lm(medv~lstat+age, Boston)
##
## Call:
## lm(formula = medv ~ lstat + age, data = Boston)
##
## Coefficients:
## (Intercept) lstat age
## 33.22276 -1.03207 0.03454
lm(medv~., Boston)
##
## Call:
## lm(formula = medv ~ ., data = Boston)
##
## Coefficients:
## (Intercept) crim zn indus chas nox
## 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
## rm age dis rad tax ptratio
## 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
## black lstat
## 9.312e-03 -5.248e-01
glm(medv~lstat+age, data=Boston)
##
## Call: glm(formula = medv ~ lstat + age, data = Boston)
##
## Coefficients:
## (Intercept) lstat age
## 33.22276 -1.03207 0.03454
##
## Degrees of Freedom: 505 Total (i.e. Null); 503 Residual
## Null Deviance: 42720
## Residual Deviance: 19170 AIC: 3283
lm(medv~-age, data=Boston)
##
## Call:
## lm(formula = medv ~ -age, data = Boston)
##
## Coefficients:
## (Intercept)
## 22.53
summary(Boston)
## crim zn indus chas
## Min. : 0.00632 Min. : 0.00 Min. : 0.46 Min. :0.00000
## 1st Qu.: 0.08205 1st Qu.: 0.00 1st Qu.: 5.19 1st Qu.:0.00000
## Median : 0.25651 Median : 0.00 Median : 9.69 Median :0.00000
## Mean : 3.61352 Mean : 11.36 Mean :11.14 Mean :0.06917
## 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.:18.10 3rd Qu.:0.00000
## Max. :88.97620 Max. :100.00 Max. :27.74 Max. :1.00000
## nox rm age dis
## Min. :0.3850 Min. :3.561 Min. : 2.90 Min. : 1.130
## 1st Qu.:0.4490 1st Qu.:5.886 1st Qu.: 45.02 1st Qu.: 2.100
## Median :0.5380 Median :6.208 Median : 77.50 Median : 3.207
## Mean :0.5547 Mean :6.285 Mean : 68.57 Mean : 3.795
## 3rd Qu.:0.6240 3rd Qu.:6.623 3rd Qu.: 94.08 3rd Qu.: 5.188
## Max. :0.8710 Max. :8.780 Max. :100.00 Max. :12.127
## rad tax ptratio black
## Min. : 1.000 Min. :187.0 Min. :12.60 Min. : 0.32
## 1st Qu.: 4.000 1st Qu.:279.0 1st Qu.:17.40 1st Qu.:375.38
## Median : 5.000 Median :330.0 Median :19.05 Median :391.44
## Mean : 9.549 Mean :408.2 Mean :18.46 Mean :356.67
## 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.23
## Max. :24.000 Max. :711.0 Max. :22.00 Max. :396.90
## lstat medv
## Min. : 1.73 Min. : 5.00
## 1st Qu.: 6.95 1st Qu.:17.02
## Median :11.36 Median :21.20
## Mean :12.65 Mean :22.53
## 3rd Qu.:16.95 3rd Qu.:25.00
## Max. :37.97 Max. :50.00
mediana_crim <- median(Boston$crim)
Boston$Direction <- ifelse(Boston$crim > mediana_crim, 1, 0)
Boston$Direction <- as.factor(Boston$Direction)
glm.fit<-glm(Direction~. -chas -crim,data=Boston,family=binomial)
library(caTools)
set.seed(123)
Boston_split <- sample.split(Boston$Direction, SplitRatio = 0.80)
train <- subset(Boston, Boston_split == TRUE)
test <- subset(Boston, Boston_split == FALSE)
glm.fit <- glm(Direction~. -chas -crim, data = train, family = binomial)
glm.probs <- predict(glm.fit, test, type = "response")
glm.pred <-ifelse(glm.probs > 0.5, 1, 0)
glm.pred <- as.factor(glm.pred)
table(glm.pred, test$Direction)
##
## glm.pred 0 1
## 0 46 5
## 1 5 46
mean(glm.pred == test$Direction)
## [1] 0.9019608
mean(glm.pred != test$Direction)
## [1] 0.09803922
library(ISLR)
library(MASS)
attach(Smarket)
train<-(Year<2005)
Smarket.2005<-Smarket[!train,]
Direction.2005<-Smarket$Direction[!train]
qda.fit <- qda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train)
qda.pred <- predict(qda.fit, Smarket.2005)
qda.class <- qda.pred$class
table(qda.class, Direction.2005)
## Direction.2005
## qda.class Down Up
## Down 30 20
## Up 81 121
mean(qda.class == Direction.2005)
## [1] 0.5992063
train<-(Year<2005)
Smarket.2005<-Smarket[!train,]
Direction.2005<-Smarket$Direction[!train]
lda.fit <- lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train)
lda.pred <- predict(lda.fit, Smarket.2005)
lda.class <- lda.pred$class
table(lda.class, Direction.2005)
## Direction.2005
## lda.class Down Up
## Down 35 35
## Up 76 106
mean(lda.class == Direction.2005)
## [1] 0.5595238
library(MASS)
attach(Cars93)
summary(Cars93)
## Manufacturer Model Type Min.Price Price
## Chevrolet: 8 100 : 1 Compact:16 Min. : 6.70 Min. : 7.40
## Ford : 8 190E : 1 Large :11 1st Qu.:10.80 1st Qu.:12.20
## Dodge : 6 240 : 1 Midsize:22 Median :14.70 Median :17.70
## Mazda : 5 300E : 1 Small :21 Mean :17.13 Mean :19.51
## Pontiac : 5 323 : 1 Sporty :14 3rd Qu.:20.30 3rd Qu.:23.30
## Buick : 4 535i : 1 Van : 9 Max. :45.40 Max. :61.90
## (Other) :57 (Other):87
## Max.Price MPG.city MPG.highway AirBags
## Min. : 7.9 Min. :15.00 Min. :20.00 Driver & Passenger:16
## 1st Qu.:14.7 1st Qu.:18.00 1st Qu.:26.00 Driver only :43
## Median :19.6 Median :21.00 Median :28.00 None :34
## Mean :21.9 Mean :22.37 Mean :29.09
## 3rd Qu.:25.3 3rd Qu.:25.00 3rd Qu.:31.00
## Max. :80.0 Max. :46.00 Max. :50.00
##
## DriveTrain Cylinders EngineSize Horsepower RPM
## 4WD :10 3 : 3 Min. :1.000 Min. : 55.0 Min. :3800
## Front:67 4 :49 1st Qu.:1.800 1st Qu.:103.0 1st Qu.:4800
## Rear :16 5 : 2 Median :2.400 Median :140.0 Median :5200
## 6 :31 Mean :2.668 Mean :143.8 Mean :5281
## 8 : 7 3rd Qu.:3.300 3rd Qu.:170.0 3rd Qu.:5750
## rotary: 1 Max. :5.700 Max. :300.0 Max. :6500
##
## Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers
## Min. :1320 No :32 Min. : 9.20 Min. :2.000
## 1st Qu.:1985 Yes:61 1st Qu.:14.50 1st Qu.:4.000
## Median :2340 Median :16.40 Median :5.000
## Mean :2332 Mean :16.66 Mean :5.086
## 3rd Qu.:2565 3rd Qu.:18.80 3rd Qu.:6.000
## Max. :3755 Max. :27.00 Max. :8.000
##
## Length Wheelbase Width Turn.circle
## Min. :141.0 Min. : 90.0 Min. :60.00 Min. :32.00
## 1st Qu.:174.0 1st Qu.: 98.0 1st Qu.:67.00 1st Qu.:37.00
## Median :183.0 Median :103.0 Median :69.00 Median :39.00
## Mean :183.2 Mean :103.9 Mean :69.38 Mean :38.96
## 3rd Qu.:192.0 3rd Qu.:110.0 3rd Qu.:72.00 3rd Qu.:41.00
## Max. :219.0 Max. :119.0 Max. :78.00 Max. :45.00
##
## Rear.seat.room Luggage.room Weight Origin Make
## Min. :19.00 Min. : 6.00 Min. :1695 USA :48 Acura Integra: 1
## 1st Qu.:26.00 1st Qu.:12.00 1st Qu.:2620 non-USA:45 Acura Legend : 1
## Median :27.50 Median :14.00 Median :3040 Audi 100 : 1
## Mean :27.83 Mean :13.89 Mean :3073 Audi 90 : 1
## 3rd Qu.:30.00 3rd Qu.:15.00 3rd Qu.:3525 BMW 535i : 1
## Max. :36.00 Max. :22.00 Max. :4105 Buick Century: 1
## NA's :2 NA's :11 (Other) :87
lm.fit <- lm(Width ~ ., data = Cars93)
summary(lm.fit)
