clmm2.control
(Package: ordinal) :
Set control parameters for cumulative link mixed models
Set control parameters for cumulative link mixed models
● Data Source:
CranContrib
● Keywords: models
● Alias: clmm2.control
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The VarCorr function extracts the variance and (if present) correlation parameters for random effect terms in a cumulative link mixed model (CLMM) fitted with clmm.
● Data Source:
CranContrib
● Keywords: models
● Alias: VarCorr, VarCorr.clmm
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Set control parameters for cumulative link mixed models
● Data Source:
CranContrib
● Keywords: models
● Alias: clmm.control
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Computes confidence intervals from the profiled likelihood for one or more parameters in a fitted cumulative link model, or plots the profile likelihood function.
● Data Source:
CranContrib
● Keywords: models
● Alias: confint.clm2, confint.profile.clm2, plot.profile.clm2, profile.clm2
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This package facilitates analysis of ordinal (ordered categorical data) via cumulative link models (CLMs) and cumulative link mixed models (CLMMs). Robust and efficient computational methods gives speedy and accurate estimation. A wide range of methods for model fits aids the data analysis.
● Data Source:
CranContrib
● Keywords: package
● Alias: ordinal, ordinal-package
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Coefficients (columns) are dropped from a design matrix to ensure that it has full rank.
● Data Source:
CranContrib
● Keywords: models
● Alias: drop.coef
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Fits cumulative link models (CLMs) such as the propotional odds model. The model allows for various link functions and structured thresholds that restricts the thresholds or cut-points to be e.g., equidistant or symmetrically arranged around the central threshold(s). Nominal effects (partial proportional odds with the logit link) are also allowed. A modified Newton algorithm is used to optimize the likelihood function.
● Data Source:
CranContrib
● Keywords: models
● Alias: clm
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update.clm2
(Package: ordinal) :
Update method for cumulative link models
Update method for cumulative link models fitted with clm2. This makes it possible to use e.g. update(obj, location = ~ . - var1, scale = ~ . + var2)
● Data Source:
CranContrib
● Keywords: models
● Alias: update.clm2, update.clmm2
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Try fitting all models that differ from the current model by adding or deleting a single term from those supplied while maintaining marginality.
● Data Source:
CranContrib
● Keywords: models
● Alias: addterm.clm2, dropterm.clm2
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The ranef function extracts the conditional modes of the random effects from a clmm object. That is, the modes of the distributions for the random effects given the observed data and estimated model parameters. In a Bayesian language they are posterior modes.
● Data Source:
CranContrib
● Keywords: models
● Alias: condVar, condVar.clmm, ranef, ranef.clmm
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providing 
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