This function uses information criteria to find a specified number of best models containing one, two, or three variables, and so on, up to the single model containing all of the explanatory variables.
bestsubset(data, y, exclude = NULL, include = NULL, Class = NULL,
weights = c(rep(1, nrow(data))),select = "SBC", tolerance = 1e-07, best = 5)Data set including dependent and independent variables to be analyzed
A character or numeric vector indicating the subset of dependent variables
A character or numeric vector indicating the subset of independent variables removed from datasets
Forces the effects vector listed in the data to be included in all models. The selection methods are performed on the other effects in the data set
Class effect variable
The weights names numeric vector to provide a weight for each observation in the input data set. And note that weights should be ranged from 0 to 1, while negative numbers are forcibly converted to 0, and numbers greater than 1 are forcibly converted to 1. If you do not specify a weight vector, each observation has a default weight of 1.
Specifies the criterion that uses to calculate all models including Akaike Information Criterion(AIC), the Corrected form of Akaike Information Criterion(AICc),Bayesian Information Criterion(BIC),Schwarz criterion(SBC),Hannan and Quinn Information Criterion(HQ), R-square statistic(Rsq), adjusted R-square statistic(adjRsq) and Mallows Cp statistic(CP)
Tolerance value for multicollinearity, default is 1e-7
Controls the number of models displayed in the output, default is 5
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# NOT RUN {
set.seed(4)
dfY <- data.frame(matrix(c(rnorm(20,0,2),c(rep(1,10),rep(2,10)),rnorm(20,2,3)),20,3))
colnames(dfY) <- paste("Y",1:3,sep="")
dfX <- data.frame(matrix(c(rnorm(100,0,2),rnorm(100,2,1)),20,10))
colnames(dfX) <- paste("X",1:10,sep="")
yx <- cbind(dfY,dfX)
bestsubset(yx,y="Y1",exclude="Y3",include="Y2",Class="Y2",
weights=c(rep(0.5,2),rep(1,18)),select="SBC",tolerance=1e-7,best=5)
# }
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