ pot - 1, family  =  student(3), 
                  data = darwin)
## Maximum likelihood estimates
darwin.rsm <- rsm(cross - self ~ pot - 1, family = Huber, data = darwin)
## M-estimates

Diagnostics for Regression-Scale Models

Description

Calculates different types of residuals, Cook's distance and the leverages for a regression-scale model.

Usage

rsm.diag(rsmfit, weighting = "observed")
rsm.diag(rsmfit, weighting = "observed")

Arguments

`rsmfit`	an `rsm` object, i.e. the result of a call to `rsm`.
`weighting`	character string; defines the weight matrix that should be used in the calculation of the residuals and diagnostics. Possible choices are `"observed"`, `"score"`, `"deviance"` and `"max"`; see Jorgensen (1984) for their definition. The default is `"observed"`.

Details

If the weighting scheme is "observed", the weights used are the values stored in the q2 component of the rsm object rsmfit. Otherwise, they are calculated by rsm.diag. Some of the IRLS weights returned by rsm may be negative if the error distribution is Student's t or user-defined. In order to avoid missing values in the residuals and regression diagnostics, the default weighting scheme used in rsm.diag switches automatically from "observed" to "score" unless otherwise specified. The "score" weights are also used by default if Huber's least favourable error distribution is used.

There are three types of residuals. The response residuals are taken on the response scale, whereas the probability transform residuals are on the $U(0,1)$ scale. The remaining ones follow approximately the standard normal distribution.

More details and in particular the definitions of the above residuals and diagnostics can be found in Brazzale (2000, Section 6.3.1).

Value

Returns a list with the following components:

`resid`	the response residuals on the response scale.
`rd`	the standardized deviance residuals from the IRLS fit.
`rp`	the standardized Pearson residuals from the IRLS fit.
`rg`	the deletion residuals from the IRLS fit.
`rs`	the $r^*$ residuals from the IRLS fit.
`rcs`	the probability transform residuals from the IRLS fit.
`cook`	Cook's distance.
`h`	the leverages of the observations.
`dispersion`	the value of the scale parameter.

Acknowledgments

This function is based on A.J. Canty's function glm.diag contained in the package boot.

Note

Huber's least favourable distribution represents a special case. The regression diagnostics are only meaningful if the errors truly follow a Huber-type distribution. This no longer holds if the option family = Huber in rsm is used to obtain the M-estimates of the parameters in place or the maximum likelihood estimates.

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

Jorgensen, B. (1984) The delta algorithm and GLIM. Int. Stat. Rev., 52, 283–300.

Davison, A. C. and Snell, E. J. (1991) Residuals and diagnostics. In Statistical Theory and Modelling: In Honour of Sir David Cox (eds. D. V. Hinkley, N. Reid, and E. J. Snell), 83–106. London: Chapman & Hall.

Davison, A. C. and Tsai, C.-L. (1992) Regression model diagnostics. Int. Stat. Rev., 60, 337–353.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
venice.diag <- rsm.diag(venice.rsm)
## observed weights
detach()

## Darwin's Data on Growth Rates of Plants
data(darwin)
darwin.rsm <- rsm(cross-self ~ pot - 1, family = Huber, data = darwin)
darwin.diag <- rsm.diag(darwin.rsm)
## score weights
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
venice.diag <- rsm.diag(venice.rsm)
## observed weights
detach()

## Darwin's Data on Growth Rates of Plants
data(darwin)
darwin.rsm <- rsm(cross-self ~ pot - 1, family = Huber, data = darwin)
darwin.diag <- rsm.diag(darwin.rsm)
## score weights

Diagnostic Plots for Regression-Scale Models

Description

Generates diagnostic plots for a regression-scale model using different types of residuals, Cook's distance and the leverages.

Usage

rsm.diag.plots(rsmfit, rsmdiag = NULL, weighting = NULL, 
               which = NULL, subset = NULL, iden = FALSE, 
               labels = NULL, ret = FALSE, ...)
## S3 method for class 'rsm'
plot(x, ...)
rsm.diag.plots(rsmfit, rsmdiag = NULL, weighting = NULL, 
               which = NULL, subset = NULL, iden = FALSE, 
               labels = NULL, ret = FALSE, ...)
## S3 method for class 'rsm'
plot(x, ...)

Arguments

`rsmfit`, `x`	a `rsm` object, i.e. the result of a call to `rsm`.
`rsmdiag`	the object returned by a call to `rsm.diag` containing the regression diagnostics for the regression-scale model defined by `rsmfit`. If not supplied, this object is created by `rsm.diag.plots` and returned upon request (if `ret = TRUE`).
`weighting`	character string; defines the weight matrix that should be used in the calculation of the residuals and diagnostics. Possible choices are `"observed"`, `"score"`, `"deviance"` and `"max"`; see Jorgensen (1984) for their definition. Will only be used if the `rsmdiag` argument is missing.
`which`	which plot to print. Admissible values are `2` to `7` corresponding to the choices in the menu below.
`subset`	subset of data used in the original `rsm` fit: should be the same than the `subset` option used in the call to `rsm` which generated `rsmfit`. Needed only if the `subset` option was used in the call to `rsm`.
`iden`	logical argument. If `TRUE`, the user will be prompted after the plots are drawn. A positive integer will select a plot and invoke `identify()` on that plot. After exiting `identify()`, the user is again prompted, this loop continuing until the user responds to the prompt with `0`. If `iden` is `FALSE` (default) the user cannot interact with the plots.
`labels`	a vector of labels for use with `identify()` if `iden` is `TRUE`. If it is not supplied, then the labels are derived from `rsmfit`.
`ret`	logical argument indicating if `rsmdiag` should be returned; the default is `FALSE`.
`...`	additional arguments such as graphical parameters.

Details

The diagnostics required for the plots are calculated by rsm.diag. These are then used to produce the plots on the current graphics device.

A menu lists all the plots that can be produced. They may be one or all of the following:

 Make a plot selection (or 0 to exit)

1: All
2: Response residuals against fitted values
3: Deviance residuals against fitted values
4: QQ-plot of deviance residuals
5: Normal QQ-plot of r* residuals
6: Cook statistic against h/(1-h)
7: Cook statistic against observation number

Selection:

In the normal scores plots, the dotted line represents the expected line if the residuals are normally distributed, i.e. it is the line with intercept 0 and slope 1.

In general, when plotting Cook's distance against the standardized leverages, there will be two dotted lines on the plot. The horizontal line is at $8/(n-2p)$ , where $n$ is the number of observations and $p$ is the number of estimated parameters. Points above this line may be points with high influence on the model. The vertical line is at $2p/(n-2p)$ and points to the right of this line have high leverage compared to the variance of the raw residual at that point. If all points are below the horizontal line or to the left of the vertical line then the line is not shown.

Use of iden = TRUE is encouraged for proper exploration of these plots as a guide to how well the model fits the data and whether certain observations have an unduly large effect on parameter estimates.

Value

If ret is TRUE then the value of rsmdiag is returned, otherwise there is no returned value.

Side Effects

The current device is cleared. If iden = TRUE, interactive identification of points is enabled. All screens are closed, but not cleared, on termination of the function.

Acknowledgments

This function is based on A. J. Canty's function glm.diag.plots contained in the package boot.

References

Davison, A. C. and Snell, E. J. (1991) Residuals and diagnostics. In Statistical Theory and Modelling: In Honour of Sir David Cox (eds. D. V. Hinkley, N. Reid, and E. J. Snell), 83–106. London: Chapman & Hall, London.

Davison, A. C. and Tsai, C.-L. (1992) Regression model diagnostics. Int. Stat. Rev., 60, 337–353.

Jorgensen, B. (1984) The Delta Algorithm and GLIM. Int. Stat. Rev., 52, 283–300.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
## Not run: 
rsm.diag.plots(venice.rsm, which = 3)

## End(Not run)
## or
## Not run: 
plot(venice.rsm)

## End(Not run)
## menu-driven
##
rsm.diag.plots(venice.rsm, which = 5, las = 1)
## normal QQ-plot of r* residuals 
## Not run: 
rsm.diag.plots(venice.rsm, which = 7, iden = T, labels = paste(1931:1981))

## End(Not run)
## year 1932 highly influential
detach()
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
## Not run: 
rsm.diag.plots(venice.rsm, which = 3)

## End(Not run)
## or
## Not run: 
plot(venice.rsm)

## End(Not run)
## menu-driven
##
rsm.diag.plots(venice.rsm, which = 5, las = 1)
## normal QQ-plot of r* residuals 
## Not run: 
rsm.diag.plots(venice.rsm, which = 7, iden = T, labels = paste(1931:1981))

## End(Not run)
## year 1932 highly influential
detach()

Generate a RSM Family Object

Description

Generates a family.rsm object containing a list of functions and expressions used by rsm.

