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#Com.numericalmethod.suanshu.stats.regression.linear.glm Classes and Interfaces - 44 results found.
| Name | Description | Type | Package | Framework |
| BackwardElimination | Constructs a GLM model for a set of observations using the backward elimination method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| BackwardElimination .Step | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu | |
| EliminationByAIC | In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| EliminationByZValue | In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| ForwardSelection | Constructs a GLM model for a set of observations using the forward selection method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| ForwardSelection .Step | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu | |
| GeneralizedLinearModel | The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| GeneralizedLinearModelQuasiFamily | GLM for the quasi-families. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| GLMBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| GLMBinomial | This is the Binomial distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMExponentialDistribution | This interface represents a probability distribution from the exponential family. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMFamily | Family provides a convenient way to specify the error distribution and link function used in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMFitting | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| GLMGamma | This is the Gamma distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMGaussian | This is the Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMInverseGaussian | This is the Inverse Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMModelSelection | Given a set of observations {y, X}, we would like to construct a GLM to explain the data. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| GLMModelSelection .ModelNotFound | Throw a ModelNotFound exception when fail to construct a model toSee Also:Serialized Form | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| GLMPoisson | This is the Poisson distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMProblem | This is a Generalized Linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| GLMResiduals | Residual analysis of the results of a Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| IWLS | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| LinkCloglog | This class represents the complementary log-log link function: g(x) = log(-log(1 - x)) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkFunction | This interface represents a link function g(x) in Generalized Linear Model (GLM). | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkIdentity | This class represents the identity link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkInverse | This class represents the inverse link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkInverseSquared | This class represents the inverse-squared link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkLog | This class represents the log link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkLogit | This class represents the logit link function: g(x) = log(frac{mu}{1-mu}) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkProbit | This class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkSqrt | This class represents the square-root link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| QuasiBinomial | This is the quasi Binomial distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiDistribution | This interface represents the quasi-distribution used in GLM. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiFamily | This interface represents the quasi-family used in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGamma | This is the quasi Gamma distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGaussian | This is the quasi Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGLMBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu | |
| QuasiGLMNewtonRaphson | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu | |
| QuasiGLMProblem | This class represents a quasi generalized linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| QuasiGLMResiduals | Residual analysis of the results of a quasi Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| QuasiInverseGaussian | This is the quasi Inverse-Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiPoisson | This is the quasi Poisson distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| SelectionByAIC | In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| SelectionByZValue | In each step, the most significant factor is added, until all remaining factors are insignificant. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |