Detroit homicide data for 1961-73 used in the book Subset Regression by A.J. Miller
For convenience we have labelled the input variables 1 through 11 to be consistent with the notation used in Miller (2002). Only the first 11 variables were used in Miller's analyses. The best fitting subset regression with these 11 variables, uses only 3 inputs and has a residual sum of squares of 6.77 while using forward selection produces a best fit with 3 inputs with residual sum of squares 21.19. Backward selection and stagewise methods produce similar results. It is remarkable that there is such a big difference. Note that the usual forward and backward selection algorithms may fail since the linear regression using 11 variables gives essentially a perfect fit.
A data frame with 13 observations on the following 14 variables.
Full-time police per 100,000 population
Percent unemployed in the population
Number of manufacturing workers in thousands
Number of handgun licences per 100,000 population
Number of handgun registrations per 100,000 population
Percent homicides cleared by arrests
Number of white males in the population
Number of non-manufacturing workers in thousands
Number of government workers in thousands
Average hourly earnings
Average weekly earnings
Death rate in accidents per 100,000 population
Number of assaults per 100,000 population
Number of homicides per 100,000 of population
The data were orginally collected and discussed by Fisher (1976) but the complete dataset first appeared in Gunst and Mason (1980, Appendix A). Miller (2002) discusses this dataset throughout his book. The data were obtained from StatLib.
Fisher, J.C. (1976). Homicide in Detroit: The Role of Firearms. Criminology, vol.14, 387-400.
Gunst, R.F. and Mason, R.L. (1980). Regression analysis and its application: A data-oriented approach. Marcel Dekker.
Miller, A. J. (2002). Subset Selection in Regression. 2nd Ed. Chapman & Hall/CRC. Boca Raton.
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#Detroit data example data(Detroit) #As in Miller (2002) columns 1-11 are used as inputs p<-11 #For possible comparison with other algorithms such as LARS # it is preferable to work with the scaled inputs. #From Miller (2002, Table 3.14), we see that the #best six inputs are: 1, 2, 4, 6, 7, 11 X<-as.data.frame(scale(Detroit[,c(1,2,4,6,7,11)])) y<-Detroit[,ncol(Detroit)] Xy<-cbind(X,HOM=y) #Use backward stepwise regression with BIC selects full model out <- lm(HOM~., data=Xy) step(out, k=log(nrow(Xy))) # #Same story with exhaustive search algorithm out<-bestglm(Xy, IC="BIC") out #But many coefficients have p-values that are quite large considering # the selection bias. Note: 1, 6 and 7 are all about 5% only. #We can use BICq to reduce the number of variables. #The qTable let's choose q for other possible models, out$qTable #This suggest we try q=0.05 or q=0.0005 bestglm(Xy,IC="BICq", t=0.05) bestglm(Xy,IC="BICq", t=0.00005) #It is interesting that the subset model of size 2 is not a subset # itself of the size 3 model. These results agree with Miller (2002, Table 3.14). # #Using delete-d CV with d=4 suggests variables 2,4,6,11 set.seed(1233211) bestglm(Xy, IC="CV", CVArgs=list(Method="d", K=4, REP=50))
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