Usage

extreme()
Huber(k = 1.345)
logistic()
logWeibull()
student(df = stop("Argument \"df\" is missing, with no default"))
extreme()
Huber(k = 1.345)
logistic()
logWeibull()
student(df = stop("Argument \"df\" is missing, with no default"))

Arguments

`k`	the tuning constant in Huber's least favourable distribution.
`df`	the degrees of freedom in Student's t distribution.

Details

Each of the names are associated with a member of the class of error distributions for regression-scale models. Users can construct their own families, as long as they have components compatible with those given in rsm.distributions. The demonstration file ‘margdemo.R’ that accompanies the package shows how to create a new generator function. When passed as an argument to rsm with the default setting, the empty parentheses () can be omitted. There is a print method for the class family.rsm.

Value

A family.rsm object, which is a list of functions and expressions used by rsm in the iteratively reweighted least-squares algorithm. See family.rsm.object for details.

Examples

student(df = 3) ## generates Student's t error distribution with 3 d.f.
## Not run: 
rsm(formula = value, data = value, family = extreme)

## End(Not run)
student(df = 3) ## generates Student's t error distribution with 3 d.f.
## Not run: 
rsm(formula = value, data = value, family = extreme)

## End(Not run)

Fit a Regression-Scale Model Without Computing the Model Matrix

Description

Fits a rsm model without computing the model matrix of the response vector.

Usage

rsm.fit(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, 
        trace, ...)
rsm.fit(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, 
        trace, ...)

Arguments

`X`	the model matrix (design matrix).
`Y`	the response vector.
`dispersion`	if `NULL`, the MLE of the scale parameter is returned, otherwise the scale parameter is fixed to the numerical value passed through the argument. If Huber's least favourable distribution is used and `dispersion` is `TRUE`, the MAD is computed and the scale parameter fixed to this value in subsequent calculations.
`score.dispersion`	must default to `NULL`.
`offset`	optional offset added to the linear predictor.
`family`	a `family.rsm` object, i.e. a list of functions and expressions characterizing the error distribution. Families supported are `gaussian`, `student` (Student's t), `extreme` (Gumbel or extreme value), `logistic`, `logWeibull`, `logExponential`, `logRayleigh` and `Huber` (Huber's least favourable). Users can construct their own families, as long as they have components compatible with those given in `rsm.distributions`. The demonstration file ‘margdemo.R’ that ships with the package shows how to create a new generator function.
`maxit`	maximum number of iterations allowed.
`epsilon`	convergence threshold.
`trace`	if `TRUE`, iterations details are printed during execution.
`...`	not used, but do absorb any redundant argument.

Details

The rsm.fit function is called internally by the rsm routine to do the actual model fitting. Although it is not intended to be used directly by the user, it may be useful when the same data frame is used over and over again. It might save computational time, since the model matrix is not created. No formula needs to be specified as an argument. As no weights argument is available, the response Y and the model matrix X must already include the weights if weighting is desired.

Value

an object which is a subset of a rsm object.

Note

The rsm.fit function is the workhorse of the rsm fitting routine for the student (with df $\leq$ 2), Huber and user-defined error distributions. It receives X and Y data rather than a formula, but still uses the family.rsm object to define the IRLS steps. Users can write their own versions of rsm.fit, and pass the name of their function via the method argument to rsm. Care should be taken to include as many of the arguments as feasible, but definitely the ... argument, which will absorb any additional argument given in the call from rsm.

Fit an Empty Regression-Scale Model

Description

Fits a rsm model with empty model matrix.

