RSDA (version 1.1)

sym.lm: CM and CRM Linear regression model

Description

To execute the Center Method (CR) and Center and Range Method (CRM) to Linear regression

Usage

sym.lm(formula, sym.data, method = c("cm", "crm"))

Arguments

formula

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

sym.data

Should be a symbolic data table read with the function read.sym.table(...).

method

"cm" to Center Method and "crm" to Center and Range Method.

Value

• sym.lm returns an object of class "lm" or for multiple responses of class c("mlm", "lm")

Details

Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515. LIMA-NETO, E.A., 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

sym.glm

Examples

data(int_prost_train)
data(int_prost_test)
res.cm<-sym.lm(lpsa~.,sym.data=int_prost_train,method='cm')
pred.cm<-predictsym.lm(res.cm,int_prost_test,method='cm')
RMSE.L(sym.var(int_prost_test,9),pred.cm$Fitted)
RMSE.U(sym.var(int_prost_test,9),pred.cm$Fitted)
R2.L(sym.var(int_prost_test,9),pred.cm$Fitted)
R2.U(sym.var(int_prost_test,9),pred.cm$Fitted)
deter.coefficient(sym.var(int_prost_test,9),pred.cm$Fitted)