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## AnovaModel Class | Extreme Optimization Numerical Libraries for .NET Professional |

Extreme.DataAnalysis.Models

Extreme.Statistics

Extreme.Statistics

Extreme.Statistics

Extreme.Statistics

**Namespace:**Extreme.Statistics

**Assembly:**Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

The AnovaModel type exposes the following members.

Name | Description | |
---|---|---|

AdjustedRSquared |
Gets the adjusted R Squared value for the regression.
| |

AnovaTable |
Gets the AnovaTable that summarizes the results of this model.
| |

BaseFeatureIndex |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model.) | |

Computed | Obsolete.
Gets whether the model has been computed.
(Inherited from Model.) | |

CovarianceMatrix |
Gets the covariance matrix of the model parameters.
| |

Data |
Gets an object that contains all the data used as input to the model.
(Inherited from Model.) | |

DegreesOfFreedom |
Gets the total degrees of freedom of the data.
| |

DependentVariable |
Gets or sets the dependent variable in the ANOVA model.
| |

Fitted |
Gets whether the model has been computed.
(Inherited from Model.) | |

FStatistic |
Gets the F statistic for the regression.
| |

Grouping |
Gets the grouping object that maps observations to their cell.
| |

InputSchema |
Gets the schema for the features used for fitting the model.
(Inherited from Model.) | |

IsBalanced |
Gets whether all the cells in the ANOVA design have the
same number of observations.
| |

LogLikelihood |
Gets the log-likelihood that the model generated the data.
| |

MaxDegreeOfParallelism |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.) | |

ModelSchema |
Gets the collection of variables used in the model.
(Inherited from Model.) | |

NumberOfObservations |
Gets the number of observations the model is based on.
(Inherited from Model.) | |

ObservationsPerCell |
Gets the number of observations per cell.
| |

ParallelOptions |
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.) | |

Parameters |
Gets a vector containing the estimated values of the model parameters.
| |

PValue |
Gets the probability corresponding to the F statistic for the regression.
| |

RSquared |
Gets the R Squared value for the regression.
| |

StandardError |
Gets the standard error of the regression.
| |

Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model.) | |

SupportsWeights |
Indicates whether the model supports case weights.
(Inherited from Model.) | |

Weights |
Gets or sets the actual weights.
(Inherited from Model.) |

Name | Description | |
---|---|---|

Compute | Obsolete.
Computes the model.
(Inherited from Model.) | |

Compute(ParallelOptions) | Obsolete.
Computes the model.
(Inherited from Model.) | |

Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |

Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |

Fit |
Fits the model to the data.
(Inherited from Model.) | |

Fit(ParallelOptions) |
Fits the model to the data.
(Inherited from Model.) | |

FitCore |
Computes the model to the specified input
using the specified parallelization options.
(Inherited from Model.) | |

GetAkaikeInformationCriterion |
Returns the Akaike information criterion (AIC) value for the model.
| |

GetBartlettTest |
Returns Bartlett's test to verify that the cells have the same variance.
| |

GetBayesianInformationCriterion |
Returns the Bayesian information criterion (BIC) value for the model.
| |

GetFactor(Int32) |
Gets the factor corresponding to the variable with the specified index.
| |

GetFactor |
Gets the strongly typed factor corresponding to the variable
at the specified position.
| |

GetHashCode | Serves as the default hash function. (Inherited from Object.) | |

GetHomogeneityOfVariancesTest |
Returns a test to verify that the cells have the same variance.
| |

GetHomogeneityOfVariancesTest(TestOfHomogeneityOfVariances) |
Returns a test to verify that the cells have the same variance.
| |

GetLeveneTest |
Returns Levene's test to verify that the cells have the same variance.
| |

GetType | Gets the Type of the current instance. (Inherited from Object.) | |

MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |

ResetComputation | Obsolete.
Clears all fitted model parameters.
(Inherited from Model.) | |

ResetFit |
Clears all fitted model parameters.
(Inherited from Model.) | |

SetDataSource |
Uses the specified data frame as the source for all input variables.
(Inherited from Model.) | |

Summarize |
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model.) | |

Summarize(SummaryOptions) |
Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model | |

ToString |
Returns a string representation of this instance.
(Overrides Model |

The AnovaModel class is an abstract base class
for all classes that implement Analysis of Variance (ANOVA) models. It
inherits from RegressionModel

ANOVA models are a specialized form of regression model whose independent variables or predictors are all categorical in nature. The categorical scales are called factors. Use the GetFactor(Int32) method to get the factor that corresponds to a specific independent variable.

The Cells property returns a vector or matrix of Cell objects. There is one cell for every combination of factor levels. The IsBalanced property indicates whether all cells have the same number of observations.

One of the assumptions in analysis of variance is that the variances of the data in each cell are the same. The GetHomogeneityOfVariancesTest(TestOfHomogeneityOfVariances) returns a hypothesis test object /// that allows you to verify this assumption.

This is an abstract base class, and cannot be instantiated directly. Instead, use one of the derived classes listed in the following table.

Class | Description |
---|---|

OneWayAnovaModel | Represents a one-way analysis of variance model. |

OneWayRAnovaModel | Represents a one-way analysis of variance model with repeated measures. |

TwoWayAnovaModel | Represents a two-way analysis of variance. |

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