Usage

rsm.null(X = NULL, Y, offset, family, dispersion, score.dispersion, maxit, 
         epsilon, trace, ...)
rsm.null(X = NULL, Y, offset, family, dispersion, score.dispersion, maxit, 
         epsilon, trace, ...)

Arguments

`X`	defaults to `NULL`.
`Y`	the response vector.
`dispersion`	either `NULL` or `TRUE`. If `NULL`, the MLE of the scale parameter is returned. If Huber's least favourable distribution is used and `dispersion` is `TRUE`, the MAD is computed and the scale parameter fixed to this value in subsequent calculations.
`score.dispersion`	must default to `NULL`.
`offset`	optional offset added to the linear predictor.
`family`	a `family.rsm` object, i.e. a list of functions and expressions characterizing the error distribution. Families supported are `gaussian`, `student` (Student's t), `extreme` (Gumbel or extreme value), `logistic`, `logWeibull`, `logExponential`, `logRayleigh` and `Huber` (Huber's least favourable). Users can construct their own families, as long as they have components compatible with those given in `rsm.distributions`. The demonstration file ‘margdemo.R’ that ships with the package shows how to create a new generator function.
`maxit`	maximum number of iterations allowed.
`epsilon`	convergence threshold.
`trace`	if `TRUE`, iterations details are printed during execution.
`...`	not used, but do absorb any redundant argument.

Details

The rsm.null function is called internally by the rsm routine to do the actual model fitting in case of an empty model. It is not intended to be used directly by the user. As no weights argument is available, the response Y and the model matrix X must already include the weights if weighting is desired.

Value

an object which is a subset of a rsm object.

Regression-Scale Model Object

Description

Class of objects returned when fitting a regression-scale model.

Arguments

The following components must be included in a rsm object:

`coefficients`	the coefficients of the linear predictor, which multiply the columns of the model matrix. The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to inestimable coefficients.
`dispersion`	the (estimated or known) value of the scale parameter.
`fixed`	a logical value. If `TRUE`, the scale parameter is fixed.
`residuals`	the response residuals from the fit. If weights were used, they are not taken into account. If you need other kinds of residuals, use the `residuals.rsm` function.
`fitted.values`	the fitted values from the fit. If weights were used, the fitted values are not adjusted for the weights.
`loglik`	the log likelihood from the fit.
`q1`	the value of the first derivative of minus the log density for each observation.
`q2`	the value of the second derivative of minus the log density for each observation.
`rank`	the computed rank (number of linearly independent columns in the model matrix).
`R`	the unscaled observed information matrix.
`score.dispersion`	a list containing the value of the objective function, its gradient and the convergence diagnostic, that result from estimating the scale parameter.
`iter`	the number of IRLS iterations used to compute the estimates.
`weights`	the (optional) weights used for the fit.
`assign`	the list of assignments of coefficients (and effects) to the terms in the model. The names of this list are the names of the terms. The ith element of the list is the vector saying which coefficients correspond to the ith term. It may be of length 0 if there were no estimable effects for the term.
`df.residuals`	the number of degrees of freedom for residuals.
`family`	the entire `family.rsm` object used.
`user.def`	a logical value. If `TRUE`, the error distribution is user-defined.
`dist`	a character string representing the name of the error distribution.
`formula`	the model formula.
`data`	the data frame in which to interpret the variables occurring in the model formula, or in the `subset` and the `weights` arguments to `rsm`.
`terms`	an object of mode `expression` and class `term` summarizing the formula.
`contrasts`	a list containing sufficient information to construct the contrasts used to fit any factors occurring in the model. The list contains entries that are either matrices or character vectors. When a factor is coded by contrasts, the corresponding contrast matrix is stored in this list. Factors that appear only as dummy variables and variables in the model that are matrices correspond to character vectors in the list. The character vector has the level names for a factor or the column labels for a matrix.
`control`	a list of iteration and algorithmic constants used in `rsm` to fit the model.
`call`	an image of the call that produced the object, but with the arguments all named and with the actual formula included as the formula argument.
`y`	optionally the response, if `y = TRUE` in the original `rsm` call.
`x`	optionally the model matrix, if `x = TRUE` in the original `rsm` call.
`model`	optionally the model frame, if `model = TRUE` in the original `rsm` call.

