| title | author | date | output | ||||
|---|---|---|---|---|---|---|---|
Lab 5 Henry Jones |
Henry Jones |
2022-04-14 |
|
crime <- read.csv("/Users/henryjones/Desktop/Math_CC/MA340_stats/crime.csv")
#View(crime)"Show all your steps in finding what you consider to be the best predictive models for reported violent and non-violent crimes (2 models) using census information. Your submission will be the steps you took to arrive at the models."
First we tried a random forest model with m = 34.
set.seed(1)
library(tree)
library(randomForest)## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
bag.tr1 <- randomForest(violentPerPop ~., data = na.omit(crime), mtry = 34, importance = T)
bag.tr1 #significant decrease in error##
## Call:
## randomForest(formula = violentPerPop ~ ., data = na.omit(crime), mtry = 34, importance = T)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 34
##
## Mean of squared residuals: 108603.5
## % Var explained: 70.65
#bag.tr1$cv.error
varImpPlot(bag.tr1)
importance(bag.tr1)## %IncMSE IncNodePurity
## pop 5.3578568 2247614.1
## perHoush 3.7743040 1313569.8
## pctBlack 11.2226269 15689519.9
## pctWhite 15.4031901 42675593.7
## pctAsian 3.4080949 1720059.0
## pctHisp 7.6164520 4484121.7
## pct12_21 3.7312542 1411679.4
## pct12_29 3.0839926 1327766.7
## pct16_24 4.8492257 1561857.3
## pct65up 4.2322122 1479522.8
## persUrban 5.2520753 2386607.7
## pctUrban 2.4207663 548199.7
## medIncome 2.5381548 1369200.9
## pctWwage 5.2768189 1506789.6
## pctWfarm -0.3173078 2575328.6
## pctWdiv 6.1667207 7226038.6
## pctWsocsec 3.7455744 1927089.4
## pctPubAsst 8.0134646 3670755.5
## pctRetire 3.0402645 1761248.2
## medFamIncome 3.9147275 1626666.3
## perCapInc 4.3891453 1242296.5
## whitePerCap 3.5649902 1571087.9
## blackPerCap 3.4372375 2179636.3
## NAperCap 2.1660987 1884409.7
## asianPerCap 4.4935072 3058354.2
## otherPerCap 6.1671746 3779160.2
## hispPerCap 6.0650914 2963270.1
## persPoverty 7.0631072 5194924.4
## pctPoverty 6.3815467 3477388.6
## pctLowEdu 6.5674943 1953389.2
## pctNotHSgrad 5.4652185 1855640.3
## pctCollGrad 4.2958819 1978405.9
## pctUnemploy 5.7230003 2803656.7
## pctEmploy 3.2537071 1638425.8
## pctEmployMfg 2.8521042 2643813.0
## pctEmployProfServ 0.1874221 2199748.5
## pctOccupManu 3.8842293 1771653.8
## pctOccupMgmt 4.8799927 2336363.9
## pctMaleDivorc 4.8533694 5771418.7
## pctMaleNevMar 4.1570170 2310372.8
## pctFemDivorc 6.8948628 5898981.6
## pctAllDivorc 6.9289975 5896409.8
## persPerFam 4.8650284 1714215.1
## pct2Par 11.1953514 36908846.1
## pctKids2Par 15.6017372 104757722.9
## pctKids_4w2Par 4.9092242 8887104.5
## pct12_17w2Par 5.8108272 12705128.3
## pctWorkMom_6 3.8793934 2199126.9
## pctWorkMom_18 4.0029310 2090784.3
## kidsBornNevrMarr 11.1379623 38996140.0
## pctKidsBornNevrMarr 16.7911747 114366130.4
## numForeignBorn 8.0155767 4020155.2
## pctFgnImmig_3 3.6091652 1653988.8
## pctFgnImmig_5 3.6364453 1479824.5
## pctFgnImmig_8 4.5550797 1782089.0
## pctFgnImmig_10 4.2964035 2018378.0
## pctImmig_3 4.0641335 1167977.9
## pctImmig_5 2.6994006 1510138.5
## pctImmig_8 4.4928669 1482418.2
## pctImmig_10 3.5719926 1571411.9
## pctSpeakOnlyEng 6.3464507 2804822.5
## pctNotSpeakEng 6.1006142 2801454.8
## pctLargHousFam 6.0146831 3963697.4
## pctLargHous 5.0838350 2818516.3
## persPerOccupHous 2.9400029 1558892.6
## persPerOwnOccup 4.6946547 1709088.0
## persPerRenterOccup 7.1911155 2345274.3
## pctPersOwnOccup 4.7285456 1802648.1
## pctPopDenseHous 11.9965870 8478745.1
## pctSmallHousUnits 3.7352432 4852827.7
## medNumBedrm -0.1156863 176353.1
## houseVacant 8.1240814 4793169.4
## pctHousOccup 2.7780822 3020704.4
## pctHousOwnerOccup 5.5536475 1526612.2
## pctVacantBoarded 1.6436468 5112201.0
## pctVacant6up 0.9242036 1809157.8
## medYrHousBuilt 2.7483376 1937177.2
## pctHousWOphone 3.2656394 2109463.9
## pctHousWOplumb 3.2892110 2204291.3
## ownHousLowQ 3.1412610 1750358.3
## ownHousMed 3.9624690 1296230.0
## ownHousUperQ 3.9059803 1468917.4
## ownHousQrange 7.1721099 2167156.4
## rentLowQ 2.2496879 1403502.6
## rentMed 4.1405459 1422361.7
## rentUpperQ 3.9426940 1569484.3
## rentQrange 6.8883259 3700993.7
## medGrossRent 5.0412412 1761025.8
## medRentpctHousInc 6.7881449 2452530.6
## medOwnCostpct 5.3239613 2229310.8
## medOwnCostPctWO 5.7066410 2439874.1
## persEmergShelt 4.8160028 2337500.6
## persHomeless 6.8048531 5730997.4
## pctForeignBorn 4.9478167 2380279.0
## pctBornStateResid 6.5178009 2181712.3
## pctSameHouse_5 4.1321691 1691147.8
## pctSameCounty_5 3.5844431 2149825.5
## pctSameState_5 4.0939219 1936105.7
## landArea 3.5633857 2138787.0
## popDensity 4.9372290 3562054.7
## pctUsePubTrans 4.9515560 5550486.7
## pctOfficDrugUnit -0.2464789 901010.6
## nonViolPerPop 32.2415156 81608474.5
We have a reported training MSE of 108603.5.
Next we tried a non-linear model using smoothing splines. We first determined our subset of predictors by using the regsubsets() function for forward selection. After determining this subset, we plotted all these predictors against violentPerPop to check for nonlinearity. In doing so, we selected "pctWdiv", "ownHousLowQ", "pctWorkMom_6", and "asianPerCap" as our most obviously nonlinear terms. In the creation of our next model, we fit these four predictors with cubic smoothing splines and left all others predictors as linear.
library(gam)## Loading required package: splines
## Loading required package: foreach
## Loaded gam 1.22-2
library(glmnet)## Loading required package: Matrix
## Loaded glmnet 4.1-7
library(leaps)
library(splines)
library(boot)
set.seed(1)
reg.fit.fwd <- regsubsets(violentPerPop ~., data = crime, nvmax = 50, method = "forward", really.big = T)## Warning in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
## force.in, : 2 linear dependencies found
## Reordering variables and trying again:
summari <- summary(reg.fit.fwd)
summari## Subset selection object
## Call: regsubsets.formula(violentPerPop ~ ., data = crime, nvmax = 50,
## method = "forward", really.big = T)
## 103 Variables (and intercept)
## Forced in Forced out
## pop FALSE FALSE
## perHoush FALSE FALSE
## pctBlack FALSE FALSE
## pctWhite FALSE FALSE
## pctAsian FALSE FALSE
## pctHisp FALSE FALSE
## pct12_21 FALSE FALSE
## pct12_29 FALSE FALSE
## pct16_24 FALSE FALSE
## pct65up FALSE FALSE
## persUrban FALSE FALSE
## pctUrban FALSE FALSE
## medIncome FALSE FALSE
## pctWwage FALSE FALSE
## pctWfarm FALSE FALSE
## pctWdiv FALSE FALSE
## pctWsocsec FALSE FALSE
## pctPubAsst FALSE FALSE
## pctRetire FALSE FALSE
## medFamIncome FALSE FALSE
## perCapInc FALSE FALSE
## whitePerCap FALSE FALSE
## blackPerCap FALSE FALSE
## NAperCap FALSE FALSE
## asianPerCap FALSE FALSE
## otherPerCap FALSE FALSE
## hispPerCap FALSE FALSE
## persPoverty FALSE FALSE
## pctPoverty FALSE FALSE
## pctLowEdu FALSE FALSE
## pctNotHSgrad FALSE FALSE
## pctCollGrad FALSE FALSE
## pctUnemploy FALSE FALSE
## pctEmploy FALSE FALSE
## pctEmployMfg FALSE FALSE
## pctEmployProfServ FALSE FALSE
## pctOccupManu FALSE FALSE
## pctOccupMgmt FALSE FALSE
## pctMaleDivorc FALSE FALSE
## pctMaleNevMar FALSE FALSE
## pctFemDivorc FALSE FALSE
## pctAllDivorc FALSE FALSE
## persPerFam FALSE FALSE
## pct2Par FALSE FALSE
## pctKids2Par FALSE FALSE
## pctKids_4w2Par FALSE FALSE
## pct12_17w2Par FALSE FALSE
## pctWorkMom_6 FALSE FALSE
## pctWorkMom_18 FALSE FALSE
## kidsBornNevrMarr FALSE FALSE
## pctKidsBornNevrMarr FALSE FALSE
## numForeignBorn FALSE FALSE
## pctFgnImmig_3 FALSE FALSE
## pctFgnImmig_5 FALSE FALSE
## pctFgnImmig_8 FALSE FALSE
## pctFgnImmig_10 FALSE FALSE
## pctImmig_3 FALSE FALSE
## pctImmig_5 FALSE FALSE
## pctImmig_8 FALSE FALSE
## pctImmig_10 FALSE FALSE
## pctSpeakOnlyEng FALSE FALSE
## pctNotSpeakEng FALSE FALSE
## pctLargHousFam FALSE FALSE
## pctLargHous FALSE FALSE
## persPerOccupHous FALSE FALSE
## persPerOwnOccup FALSE FALSE
## persPerRenterOccup FALSE FALSE
## pctPersOwnOccup FALSE FALSE
## pctPopDenseHous FALSE FALSE
## pctSmallHousUnits FALSE FALSE
## medNumBedrm FALSE FALSE
## houseVacant FALSE FALSE
## pctHousOccup FALSE FALSE
## pctHousOwnerOccup FALSE FALSE
## pctVacantBoarded FALSE FALSE
## pctVacant6up FALSE FALSE
## medYrHousBuilt FALSE FALSE
## pctHousWOphone FALSE FALSE
## pctHousWOplumb FALSE FALSE
## ownHousLowQ FALSE FALSE
## ownHousMed FALSE FALSE
## ownHousUperQ FALSE FALSE
## rentLowQ FALSE FALSE
## rentMed FALSE FALSE
## rentUpperQ FALSE FALSE
## medGrossRent FALSE FALSE
## medRentpctHousInc FALSE FALSE
## medOwnCostpct FALSE FALSE
## medOwnCostPctWO FALSE FALSE
## persEmergShelt FALSE FALSE
## persHomeless FALSE FALSE
## pctForeignBorn FALSE FALSE
## pctBornStateResid FALSE FALSE
## pctSameHouse_5 FALSE FALSE
## pctSameCounty_5 FALSE FALSE
## pctSameState_5 FALSE FALSE
## landArea FALSE FALSE
## popDensity FALSE FALSE
## pctUsePubTrans FALSE FALSE
## pctOfficDrugUnit FALSE FALSE
## nonViolPerPop FALSE FALSE
## ownHousQrange FALSE FALSE
## rentQrange FALSE FALSE
## 1 subsets of each size up to 51
## Selection Algorithm: forward
## pop perHoush pctBlack pctWhite pctAsian pctHisp pct12_21 pct12_29
## 1 ( 1 ) " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " "*" " " " " " " " "
## 4 ( 1 ) " " " " " " "*" " " " " " " " "
## 5 ( 1 ) " " " " " " "*" " " " " " " "*"
## 6 ( 1 ) " " " " " " "*" " " " " " " "*"
## 7 ( 1 ) " " " " " " "*" " " " " " " "*"
## 8 ( 1 ) " " " " " " "*" " " " " " " "*"
## 9 ( 1 ) " " " " " " "*" " " " " " " "*"
## 10 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 11 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 12 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 13 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 14 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 15 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 16 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 17 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 18 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 19 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 20 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 21 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 22 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 23 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 24 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 25 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 26 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 27 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 28 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 29 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 30 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 31 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 32 ( 1 ) " " " " "*" "*" " " " " " " "*"
## 33 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 34 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 35 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 36 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 37 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 38 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 39 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 40 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 41 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 42 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 43 ( 1 ) "*" " " "*" "*" " " " " " " "*"
## 44 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 45 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 46 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 47 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 48 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 49 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 50 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## 51 ( 1 ) "*" "*" "*" "*" " " " " " " "*"
## pct16_24 pct65up persUrban pctUrban medIncome pctWwage pctWfarm
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " " " "
## 16 ( 1 ) "*" " " " " " " " " " " " "
## 17 ( 1 ) "*" " " " " " " " " " " " "
## 18 ( 1 ) "*" " " " " " " " " " " " "
## 19 ( 1 ) "*" " " " " "*" " " " " " "
## 20 ( 1 ) "*" " " " " "*" " " " " " "
## 21 ( 1 ) "*" " " " " "*" " " " " " "
## 22 ( 1 ) "*" " " " " "*" " " " " " "
## 23 ( 1 ) "*" " " " " "*" " " " " " "
## 24 ( 1 ) "*" " " " " "*" " " " " " "
## 25 ( 1 ) "*" " " " " "*" " " " " " "
## 26 ( 1 ) "*" " " " " "*" " " " " " "
## 27 ( 1 ) "*" " " " " "*" " " " " " "
## 28 ( 1 ) "*" " " " " "*" " " " " " "
## 29 ( 1 ) "*" " " " " "*" " " " " " "
## 30 ( 1 ) "*" " " " " "*" " " " " " "
## 31 ( 1 ) "*" " " " " "*" " " " " " "
## 32 ( 1 ) "*" " " " " "*" " " " " " "
## 33 ( 1 ) "*" " " " " "*" " " " " " "
## 34 ( 1 ) "*" " " " " "*" " " " " "*"
## 35 ( 1 ) "*" " " " " "*" " " " " "*"
## 36 ( 1 ) "*" " " " " "*" " " " " "*"
## 37 ( 1 ) "*" " " " " "*" " " " " "*"
## 38 ( 1 ) "*" " " " " "*" " " " " "*"
## 39 ( 1 ) "*" " " " " "*" " " " " "*"
## 40 ( 1 ) "*" " " " " "*" " " " " "*"
## 41 ( 1 ) "*" " " " " "*" " " "*" "*"
## 42 ( 1 ) "*" " " " " "*" " " "*" "*"
## 43 ( 1 ) "*" " " " " "*" " " "*" "*"
## 44 ( 1 ) "*" " " " " "*" " " "*" "*"
## 45 ( 1 ) "*" " " " " "*" " " "*" "*"
## 46 ( 1 ) "*" " " " " "*" " " "*" "*"
## 47 ( 1 ) "*" " " " " "*" " " "*" "*"
## 48 ( 1 ) "*" " " " " "*" " " "*" "*"
## 49 ( 1 ) "*" " " " " "*" " " "*" "*"
## 50 ( 1 ) "*" " " " " "*" " " "*" "*"
## 51 ( 1 ) "*" " " " " "*" " " "*" "*"
## pctWdiv pctWsocsec pctPubAsst pctRetire medFamIncome perCapInc
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " "
## 15 ( 1 ) "*" " " " " " " " " " "
## 16 ( 1 ) "*" " " " " " " " " " "
## 17 ( 1 ) "*" " " " " " " " " " "
## 18 ( 1 ) "*" " " " " " " " " " "
## 19 ( 1 ) "*" " " " " " " " " " "
## 20 ( 1 ) "*" " " " " " " " " " "
## 21 ( 1 ) "*" " " "*" " " " " " "
## 22 ( 1 ) "*" " " "*" "*" " " " "
## 23 ( 1 ) "*" " " "*" "*" " " " "
## 24 ( 1 ) "*" " " "*" "*" " " " "
## 25 ( 1 ) "*" " " "*" "*" " " " "
## 26 ( 1 ) "*" " " "*" "*" " " " "
## 27 ( 1 ) "*" " " "*" "*" " " " "
## 28 ( 1 ) "*" " " "*" "*" " " " "
## 29 ( 1 ) "*" " " "*" "*" " " " "
## 30 ( 1 ) "*" " " "*" "*" " " " "
## 31 ( 1 ) "*" " " "*" "*" " " " "
## 32 ( 1 ) "*" " " "*" "*" " " " "
## 33 ( 1 ) "*" " " "*" "*" " " " "
## 34 ( 1 ) "*" " " "*" "*" " " " "
## 35 ( 1 ) "*" " " "*" "*" " " " "
## 36 ( 1 ) "*" " " "*" "*" " " " "
## 37 ( 1 ) "*" " " "*" "*" " " " "
## 38 ( 1 ) "*" " " "*" "*" " " " "
## 39 ( 1 ) "*" " " "*" "*" " " " "
## 40 ( 1 ) "*" " " "*" "*" " " " "
## 41 ( 1 ) "*" " " "*" "*" " " " "
## 42 ( 1 ) "*" "*" "*" "*" " " " "
## 43 ( 1 ) "*" "*" "*" "*" " " " "
## 44 ( 1 ) "*" "*" "*" "*" " " " "
## 45 ( 1 ) "*" "*" "*" "*" " " " "
## 46 ( 1 ) "*" "*" "*" "*" " " " "
## 47 ( 1 ) "*" "*" "*" "*" " " " "
## 48 ( 1 ) "*" "*" "*" "*" " " " "
## 49 ( 1 ) "*" "*" "*" "*" " " " "
## 50 ( 1 ) "*" "*" "*" "*" " " " "
## 51 ( 1 ) "*" "*" "*" "*" " " " "
## whitePerCap blackPerCap NAperCap asianPerCap otherPerCap hispPerCap
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " " " "
## 20 ( 1 ) " " " " " " "*" " " " "
## 21 ( 1 ) " " " " " " "*" " " " "
## 22 ( 1 ) " " " " " " "*" " " " "
## 23 ( 1 ) " " " " " " "*" " " " "
## 24 ( 1 ) " " " " " " "*" " " " "
## 25 ( 1 ) " " " " " " "*" "*" " "
## 26 ( 1 ) " " " " " " "*" "*" " "
## 27 ( 1 ) " " " " " " "*" "*" " "
## 28 ( 1 ) " " " " " " "*" "*" " "
## 29 ( 1 ) "*" " " " " "*" "*" " "
## 30 ( 1 ) "*" " " " " "*" "*" " "
## 31 ( 1 ) "*" " " " " "*" "*" " "
## 32 ( 1 ) "*" " " " " "*" "*" " "
## 33 ( 1 ) "*" " " " " "*" "*" " "
## 34 ( 1 ) "*" " " " " "*" "*" " "
## 35 ( 1 ) "*" " " " " "*" "*" " "
## 36 ( 1 ) "*" " " " " "*" "*" " "
## 37 ( 1 ) "*" " " " " "*" "*" " "
## 38 ( 1 ) "*" " " " " "*" "*" " "
## 39 ( 1 ) "*" " " " " "*" "*" " "
## 40 ( 1 ) "*" " " " " "*" "*" " "
## 41 ( 1 ) "*" " " " " "*" "*" " "
## 42 ( 1 ) "*" " " " " "*" "*" " "
## 43 ( 1 ) "*" " " " " "*" "*" " "
## 44 ( 1 ) "*" " " " " "*" "*" " "
## 45 ( 1 ) "*" " " " " "*" "*" " "
## 46 ( 1 ) "*" " " " " "*" "*" " "
## 47 ( 1 ) "*" " " " " "*" "*" " "
## 48 ( 1 ) "*" " " " " "*" "*" " "
## 49 ( 1 ) "*" " " " " "*" "*" " "
## 50 ( 1 ) "*" " " " " "*" "*" " "
## 51 ( 1 ) "*" " " " " "*" "*" " "
## persPoverty pctPoverty pctLowEdu pctNotHSgrad pctCollGrad pctUnemploy
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " "
## 14 ( 1 ) " " "*" " " " " " " " "
## 15 ( 1 ) " " "*" " " " " " " " "
## 16 ( 1 ) " " "*" " " " " " " " "
## 17 ( 1 ) " " "*" "*" " " " " " "
## 18 ( 1 ) " " "*" "*" " " " " " "
## 19 ( 1 ) " " "*" "*" " " " " " "
## 20 ( 1 ) " " "*" "*" " " " " " "
## 21 ( 1 ) " " "*" "*" " " " " " "
## 22 ( 1 ) " " "*" "*" " " " " " "
## 23 ( 1 ) " " "*" "*" " " " " " "
## 24 ( 1 ) " " "*" "*" " " " " " "
## 25 ( 1 ) " " "*" "*" " " " " " "
## 26 ( 1 ) " " "*" "*" " " " " " "
## 27 ( 1 ) " " "*" "*" "*" " " " "
## 28 ( 1 ) " " "*" "*" "*" " " " "
## 29 ( 1 ) " " "*" "*" "*" " " " "
## 30 ( 1 ) " " "*" "*" "*" "*" " "
## 31 ( 1 ) " " "*" "*" "*" "*" " "
## 32 ( 1 ) " " "*" "*" "*" "*" " "
## 33 ( 1 ) " " "*" "*" "*" "*" " "
## 34 ( 1 ) " " "*" "*" "*" "*" " "
## 35 ( 1 ) " " "*" "*" "*" "*" " "
## 36 ( 1 ) " " "*" "*" "*" "*" " "
## 37 ( 1 ) " " "*" "*" "*" "*" " "
## 38 ( 1 ) " " "*" "*" "*" "*" " "
## 39 ( 1 ) " " "*" "*" "*" "*" " "
## 40 ( 1 ) " " "*" "*" "*" "*" " "
## 41 ( 1 ) " " "*" "*" "*" "*" " "
## 42 ( 1 ) " " "*" "*" "*" "*" " "
## 43 ( 1 ) " " "*" "*" "*" "*" " "
## 44 ( 1 ) " " "*" "*" "*" "*" " "
## 45 ( 1 ) " " "*" "*" "*" "*" " "
## 46 ( 1 ) " " "*" "*" "*" "*" " "
## 47 ( 1 ) " " "*" "*" "*" "*" " "
## 48 ( 1 ) " " "*" "*" "*" "*" " "
## 49 ( 1 ) " " "*" "*" "*" "*" " "
## 50 ( 1 ) " " "*" "*" "*" "*" " "
## 51 ( 1 ) " " "*" "*" "*" "*" " "
## pctEmploy pctEmployMfg pctEmployProfServ pctOccupManu pctOccupMgmt
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " "*" " " " " " "
## 27 ( 1 ) " " "*" " " " " " "
## 28 ( 1 ) " " "*" " " " " " "
## 29 ( 1 ) " " "*" " " " " " "
## 30 ( 1 ) " " "*" " " " " " "
## 31 ( 1 ) " " "*" " " " " " "
## 32 ( 1 ) " " "*" " " " " " "
## 33 ( 1 ) " " "*" " " " " " "
## 34 ( 1 ) " " "*" " " " " " "
## 35 ( 1 ) " " "*" " " " " " "
## 36 ( 1 ) " " "*" " " " " " "
## 37 ( 1 ) " " "*" " " " " " "
## 38 ( 1 ) " " "*" " " " " " "
## 39 ( 1 ) " " "*" " " " " " "
## 40 ( 1 ) " " "*" " " " " " "
## 41 ( 1 ) " " "*" " " " " " "
## 42 ( 1 ) " " "*" " " " " " "
## 43 ( 1 ) " " "*" " " " " " "
## 44 ( 1 ) " " "*" " " " " " "
## 45 ( 1 ) " " "*" " " " " " "
## 46 ( 1 ) " " "*" " " " " " "
## 47 ( 1 ) " " "*" " " " " " "
## 48 ( 1 ) " " "*" " " " " " "
## 49 ( 1 ) " " "*" " " " " " "
## 50 ( 1 ) " " "*" " " " " " "
## 51 ( 1 ) " " "*" " " " " " "
## pctMaleDivorc pctMaleNevMar pctFemDivorc pctAllDivorc persPerFam
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) "*" " " " " " " " "
## 9 ( 1 ) "*" " " " " " " " "
## 10 ( 1 ) "*" " " " " " " " "
## 11 ( 1 ) "*" " " " " " " " "
## 12 ( 1 ) "*" " " " " " " " "
## 13 ( 1 ) "*" " " " " " " " "
## 14 ( 1 ) "*" " " " " " " " "
## 15 ( 1 ) "*" " " " " " " " "
## 16 ( 1 ) "*" " " " " " " " "
## 17 ( 1 ) "*" " " " " " " " "
## 18 ( 1 ) "*" " " " " "*" " "
## 19 ( 1 ) "*" " " " " "*" " "
## 20 ( 1 ) "*" " " " " "*" " "
## 21 ( 1 ) "*" " " " " "*" " "
## 22 ( 1 ) "*" " " " " "*" " "
## 23 ( 1 ) "*" " " " " "*" " "
## 24 ( 1 ) "*" " " "*" "*" " "
## 25 ( 1 ) "*" " " "*" "*" " "
## 26 ( 1 ) "*" " " "*" "*" " "
## 27 ( 1 ) "*" " " "*" "*" " "
## 28 ( 1 ) "*" " " "*" "*" " "
## 29 ( 1 ) "*" " " "*" "*" " "
## 30 ( 1 ) "*" " " "*" "*" " "
## 31 ( 1 ) "*" " " "*" "*" " "
## 32 ( 1 ) "*" " " "*" "*" " "
## 33 ( 1 ) "*" " " "*" "*" " "
## 34 ( 1 ) "*" " " "*" "*" " "
## 35 ( 1 ) "*" " " "*" "*" " "
## 36 ( 1 ) "*" " " "*" "*" " "
## 37 ( 1 ) "*" " " "*" "*" " "
## 38 ( 1 ) "*" " " "*" "*" " "
## 39 ( 1 ) "*" " " "*" "*" " "
## 40 ( 1 ) "*" " " "*" "*" " "
## 41 ( 1 ) "*" " " "*" "*" " "
## 42 ( 1 ) "*" " " "*" "*" " "
## 43 ( 1 ) "*" " " "*" "*" " "
## 44 ( 1 ) "*" " " "*" "*" " "
## 45 ( 1 ) "*" " " "*" "*" " "
## 46 ( 1 ) "*" " " "*" "*" " "
## 47 ( 1 ) "*" " " "*" "*" " "
## 48 ( 1 ) "*" " " "*" "*" " "
## 49 ( 1 ) "*" " " "*" "*" " "
## 50 ( 1 ) "*" " " "*" "*" " "
## 51 ( 1 ) "*" " " "*" "*" " "
## pct2Par pctKids2Par pctKids_4w2Par pct12_17w2Par pctWorkMom_6
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " "*" " " " " " "
## 24 ( 1 ) " " "*" " " " " " "
## 25 ( 1 ) " " "*" " " " " " "
## 26 ( 1 ) " " "*" " " " " " "
## 27 ( 1 ) " " "*" " " " " " "
## 28 ( 1 ) " " "*" " " " " " "
## 29 ( 1 ) " " "*" " " " " " "
## 30 ( 1 ) " " "*" " " " " " "
## 31 ( 1 ) " " "*" " " " " " "
## 32 ( 1 ) " " "*" " " " " " "
## 33 ( 1 ) " " "*" " " " " " "
## 34 ( 1 ) " " "*" " " " " " "
## 35 ( 1 ) " " "*" " " " " " "
## 36 ( 1 ) " " "*" " " " " " "
## 37 ( 1 ) " " "*" " " " " " "
## 38 ( 1 ) " " "*" " " " " " "
## 39 ( 1 ) " " "*" " " " " "*"
## 40 ( 1 ) " " "*" " " " " "*"
## 41 ( 1 ) " " "*" " " " " "*"
## 42 ( 1 ) " " "*" " " " " "*"
## 43 ( 1 ) " " "*" " " " " "*"
## 44 ( 1 ) " " "*" " " " " "*"
## 45 ( 1 ) " " "*" " " " " "*"
## 46 ( 1 ) " " "*" " " " " "*"
## 47 ( 1 ) " " "*" " " " " "*"
## 48 ( 1 ) " " "*" " " " " "*"
## 49 ( 1 ) " " "*" " " " " "*"
## 50 ( 1 ) " " "*" "*" " " "*"
## 51 ( 1 ) " " "*" "*" " " "*"
## pctWorkMom_18 kidsBornNevrMarr pctKidsBornNevrMarr numForeignBorn
## 1 ( 1 ) " " " " "*" " "
## 2 ( 1 ) " " " " "*" " "
## 3 ( 1 ) " " " " "*" " "
## 4 ( 1 ) " " " " "*" " "
## 5 ( 1 ) " " " " "*" " "
## 6 ( 1 ) " " " " "*" " "
## 7 ( 1 ) " " " " "*" " "
## 8 ( 1 ) " " " " "*" " "
## 9 ( 1 ) " " " " "*" " "
## 10 ( 1 ) " " " " "*" " "
## 11 ( 1 ) "*" " " "*" " "
## 12 ( 1 ) "*" " " "*" " "
## 13 ( 1 ) "*" " " "*" " "
## 14 ( 1 ) "*" " " "*" " "
## 15 ( 1 ) "*" " " "*" " "
## 16 ( 1 ) "*" " " "*" " "
## 17 ( 1 ) "*" " " "*" " "
## 18 ( 1 ) "*" " " "*" " "
## 19 ( 1 ) "*" " " "*" " "
## 20 ( 1 ) "*" " " "*" " "
## 21 ( 1 ) "*" " " "*" " "
## 22 ( 1 ) "*" " " "*" " "
## 23 ( 1 ) "*" " " "*" " "
## 24 ( 1 ) "*" " " "*" " "
## 25 ( 1 ) "*" " " "*" " "
## 26 ( 1 ) "*" " " "*" " "
## 27 ( 1 ) "*" " " "*" " "
## 28 ( 1 ) "*" " " "*" " "
## 29 ( 1 ) "*" " " "*" " "
## 30 ( 1 ) "*" " " "*" " "
## 31 ( 1 ) "*" " " "*" " "
## 32 ( 1 ) "*" " " "*" " "
## 33 ( 1 ) "*" " " "*" " "
## 34 ( 1 ) "*" " " "*" " "
## 35 ( 1 ) "*" " " "*" "*"
## 36 ( 1 ) "*" " " "*" "*"
## 37 ( 1 ) "*" " " "*" "*"
## 38 ( 1 ) "*" " " "*" "*"
## 39 ( 1 ) "*" " " "*" "*"
## 40 ( 1 ) "*" " " "*" "*"
## 41 ( 1 ) "*" " " "*" "*"
## 42 ( 1 ) "*" " " "*" "*"
## 43 ( 1 ) "*" " " "*" "*"
## 44 ( 1 ) "*" " " "*" "*"
## 45 ( 1 ) "*" " " "*" "*"
## 46 ( 1 ) "*" " " "*" "*"
## 47 ( 1 ) "*" " " "*" "*"
## 48 ( 1 ) "*" " " "*" "*"
## 49 ( 1 ) "*" " " "*" "*"
## 50 ( 1 ) "*" " " "*" "*"
## 51 ( 1 ) "*" " " "*" "*"
## pctFgnImmig_3 pctFgnImmig_5 pctFgnImmig_8 pctFgnImmig_10 pctImmig_3
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " " "
## 32 ( 1 ) " " " " " " " " " "
## 33 ( 1 ) " " " " " " " " " "
## 34 ( 1 ) " " " " " " " " " "
## 35 ( 1 ) " " " " " " " " " "
## 36 ( 1 ) " " " " " " " " " "
## 37 ( 1 ) " " " " " " " " " "
## 38 ( 1 ) " " " " " " " " " "
## 39 ( 1 ) " " " " " " " " " "
## 40 ( 1 ) " " " " " " " " " "
## 41 ( 1 ) " " " " " " " " " "
## 42 ( 1 ) " " " " " " " " " "
## 43 ( 1 ) " " " " " " " " " "
## 44 ( 1 ) " " " " " " " " " "
## 45 ( 1 ) " " " " " " " " " "
## 46 ( 1 ) " " " " " " " " " "
## 47 ( 1 ) " " " " " " " " " "
## 48 ( 1 ) " " " " "*" " " " "
## 49 ( 1 ) "*" " " "*" " " " "
## 50 ( 1 ) "*" " " "*" " " " "
## 51 ( 1 ) "*" " " "*" " " " "
## pctImmig_5 pctImmig_8 pctImmig_10 pctSpeakOnlyEng pctNotSpeakEng
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " " "
## 32 ( 1 ) " " " " " " " " " "
## 33 ( 1 ) " " " " " " " " " "
## 34 ( 1 ) " " " " " " " " " "
## 35 ( 1 ) " " " " " " " " " "
## 36 ( 1 ) " " " " " " " " "*"
## 37 ( 1 ) " " " " " " " " "*"
## 38 ( 1 ) " " " " " " " " "*"
## 39 ( 1 ) " " " " " " " " "*"
## 40 ( 1 ) " " " " " " " " "*"
## 41 ( 1 ) " " " " " " " " "*"
## 42 ( 1 ) " " " " " " " " "*"
## 43 ( 1 ) " " " " " " " " "*"
## 44 ( 1 ) " " " " " " " " "*"
## 45 ( 1 ) " " " " " " " " "*"
## 46 ( 1 ) " " " " " " " " "*"
## 47 ( 1 ) " " " " " " " " "*"
## 48 ( 1 ) " " " " " " " " "*"
## 49 ( 1 ) " " " " " " " " "*"
## 50 ( 1 ) " " " " " " " " "*"
## 51 ( 1 ) " " " " " " " " "*"
## pctLargHousFam pctLargHous persPerOccupHous persPerOwnOccup
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " " " " " " "
## 9 ( 1 ) " " " " " " " "
## 10 ( 1 ) " " " " " " " "
## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " " " " " " "
## 13 ( 1 ) " " " " " " " "
## 14 ( 1 ) " " " " " " " "
## 15 ( 1 ) " " " " " " " "
## 16 ( 1 ) " " " " " " " "
## 17 ( 1 ) " " " " " " " "
## 18 ( 1 ) " " " " " " " "
## 19 ( 1 ) " " " " " " " "
## 20 ( 1 ) " " " " " " " "
## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " " " " "
## 24 ( 1 ) " " " " " " " "
## 25 ( 1 ) " " " " " " " "
## 26 ( 1 ) " " " " " " " "
## 27 ( 1 ) " " " " " " " "
## 28 ( 1 ) " " "*" " " " "
## 29 ( 1 ) " " "*" " " " "
## 30 ( 1 ) " " "*" " " " "
## 31 ( 1 ) " " "*" " " " "
## 32 ( 1 ) " " "*" " " " "
## 33 ( 1 ) " " "*" " " " "
## 34 ( 1 ) " " "*" " " " "
## 35 ( 1 ) " " "*" " " " "
## 36 ( 1 ) " " "*" " " " "
## 37 ( 1 ) " " "*" " " " "
## 38 ( 1 ) "*" "*" " " " "
## 39 ( 1 ) "*" "*" " " " "
## 40 ( 1 ) "*" "*" "*" " "
## 41 ( 1 ) "*" "*" "*" " "
## 42 ( 1 ) "*" "*" "*" " "
## 43 ( 1 ) "*" "*" "*" " "
## 44 ( 1 ) "*" "*" "*" " "
## 45 ( 1 ) "*" "*" "*" " "
## 46 ( 1 ) "*" "*" "*" " "
## 47 ( 1 ) "*" "*" "*" " "
## 48 ( 1 ) "*" "*" "*" " "
## 49 ( 1 ) "*" "*" "*" " "
## 50 ( 1 ) "*" "*" "*" " "
## 51 ( 1 ) "*" "*" "*" " "
## persPerRenterOccup pctPersOwnOccup pctPopDenseHous pctSmallHousUnits
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " "*" " "
## 7 ( 1 ) " " " " "*" " "
## 8 ( 1 ) " " " " "*" " "
## 9 ( 1 ) " " " " "*" " "
## 10 ( 1 ) " " " " "*" " "
## 11 ( 1 ) " " " " "*" " "
## 12 ( 1 ) " " " " "*" " "
## 13 ( 1 ) " " " " "*" "*"
## 14 ( 1 ) " " " " "*" "*"
## 15 ( 1 ) " " " " "*" "*"
## 16 ( 1 ) " " " " "*" "*"
## 17 ( 1 ) " " " " "*" "*"
## 18 ( 1 ) " " " " "*" "*"
## 19 ( 1 ) " " " " "*" "*"
## 20 ( 1 ) " " " " "*" "*"
## 21 ( 1 ) " " " " "*" "*"
## 22 ( 1 ) " " " " "*" "*"
## 23 ( 1 ) " " " " "*" "*"
## 24 ( 1 ) " " " " "*" "*"
## 25 ( 1 ) " " " " "*" "*"
## 26 ( 1 ) " " " " "*" "*"
## 27 ( 1 ) " " " " "*" "*"
## 28 ( 1 ) " " " " "*" "*"
## 29 ( 1 ) " " " " "*" "*"
## 30 ( 1 ) " " " " "*" "*"
## 31 ( 1 ) " " " " "*" "*"
## 32 ( 1 ) " " " " "*" "*"
## 33 ( 1 ) " " " " "*" "*"
## 34 ( 1 ) " " " " "*" "*"
## 35 ( 1 ) " " " " "*" "*"
## 36 ( 1 ) " " " " "*" "*"
## 37 ( 1 ) " " " " "*" "*"
## 38 ( 1 ) " " " " "*" "*"
## 39 ( 1 ) " " " " "*" "*"
## 40 ( 1 ) " " " " "*" "*"
## 41 ( 1 ) " " " " "*" "*"
## 42 ( 1 ) " " " " "*" "*"
## 43 ( 1 ) " " " " "*" "*"
## 44 ( 1 ) " " " " "*" "*"
## 45 ( 1 ) " " " " "*" "*"
## 46 ( 1 ) " " " " "*" "*"
## 47 ( 1 ) " " " " "*" "*"
## 48 ( 1 ) " " " " "*" "*"
## 49 ( 1 ) " " " " "*" "*"
## 50 ( 1 ) " " " " "*" "*"
## 51 ( 1 ) " " " " "*" "*"
## medNumBedrm houseVacant pctHousOccup pctHousOwnerOccup
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " "*" " " " "
## 5 ( 1 ) " " "*" " " " "
## 6 ( 1 ) " " "*" " " " "
## 7 ( 1 ) " " "*" " " " "
## 8 ( 1 ) " " "*" " " " "
## 9 ( 1 ) " " "*" " " " "
## 10 ( 1 ) " " "*" " " " "
## 11 ( 1 ) " " "*" " " " "
## 12 ( 1 ) " " "*" " " " "
## 13 ( 1 ) " " "*" " " " "
## 14 ( 1 ) " " "*" " " " "
## 15 ( 1 ) " " "*" " " " "
## 16 ( 1 ) " " "*" " " " "
## 17 ( 1 ) " " "*" " " " "
## 18 ( 1 ) " " "*" " " " "
## 19 ( 1 ) " " "*" " " " "
## 20 ( 1 ) " " "*" " " " "
## 21 ( 1 ) " " "*" " " " "
## 22 ( 1 ) " " "*" " " " "
## 23 ( 1 ) " " "*" " " " "
## 24 ( 1 ) " " "*" " " " "
## 25 ( 1 ) " " "*" " " " "
## 26 ( 1 ) " " "*" " " " "
## 27 ( 1 ) " " "*" " " " "
## 28 ( 1 ) " " "*" " " " "
## 29 ( 1 ) " " "*" " " " "
## 30 ( 1 ) " " "*" " " " "
## 31 ( 1 ) " " "*" " " " "
## 32 ( 1 ) " " "*" " " " "
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## 34 ( 1 ) " " "*" " " " "
## 35 ( 1 ) " " "*" " " " "
## 36 ( 1 ) " " "*" " " " "
## 37 ( 1 ) " " "*" " " " "
## 38 ( 1 ) " " "*" " " " "
## 39 ( 1 ) " " "*" " " " "
## 40 ( 1 ) " " "*" " " " "
## 41 ( 1 ) " " "*" " " " "
## 42 ( 1 ) " " "*" " " " "
## 43 ( 1 ) " " "*" " " " "
## 44 ( 1 ) " " "*" " " " "
## 45 ( 1 ) " " "*" " " " "
## 46 ( 1 ) " " "*" " " " "
## 47 ( 1 ) " " "*" " " " "
## 48 ( 1 ) " " "*" " " " "
## 49 ( 1 ) " " "*" " " " "
## 50 ( 1 ) " " "*" " " " "
## 51 ( 1 ) " " "*" " " " "
## pctVacantBoarded pctVacant6up medYrHousBuilt pctHousWOphone
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) "*" " " " " " "
## 8 ( 1 ) "*" " " " " " "
## 9 ( 1 ) "*" " " " " " "
## 10 ( 1 ) "*" " " " " " "
## 11 ( 1 ) "*" " " " " " "
## 12 ( 1 ) "*" " " " " " "
## 13 ( 1 ) "*" " " " " " "
## 14 ( 1 ) "*" " " " " " "
## 15 ( 1 ) "*" " " " " " "
## 16 ( 1 ) "*" " " " " " "
## 17 ( 1 ) "*" " " " " " "
## 18 ( 1 ) "*" " " " " " "
## 19 ( 1 ) "*" " " " " " "
## 20 ( 1 ) "*" " " " " " "
## 21 ( 1 ) "*" " " " " " "
## 22 ( 1 ) "*" " " " " " "
## 23 ( 1 ) "*" " " " " " "
## 24 ( 1 ) "*" " " " " " "
## 25 ( 1 ) "*" " " " " " "
## 26 ( 1 ) "*" " " " " " "
## 27 ( 1 ) "*" " " " " " "
## 28 ( 1 ) "*" " " " " " "
## 29 ( 1 ) "*" " " " " " "
## 30 ( 1 ) "*" " " " " " "
## 31 ( 1 ) "*" " " " " " "
## 32 ( 1 ) "*" " " " " " "
## 33 ( 1 ) "*" " " " " " "
## 34 ( 1 ) "*" " " " " " "
## 35 ( 1 ) "*" " " " " " "
## 36 ( 1 ) "*" " " " " " "
## 37 ( 1 ) "*" " " " " " "
## 38 ( 1 ) "*" " " " " " "
## 39 ( 1 ) "*" " " " " " "
## 40 ( 1 ) "*" " " " " " "
## 41 ( 1 ) "*" " " " " " "
## 42 ( 1 ) "*" " " " " " "
## 43 ( 1 ) "*" " " "*" " "
## 44 ( 1 ) "*" " " "*" " "
## 45 ( 1 ) "*" " " "*" " "
## 46 ( 1 ) "*" " " "*" " "
## 47 ( 1 ) "*" "*" "*" " "
## 48 ( 1 ) "*" "*" "*" " "
## 49 ( 1 ) "*" "*" "*" " "
## 50 ( 1 ) "*" "*" "*" " "
## 51 ( 1 ) "*" "*" "*" " "
## pctHousWOplumb ownHousLowQ ownHousMed ownHousUperQ ownHousQrange
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
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## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
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## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " " "
## 32 ( 1 ) " " "*" " " " " " "
## 33 ( 1 ) " " "*" " " " " " "
## 34 ( 1 ) " " "*" " " " " " "
## 35 ( 1 ) " " "*" " " " " " "
## 36 ( 1 ) " " "*" " " " " " "
## 37 ( 1 ) " " "*" " " " " " "
## 38 ( 1 ) " " "*" " " " " " "
## 39 ( 1 ) " " "*" " " " " " "
## 40 ( 1 ) " " "*" " " " " " "
## 41 ( 1 ) " " "*" " " " " " "
## 42 ( 1 ) " " "*" " " " " " "
## 43 ( 1 ) " " "*" " " " " " "
## 44 ( 1 ) " " "*" " " " " " "
## 45 ( 1 ) " " "*" " " " " " "
## 46 ( 1 ) " " "*" " " " " " "
## 47 ( 1 ) " " "*" " " " " " "
## 48 ( 1 ) " " "*" " " " " " "
## 49 ( 1 ) " " "*" " " " " " "
## 50 ( 1 ) " " "*" " " " " " "
## 51 ( 1 ) " " "*" " " "*" " "
## rentLowQ rentMed rentUpperQ rentQrange medGrossRent medRentpctHousInc
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " "*" " " " "
## 10 ( 1 ) " " " " " " "*" " " " "
## 11 ( 1 ) " " " " " " "*" " " " "
## 12 ( 1 ) " " " " " " "*" " " " "
## 13 ( 1 ) " " " " " " "*" " " " "
## 14 ( 1 ) " " " " " " "*" " " " "
## 15 ( 1 ) " " " " " " "*" " " " "
## 16 ( 1 ) " " " " " " "*" " " " "
## 17 ( 1 ) " " " " " " "*" " " " "
## 18 ( 1 ) " " " " " " "*" " " " "
## 19 ( 1 ) " " " " " " "*" " " " "
## 20 ( 1 ) " " " " " " "*" " " " "
## 21 ( 1 ) " " " " " " "*" " " " "
## 22 ( 1 ) " " " " " " "*" " " " "
## 23 ( 1 ) " " " " " " "*" " " " "
## 24 ( 1 ) " " " " " " "*" " " " "
## 25 ( 1 ) " " " " " " "*" " " " "
## 26 ( 1 ) " " " " " " "*" " " " "
## 27 ( 1 ) " " " " " " "*" " " " "
## 28 ( 1 ) " " " " " " "*" " " " "
## 29 ( 1 ) " " " " " " "*" " " " "
## 30 ( 1 ) " " " " " " "*" " " " "
## 31 ( 1 ) " " " " " " "*" " " " "
## 32 ( 1 ) " " " " " " "*" " " " "
## 33 ( 1 ) " " " " " " "*" " " " "
## 34 ( 1 ) " " " " " " "*" " " " "
## 35 ( 1 ) " " " " " " "*" " " " "
## 36 ( 1 ) " " " " " " "*" " " " "
## 37 ( 1 ) " " " " " " "*" " " " "
## 38 ( 1 ) " " " " " " "*" " " " "
## 39 ( 1 ) " " " " " " "*" " " " "
## 40 ( 1 ) " " " " " " "*" " " " "
## 41 ( 1 ) " " " " " " "*" " " " "
## 42 ( 1 ) " " " " " " "*" " " " "
## 43 ( 1 ) " " " " " " "*" " " " "
## 44 ( 1 ) " " " " " " "*" " " " "
## 45 ( 1 ) " " " " " " "*" "*" " "
## 46 ( 1 ) "*" " " " " "*" "*" " "
## 47 ( 1 ) "*" " " " " "*" "*" " "
## 48 ( 1 ) "*" " " " " "*" "*" " "
## 49 ( 1 ) "*" " " " " "*" "*" " "
## 50 ( 1 ) "*" " " " " "*" "*" " "
## 51 ( 1 ) "*" " " " " "*" "*" " "
## medOwnCostpct medOwnCostPctWO persEmergShelt persHomeless
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " " " " " " "
## 9 ( 1 ) " " " " " " " "
## 10 ( 1 ) " " " " " " " "
## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " "*" " " " "
## 13 ( 1 ) " " "*" " " " "
## 14 ( 1 ) " " "*" " " " "
## 15 ( 1 ) " " "*" " " " "
## 16 ( 1 ) " " "*" " " " "
## 17 ( 1 ) " " "*" " " " "
## 18 ( 1 ) " " "*" " " " "
## 19 ( 1 ) " " "*" " " " "
## 20 ( 1 ) " " "*" " " " "
## 21 ( 1 ) " " "*" " " " "
## 22 ( 1 ) " " "*" " " " "
## 23 ( 1 ) " " "*" " " " "
## 24 ( 1 ) " " "*" " " " "
## 25 ( 1 ) " " "*" " " " "
## 26 ( 1 ) " " "*" " " " "
## 27 ( 1 ) " " "*" " " " "
## 28 ( 1 ) " " "*" " " " "
## 29 ( 1 ) " " "*" " " " "
## 30 ( 1 ) " " "*" " " " "
## 31 ( 1 ) " " "*" " " " "
## 32 ( 1 ) " " "*" " " " "
## 33 ( 1 ) " " "*" " " " "
## 34 ( 1 ) " " "*" " " " "
## 35 ( 1 ) " " "*" " " " "
## 36 ( 1 ) " " "*" " " " "
## 37 ( 1 ) " " "*" "*" " "
## 38 ( 1 ) " " "*" "*" " "
## 39 ( 1 ) " " "*" "*" " "
## 40 ( 1 ) " " "*" "*" " "
## 41 ( 1 ) " " "*" "*" " "
## 42 ( 1 ) " " "*" "*" " "
## 43 ( 1 ) " " "*" "*" " "
## 44 ( 1 ) " " "*" "*" " "
## 45 ( 1 ) " " "*" "*" " "
## 46 ( 1 ) " " "*" "*" " "
## 47 ( 1 ) " " "*" "*" " "
## 48 ( 1 ) " " "*" "*" " "
## 49 ( 1 ) " " "*" "*" " "
## 50 ( 1 ) " " "*" "*" " "
## 51 ( 1 ) " " "*" "*" " "
## pctForeignBorn pctBornStateResid pctSameHouse_5 pctSameCounty_5
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " " " " " " "
## 9 ( 1 ) " " " " " " " "
## 10 ( 1 ) " " " " " " " "
## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " " " " " " "
## 13 ( 1 ) " " " " " " " "
## 14 ( 1 ) " " " " " " " "
## 15 ( 1 ) " " " " " " " "
## 16 ( 1 ) " " " " " " " "
## 17 ( 1 ) " " " " " " " "
## 18 ( 1 ) " " " " " " " "
## 19 ( 1 ) " " " " " " " "
## 20 ( 1 ) " " " " " " " "
## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " " " " "
## 24 ( 1 ) " " " " " " " "
## 25 ( 1 ) " " " " " " " "
## 26 ( 1 ) " " " " " " " "
## 27 ( 1 ) " " " " " " " "
## 28 ( 1 ) " " " " " " " "
## 29 ( 1 ) " " " " " " " "
## 30 ( 1 ) " " " " " " " "
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## 48 ( 1 ) " " " " " " " "
## 49 ( 1 ) " " " " " " " "
## 50 ( 1 ) " " " " " " " "
## 51 ( 1 ) " " " " " " " "
## pctSameState_5 landArea popDensity pctUsePubTrans pctOfficDrugUnit
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " "*"
## 32 ( 1 ) " " " " " " " " "*"
## 33 ( 1 ) " " " " " " " " "*"
## 34 ( 1 ) " " " " " " " " "*"
## 35 ( 1 ) " " " " " " " " "*"
## 36 ( 1 ) " " " " " " " " "*"
## 37 ( 1 ) " " " " " " " " "*"
## 38 ( 1 ) " " " " " " " " "*"
## 39 ( 1 ) " " " " " " " " "*"
## 40 ( 1 ) " " " " " " " " "*"
## 41 ( 1 ) " " " " " " " " "*"
## 42 ( 1 ) " " " " " " " " "*"
## 43 ( 1 ) " " " " " " " " "*"
## 44 ( 1 ) " " " " " " " " "*"
## 45 ( 1 ) " " " " " " " " "*"
## 46 ( 1 ) " " " " " " " " "*"
## 47 ( 1 ) " " " " " " " " "*"
## 48 ( 1 ) " " " " " " " " "*"
## 49 ( 1 ) " " " " " " " " "*"
## 50 ( 1 ) " " " " " " " " "*"
## 51 ( 1 ) " " " " " " " " "*"
## nonViolPerPop
## 1 ( 1 ) " "
## 2 ( 1 ) "*"
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"
## 16 ( 1 ) "*"
## 17 ( 1 ) "*"
## 18 ( 1 ) "*"
## 19 ( 1 ) "*"
## 20 ( 1 ) "*"
## 21 ( 1 ) "*"
## 22 ( 1 ) "*"
## 23 ( 1 ) "*"
## 24 ( 1 ) "*"
## 25 ( 1 ) "*"
## 26 ( 1 ) "*"
## 27 ( 1 ) "*"
## 28 ( 1 ) "*"
## 29 ( 1 ) "*"
## 30 ( 1 ) "*"
## 31 ( 1 ) "*"
## 32 ( 1 ) "*"
## 33 ( 1 ) "*"
## 34 ( 1 ) "*"
## 35 ( 1 ) "*"
## 36 ( 1 ) "*"
## 37 ( 1 ) "*"
## 38 ( 1 ) "*"
## 39 ( 1 ) "*"
## 40 ( 1 ) "*"
## 41 ( 1 ) "*"
## 42 ( 1 ) "*"
## 43 ( 1 ) "*"
## 44 ( 1 ) "*"
## 45 ( 1 ) "*"
## 46 ( 1 ) "*"
## 47 ( 1 ) "*"
## 48 ( 1 ) "*"
## 49 ( 1 ) "*"
## 50 ( 1 ) "*"
## 51 ( 1 ) "*"
coefs <- coef(reg.fit.fwd,43)[-1]
predict_names <- names(coefs)
namesof <- numeric()
#examine plots of the selected subset to choose non-linear vars for the model
plot(violentPerPop ~ ., data = na.omit(crime[,c(predict_names,"violentPerPop")]))
plot(summari$cp)
#we get a minimum error with 43 degrees of freedom.
which.min(summari$cp)## [1] 43
#reg.fit.fwd
#create out nonlinear GAM model
set.seed(1)
gam.1 <- glm(violentPerPop ~ bs((pctWdiv), 3) + s(ownHousLowQ,3) + s(pctWorkMom_6,3) + s(asianPerCap,3) + ., data = na.omit(crime))
#summary(gam.1)
#make predictions
pred_gam1 <- predict(gam.1, newdata = na.omit(crime))## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
crime <- na.omit(crime)
set.seed(1)
cv.glm(crime, gam.1,K=10)$delta[1]## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## [1] 142574.1
Our model produces a cross validation error of 142574.1
Next we made a simple least squares model with the subset determined by regsubsets().
set.seed(1)
l1 <- glm(violentPerPop ~ ., data = crime[,c(predict_names, "violentPerPop")])
summary(l1)##
## Call:
## glm(formula = violentPerPop ~ ., data = crime[, c(predict_names,
## "violentPerPop")])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1529.68 -181.63 -38.98 120.29 2168.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.457e+02 2.263e+03 0.285 0.775454
## pop -7.034e-04 2.856e-04 -2.463 0.013881 *
## pctBlack 7.262e+00 2.211e+00 3.285 0.001040 **
## pctWhite 8.151e-01 1.968e+00 0.414 0.678711
## pct12_29 -3.309e+01 9.447e+00 -3.502 0.000472 ***
## pct16_24 2.695e+01 8.650e+00 3.115 0.001866 **
## pctUrban 1.096e+00 2.441e-01 4.492 7.49e-06 ***
## pctWwage -7.614e+00 4.954e+00 -1.537 0.124444
## pctWfarm 2.312e+01 1.514e+01 1.527 0.126839
## pctWdiv -3.948e+00 2.303e+00 -1.714 0.086609 .
## pctWsocsec -3.671e+00 4.607e+00 -0.797 0.425663
## pctPubAsst 8.160e+00 4.990e+00 1.635 0.102160
## pctRetire -1.204e+01 3.289e+00 -3.661 0.000259 ***
## whitePerCap -4.917e-03 4.161e-03 -1.182 0.237423
## asianPerCap 2.034e-03 9.832e-04 2.068 0.038755 *
## otherPerCap 3.288e-03 1.098e-03 2.995 0.002784 **
## pctPoverty -8.939e+00 3.498e+00 -2.556 0.010676 *
## pctLowEdu -1.210e+01 5.904e+00 -2.049 0.040633 *
## pctNotHSgrad 4.162e+00 4.454e+00 0.934 0.350287
## pctCollGrad 3.275e+00 2.162e+00 1.515 0.129948
## pctEmployMfg -3.407e+00 1.340e+00 -2.542 0.011096 *
## pctMaleDivorc 2.123e+02 5.305e+01 4.002 6.52e-05 ***
## pctFemDivorc 1.707e+02 5.681e+01 3.005 0.002688 **
## pctAllDivorc -3.652e+02 1.089e+02 -3.354 0.000812 ***
## pctKids2Par -1.771e+01 3.531e+00 -5.017 5.75e-07 ***
## pctWorkMom_6 2.704e+00 2.672e+00 1.012 0.311589
## pctWorkMom_18 -7.875e+00 3.728e+00 -2.112 0.034796 *
## pctKidsBornNevrMarr 3.760e+01 8.679e+00 4.332 1.55e-05 ***
## numForeignBorn 6.888e-04 6.015e-04 1.145 0.252269
## pctNotSpeakEng -1.337e+01 8.122e+00 -1.646 0.099894 .
## pctLargHousFam 3.582e+01 2.099e+01 1.707 0.087990 .
## pctLargHous -5.479e+01 2.643e+01 -2.074 0.038259 *
## persPerOccupHous 1.370e+02 1.355e+02 1.012 0.311878
## pctPopDenseHous 2.578e+01 6.305e+00 4.089 4.52e-05 ***
## pctSmallHousUnits 1.588e+00 1.681e+00 0.945 0.344934
## houseVacant 2.271e-02 5.394e-03 4.210 2.68e-05 ***
## pctVacantBoarded 1.013e+01 3.335e+00 3.038 0.002411 **
## medYrHousBuilt 1.183e+00 1.088e+00 1.087 0.277366
## ownHousLowQ -1.343e-04 3.098e-04 -0.433 0.664711
## medRentpctHousInc -4.426e+00 4.260e+00 -1.039 0.298925
## persHomeless 6.461e-02 1.004e-01 0.643 0.520138
## pctForeignBorn 5.001e+00 3.315e+00 1.508 0.131597
## ownHousQrange -5.352e-04 3.698e-04 -1.447 0.148034
## rentQrange 3.791e-01 1.505e-01 2.519 0.011841 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 126350.7)
##
## Null deviance: 703355849 on 1900 degrees of freedom
## Residual deviance: 234633282 on 1857 degrees of freedom
## AIC: 27771
##
## Number of Fisher Scoring iterations: 2
predicts <- predict(l1, data = crime)
cv.glm(crime,l1,K=10)$delta[1]## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## [1] 141289.5
We just a cross validation error of 141289.5
Again we make a RF with m = 34.
