RSDA (version 2.0.8)

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.

Examples

# NOT RUN {
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)
# }