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hoa (version 2.1.4)

Higher Order Likelihood Inference

Description

Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).

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Version

Install

install.packages('hoa')

Monthly Downloads

36

Version

2.1.4

License

GPL (>= 2) | file LICENCE

Homepage

http://www.r-project.org

Maintainer

Alex-Antoine Fortin

Last Published

August 12th, 2015

Functions in hoa (2.1.4)

`Daphnia Magna' Data

Use family() on a ``rsm'' object

Convert Covariance Matrix to Correlation Matrix --- Generic Function

contour.all.nlreg.profiles

Contour Method for `nlreg' Objects

Use print() on a ``rsm'' object

family.rsm.object

Family Object for Regression-Scale Models

Nuclear Power Station Data

Use param() on a `nlreg' object

Metsulfuron Methyl Data

print.summary.cond

Use print() on a ``summary.cond'' object

Approximate Conditional Inference Object

Crying Babies Data

Fit a Nonlinear Heteroscedastic Model via Maximum Likelihood

Fit a Regression-Scale Model Without Computing the Model Matrix

print.family.rsm

Use print() on a ``family.rsm'' object

Plot a fr Object

Summary Method for `mpl' Objects

Regression-Scale Model Object

Extract All Parameters from a Model --- Generic Function

var2cor.nlregmpl

Use var2cor() on a `nlreg' and `mpl' object

residuals.nlreg

Use residuals() on a `nlreg' object

summary.nlreg.profile

Summary Method for Objects of Class `nlreg.profile'

Nonlinear Heteroscedastic Model Diagnostics

Use print() on a `nlreg' object

nlreg.contours.object

Contour Object for Nonlinear Heteroscedastic Models

Six Herbicide Data Sets

Huber's Least Favourable Distribution

Approximate Conditional Inference in Regression-Scale Models

Use fitted() on a `nlreg' object

Darwin's Data on Growth Rates of Plants

Compute the Log Likelihood for Regression-Scale Models

Returns the Observed Information Matrix --- Generic Function

print.summary.rsm

Use print() on a ``summary.rsm'' object

Use print() on a `mpl' object

Generate a RSM Family Object

Support for Functions rsm.fit and rsm.surv

Approximate Conditional Inference - Generic Function

AIDS Symptoms and AZT Use Data

Approximate Marginal Inference Object

Use print() on a ``marg'' object

print.summary.mpl

Use print() on a `summary.mpl' object

Maximum Adjusted Profile Likelihood Estimates for a `nlreg' Object

Profile Method for `nlreg' Objects

Summary Method for Nonlinear Heteroscedastic Models

Summary Method for Objects of Class ``cond''

Chlorsulfuron Data

Expected Information Matrix for `nlreg' Objects

Use family() on a ``cond'' object

Higher Order Likelihood Inference

plot.nlreg.contours

Use plot() on a `nlreg.contours' object

family.summary.cond

Use family() on a ``summary.cond'' object

Use anova() on a ``rsmlist'' object

Support for `nlreg' package of `hoa' bundle

Fit a Regression-Scale Model

print.summary.nlreg.profiles

Use print() on a `summary.nlreg.profile' and `summary.all.nlreg.profiles' object

Tangent exponential model: Higher Order Likelihood Approximation

Differentiate the Mean Function of a Nonlinear Model

ANOVA Table for a RSM Object

all.profiles.nlreg

Support for function `profile.nlreg'

Fungal Infections Treatment Data

Approximate Conditional Inference for Logistic and Loglinear Models

Maximum Adjusted Profile Likelihood Object

Fraudulent Automobile Insurance Claims Data

Compute the Log Likelihood for Nonlinear Heteroscedastic Models

Generate Plots for an Approximate Conditional Inference Object

Helicopter Data

plot.nlreg.diag

Diagnostic Plots for Nonlinear Heteroscedastic Models

Use coef() on a `nlreg' object

Compute Residuals for Regression-Scale Models

Nonlinear Heteroscedastic Model Object

Generate Plots for an Approximate Marginal Inference Object

Fit a Regression-Scale Model Without Computing the Model Matrix

print.summary.marg

Use print() on a ``summary.marg'' object

summary.all.nlreg.profiles

Summary Method for Objects of Class `all.nlreg.profiles'

Summary Method for Objects of Class ``marg''

Summary Method for Regression-Scale Models

Observed Information Matrix for `nlreg' Objects

Calculate Variance-Covariance Matrix for a Fitted RSM Model

nlreg.profile.objects

Profile Objects for Nonlinear Heteroscedastic Models

Differentiate the Variance Function of a Nonlinear Model

make.family.rsm

Support for RSM Family Functions

print.nlreg.contours

Use print() on a `nlreg.contours' object

print.summary.nlreg

Use print() on a `summary.nlreg' object

Diagnostics for Regression-Scale Models

Update and Re-fit a RSM Model Call

House Price Data

Maximum Adjusted Profile Likelihood Estimation --- Generic Function

Returns the Expected Information Matrix --- Generic Function

Use print() on a ``cond'' object

print.nlreg.profiles

Use print() on a `nlreg.profile' and `all.nlreg.profiles' object

plot.nlreg.profiles

Use plot() on a `profile.nlreg' and `all.profiles.nlreg' object

Diagnostic Plots for Regression-Scale Models

rsm.distributions

RSM Family Support Object

Likelihood-Based Confidence Intervals Based on fr Object

Fit an Empty Regression-Scale Model

Radioimmunoassay Data