set.seed(1)
library(tree)
library(randomForest)
bag.tr1 <- randomForest(nonViolPerPop ~., data = na.omit(crime), mtry = 34, importance = T)
bag.tr1 #significant decrease in error##
## Call:
## randomForest(formula = nonViolPerPop ~ ., data = na.omit(crime), mtry = 34, importance = T)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 34
##
## Mean of squared residuals: 3111366
## % Var explained: 59.92
#bag.tr1$cv.error
varImpPlot(bag.tr1)
importance(bag.tr1)## %IncMSE IncNodePurity
## pop 5.6414696 75012480
## perHoush 5.8515316 41626665
## pctBlack 8.4866284 74797005
## pctWhite 10.7851889 126567396
## pctAsian 4.8621899 55615634
## pctHisp 4.0866629 55572239
## pct12_21 4.9163809 60311365
## pct12_29 3.5245460 49579865
## pct16_24 5.3525090 54532764
## pct65up 3.5055225 42379037
## persUrban 5.4404371 26064980
## pctUrban 2.4897800 6772522
## medIncome 5.2341361 71639083
## pctWwage 2.8569862 37391599
## pctWfarm 4.9833108 71527256
## pctWdiv 3.5279920 46906616
## pctWsocsec 4.0146473 34181441
## pctPubAsst 4.8532130 60496847
## pctRetire 4.6641863 54550819
## medFamIncome 2.1935196 52935571
## perCapInc 5.3720012 38915120
## whitePerCap 4.9611730 64377732
## blackPerCap 5.7252329 65024826
## NAperCap 4.0078241 58234800
## asianPerCap 1.6397353 72929093
## otherPerCap 7.1374815 66638038
## hispPerCap 5.2572811 63977254
## persPoverty 6.4008039 217425153
## pctPoverty 4.8215490 165805534
## pctLowEdu 3.1635838 53322195
## pctNotHSgrad 4.7337816 41392304
## pctCollGrad 6.2832935 49526545
## pctUnemploy 5.1702943 58011486
## pctEmploy 4.7082043 54014990
## pctEmployMfg 2.1119959 88276806
## pctEmployProfServ 4.5704724 62802617
## pctOccupManu 5.7542070 61560273
## pctOccupMgmt 7.0428666 56410543
## pctMaleDivorc 6.9041678 373811135
## pctMaleNevMar 3.3979321 47994245
## pctFemDivorc 11.3696687 613289407
## pctAllDivorc 9.0859517 582951232
## persPerFam 4.3599703 37774486
## pct2Par 12.6531337 1358129306
## pctKids2Par 9.5824481 1412489971
## pctKids_4w2Par 5.8632687 439268752
## pct12_17w2Par 6.5638913 403949027
## pctWorkMom_6 0.2286087 71513107
## pctWorkMom_18 2.3358613 42958525
## kidsBornNevrMarr 5.7772113 110947838
## pctKidsBornNevrMarr 2.7250492 140570743
## numForeignBorn 3.4766151 40438964
## pctFgnImmig_3 4.1613967 42742576
## pctFgnImmig_5 3.5235745 37911748
## pctFgnImmig_8 2.8441181 55494557
## pctFgnImmig_10 2.8281120 115134239
## pctImmig_3 3.8281123 26781508
## pctImmig_5 2.7664148 28470729
## pctImmig_8 4.8683377 26978584
## pctImmig_10 2.2035301 33365850
## pctSpeakOnlyEng 3.6385571 41333669
## pctNotSpeakEng 4.0511781 41783448
## pctLargHousFam 4.7128130 37297079
## pctLargHous 4.9537102 36168482
## persPerOccupHous 5.1169323 38272780
## persPerOwnOccup 6.2062343 64040173
## persPerRenterOccup 4.5444393 55652253
## pctPersOwnOccup 2.7382238 91119283
## pctPopDenseHous 6.7464727 113582511
## pctSmallHousUnits 3.0867384 84907597
## medNumBedrm 0.7047339 3466818
## houseVacant 8.5625299 105193208
## pctHousOccup 4.9122143 136769820
## pctHousOwnerOccup 3.7802481 44982338
## pctVacantBoarded 3.8154358 59975310
## pctVacant6up 4.8603337 68402161
## medYrHousBuilt 6.3708848 70792718
## pctHousWOphone 4.3874482 82751615
## pctHousWOplumb 3.9908674 58777171
## ownHousLowQ 7.4596479 60351821
## ownHousMed 5.0833035 46349323
## ownHousUperQ 4.9507971 37134214
## ownHousQrange 5.4620043 52994437
## rentLowQ 8.3008712 165985683
## rentMed 2.2406957 65686885
## rentUpperQ 2.3568714 36069174
## rentQrange 7.1348298 89632948
## medGrossRent 2.9071429 59831862
## medRentpctHousInc 3.3495557 51000397
## medOwnCostpct 7.0310236 53663113
## medOwnCostPctWO 2.8302661 50058996
## persEmergShelt 6.6944912 48607891
## persHomeless 7.7784201 106869132
## pctForeignBorn 5.1399739 31139196
## pctBornStateResid 7.9983639 76022861
## pctSameHouse_5 2.9102719 59836994
## pctSameCounty_5 1.4664479 113414082
## pctSameState_5 4.7443345 95131242
## landArea 7.6605849 77461921
## popDensity 4.7343289 69904511
## pctUsePubTrans 3.5830303 44644208
## pctOfficDrugUnit 5.2839732 17053693
## violentPerPop 34.7562693 3185766039
Our model makes a training MSE of 3111366.
Again we make a nonlinear model, this time a larger subset of predictors as before because the regsubset() function showed minimal error for a 49 predictor model.
library(gam)
library(glmnet)
library(leaps)
library(splines)
library(boot)
set.seed(1)
reg.fit.fwd2 <- regsubsets(nonViolPerPop ~., data = crime, nvmax = 50, method = "forward", really.big = T)## Warning in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
## force.in, : 2 linear dependencies found
## Reordering variables and trying again:
summari <- summary(reg.fit.fwd2)
summari## Subset selection object
## Call: regsubsets.formula(nonViolPerPop ~ ., data = crime, nvmax = 50,
## method = "forward", really.big = T)
## 103 Variables (and intercept)
## Forced in Forced out
## pop FALSE FALSE
## perHoush FALSE FALSE
## pctBlack FALSE FALSE
## pctWhite FALSE FALSE
## pctAsian FALSE FALSE
## pctHisp FALSE FALSE
## pct12_21 FALSE FALSE
## pct12_29 FALSE FALSE
## pct16_24 FALSE FALSE
## pct65up FALSE FALSE
## persUrban FALSE FALSE
## pctUrban FALSE FALSE
## medIncome FALSE FALSE
## pctWwage FALSE FALSE
## pctWfarm FALSE FALSE
## pctWdiv FALSE FALSE
## pctWsocsec FALSE FALSE
## pctPubAsst FALSE FALSE
## pctRetire FALSE FALSE
## medFamIncome FALSE FALSE
## perCapInc FALSE FALSE
## whitePerCap FALSE FALSE
## blackPerCap FALSE FALSE
## NAperCap FALSE FALSE
## asianPerCap FALSE FALSE
## otherPerCap FALSE FALSE
## hispPerCap FALSE FALSE
## persPoverty FALSE FALSE
## pctPoverty FALSE FALSE
## pctLowEdu FALSE FALSE
## pctNotHSgrad FALSE FALSE
## pctCollGrad FALSE FALSE
## pctUnemploy FALSE FALSE
## pctEmploy FALSE FALSE
## pctEmployMfg FALSE FALSE
## pctEmployProfServ FALSE FALSE
## pctOccupManu FALSE FALSE
## pctOccupMgmt FALSE FALSE
## pctMaleDivorc FALSE FALSE
## pctMaleNevMar FALSE FALSE
## pctFemDivorc FALSE FALSE
## pctAllDivorc FALSE FALSE
## persPerFam FALSE FALSE
## pct2Par FALSE FALSE
## pctKids2Par FALSE FALSE
## pctKids_4w2Par FALSE FALSE
## pct12_17w2Par FALSE FALSE
## pctWorkMom_6 FALSE FALSE
## pctWorkMom_18 FALSE FALSE
## kidsBornNevrMarr FALSE FALSE
## pctKidsBornNevrMarr FALSE FALSE
## numForeignBorn FALSE FALSE
## pctFgnImmig_3 FALSE FALSE
## pctFgnImmig_5 FALSE FALSE
## pctFgnImmig_8 FALSE FALSE
## pctFgnImmig_10 FALSE FALSE
## pctImmig_3 FALSE FALSE
## pctImmig_5 FALSE FALSE
## pctImmig_8 FALSE FALSE
## pctImmig_10 FALSE FALSE
## pctSpeakOnlyEng FALSE FALSE
## pctNotSpeakEng FALSE FALSE
## pctLargHousFam FALSE FALSE
## pctLargHous FALSE FALSE
## persPerOccupHous FALSE FALSE
## persPerOwnOccup FALSE FALSE
## persPerRenterOccup FALSE FALSE
## pctPersOwnOccup FALSE FALSE
## pctPopDenseHous FALSE FALSE
## pctSmallHousUnits FALSE FALSE
## medNumBedrm FALSE FALSE
## houseVacant FALSE FALSE
## pctHousOccup FALSE FALSE
## pctHousOwnerOccup FALSE FALSE
## pctVacantBoarded FALSE FALSE
## pctVacant6up FALSE FALSE
## medYrHousBuilt FALSE FALSE
## pctHousWOphone FALSE FALSE
## pctHousWOplumb FALSE FALSE
## ownHousLowQ FALSE FALSE
## ownHousMed FALSE FALSE
## ownHousUperQ FALSE FALSE
## rentLowQ FALSE FALSE
## rentMed FALSE FALSE
## rentUpperQ FALSE FALSE
## medGrossRent FALSE FALSE
## medRentpctHousInc FALSE FALSE
## medOwnCostpct FALSE FALSE
## medOwnCostPctWO FALSE FALSE
## persEmergShelt FALSE FALSE
## persHomeless FALSE FALSE
## pctForeignBorn FALSE FALSE
## pctBornStateResid FALSE FALSE
## pctSameHouse_5 FALSE FALSE
## pctSameCounty_5 FALSE FALSE
## pctSameState_5 FALSE FALSE
## landArea FALSE FALSE
## popDensity FALSE FALSE
## pctUsePubTrans FALSE FALSE
## pctOfficDrugUnit FALSE FALSE
## violentPerPop FALSE FALSE
## ownHousQrange FALSE FALSE
## rentQrange FALSE FALSE
## 1 subsets of each size up to 51
## Selection Algorithm: forward
## pop perHoush pctBlack pctWhite pctAsian pctHisp pct12_21 pct12_29
## 1 ( 1 ) " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " " " "
## 6 ( 1 ) " " "*" " " " " " " " " " " " "
## 7 ( 1 ) " " "*" " " " " " " " " " " " "
## 8 ( 1 ) " " "*" " " " " " " " " " " " "
## 9 ( 1 ) " " "*" " " " " " " " " " " " "
## 10 ( 1 ) " " "*" " " " " " " " " " " " "
## 11 ( 1 ) " " "*" " " " " " " " " " " " "
## 12 ( 1 ) " " "*" " " " " " " " " " " " "
## 13 ( 1 ) " " "*" " " " " " " " " " " " "
## 14 ( 1 ) " " "*" " " " " " " " " " " " "
## 15 ( 1 ) " " "*" " " " " " " " " " " " "
## 16 ( 1 ) " " "*" " " " " " " " " " " " "
## 17 ( 1 ) " " "*" " " " " " " " " " " " "
## 18 ( 1 ) " " "*" " " " " " " " " " " " "
## 19 ( 1 ) " " "*" " " " " " " " " " " " "
## 20 ( 1 ) " " "*" " " " " " " " " " " " "
## 21 ( 1 ) " " "*" " " " " " " " " " " " "
## 22 ( 1 ) " " "*" " " " " " " " " " " " "
## 23 ( 1 ) " " "*" " " " " " " " " " " " "
## 24 ( 1 ) " " "*" " " " " " " " " " " " "
## 25 ( 1 ) " " "*" " " " " " " " " " " " "
## 26 ( 1 ) " " "*" " " " " " " " " " " " "
## 27 ( 1 ) " " "*" " " " " " " " " " " " "
## 28 ( 1 ) " " "*" " " " " " " " " " " " "
## 29 ( 1 ) " " "*" " " " " " " " " " " " "
## 30 ( 1 ) " " "*" " " " " " " " " " " " "
## 31 ( 1 ) " " "*" " " " " " " " " " " " "
## 32 ( 1 ) " " "*" " " " " " " " " " " " "
## 33 ( 1 ) " " "*" " " " " " " " " " " " "
## 34 ( 1 ) " " "*" "*" " " " " " " " " " "
## 35 ( 1 ) " " "*" "*" " " " " " " " " " "
## 36 ( 1 ) " " "*" "*" " " " " " " " " " "
## 37 ( 1 ) " " "*" "*" " " " " " " " " " "
## 38 ( 1 ) " " "*" "*" " " " " " " " " " "
## 39 ( 1 ) " " "*" "*" " " " " " " " " " "
## 40 ( 1 ) " " "*" "*" " " " " " " " " " "
## 41 ( 1 ) " " "*" "*" " " " " " " " " " "
## 42 ( 1 ) " " "*" "*" " " " " " " " " " "
## 43 ( 1 ) " " "*" "*" " " " " " " " " " "
## 44 ( 1 ) " " "*" "*" " " " " " " " " " "
## 45 ( 1 ) " " "*" "*" " " " " " " " " " "
## 46 ( 1 ) " " "*" "*" " " " " " " " " " "
## 47 ( 1 ) " " "*" "*" " " " " " " " " " "
## 48 ( 1 ) " " "*" "*" " " " " " " " " " "
## 49 ( 1 ) " " "*" "*" " " " " " " " " " "
## 50 ( 1 ) " " "*" "*" " " " " " " " " " "
## 51 ( 1 ) " " "*" "*" " " " " " " " " " "
## pct16_24 pct65up persUrban pctUrban medIncome pctWwage pctWfarm
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " "*" " "