Generation

This class of objects is returned by the rsm function to represent a fitted regression-scale model. Class rsm inherits from classes glm and lm, since it is fitted by iteratively reweighted least squares. The object returned has all the components of a weighted least squares object.

Methods

Objects of this class have methods for the functions print, summary, anova and fitted among others.

Note

The residuals, fitted values and coefficients should be extracted by the generic functions of the same name, rather than by the $ operator.

Fit a Regression-Scale Model Without Computing the Model Matrix

Description

Fits a rsm model without computing the model matrix of the response vector.

Usage

rsm.surv(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, 
         trace, ...)
rsm.surv(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, 
         trace, ...)

Arguments

`X`	the model matrix (design matrix).
`Y`	the response vector.
`offset`	optional offset added to the linear predictor.
`family`	a `family.rsm` object, i.e. a list of functions and expressions characterizing the error distribution. Families supported are `extreme` (Gumbel or extreme value), `logWeibull`, `logExponential`, `logRayleigh`, `logistic` and `student` (Student's t) with `df` $>$ 2.
`dispersion`	if `NULL`, the MLE of the scale parameter is returned, otherwise the scale parameter is fixed to the numerical value passed through the argument.
`score.dispersion`	must default to `NULL`.
`maxit`	maximum number of iterations.
`epsilon`	convergence threshold.
`trace`	if `TRUE`, iterations details are printed during execution.
`...`	not used, but do absorb any redundant argument.

Details

The rsm.surv function is called internally by the rsm routine to do the actual model fitting. Although it is not intended to be used directly by the user, it may be useful when the same data frame is used over and over again. It might save computational time, since the model matrix is not created. No formula needs to be specified as an argument. As no weights argument is available, the response Y and the model matrix X must already include the weights if weighting is desired.

Value

an object, which is a subset of a rsm object.

Note

The rsm.surv function is the default option for rsm for the extreme, logistic, logWeibull, logExponential, logRayleigh and student (with df larger than 2) error distributions. It makes use of the survreg.fit routine to estimate parametric survival models. It receives X and Y data rather than a formula, but still uses the family.rsm object to define the IRLS steps. The rsm.surv routine cannot be used for Huber-type and user-defined error distributions.

Summary Method for Objects of Class “marg”

Description

Returns a summary list for objects of class marg.

Usage

## S3 method for class 'marg'
summary(object, alpha = 0.05, test = NULL, all = FALSE, 
        coef = TRUE, int = ifelse((is.null(test) || all), TRUE, FALSE), 
        digits = NULL, ...)
## S3 method for class 'marg'
summary(object, alpha = 0.05, test = NULL, all = FALSE, 
        coef = TRUE, int = ifelse((is.null(test) || all), TRUE, FALSE), 
        digits = NULL, ...)

Arguments

`object`	a `marg` object. This is assumed to be the result returned by the `cond.rsm` function.
`alpha`	a vector of levels for confidence intervals; the default is 5%.
`test`	a vector of values of the parameter of interest one wants to test for. If `NULL` no test is performed. The default is `NULL`.
`all`	logical value; if `TRUE` all the information stored in the `summary.marg` object is printed, else only a subset of it. The default is `FALSE`.
`coef`	logical value; if `TRUE` the unconditional and approximate conditional/marginal MLEs are printed. The default is `TRUE`.
`int`	logical value; if `TRUE` confidence intervals are printed. The default is `TRUE`.
`digits`	the number of significant digits to be printed. The default depends on the value of `digits` set by `options`
`...`	absorbs any additional argument.

Details

This function is a method for the generic function summary() for objects of class marg. It can be invoked by calling summary or directly summary.marg for an object of the appropriate class.

