bootFreq {MMIX} | R Documentation |
This function analyses the stability of the stepwise selection and mixing methods using a bootstrap procedure.
bootFreq(data, family, nboot = 100, method = 1, file = NULL, ...)
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. |
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.
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. |
This function does not accept or treat missing values.
Marie Morfin and David Makowski makowski@grignon.inra.fr
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.
fullModel
, stepSel
, bmaBic
,
mixAic
, arms
##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)