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MultivariateSampleLogisticLinearRegression Method

Performs a linear logistic regression analysis.

Namespace:  Meta.Numerics.Statistics
Assembly:  Meta.Numerics (in Meta.Numerics.dll) Version: 4.1.4
public MultiLinearLogisticRegressionResult LogisticLinearRegression(
	int outputIndex


Type: SystemInt32
The index of the column to predict.

Return Value

Type: MultiLinearLogisticRegressionResult
A logistic multi-linear model fit. The kth parameter is the slope of the multi-linear model with respect to the kth column, except for k equal to the outputIndex, for which it is the intercept.
InvalidOperationExceptionThe column to be predicted contains values other than 0 and 1.
InsufficientDataExceptionThere are not more rows in the sample than columns.
DivideByZeroExceptionThe curvature matrix is singular, indicating that the data is independent of one or more parameters, or that two or more parameters are linearly dependent.
Logistic linear regression is suited to situations where multiple input variables, either continuous or binary indicators, are used to predict the value of a binary output variable. Like a linear regression, a logistic linear regression tries to find a model that predicts the output variable using a linear combination of input variables. Unlike a simple linear regression, the model does not assume that this linear function predicts the output directly; instead it assumes that this function value is then fed into a logit link function, which maps the real numbers into the interval (0, 1), and interprets the value of this link function as the probability of obtaining success value for the output variable.
See Also