## 18 ( 1 ) " " " " " " " " " " "*" " "
## 19 ( 1 ) " " " " " " " " " " "*" " "
## 20 ( 1 ) " " " " " " " " " " "*" " "
## 21 ( 1 ) " " " " " " " " " " "*" " "
## 22 ( 1 ) " " " " " " " " " " "*" " "
## 23 ( 1 ) " " " " " " " " " " "*" " "
## 24 ( 1 ) " " " " " " " " " " "*" " "
## 25 ( 1 ) " " " " " " " " " " "*" " "
## 26 ( 1 ) " " " " " " " " " " "*" " "
## 27 ( 1 ) " " " " " " " " " " "*" " "
## 28 ( 1 ) " " " " " " " " " " "*" " "
## 29 ( 1 ) " " " " " " " " " " "*" " "
## 30 ( 1 ) " " " " " " " " " " "*" " "
## 31 ( 1 ) " " " " " " " " " " "*" " "
## 32 ( 1 ) " " " " " " " " " " "*" " "
## 33 ( 1 ) " " " " " " " " " " "*" " "
## 34 ( 1 ) " " " " " " " " " " "*" " "
## 35 ( 1 ) " " " " " " " " " " "*" " "
## 36 ( 1 ) " " " " " " " " " " "*" " "
## 37 ( 1 ) " " " " " " " " " " "*" " "
## 38 ( 1 ) " " " " " " " " " " "*" " "
## 39 ( 1 ) " " " " " " " " " " "*" " "
## 40 ( 1 ) " " " " " " " " " " "*" " "
## 41 ( 1 ) " " " " " " " " " " "*" " "
## 42 ( 1 ) " " " " " " " " " " "*" " "
## 43 ( 1 ) " " " " " " " " " " "*" " "
## 44 ( 1 ) " " " " " " " " " " "*" " "
## 45 ( 1 ) " " " " " " " " " " "*" " "
## 46 ( 1 ) " " " " " " " " " " "*" " "
## 47 ( 1 ) " " " " " " " " " " "*" " "
## 48 ( 1 ) " " " " "*" " " " " "*" " "
## 49 ( 1 ) " " " " "*" " " " " "*" " "
## 50 ( 1 ) " " " " "*" " " " " "*" " "
## 51 ( 1 ) " " " " "*" " " " " "*" " "
## pctWdiv pctWsocsec pctPubAsst pctRetire medFamIncome perCapInc
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) "*" " " " " " " " " " "
## 5 ( 1 ) "*" " " " " " " " " " "
## 6 ( 1 ) "*" " " " " " " " " " "
## 7 ( 1 ) "*" " " " " " " " " " "
## 8 ( 1 ) "*" " " " " " " " " " "
## 9 ( 1 ) "*" " " " " " " " " " "
## 10 ( 1 ) "*" " " " " " " " " " "
## 11 ( 1 ) "*" " " " " " " " " " "
## 12 ( 1 ) "*" " " " " " " " " " "
## 13 ( 1 ) "*" " " " " " " " " " "
## 14 ( 1 ) "*" " " " " " " " " " "
## 15 ( 1 ) "*" " " " " " " " " " "
## 16 ( 1 ) "*" "*" " " " " " " " "
## 17 ( 1 ) "*" "*" " " " " " " " "
## 18 ( 1 ) "*" "*" " " "*" " " " "
## 19 ( 1 ) "*" "*" " " "*" " " " "
## 20 ( 1 ) "*" "*" " " "*" " " " "
## 21 ( 1 ) "*" "*" " " "*" " " " "
## 22 ( 1 ) "*" "*" " " "*" " " " "
## 23 ( 1 ) "*" "*" " " "*" " " " "
## 24 ( 1 ) "*" "*" " " "*" " " " "
## 25 ( 1 ) "*" "*" " " "*" " " " "
## 26 ( 1 ) "*" "*" " " "*" " " " "
## 27 ( 1 ) "*" "*" " " "*" " " " "
## 28 ( 1 ) "*" "*" " " "*" " " " "
## 29 ( 1 ) "*" "*" " " "*" " " " "
## 30 ( 1 ) "*" "*" " " "*" " " " "
## 31 ( 1 ) "*" "*" " " "*" " " " "
## 32 ( 1 ) "*" "*" " " "*" " " " "
## 33 ( 1 ) "*" "*" " " "*" "*" " "
## 34 ( 1 ) "*" "*" " " "*" "*" " "
## 35 ( 1 ) "*" "*" " " "*" "*" " "
## 36 ( 1 ) "*" "*" " " "*" "*" " "
## 37 ( 1 ) "*" "*" " " "*" "*" " "
## 38 ( 1 ) "*" "*" " " "*" "*" " "
## 39 ( 1 ) "*" "*" " " "*" "*" " "
## 40 ( 1 ) "*" "*" " " "*" "*" " "
## 41 ( 1 ) "*" "*" " " "*" "*" " "
## 42 ( 1 ) "*" "*" " " "*" "*" " "
## 43 ( 1 ) "*" "*" " " "*" "*" " "
## 44 ( 1 ) "*" "*" " " "*" "*" " "
## 45 ( 1 ) "*" "*" " " "*" "*" " "
## 46 ( 1 ) "*" "*" " " "*" "*" " "
## 47 ( 1 ) "*" "*" " " "*" "*" " "
## 48 ( 1 ) "*" "*" " " "*" "*" " "
## 49 ( 1 ) "*" "*" " " "*" "*" " "
## 50 ( 1 ) "*" "*" " " "*" "*" " "
## 51 ( 1 ) "*" "*" " " "*" "*" " "
## whitePerCap blackPerCap NAperCap asianPerCap otherPerCap hispPerCap
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " " " "
## 21 ( 1 ) "*" " " " " " " " " " "
## 22 ( 1 ) "*" " " " " " " " " " "
## 23 ( 1 ) "*" " " " " " " " " " "
## 24 ( 1 ) "*" " " " " " " " " " "
## 25 ( 1 ) "*" " " " " " " " " " "
## 26 ( 1 ) "*" " " " " " " " " " "
## 27 ( 1 ) "*" " " " " " " " " " "
## 28 ( 1 ) "*" " " " " " " " " " "
## 29 ( 1 ) "*" " " " " " " " " " "
## 30 ( 1 ) "*" " " " " " " " " " "
## 31 ( 1 ) "*" " " " " " " " " " "
## 32 ( 1 ) "*" " " " " " " " " " "
## 33 ( 1 ) "*" " " " " " " " " " "
## 34 ( 1 ) "*" " " " " " " " " " "
## 35 ( 1 ) "*" " " " " " " " " " "
## 36 ( 1 ) "*" " " " " " " " " " "
## 37 ( 1 ) "*" " " " " " " " " " "
## 38 ( 1 ) "*" " " " " " " " " " "
## 39 ( 1 ) "*" " " " " "*" " " " "
## 40 ( 1 ) "*" " " " " "*" " " " "
## 41 ( 1 ) "*" " " " " "*" " " " "
## 42 ( 1 ) "*" " " " " "*" " " " "
## 43 ( 1 ) "*" " " " " "*" " " " "
## 44 ( 1 ) "*" " " " " "*" " " " "
## 45 ( 1 ) "*" " " " " "*" " " " "
## 46 ( 1 ) "*" " " " " "*" " " " "
## 47 ( 1 ) "*" " " " " "*" "*" " "
## 48 ( 1 ) "*" " " " " "*" "*" " "
## 49 ( 1 ) "*" " " " " "*" "*" " "
## 50 ( 1 ) "*" "*" " " "*" "*" " "
## 51 ( 1 ) "*" "*" " " "*" "*" " "
## persPoverty pctPoverty pctLowEdu pctNotHSgrad pctCollGrad pctUnemploy
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " "*" " " " " " " " "
## 4 ( 1 ) " " "*" " " " " " " " "
## 5 ( 1 ) " " "*" " " " " " " " "
## 6 ( 1 ) " " "*" " " " " " " " "
## 7 ( 1 ) " " "*" " " " " " " " "
## 8 ( 1 ) " " "*" " " " " " " " "
## 9 ( 1 ) " " "*" " " " " " " " "
## 10 ( 1 ) " " "*" " " " " " " " "
## 11 ( 1 ) " " "*" " " " " " " " "
## 12 ( 1 ) " " "*" " " " " " " " "
## 13 ( 1 ) " " "*" " " " " " " " "
## 14 ( 1 ) " " "*" " " " " " " " "
## 15 ( 1 ) " " "*" " " "*" " " " "
## 16 ( 1 ) " " "*" " " "*" " " " "
## 17 ( 1 ) " " "*" " " "*" " " " "
## 18 ( 1 ) " " "*" " " "*" " " " "
## 19 ( 1 ) " " "*" " " "*" " " " "
## 20 ( 1 ) " " "*" " " "*" " " " "
## 21 ( 1 ) " " "*" " " "*" " " " "
## 22 ( 1 ) " " "*" " " "*" " " " "
## 23 ( 1 ) " " "*" " " "*" " " " "
## 24 ( 1 ) " " "*" " " "*" " " " "
## 25 ( 1 ) " " "*" " " "*" " " " "
## 26 ( 1 ) " " "*" " " "*" " " " "
## 27 ( 1 ) " " "*" " " "*" " " " "
## 28 ( 1 ) " " "*" " " "*" " " " "
## 29 ( 1 ) " " "*" " " "*" " " " "
## 30 ( 1 ) " " "*" " " "*" " " " "
## 31 ( 1 ) " " "*" " " "*" " " " "
## 32 ( 1 ) " " "*" " " "*" " " " "
## 33 ( 1 ) " " "*" " " "*" " " " "
## 34 ( 1 ) " " "*" " " "*" " " " "
## 35 ( 1 ) " " "*" " " "*" " " " "
## 36 ( 1 ) " " "*" " " "*" " " " "
## 37 ( 1 ) " " "*" " " "*" " " " "
## 38 ( 1 ) " " "*" " " "*" "*" " "
## 39 ( 1 ) " " "*" " " "*" "*" " "
## 40 ( 1 ) " " "*" " " "*" "*" " "
## 41 ( 1 ) "*" "*" " " "*" "*" " "
## 42 ( 1 ) "*" "*" " " "*" "*" " "
## 43 ( 1 ) "*" "*" " " "*" "*" " "
## 44 ( 1 ) "*" "*" " " "*" "*" " "
## 45 ( 1 ) "*" "*" " " "*" "*" " "
## 46 ( 1 ) "*" "*" " " "*" "*" " "
## 47 ( 1 ) "*" "*" " " "*" "*" " "
## 48 ( 1 ) "*" "*" " " "*" "*" " "
## 49 ( 1 ) "*" "*" " " "*" "*" " "
## 50 ( 1 ) "*" "*" " " "*" "*" " "
## 51 ( 1 ) "*" "*" " " "*" "*" " "
## pctEmploy pctEmployMfg pctEmployProfServ pctOccupManu pctOccupMgmt
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) "*" " " " " " " " "
## 20 ( 1 ) "*" " " " " " " " "
## 21 ( 1 ) "*" " " " " " " " "
## 22 ( 1 ) "*" " " " " " " " "
## 23 ( 1 ) "*" " " " " " " " "
## 24 ( 1 ) "*" " " " " " " " "
## 25 ( 1 ) "*" " " "*" " " " "
## 26 ( 1 ) "*" " " "*" " " " "
## 27 ( 1 ) "*" " " "*" " " " "
## 28 ( 1 ) "*" " " "*" " " " "
## 29 ( 1 ) "*" "*" "*" " " " "
## 30 ( 1 ) "*" "*" "*" " " " "
## 31 ( 1 ) "*" "*" "*" " " " "
## 32 ( 1 ) "*" "*" "*" " " " "
## 33 ( 1 ) "*" "*" "*" " " " "
## 34 ( 1 ) "*" "*" "*" " " " "
## 35 ( 1 ) "*" "*" "*" " " " "
## 36 ( 1 ) "*" "*" "*" " " "*"
## 37 ( 1 ) "*" "*" "*" " " "*"
## 38 ( 1 ) "*" "*" "*" " " "*"
## 39 ( 1 ) "*" "*" "*" " " "*"
## 40 ( 1 ) "*" "*" "*" " " "*"
## 41 ( 1 ) "*" "*" "*" " " "*"
## 42 ( 1 ) "*" "*" "*" " " "*"
## 43 ( 1 ) "*" "*" "*" " " "*"
## 44 ( 1 ) "*" "*" "*" " " "*"
## 45 ( 1 ) "*" "*" "*" " " "*"
## 46 ( 1 ) "*" "*" "*" " " "*"
## 47 ( 1 ) "*" "*" "*" " " "*"
## 48 ( 1 ) "*" "*" "*" " " "*"
## 49 ( 1 ) "*" "*" "*" " " "*"
## 50 ( 1 ) "*" "*" "*" " " "*"
## 51 ( 1 ) "*" "*" "*" " " "*"
## pctMaleDivorc pctMaleNevMar pctFemDivorc pctAllDivorc persPerFam
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " "*" " "
## 3 ( 1 ) " " " " " " "*" " "
## 4 ( 1 ) " " " " " " "*" " "
## 5 ( 1 ) " " " " " " "*" " "
## 6 ( 1 ) " " " " " " "*" " "
## 7 ( 1 ) " " " " " " "*" " "
## 8 ( 1 ) " " " " " " "*" " "
## 9 ( 1 ) " " " " " " "*" " "
## 10 ( 1 ) " " " " " " "*" " "
## 11 ( 1 ) " " " " " " "*" " "
## 12 ( 1 ) " " " " " " "*" " "
## 13 ( 1 ) " " " " " " "*" " "
## 14 ( 1 ) " " " " " " "*" " "
## 15 ( 1 ) " " " " " " "*" " "
## 16 ( 1 ) " " " " " " "*" " "
## 17 ( 1 ) " " " " " " "*" " "
## 18 ( 1 ) " " " " " " "*" " "
## 19 ( 1 ) " " " " " " "*" " "
## 20 ( 1 ) " " " " " " "*" " "
## 21 ( 1 ) " " " " " " "*" " "
## 22 ( 1 ) " " "*" " " "*" " "
## 23 ( 1 ) " " "*" " " "*" " "
## 24 ( 1 ) " " "*" " " "*" " "
## 25 ( 1 ) " " "*" " " "*" " "
## 26 ( 1 ) " " "*" " " "*" " "
## 27 ( 1 ) " " "*" " " "*" " "
## 28 ( 1 ) " " "*" " " "*" " "
## 29 ( 1 ) " " "*" " " "*" " "
## 30 ( 1 ) " " "*" " " "*" " "
## 31 ( 1 ) " " "*" " " "*" " "
## 32 ( 1 ) " " "*" " " "*" " "
## 33 ( 1 ) " " "*" " " "*" " "
## 34 ( 1 ) " " "*" " " "*" " "
## 35 ( 1 ) " " "*" " " "*" " "
## 36 ( 1 ) " " "*" " " "*" " "
## 37 ( 1 ) " " "*" " " "*" " "
## 38 ( 1 ) " " "*" " " "*" " "
## 39 ( 1 ) " " "*" " " "*" " "
## 40 ( 1 ) " " "*" " " "*" " "
## 41 ( 1 ) " " "*" " " "*" " "
## 42 ( 1 ) " " "*" " " "*" " "
## 43 ( 1 ) " " "*" " " "*" " "
## 44 ( 1 ) " " "*" " " "*" " "
## 45 ( 1 ) " " "*" " " "*" " "
## 46 ( 1 ) " " "*" " " "*" " "
## 47 ( 1 ) " " "*" " " "*" " "
## 48 ( 1 ) " " "*" " " "*" " "
## 49 ( 1 ) " " "*" " " "*" " "
## 50 ( 1 ) " " "*" " " "*" " "
## 51 ( 1 ) " " "*" " " "*" " "
## pct2Par pctKids2Par pctKids_4w2Par pct12_17w2Par pctWorkMom_6
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " "*" " " " " " "
