# October 27, 2005 > senic <- read.table("APPENC01.txt") # 113 by 12 > y <- senic[,2] > X <- as.matrix( senic[,3:12] ) > dimnames(X) <- list(NULL,c("age","infect","culture","x-ray","beds","medsc", "region","census","nurses","services")) > ans <- lsfit(X,y) > ls.print(ans) Residual Standard Error = 1.2229, Multiple R-Square = 0.6273 N = 113, F-statistic = 17.1645 on 10 and 102 df, p-value = 0 coef std.err t.stat p.value Intercept 3.7204 1.8881 1.9705 0.0515 age 0.0852 0.0273 3.1221 0.0023 infect 0.4264 0.1244 3.4279 0.0009 culture 0.0079 0.0156 0.5063 0.6137 x-ray 0.0125 0.0071 1.7643 0.0807 beds -0.0054 0.0035 -1.5381 0.1271 medsc -0.2042 0.4302 -0.4746 0.6361 region -0.5801 0.1321 -4.3921 0.0000 census 0.0160 0.0043 3.7340 0.0003 nurses -0.0059 0.0022 -2.6852 0.0085 services -0.0126 0.0136 -0.9288 0.3552 > stepwise(X,y) $rss: [1] 292.7645 228.2800 200.8756 178.5708 165.5764 159.9998 154.7491 $size: [1] 1 2 3 4 5 6 7 $which: age infect culture x-ray beds medsc region census nurses services 1(+2) F T F F F F F F F F 2(+7) F T F F F F T F F F 3(+8) F T F F F F T T F F 4(+9) F T F F F F T T T F 5(+1) T T F F F F T T T F 6(+4) T T F T F F T T T F 7(+5) T T F T T F T T T F $f.stat: [1] 44.149782 31.072769 14.870344 13.489993 8.397299 3.694539 3.562655 $method: [1] "efroymson" > stepwise(X,y,f.crit=.1) $rss: [1] 292.7645 228.2800 200.8756 178.5708 165.5764 159.9998 154.7491 153.4547 [9] 152.8682 152.5314 $size: [1] 1 2 3 4 5 6 7 8 9 10 $which: age infect culture x-ray beds medsc region census nurses services 1(+ 2) F T F F F F F F F F 2(+ 7) F T F F F F T F F F 3(+ 8) F T F F F F T T F F 4(+ 9) F T F F F F T T T F 5(+ 1) T T F F F F T T T F 6(+ 4) T T F T F F T T T F 7(+ 5) T T F T T F T T T F 8(+10) T T F T T F T T T T 9(+ 3) T T T T T F T T T T 10(+ 6) T T T T T T T T T T $f.stat: [1] 44.1497820 31.0727686 14.8703435 13.4899934 8.3972993 3.6945394 [7] 3.5626552 0.8772546 0.3951858 0.2252395 $method: [1] "efroymson" > stepwise(X,y,method="backward") $rss: [1] 152.8682 153.4547 154.7491 159.9998 165.5764 178.5708 200.8756 228.2800 [9] 292.7645 409.2104 $size: [1] 9 8 7 6 5 4 3 2 1 0 $which: age infect culture x-ray beds medsc region census nurses services 9(- 6) T T T T T F T T T T 8(- 3) T T F T T F T T T T 7(-10) T T F T T F T T T F 6(- 5) T T F T F F T T T F 5(- 4) T T F F F F T T T F 4(- 1) F T F F F F T T T F 3(- 9) F T F F F F T T F F 2(- 8) F T F F F F T F F F 1(- 7) F T F F F F F F F F 0(- 2) F F F F F F F F F F $f.stat: [1] 0.2252395 0.3951858 0.8772546 3.5626552 3.6945394 8.3972993 [7] 13.4899934 14.8703435 31.0727686 44.1497820 $method: [1] "backward" > stepwise(X,y,method="exhaustive",nbest=100) ## too many to show