##
## Call:
## lm(formula = Width ~ ., data = Cars93)
##
## Residuals:
## ALL 82 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (143 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.100e+01 NaN NaN NaN
## ManufacturerAudi -1.000e+00 NaN NaN NaN
## ManufacturerBMW -2.000e+00 NaN NaN NaN
## ManufacturerBuick 7.000e+00 NaN NaN NaN
## ManufacturerCadillac 3.000e+00 NaN NaN NaN
## ManufacturerChevrolet -1.218e-14 NaN NaN NaN
## ManufacturerChrylser 3.000e+00 NaN NaN NaN
## ManufacturerChrysler -3.000e+00 NaN NaN NaN
## ManufacturerDodge 1.000e+00 NaN NaN NaN
## ManufacturerEagle 3.000e+00 NaN NaN NaN
## ManufacturerFord -3.000e+00 NaN NaN NaN
## ManufacturerGeo -4.000e+00 NaN NaN NaN
## ManufacturerHonda -1.000e+00 NaN NaN NaN
## ManufacturerHyundai -2.000e+00 NaN NaN NaN
## ManufacturerInfiniti 1.000e+00 NaN NaN NaN
## ManufacturerLexus -1.662e-14 NaN NaN NaN
## ManufacturerLincoln 6.000e+00 NaN NaN NaN
## ManufacturerMazda -5.000e+00 NaN NaN NaN
## ManufacturerMercedes-Benz -2.000e+00 NaN NaN NaN
## ManufacturerMercury 2.000e+00 NaN NaN NaN
## ManufacturerMitsubishi -4.000e+00 NaN NaN NaN
## ManufacturerNissan -5.000e+00 NaN NaN NaN
## ManufacturerOldsmobile 3.000e+00 NaN NaN NaN
## ManufacturerPlymouth -4.000e+00 NaN NaN NaN
## ManufacturerPontiac -5.000e+00 NaN NaN NaN
## ManufacturerSaab -4.000e+00 NaN NaN NaN
## ManufacturerSaturn -3.000e+00 NaN NaN NaN
## ManufacturerSubaru -6.000e+00 NaN NaN NaN
## ManufacturerSuzuki -8.000e+00 NaN NaN NaN
## ManufacturerToyota -6.000e+00 NaN NaN NaN
## ManufacturerVolkswagen -4.000e+00 NaN NaN NaN
## ManufacturerVolvo -2.000e+00 NaN NaN NaN
## Model190E -2.000e+00 NaN NaN NaN
## Model240 -2.000e+00 NaN NaN NaN
## Model300E NA NA NA NA
## Model323 3.535e-15 NaN NaN NaN
## Model535i NA NA NA NA
## Model626 3.000e+00 NaN NaN NaN
## Model850 NA NA NA NA
## Model90 -3.000e+00 NaN NaN NaN
## Model900 NA NA NA NA
## ModelAccord -3.000e+00 NaN NaN NaN
## ModelAchieva -7.000e+00 NaN NaN NaN
## ModelAltima 1.000e+00 NaN NaN NaN
## ModelBonneville 8.000e+00 NaN NaN NaN
## ModelCamaro 3.000e+00 NaN NaN NaN
## ModelCamry 5.000e+00 NaN NaN NaN
## ModelCapri -8.000e+00 NaN NaN NaN
## ModelCaprice 6.000e+00 NaN NaN NaN
## ModelCavalier -5.000e+00 NaN NaN NaN
## ModelCelica 4.000e+00 NaN NaN NaN
## ModelCentury -9.000e+00 NaN NaN NaN
## ModelCivic -3.000e+00 NaN NaN NaN
## ModelColt -6.000e+00 NaN NaN NaN
## ModelConcorde NA NA NA NA
## ModelContinental -4.000e+00 NaN NaN NaN
## ModelCorrado -1.000e+00 NaN NaN NaN
## ModelCorsica -3.000e+00 NaN NaN NaN
## ModelCougar NA NA NA NA
## ModelCrown_Victoria 1.000e+01 NaN NaN NaN
## ModelCutlass_Ciera -4.000e+00 NaN NaN NaN
## ModelDeVille -1.000e+00 NaN NaN NaN
## ModelDiamante 3.000e+00 NaN NaN NaN
## ModelDynasty -3.000e+00 NaN NaN NaN
## ModelES300 -1.000e+00 NaN NaN NaN
## ModelEighty-Eight NA NA NA NA
## ModelElantra -3.000e+00 NaN NaN NaN
## ModelEscort -1.000e+00 NaN NaN NaN
## ModelExcel -6.000e+00 NaN NaN NaN
## ModelFestiva -5.000e+00 NaN NaN NaN
## ModelFirebird 9.000e+00 NaN NaN NaN
## ModelFox -4.000e+00 NaN NaN NaN
## ModelGrand_Prix 6.000e+00 NaN NaN NaN
## ModelImperial 1.000e+00 NaN NaN NaN
## ModelIntegra -3.000e+00 NaN NaN NaN
## ModelJusty -5.000e+00 NaN NaN NaN
## ModelLaser NA NA NA NA
## ModelLeBaron NA NA NA NA
## ModelLeMans 1.995e-15 NaN NaN NaN
