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Showing results 1 to 10 of 4,248.


Function aodml [aods3 v0.4-1]
keywords
regression
title
ML Estimation of Generalized Linear Models for Overdispersed Count Data
description
The function fits a beta-binomial (BB) or a negative binomial (NB) generalized linear model from clustered data. For the BB model, data have the form {$(n_1, m_1), (n_2, m_2), ..., (n_N, m_N)$}, where $n_i$ is the size of cluster $i$, $m_i$ the number of successes, and $N$ the number of clusters. The response is the proportion $y = m/n.$ For the NB model, data can be of two types. When modeling simple counts, data have the form {$m_1, m_2, ..., m_N$}, where $m_i$ is the number of occurences of the event under study. When modeling rates (e.g. hazard rates), data have the same form as for the BB model, where $n_i$ is the denominator of the rate for cluster $i$ (considered as an offset, i.e. a constant known value) and $m_i$ the number of occurences of the event. For both types of data, the response is the count $y = m$.
Function aodql [aods3 v0.4-1]
keywords
regression
title
QL/MM Estimation of Generalized Linear Models for Overdispersed Count Data
description
From clustered data, the function fits generalized linear models containing an over-dispersion parameter $\Phi$ using quasi-likelihood estimating equations for the mean $\mu$ and a moment estimator for $\Phi$. For binomial-type models, data have the form {$(n_1, m_1), (n_2, m_2), ..., (n_N, m_N)$}, where $n_i$ is the size of cluster $i$, $m_i$ the number of successes, and $N$ the number of clusters. The response is the proportion $y = m/n$. For Poisson-type models, data can be of two forms. When modeling simple counts, data have the form {$m_1, m_2, ..., m_N$}, where $m_i$ is the number of occurences of the event under study. When modeling rates (e.g. hazard rates), data have the same form as for the BB model, where $n_i$ is the denominator of the rate for cluster $i$ (considered as an offset, i.e. a constant known value) and $m_i$ the number of occurences of the event. For both forms of data, the response is the count $y = m$.
Function stls [truncSP v1.2.2]
keywords
regression
title
Estimation of truncated regression models using the Symmetrically Trimmed Least Squares (STLS) estimator
description
Function for estimation of linear regression models with truncated response variables (fixed truncation point), using the STLS estimator (Powell 1986)
Function qme [truncSP v1.2.2]
keywords
regression
title
Estimation of truncated regression models using the Quadratic Mode Estimator (QME)
description
Estimation of linear regression models with truncated response variables (fixed truncation point), using the Quadratic Mode Estimator (QME) (Lee 1993 and Laitila 2001)
Function lt [truncSP v1.2.2]
keywords
regression
title
Estimation of truncated regression models using the Left Truncated (LT) estimator
description
Estimates linear regression models with truncated response variables (fixed truncation point), using the LT estimator (Karlsson 2006).
Function plinks [glmx v0.1-1]
keywords
regression
title
Parametric Links for Binomial Generalized Linear Models
description
Various symmetric and asymmetric parametric links for use as link function for binomial generalized linear models.
Function breakpoints.glogisfit [glogis v1.0-0]
keywords
regression
title
Segmented Fitting of the Generalized Logistic Distribution
description
Fitting univariate generalized logisitc distributions (Type I: skew-logistic with location, scale, and shape parameters) to segments of time series data.
Function glogisfit [glogis v1.0-0]
keywords
regression
title
Fitting the Generalized Logistic Distribution
description
Fit a univariate generalized logisitc distribution (Type I: skew-logistic with location, scale, and shape parameters) to a sample of observations.
Function glmx.control [glmx v0.1-1]
keywords
regression
title
Control Parameters for GLMs with Extra Parameters
description
Various parameters that control fitting of generalized linear models with extra parameters using glmx.
Function hetglm.control [glmx v0.1-1]
keywords
regression
title
Control Parameters for Heteroskedastic Binary Response GLMs
description
Various parameters that control fitting of heteroskedastic binary response models using hetglm.