stepSel {MMIX} | R Documentation |
Select and fit a model by stepwise regression, for linear and logistic models.
stepSel(data, family, direction = "both", criterion, trace = 0)
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”)). |
direction |
the type of stepwise search, can be one of “both” (the default), “backward” or “forward” |
criterion |
selection criterion at each step of the procedure.
criterion = “aic” for the Akaike Information Criterion, and
criterion = “bic” for the Bayesian Information Criterion. |
trace |
print information during the run if trace = 1. Larger
values may give more information. If trace = 0 no information is
printed. |
This function uses the function step
.
stepSel
returns an object of class "MMIXclass"
. A data frame
with the main results is printed whith the function "print"
, and the
detailed results are obtained with the function "summary"
. A
stepSel
object is a list including the following components:
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 logartithm 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. |
Marie Morfin and David Makowski
Akaike H. (1974) A new look at the statistical model identification, IEEE Transactions on Automatic Control 19, 716-723.
Miller A. (2002) Subset selection in regression, 2nd edition Chapman & Hall/CRC, New York.
Schwarz, G. (1978) Estimating the dimension of a model, Annals of Statistics 6, 461-464.
Whittingham M.J., Stephens P., Bradbury R.B.. Freckleton R.P. (2006) Why do we still use stepwise modelling in ecology and behaviour?, J. Anim. Ecol. 75, 1182-1189.
##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) ##stepSel method stepResult1<-stepSel(data=data1,family=gaussian("identity"),criterion="bic", direction="both") stepResult1 summary(stepResult1) stepResult2<-stepSel(data=data2,family=binomial("logit"),criterion="bic", direction="both") stepResult2 summary(stepResult2)