iRegression (version 1.2.1)

ccrm: Constrained Centre and Range Method

Description

ccrm is used to fit a linear regression model to symbolic interval-valued variables based on the inequality constraints over the range variables (Lima Neto and De Carvalho, 2010).

Usage

ccrm(formula1, formula2, data, ...)

Arguments

formula1

an object of class "formula": the description of the first model to be fitted.

formula2

an object of class "formula": the description of the second model to be fitted.

data

an optional data frame containing the variables in the model.

...

other arguments.

Value

ccrm returns an object of class "ccrm" including at least the following elements:

coefficients.C

a named vector of coefficients for the Centre's explanatory variables.

coefficients.R

a named vector of coefficients for the Range's explanatory variables.

sigma.C

an estimative of the standard deviation for the Centre's response variable.

sigma.R

an estimative of the standard deviation for the Range's response variable.

df.C

the degrees of freedom for the Centre residuals

df.R

the degrees of freedom for the Range residuals

fitted.values.l

the fitted values for the lower interval bound.

fitted.values.u

the fitted values for the upper interval bound.

residuals.l

the ordinary residuals for the lower interval bound.

residuals.u

the ordinary residuals for the upper interval bound.

Details

The Constrained Centre and Range method (CCRM) was proposed by Lima Neto and De Carvalho (2010) and fits two independent linear regression models on the midpoint and range of the intervals. In the Constrained Centre and Range Method, the estimative of the parameters of the range's model is based on inequality constraints. There is no constraints over the parameters estimates for the midpoint regression equation. The aim is to guarantee mathematical coherence between the predicted values of the lower and upper bounds of the response interval-valued variable Y, i.e., yL < yU.

References

Lima Neto, E.A. and De Carvalho, F.A.T. (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis, 54, 333--347.

See Also

summary.ccrm, coef.ccrm, fitted.ccrm, residuals.ccrm, formula

Examples

data("Cardiological.CR", package = "iRegression")
ex.ccrm <- ccrm("PulseC~SystC+DiastC","PulseR~SystR+DiastR",data=Cardiological.CR)
ex.ccrm