## 13 ( 1 ) " " "*" " " " " " "
## 14 ( 1 ) " " "*" " " " " " "
## 15 ( 1 ) " " "*" " " " " " "
## 16 ( 1 ) " " "*" " " " " " "
## 17 ( 1 ) " " "*" " " " " " "
## 18 ( 1 ) " " "*" " " " " " "
## 19 ( 1 ) " " "*" " " " " " "
## 20 ( 1 ) " " "*" " " " " " "
## 21 ( 1 ) " " "*" " " " " " "
## 22 ( 1 ) " " "*" " " " " " "
## 23 ( 1 ) " " "*" " " " " " "
## 24 ( 1 ) " " "*" " " " " " "
## 25 ( 1 ) " " "*" " " " " " "
## 26 ( 1 ) " " "*" " " " " " "
## 27 ( 1 ) " " "*" " " " " " "
## 28 ( 1 ) " " "*" " " " " " "
## 29 ( 1 ) " " "*" " " " " " "
## 30 ( 1 ) " " "*" " " " " " "
## 31 ( 1 ) " " "*" " " " " " "
## 32 ( 1 ) " " "*" " " " " " "
## 33 ( 1 ) " " "*" " " " " " "
## 34 ( 1 ) " " "*" " " " " " "
## 35 ( 1 ) " " "*" " " " " " "
## 36 ( 1 ) " " "*" " " " " " "
## 37 ( 1 ) " " "*" " " " " " "
## 38 ( 1 ) " " "*" " " " " " "
## 39 ( 1 ) " " "*" " " " " " "
## 40 ( 1 ) " " "*" " " " " " "
## 41 ( 1 ) " " "*" " " " " " "
## 42 ( 1 ) " " "*" " " " " " "
## 43 ( 1 ) " " "*" " " " " " "
## 44 ( 1 ) " " "*" " " " " " "
## 45 ( 1 ) " " "*" " " " " " "
## 46 ( 1 ) " " "*" " " " " " "
## 47 ( 1 ) " " "*" " " " " " "
## 48 ( 1 ) " " "*" " " " " " "
## 49 ( 1 ) " " "*" " " " " " "
## 50 ( 1 ) " " "*" " " " " " "
## 51 ( 1 ) " " "*" " " " " "*"
## pctWorkMom_18 kidsBornNevrMarr pctKidsBornNevrMarr numForeignBorn
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " " " " " " "
## 9 ( 1 ) " " " " " " " "
## 10 ( 1 ) " " " " " " " "
## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " " " " " " "
## 13 ( 1 ) " " " " " " " "
## 14 ( 1 ) " " " " " " " "
## 15 ( 1 ) " " " " " " " "
## 16 ( 1 ) " " " " " " " "
## 17 ( 1 ) " " " " " " " "
## 18 ( 1 ) " " " " " " " "
## 19 ( 1 ) " " " " " " " "
## 20 ( 1 ) " " " " " " " "
## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " " " " "
## 24 ( 1 ) " " " " " " " "
## 25 ( 1 ) " " " " " " " "
## 26 ( 1 ) " " " " " " " "
## 27 ( 1 ) " " " " " " " "
## 28 ( 1 ) " " " " " " " "
## 29 ( 1 ) " " " " " " " "
## 30 ( 1 ) " " " " " " " "
## 31 ( 1 ) " " " " " " " "
## 32 ( 1 ) " " " " " " " "
## 33 ( 1 ) " " " " " " " "
## 34 ( 1 ) " " " " " " " "
## 35 ( 1 ) " " " " " " " "
## 36 ( 1 ) " " " " " " " "
## 37 ( 1 ) " " " " " " " "
## 38 ( 1 ) " " " " " " " "
## 39 ( 1 ) " " " " " " " "
## 40 ( 1 ) " " "*" " " " "
## 41 ( 1 ) " " "*" " " " "
## 42 ( 1 ) " " "*" " " " "
## 43 ( 1 ) " " "*" " " " "
## 44 ( 1 ) " " "*" " " " "
## 45 ( 1 ) " " "*" " " " "
## 46 ( 1 ) " " "*" " " " "
## 47 ( 1 ) " " "*" " " " "
## 48 ( 1 ) " " "*" " " " "
## 49 ( 1 ) " " "*" " " " "
## 50 ( 1 ) " " "*" " " " "
## 51 ( 1 ) " " "*" " " " "
## pctFgnImmig_3 pctFgnImmig_5 pctFgnImmig_8 pctFgnImmig_10 pctImmig_3
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " "*"
## 32 ( 1 ) " " " " " " "*" "*"
## 33 ( 1 ) " " " " " " "*" "*"
## 34 ( 1 ) " " " " " " "*" "*"
## 35 ( 1 ) " " " " " " "*" "*"
## 36 ( 1 ) " " " " " " "*" "*"
## 37 ( 1 ) " " " " " " "*" "*"
## 38 ( 1 ) " " " " " " "*" "*"
## 39 ( 1 ) " " " " " " "*" "*"
## 40 ( 1 ) " " " " " " "*" "*"
## 41 ( 1 ) " " " " " " "*" "*"
## 42 ( 1 ) " " " " " " "*" "*"
## 43 ( 1 ) " " "*" " " "*" "*"
## 44 ( 1 ) " " "*" " " "*" "*"
## 45 ( 1 ) " " "*" "*" "*" "*"
## 46 ( 1 ) " " "*" "*" "*" "*"
## 47 ( 1 ) " " "*" "*" "*" "*"
## 48 ( 1 ) " " "*" "*" "*" "*"
## 49 ( 1 ) " " "*" "*" "*" "*"
## 50 ( 1 ) " " "*" "*" "*" "*"
## 51 ( 1 ) " " "*" "*" "*" "*"
## pctImmig_5 pctImmig_8 pctImmig_10 pctSpeakOnlyEng pctNotSpeakEng
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " "*" " "
## 14 ( 1 ) " " " " " " "*" " "
## 15 ( 1 ) " " " " " " "*" " "
## 16 ( 1 ) " " " " " " "*" " "
## 17 ( 1 ) " " " " " " "*" " "
## 18 ( 1 ) " " " " " " "*" " "
## 19 ( 1 ) " " " " " " "*" " "
## 20 ( 1 ) " " " " " " "*" " "
## 21 ( 1 ) " " " " " " "*" " "
## 22 ( 1 ) " " " " " " "*" " "
## 23 ( 1 ) " " " " " " "*" " "
## 24 ( 1 ) " " " " " " "*" " "
## 25 ( 1 ) " " " " " " "*" " "
## 26 ( 1 ) " " " " " " "*" " "
## 27 ( 1 ) " " " " " " "*" " "
## 28 ( 1 ) " " " " " " "*" " "
## 29 ( 1 ) " " " " " " "*" " "
## 30 ( 1 ) " " " " " " "*" " "
## 31 ( 1 ) " " " " " " "*" " "
## 32 ( 1 ) " " " " " " "*" " "
## 33 ( 1 ) " " " " " " "*" " "
## 34 ( 1 ) " " " " " " "*" " "
## 35 ( 1 ) " " " " " " "*" " "
## 36 ( 1 ) " " " " " " "*" " "
## 37 ( 1 ) " " " " " " "*" " "
## 38 ( 1 ) " " " " " " "*" " "
## 39 ( 1 ) " " " " " " "*" " "
## 40 ( 1 ) " " " " " " "*" " "
## 41 ( 1 ) " " " " " " "*" " "
## 42 ( 1 ) " " " " " " "*" " "
## 43 ( 1 ) " " " " " " "*" " "
## 44 ( 1 ) " " " " "*" "*" " "
## 45 ( 1 ) " " " " "*" "*" " "
## 46 ( 1 ) " " " " "*" "*" " "
## 47 ( 1 ) " " " " "*" "*" " "
## 48 ( 1 ) " " " " "*" "*" " "
## 49 ( 1 ) " " " " "*" "*" " "
## 50 ( 1 ) " " " " "*" "*" " "
## 51 ( 1 ) " " " " "*" "*" " "
## pctLargHousFam pctLargHous persPerOccupHous persPerOwnOccup
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " " " " " " "
## 9 ( 1 ) " " " " " " " "
## 10 ( 1 ) " " " " " " " "
## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " " " " " " "
## 13 ( 1 ) " " " " " " " "
## 14 ( 1 ) " " " " " " " "
## 15 ( 1 ) " " " " " " " "
## 16 ( 1 ) " " " " " " " "
## 17 ( 1 ) " " " " " " " "
## 18 ( 1 ) " " " " " " " "
## 19 ( 1 ) " " " " " " " "
## 20 ( 1 ) " " " " " " " "
## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " "*" " "
## 24 ( 1 ) " " " " "*" " "
## 25 ( 1 ) " " " " "*" " "
## 26 ( 1 ) " " " " "*" "*"
## 27 ( 1 ) " " " " "*" "*"
## 28 ( 1 ) " " " " "*" "*"
## 29 ( 1 ) " " " " "*" "*"
## 30 ( 1 ) " " " " "*" "*"
## 31 ( 1 ) " " " " "*" "*"
## 32 ( 1 ) " " " " "*" "*"
## 33 ( 1 ) " " " " "*" "*"
## 34 ( 1 ) " " " " "*" "*"
## 35 ( 1 ) " " " " "*" "*"
## 36 ( 1 ) " " " " "*" "*"
## 37 ( 1 ) " " " " "*" "*"
## 38 ( 1 ) " " " " "*" "*"
## 39 ( 1 ) " " " " "*" "*"
## 40 ( 1 ) " " " " "*" "*"
## 41 ( 1 ) " " " " "*" "*"
## 42 ( 1 ) " " " " "*" "*"
## 43 ( 1 ) " " " " "*" "*"
## 44 ( 1 ) " " " " "*" "*"
## 45 ( 1 ) " " " " "*" "*"
## 46 ( 1 ) " " " " "*" "*"
## 47 ( 1 ) " " " " "*" "*"
## 48 ( 1 ) " " " " "*" "*"
## 49 ( 1 ) " " " " "*" "*"
## 50 ( 1 ) " " " " "*" "*"
## 51 ( 1 ) " " " " "*" "*"
## persPerRenterOccup pctPersOwnOccup pctPopDenseHous pctSmallHousUnits
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
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## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " " " " "
## 24 ( 1 ) " " " " " " " "
## 25 ( 1 ) " " " " " " " "
## 26 ( 1 ) " " " " " " " "
## 27 ( 1 ) " " " " "*" " "
## 28 ( 1 ) " " " " "*" " "
## 29 ( 1 ) " " " " "*" " "
## 30 ( 1 ) " " " " "*" " "
## 31 ( 1 ) " " " " "*" " "
## 32 ( 1 ) " " " " "*" " "
## 33 ( 1 ) " " " " "*" " "
## 34 ( 1 ) " " " " "*" " "
## 35 ( 1 ) " " " " "*" " "
## 36 ( 1 ) " " " " "*" " "
## 37 ( 1 ) " " " " "*" " "
## 38 ( 1 ) " " " " "*" " "
## 39 ( 1 ) " " " " "*" " "
## 40 ( 1 ) " " " " "*" " "
## 41 ( 1 ) " " " " "*" " "
## 42 ( 1 ) " " " " "*" " "
## 43 ( 1 ) " " " " "*" " "
## 44 ( 1 ) " " " " "*" " "
## 45 ( 1 ) " " " " "*" " "
## 46 ( 1 ) " " " " "*" " "
## 47 ( 1 ) " " " " "*" " "
## 48 ( 1 ) " " " " "*" " "
## 49 ( 1 ) " " " " "*" " "
## 50 ( 1 ) " " " " "*" " "
## 51 ( 1 ) " " " " "*" " "
## medNumBedrm houseVacant pctHousOccup pctHousOwnerOccup
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " " " " "
## 6 ( 1 ) " " " " " " " "
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## 8 ( 1 ) " " " " " " " "
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## 11 ( 1 ) " " " " " " " "
## 12 ( 1 ) " " " " " " " "
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## 16 ( 1 ) " " " " " " " "
## 17 ( 1 ) " " " " " " " "
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## 19 ( 1 ) " " " " " " " "
## 20 ( 1 ) " " " " " " " "
## 21 ( 1 ) " " " " " " " "
## 22 ( 1 ) " " " " " " " "
## 23 ( 1 ) " " " " " " " "
## 24 ( 1 ) " " " " " " " "
## 25 ( 1 ) " " " " " " " "
## 26 ( 1 ) " " " " " " " "
## 27 ( 1 ) " " " " " " " "
## 28 ( 1 ) " " " " " " " "
## 29 ( 1 ) " " " " " " " "
## 30 ( 1 ) " " " " " " " "
## 31 ( 1 ) " " " " " " " "
## 32 ( 1 ) " " " " " " " "
## 33 ( 1 ) " " " " " " " "
## 34 ( 1 ) " " " " " " " "
## 35 ( 1 ) " " " " " " " "
## 36 ( 1 ) " " " " " " " "
## 37 ( 1 ) " " " " " " " "
## 38 ( 1 ) " " " " " " " "
## 39 ( 1 ) " " " " " " " "
## 40 ( 1 ) " " " " " " " "
## 41 ( 1 ) " " " " " " " "
## 42 ( 1 ) " " " " "*" " "
## 43 ( 1 ) " " " " "*" " "
## 44 ( 1 ) " " " " "*" " "
## 45 ( 1 ) " " " " "*" " "
## 46 ( 1 ) " " " " "*" " "
## 47 ( 1 ) " " " " "*" " "
## 48 ( 1 ) " " " " "*" " "
## 49 ( 1 ) " " " " "*" " "
## 50 ( 1 ) " " " " "*" " "
## 51 ( 1 ) " " " " "*" " "
## pctVacantBoarded pctVacant6up medYrHousBuilt pctHousWOphone
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
## 4 ( 1 ) " " " " " " " "
## 5 ( 1 ) " " " " "*" " "
## 6 ( 1 ) " " " " "*" " "
## 7 ( 1 ) " " " " "*" " "
## 8 ( 1 ) " " " " "*" " "
## 9 ( 1 ) " " " " "*" " "
## 10 ( 1 ) " " "*" "*" " "
## 11 ( 1 ) " " "*" "*" " "
## 12 ( 1 ) " " "*" "*" " "
## 13 ( 1 ) " " "*" "*" " "
## 14 ( 1 ) " " "*" "*" " "
## 15 ( 1 ) " " "*" "*" " "
## 16 ( 1 ) " " "*" "*" " "
## 17 ( 1 ) " " "*" "*" " "
## 18 ( 1 ) " " "*" "*" " "
## 19 ( 1 ) " " "*" "*" " "
## 20 ( 1 ) " " "*" "*" " "
## 21 ( 1 ) " " "*" "*" " "
## 22 ( 1 ) " " "*" "*" " "
## 23 ( 1 ) " " "*" "*" " "
## 24 ( 1 ) " " "*" "*" " "
## 25 ( 1 ) " " "*" "*" " "
## 26 ( 1 ) " " "*" "*" " "
## 27 ( 1 ) " " "*" "*" " "
## 28 ( 1 ) "*" "*" "*" " "
## 29 ( 1 ) "*" "*" "*" " "
## 30 ( 1 ) "*" "*" "*" " "
## 31 ( 1 ) "*" "*" "*" " "
## 32 ( 1 ) "*" "*" "*" " "
## 33 ( 1 ) "*" "*" "*" " "
## 34 ( 1 ) "*" "*" "*" " "
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## 36 ( 1 ) "*" "*" "*" " "
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## 38 ( 1 ) "*" "*" "*" " "
