fullModel {MMIX}R Documentation

Linear and logistic regressions

Description

Linear or logistic model without any variable selection.

Usage

fullModel(data, family)

Arguments

data a data frame including the response variable (first column) and the explanatory variables. All the variables must be numeric and the response variable value must be 0 or 1 for the logistic model.
family a description of the error distribution (gaussian("identity") or binomial("logit")).

Value

fullModel returns an object of class "MMIXclass". A data frame with the main results is printed when calling the function "print", and the detailed results are obtained with the function "summary". A fullModel object is a list including the following components:

cv logical. Was the IWLS algorithm judged to have converged?
coef a named vector of coefficients estimated by least squares or maximum likelihood.
aic Akaike Information Criterion, minus twice the maximized log-likelihood plus twice the number of coefficients.
bic Bayesian information criterion, minus twice the maximized log-likelihood plus the logarithm of the number of observation multiplied by the number of coefficients
fitted.values the fitted values, obtained by transforming the linear predictors by the inverse of the link function.

Author(s)

Marie Morfin and David Makowski

References

Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with S., Springer, New York.

See Also

family, arms, bmaBic, mixAic, stepSel, bootFreq

Examples


###Data 
#Explanatory variables 
X1<-c(-0.2,-2.4,-0.7,1.2,0.0,-1.1,-2.1,-0.3,2.0,-1.7,1.4,-1.3,-3.4,0.4,-1.3,
-4.8)
X2<- c(-3,  2,  1, -2, -2, -4,  0,  1,  1, -1, -1, -4,  0,  2,  0, -4)
X3<-c(2,1,0,-2,1,-2, 0, -1, -4, 1, -3, -3, -3, -1, 0, 2)

##Linear model
Y1<- c(8.7, 6, 9.1, 10.4, 7.6 ,10.4,  7.9, 11.9, 18, 10.5, 16.5, 8.8, 7.7, 13.5,
 8.2, 0.8)
data1<-data.frame(Y1,X1,X2,X3)
##Logistic model
Y2<-c(1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1)
data2<-data.frame(Y2,X1,X2,X3)

##fullModel method
fullResult1<-fullModel(data=data1,family=gaussian("identity"))
fullResult1
summary(fullResult1)

fullResult2<-fullModel(data=data2,family=binomial("logit"))
fullResult2
summary(fullResult2)


[Package MMIX version 1.1 Index]