Value

A list is returned with the following components:

`coefficients`	a $2\times 2$ matrix containing the unconditional and approximate conditional/marginal MLEs and their standard errors.
`conf.int`	a matrix containing, for each level given in `alpha`, the upper and lower confidence bounds derived from several first and higher order test statistics. One-sided and two-sided confidence intervals are considered. See `marg.object` for details on the test statistics used.
`signif.tests`	a list with two elements. The first (`stats`) contains, for each value given in `test`, the values and tail probabilities of several first and higher order test statistics. See `marg.object` for details on the test statistics. The second element of the list (`qTerm`) contains for each tested hypothesis the correction term used in the higher order solutions.
`call`	the function call that created the `marg` object.
`formula`	the model formula.
`family`	the name of the error distribution.
`offset`	the covariate occurring in the model formula whose coefficient represents the parameter of interest or `scale` if the parameter of interest is the scale parameter.
`alpha`	the vector of levels used to compute the confidence intervals.
`hypotheses`	the values for the parameter of interest that have been tested for.
`diagnostics`	the information and nuisance parameters aspects; see `marg.object` for details.
`n.approx`	the number of output points that have been calculated exactly.
`all`	logical value; if `TRUE`, all the information stored in the `summary.marg` object is printed.
`cf`	logical value; if `TRUE`, the parameter estimates are printed.
`int`	logical value; if `TRUE`, confidence intervals are printed.
`is.scalar`	a logical value indicating whether there are any nuisance parameters. If `FALSE` there are none.
`digits`	the number of significant digits to be printed.

Note

If the parameter of interest is the scale parameter, all calculations are performed on the log scale, though most results are reported on the original scale.

The amount of information calculated may vary depending on whether there are any nuisance parameters. A message is printed if there are none.

Examples

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
houses.marg <- cond(houses.rsm, floor)
summary(houses.marg, test = 0, coef = FALSE)
## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
houses.marg <- cond(houses.rsm, floor)
summary(houses.marg, test = 0, coef = FALSE)

Summary Method for Regression-Scale Models

Description

Returns a summary list for a fitted regression-scale model.

Usage

## S3 method for class 'rsm'
summary(object, correlation = FALSE, digits = NULL, ...)
## S3 method for class 'rsm'
summary(object, correlation = FALSE, digits = NULL, ...)

Arguments

`object`	a fitted `rsm` object. This is assumed to be the result of some fit that produces an object inheriting from the class `rsm`, in the sense that the components returned by the `rsm` function will be available.
`correlation`	logical argument. If `TRUE`, the correlation matrix for the coefficients is computed; default is `TRUE`.
`digits`	a non-null value specifies the minimum number of significant digits to be printed in values. If `NULL`, the value of `digits` set by `options` is used.
`...`	absorbs any additional argument.

Details

This function is a method for the generic function summary() for class rsm. It can be invoked by calling summary for an object of the appropriate class, or directly by calling summary.rsm regardless of the class of the object.

Value

A list is returned with the following components:

`coefficients`	a matrix with four columns, containing the coefficients, their standard errors, the $z$ -values (or Wald statistics) and the associated $p$ -values based on the standard normal approximation to the distribution of the $z$ statistic.
`dispersion`	the value of the scale parameter used in the computations.
`fixed`	a logical value. If `TRUE`, the scale parameter is known.
`residuals`	the response residuals.
`cov.unscaled`	the unscaled covariance matrix, i.e, a matrix such that multiplying it by the squared scale parameter, or an estimate thereof, produces an estimated (asymptotic) covariance matrix for the coefficients.
`correlation`	the computed correlation matrix for the coefficients in the model.
`family`	the entire `family.rsm` object used.
`loglik`	the computed log likelihood.
`terms`	an object of mode `expression` and class `term` summarizing the formula.
`df`	the number of degrees of freedom for the model and for the residuals.
`iter`	the number of IRLS iterations used to compute the estimates.
`nas`	a logical vector indicating which, if any, coefficients are missing.
`call`	an image of the call that produced the `rsm` object, but with the arguments all named and with the actual formula.
`digits`	the value of the `digits` argument.

Examples

## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
summary(houses.rsm)
## House Price Data
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
summary(houses.rsm)

Update and Re-fit a RSM Model Call

Description

update.rsm is used to update a rsm model formulae. This typically involves adding or dropping terms, but updates can be more general.

Usage

## S3 method for class 'rsm'
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'rsm'
update(object, formula., ..., evaluate = TRUE)

Arguments

`object`	a model of class `rsm` to be updated.
`formula.`	changes to the formula – see `update.formula` for details.
`...`	additional arguments to the call, or arguments with changed values. Use `name = NULL` to remove the argument `name`.
`evaluate`	if `TRUE` evaluate the new call else return the call.