## 39 ( 1 ) "*" "*" "*" " "
## 40 ( 1 ) "*" "*" "*" " "
## 41 ( 1 ) "*" "*" "*" " "
## 42 ( 1 ) "*" "*" "*" " "
## 43 ( 1 ) "*" "*" "*" " "
## 44 ( 1 ) "*" "*" "*" " "
## 45 ( 1 ) "*" "*" "*" " "
## 46 ( 1 ) "*" "*" "*" " "
## 47 ( 1 ) "*" "*" "*" " "
## 48 ( 1 ) "*" "*" "*" " "
## 49 ( 1 ) "*" "*" "*" " "
## 50 ( 1 ) "*" "*" "*" " "
## 51 ( 1 ) "*" "*" "*" " "
## pctHousWOplumb ownHousLowQ ownHousMed ownHousUperQ ownHousQrange
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
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## 11 ( 1 ) " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " "
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## 18 ( 1 ) " " " " "*" " " " "
## 19 ( 1 ) " " " " "*" " " " "
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## 22 ( 1 ) " " " " "*" " " " "
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## 26 ( 1 ) " " " " "*" " " " "
## 27 ( 1 ) " " " " "*" " " " "
## 28 ( 1 ) " " " " "*" " " " "
## 29 ( 1 ) " " " " "*" " " " "
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## 50 ( 1 ) " " " " "*" " " " "
## 51 ( 1 ) " " " " "*" " " " "
## rentLowQ rentMed rentUpperQ rentQrange medGrossRent medRentpctHousInc
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
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## 46 ( 1 ) "*" " " "*" " " " " "*"
## 47 ( 1 ) "*" " " "*" " " " " "*"
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## 49 ( 1 ) "*" " " "*" " " " " "*"
## 50 ( 1 ) "*" " " "*" " " " " "*"
## 51 ( 1 ) "*" " " "*" " " " " "*"
## medOwnCostpct medOwnCostPctWO persEmergShelt persHomeless
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
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## 50 ( 1 ) "*" "*" "*" " "
## 51 ( 1 ) "*" "*" "*" " "
## pctForeignBorn pctBornStateResid pctSameHouse_5 pctSameCounty_5
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) " " " " " " " "
## 3 ( 1 ) " " " " " " " "
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## 5 ( 1 ) " " " " " " " "
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## 7 ( 1 ) " " " " " " " "
## 8 ( 1 ) " " "*" " " " "
## 9 ( 1 ) " " "*" " " " "
## 10 ( 1 ) " " "*" " " " "
## 11 ( 1 ) " " "*" " " " "
## 12 ( 1 ) " " "*" " " " "
## 13 ( 1 ) " " "*" " " " "
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## 21 ( 1 ) " " "*" " " " "
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## 50 ( 1 ) "*" "*" " " " "
## 51 ( 1 ) "*" "*" " " " "
## pctSameState_5 landArea popDensity pctUsePubTrans pctOfficDrugUnit
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " "*"
## 8 ( 1 ) " " " " " " " " "*"
## 9 ( 1 ) " " " " "*" " " "*"
## 10 ( 1 ) " " " " "*" " " "*"
## 11 ( 1 ) " " " " "*" " " "*"
## 12 ( 1 ) " " " " "*" " " "*"
## 13 ( 1 ) " " " " "*" " " "*"
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## 20 ( 1 ) " " " " "*" " " "*"
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## 25 ( 1 ) " " " " "*" " " "*"
## 26 ( 1 ) " " " " "*" " " "*"
## 27 ( 1 ) " " " " "*" " " "*"
## 28 ( 1 ) " " " " "*" " " "*"
## 29 ( 1 ) " " " " "*" " " "*"
## 30 ( 1 ) " " " " "*" " " "*"
## 31 ( 1 ) " " " " "*" " " "*"
## 32 ( 1 ) " " " " "*" " " "*"
## 33 ( 1 ) " " " " "*" " " "*"
## 34 ( 1 ) " " " " "*" " " "*"
## 35 ( 1 ) " " " " "*" " " "*"
## 36 ( 1 ) " " " " "*" " " "*"
## 37 ( 1 ) " " "*" "*" " " "*"
## 38 ( 1 ) " " "*" "*" " " "*"
## 39 ( 1 ) " " "*" "*" " " "*"
## 40 ( 1 ) " " "*" "*" " " "*"
## 41 ( 1 ) " " "*" "*" " " "*"
## 42 ( 1 ) " " "*" "*" " " "*"
## 43 ( 1 ) " " "*" "*" " " "*"
## 44 ( 1 ) " " "*" "*" " " "*"
## 45 ( 1 ) " " "*" "*" " " "*"
## 46 ( 1 ) " " "*" "*" " " "*"
## 47 ( 1 ) " " "*" "*" " " "*"
## 48 ( 1 ) " " "*" "*" " " "*"
## 49 ( 1 ) " " "*" "*" " " "*"
## 50 ( 1 ) " " "*" "*" " " "*"
## 51 ( 1 ) " " "*" "*" " " "*"
## violentPerPop
## 1 ( 1 ) "*"
## 2 ( 1 ) "*"
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"
## 16 ( 1 ) "*"
## 17 ( 1 ) "*"
## 18 ( 1 ) "*"
## 19 ( 1 ) "*"
## 20 ( 1 ) "*"
## 21 ( 1 ) "*"
## 22 ( 1 ) "*"
## 23 ( 1 ) "*"
## 24 ( 1 ) "*"
## 25 ( 1 ) "*"
## 26 ( 1 ) "*"
## 27 ( 1 ) "*"
## 28 ( 1 ) "*"
## 29 ( 1 ) "*"
## 30 ( 1 ) "*"
## 31 ( 1 ) "*"
## 32 ( 1 ) "*"
## 33 ( 1 ) "*"
## 34 ( 1 ) "*"
## 35 ( 1 ) "*"
## 36 ( 1 ) "*"
## 37 ( 1 ) "*"
## 38 ( 1 ) "*"
## 39 ( 1 ) "*"
## 40 ( 1 ) "*"
## 41 ( 1 ) "*"
## 42 ( 1 ) "*"
## 43 ( 1 ) "*"
## 44 ( 1 ) "*"
## 45 ( 1 ) "*"
## 46 ( 1 ) "*"
## 47 ( 1 ) "*"
## 48 ( 1 ) "*"
## 49 ( 1 ) "*"
## 50 ( 1 ) "*"
## 51 ( 1 ) "*"
which.min(summari$cp)## [1] 49
coefs <- coef(reg.fit.fwd2,49)[-1]
predict_names <- names(coefs)
namesof <- numeric()
#examine non linear relationships
plot(nonViolPerPop ~ ., data = na.omit(crime[,c(predict_names,"nonViolPerPop")]))
gam.1 <- gam(nonViolPerPop ~ bs((pctWdiv), 3) + s(pctCollGrad,3) + s(medFamIncome,3) + s(rentQrange,3) + ., data = na.omit(crime))
#summary(gam.1)
pred_gam1 <- predict(gam.1, newdata = na.omit(crime))## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
crime <- na.omit(crime)
#cross validation error
cv.glm(crime, gam.1,K=10)$delta[1]## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(9.02, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in bs((pctWdiv), degree = 3L, knots = numeric(0), Boundary.knots =
## c(10.1, : some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## [1] 3194596
So our model gives a cross validation error of 3194596.
l1 <- glm(nonViolPerPop ~ ., data = crime[,c(predict_names, "nonViolPerPop")])
summary(l1)##
## Call:
## glm(formula = nonViolPerPop ~ ., data = crime[, c(predict_names,
## "nonViolPerPop")])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6301.3 -1027.6 -201.6 771.0 24846.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.922e+04 1.527e+04 -2.568 0.010309 *
## perHoush -1.357e+03 4.845e+02 -2.800 0.005169 **
## pctBlack 3.559e+00 7.305e+00 0.487 0.626143
## persUrban -3.438e-03 1.670e-03 -2.059 0.039640 *
## pctWwage 4.907e+01 2.599e+01 1.888 0.059153 .
## pctWdiv 1.730e+01 1.174e+01 1.474 0.140701
## pctWsocsec 1.336e+02 2.320e+01 5.758 9.97e-09 ***
## pctRetire -4.745e+01 1.756e+01 -2.702 0.006964 **
## medFamIncome -1.798e-02 1.849e-02 -0.972 0.331137
## whitePerCap 1.221e-01 2.863e-02 4.263 2.11e-05 ***
## asianPerCap -3.810e-03 5.187e-03 -0.735 0.462727
## otherPerCap 1.475e-02 5.748e-03 2.565 0.010382 *
## persPoverty 3.662e-02 1.190e-02 3.077 0.002119 **
## pctPoverty 5.581e+01 1.923e+01 2.902 0.003752 **
## pctNotHSgrad -5.302e+01 1.360e+01 -3.897 0.000101 ***
## pctCollGrad -3.321e+01 1.690e+01 -1.965 0.049536 *
## pctEmploy 4.410e+01 1.842e+01 2.395 0.016739 *
## pctEmployMfg -1.649e+01 7.996e+00 -2.062 0.039342 *
## pctEmployProfServ -3.593e+01 1.435e+01 -2.503 0.012388 *
## pctOccupMgmt 5.175e+01 1.982e+01 2.611 0.009089 **
## pctMaleNevMar 8.315e+01 1.545e+01 5.382 8.30e-08 ***
## pctAllDivorc 2.987e+02 4.207e+01 7.100 1.77e-12 ***
## pctKids2Par -7.861e+01 1.651e+01 -4.762 2.07e-06 ***
## kidsBornNevrMarr -6.866e-02 2.070e-02 -3.316 0.000930 ***
## pctFgnImmig_5 -2.788e+01 1.088e+01 -2.563 0.010469 *
## pctFgnImmig_8 1.576e+01 1.373e+01 1.148 0.251082
## pctFgnImmig_10 1.613e+01 9.756e+00 1.654 0.098375 .
## pctImmig_3 -5.743e+01 1.267e+02 -0.453 0.650396
## pctImmig_10 -1.583e+02 7.624e+01 -2.076 0.038046 *
## pctSpeakOnlyEng -4.368e+00 9.741e+00 -0.448 0.653882
## persPerOccupHous 4.139e+03 8.624e+02 4.800 1.72e-06 ***
## persPerOwnOccup -1.488e+03 5.607e+02 -2.653 0.008051 **
## pctPopDenseHous -3.054e+01 2.582e+01 -1.183 0.237097
## pctHousOccup -3.382e+01 1.180e+01 -2.868 0.004182 **
## pctVacantBoarded 1.595e+01 1.767e+01 0.903 0.366726
## pctVacant6up -1.300e+01 4.559e+00 -2.851 0.004409 **
## medYrHousBuilt 1.836e+01 7.204e+00 2.549 0.010885 *
## ownHousMed -5.063e-03 1.759e-03 -2.879 0.004036 **
## rentMed -2.707e+00 2.174e+00 -1.245 0.213293
## medGrossRent -9.256e-01 2.038e+00 -0.454 0.649791
## medOwnCostPctWO -3.374e+01 4.111e+01 -0.821 0.412000
## persEmergShelt 5.610e-01 3.005e-01 1.867 0.062045 .
## persHomeless -2.387e-01 6.182e-01 -0.386 0.699449
## pctForeignBorn 1.286e+02 3.089e+01 4.162 3.29e-05 ***
## pctSameHouse_5 -8.437e+00 1.178e+01 -0.716 0.474058
## pctSameCounty_5 6.460e+00 9.626e+00 0.671 0.502212
## pctUsePubTrans -1.306e+01 1.409e+01 -0.927 0.354009
## pctOfficDrugUnit 5.817e+01 1.642e+01 3.542 0.000406 ***
## ownHousQrange -6.983e-04 2.062e-03 -0.339 0.734849
## rentQrange 6.348e-01 8.221e-01 0.772 0.440158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3431285)
##
## Null deviance: 1.4756e+10 on 1900 degrees of freedom
## Residual deviance: 6.3513e+09 on 1851 degrees of freedom
## AIC: 34053
##
## Number of Fisher Scoring iterations: 2
predicts <- predict(l1, data = crime)
#MSE<- sum((predicts - crime$nonViolPerPop)^2)/length(crime$nonViolPerPop)
#MSE
#Cross validation error
cv.glm(crime,l1,K=10)$delta[1]## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## [1] 3134540
So our purely linear model gives a cross validation error of 3134540.
"Would it be reasonable to use your model to predict the true number of crimes in a community? Address this question under the following scenarios: a) The data on reported crimes is an accurate representation of the actual occurrence of crimes in a community; b) The data on reported crimes is biased and it is not an accurate representation of the actual occurrence of crimes in a community."
For both scenarios our model is not a particularly accurate predicting machine, as all cross validation errors are very high. Under scenario (a), our model would have a much better chance of being a good predictive machine of actual crime, because we have then trained on model on actual crime rate. Clearly for scenario (b), since we haven't based our model on actual crime, but instead reported crime, then we certainly will be much less accurate in predicting actual crime.