bootFreq {MMIX}R Documentation

Stability of selection and mixing methods

Description

This function analyses the stability of the stepwise selection and mixing methods using a bootstrap procedure.

Usage

bootFreq(data, family, nboot = 100, method = 1,  file = NULL, ...)

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")).
nboot number of bootstrap samples drawn.
method the statistical method used to estimate the model parameters. method = 1 for fullModel, method = 2 for stepSel, method = 3 for bmaBic, method = 4 for mixAic, method = 5 for arms.
file the path of the file where the results are stored during the run. If file = NULL no file is created.
... the specific arguments of the called method.

Details

nboot samples are generated with replacement from the original dataset. For each sample, the parameters are estimated using the chosen method. The frequency of selection of a variable is the part of the samples for which the estimated value of its coefficient is different from zero. Selection frequency is an indicator of the stability of the method. Frequencies close to 0 or 1 indicates that the corresponding variables was almost never or always selected in the bootstrap samples.

Value

bootFreq returns an object of class "classMMIX". A data frame with the main results is printed with the function "print", and a graphic with the weights of the explanatory variables is plotted with the function "plot". A bootFreq object is a list including the following components:

frequency frequency of selection of each variable across the bootstrap samples
coef estimated parameter values across the bootstrap samples; a matrix with nsample rows
mean mean of the estimated parameter values across the bootstrap samples
sd standard deviation of the estimated parameter values across the bootstrap samples
pne0 mean values of the variable weights computed using a model mixing method. Pne0 = frequency if method = 1 or 2.

Warning

This function does not accept or treat missing values.

Author(s)

Marie Morfin and David Makowski makowski@grignon.inra.fr

References

Buckland, S.T., Burnham, K.P. and Augustin, N.H. (1997) Model selection: an integral part of inference, Biometrics 53, 603-618.

Chatfield, C. (1995) Model uncertainty, data mining and statistical inference, Journal of the Royal Statistical Society /A 158, 419-466.

Efron, B. (1979) Bootstrap methods : another look at the jackknife, American Statistician 7, 1-26.

Efron, B. and Tibshirani, R.J. (1993) An introduction to the bootstrap, Chapman & Hall.

Hammersley, J.M. and Handscomb, D.C. (1964) Monte Carlo Methods, Chapman & Hall.

Miller A. (2002) Subset selection in regression, 2nd edition Chapman & Hall/CRC, New York.

Mooney, C.Z. and Duval, R.D. (1993) Bootstrapping: a nonparametric approach to statistical inference, Sage Publications, London.

Prost, L., Makowski, D. and Jeuffroy, M.-H. (2006) Comparison of stepwise selection and Bayesian model averaging for yield gap analysis, Ecological Modelling 219, 66-76.

See Also

fullModel, stepSel, bmaBic, mixAic, arms

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)

##Analysis of a stepwise selection
bootStep1<-bootFreq(data=data1,family=gaussian("identity"),nboot=50,method=2,
criterion="bic",trace=0)
bootStep1
summary(bootStep1)
plot(bootStep1)

bootStep2<-bootFreq(data=data2,family=binomial("logit"),method=2,
criterion="bic",nboot=20,trace=0)
bootStep2
summary(bootStep2)
plot(bootStep2)


[Package MMIX version 1.1 Index]