Value

If evaluate = TRUE the fitted object, otherwise the updated call.

Note

Based upon update.default.

Examples

data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
## model fit including all covariates
##
houses.rsm <- update(houses.rsm, method = "rsm.fit", 
                     control = glm.control(trace = TRUE))
## prints information about the iterative procedure at each iteration
##
update(houses.rsm, ~ . - bdroom + offset(7 * bdroom))
## "bdroom" is included as offset variable with fixed (= 7) coefficient
data(houses)
houses.rsm <- rsm(price ~ ., family = student(5), data = houses)
## model fit including all covariates
##
houses.rsm <- update(houses.rsm, method = "rsm.fit", 
                     control = glm.control(trace = TRUE))
## prints information about the iterative procedure at each iteration
##
update(houses.rsm, ~ . - bdroom + offset(7 * bdroom))
## "bdroom" is included as offset variable with fixed (= 7) coefficient

Calculate Variance-Covariance Matrix for a Fitted RSM Model

Description

Returns the variance-covariance matrix of the parameters of a fitted rsm model object.

Usage

## S3 method for class 'rsm'
vcov(object, correlation = FALSE, ...)
## S3 method for class 'rsm'
vcov(object, correlation = FALSE, ...)

Arguments

`object`	a fitted model object of class `rsm`.
`correlation`	if `TRUE` the correlation matrix is returned instead of the variance-covariance matrix.
`...`	absobs any additional argument.

Details

This is a method for function vcov for objects of class rsm.

Value

A matrix of the estimated covariances between the parameter estimates of a fitted regression-scale model, or, if dispersion = TRUE the correlation matrix.

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
vcov(venice.rsm)
vcov(venice.rsm, corr = TRUE)
##
detach()
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
vcov(venice.rsm)
vcov(venice.rsm, corr = TRUE)
##
detach()

Sea Level Data

Description

The venice data frame has 51 rows and 2 columns.

Pirazzoli (1982) collected the ten largest values of sea levels in Venice (with a few exceptions) for the years 1887–1981. The venice data frame contains the maxima for the years 1931–1981.

Usage

data(venice)data(venice)

Format

This data frame contains the following columns:

year: the years;
sea: the sea levels (in cm).

Source

The data were obtained from

Smith, R. L. (1986) Extreme value theory based on the $r$ -largest annual events. Journal of Hydrology , 86, 27–43.

References

Pirazzoli, P. (1982) Maree estreme a Venezia (periodo 1872–1981). Acqua Aria, 10, 1023–1039.

Examples

data(venice)
attach(venice)
#
plot(sea ~ year, ylab = "sea level")
##
Year <- 1:51/51
venice.l <- rsm(sea ~ Year + I(Year^2), family = extreme)
lines(year, fitted(venice.l))
##
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.p <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                family = extreme)
lines(year, fitted(venice.p), col = "red")
##
detach()
data(venice)
attach(venice)
#
plot(sea ~ year, ylab = "sea level")
##
Year <- 1:51/51
venice.l <- rsm(sea ~ Year + I(Year^2), family = extreme)
lines(year, fitted(venice.l))
##
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.p <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                family = extreme)
lines(year, fitted(venice.p), col = "red")
##
detach()

Package 'marg'

Help Index

Approximate marginal inference for regression-scale models

Description

Details

Author(s)

Approximate Conditional Inference - Generic Function

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Approximate Conditional Inference in Regression-Scale Models

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Darwin's Data on Growth Rates of Plants

Description

Usage

Format

Source

References

Examples

Use family() on a “rsm” object

Description

Usage

Arguments

See Also

Examples

Family Object for Regression-Scale Models

Description

Generation

Structure

Note

See Also

House Price Data

Description

Usage

Format

Source

Examples

Huber's Least Favourable Distribution

Description

Usage

Arguments

Details

Value

Side Effects

Background

References

Examples

Compute the Log Likelihood for Regression-Scale Models

Description

Usage

Arguments

Details

Value

Note

See Also

Examples

Approximate Marginal Inference Object

Description

Arguments

Generation

Methods

Note

References

See Also

Nuclear Power Station Data

Description

Usage