## ModelLeSabre -4.000e+00 NaN NaN NaN
## ModelLegacy 2.000e+00 NaN NaN NaN
## ModelLegend NA NA NA NA
## ModelLoyale NA NA NA NA
## ModelLumina NA NA NA NA
## ModelMaxima 3.000e+00 NaN NaN NaN
## ModelMetro -4.000e+00 NaN NaN NaN
## ModelMirage NA NA NA NA
## ModelMustang 6.410e-16 NaN NaN NaN
## ModelPassat NA NA NA NA
## ModelPrelude NA NA NA NA
## ModelProbe 2.000e+00 NaN NaN NaN
## ModelProtege NA NA NA NA
## ModelQ45 NA NA NA NA
## ModelRiviera -5.000e+00 NaN NaN NaN
## ModelRoadmaster NA NA NA NA
## ModelSC300 NA NA NA NA
## ModelSL NA NA NA NA
## ModelScoupe -5.000e+00 NaN NaN NaN
## ModelSentra NA NA NA NA
## ModelSeville NA NA NA NA
## ModelShadow -5.000e+00 NaN NaN NaN
## ModelSonata NA NA NA NA
## ModelSpirit -4.000e+00 NaN NaN NaN
## ModelStealth NA NA NA NA
## ModelStorm NA NA NA NA
## ModelSummit -8.000e+00 NaN NaN NaN
## ModelSunbird NA NA NA NA
## ModelSwift NA NA NA NA
## ModelTaurus 3.000e+00 NaN NaN NaN
## ModelTempo NA NA NA NA
## ModelTercel NA NA NA NA
## ModelTown_Car NA NA NA NA
## ModelVision NA NA NA NA
## TypeLarge NA NA NA NA
## TypeMidsize NA NA NA NA
## TypeSmall NA NA NA NA
## TypeSporty NA NA NA NA
## Min.Price NA NA NA NA
## Price NA NA NA NA
## Max.Price NA NA NA NA
## MPG.city NA NA NA NA
## MPG.highway NA NA NA NA
## AirBagsDriver only NA NA NA NA
## AirBagsNone NA NA NA NA
## DriveTrainFront NA NA NA NA
## DriveTrainRear NA NA NA NA
## Cylinders4 NA NA NA NA
## Cylinders5 NA NA NA NA
## Cylinders6 NA NA NA NA
## Cylinders8 NA NA NA NA
## EngineSize NA NA NA NA
## Horsepower NA NA NA NA
## RPM NA NA NA NA
## Rev.per.mile NA NA NA NA
## Man.trans.availYes NA NA NA NA
## Fuel.tank.capacity NA NA NA NA
## Passengers NA NA NA NA
## Length NA NA NA NA
## Wheelbase NA NA NA NA
## Turn.circle NA NA NA NA
## Rear.seat.room NA NA NA NA
## Luggage.room NA NA NA NA
## Weight NA NA NA NA
## Originnon-USA NA NA NA NA
## MakeAcura Legend NA NA NA NA
## MakeAudi 100 NA NA NA NA
## MakeAudi 90 NA NA NA NA
## MakeBMW 535i NA NA NA NA
## MakeBuick Century NA NA NA NA
## MakeBuick LeSabre NA NA NA NA
## MakeBuick Riviera NA NA NA NA
## MakeBuick Roadmaster NA NA NA NA
## MakeCadillac DeVille NA NA NA NA
## MakeCadillac Seville NA NA NA NA
## MakeChevrolet Camaro NA NA NA NA
## MakeChevrolet Caprice NA NA NA NA
## MakeChevrolet Cavalier NA NA NA NA
## MakeChevrolet Corsica NA NA NA NA
## MakeChevrolet Lumina NA NA NA NA
## MakeChrylser Concorde NA NA NA NA
## MakeChrysler Imperial NA NA NA NA
## MakeChrysler LeBaron NA NA NA NA
## MakeDodge Colt NA NA NA NA
## MakeDodge Dynasty NA NA NA NA
## MakeDodge Shadow NA NA NA NA
## MakeDodge Spirit NA NA NA NA
## MakeDodge Stealth NA NA NA NA
## MakeEagle Summit NA NA NA NA
## MakeEagle Vision NA NA NA NA
## MakeFord Crown_Victoria NA NA NA NA
## MakeFord Escort NA NA NA NA
## MakeFord Festiva NA NA NA NA
## MakeFord Mustang NA NA NA NA
## MakeFord Probe NA NA NA NA
## MakeFord Taurus NA NA NA NA
## MakeFord Tempo NA NA NA NA
## MakeGeo Metro NA NA NA NA
## MakeGeo Storm NA NA NA NA
## MakeHonda Accord NA NA NA NA
## MakeHonda Civic NA NA NA NA
## MakeHonda Prelude NA NA NA NA
## MakeHyundai Elantra NA NA NA NA
## MakeHyundai Excel NA NA NA NA
## MakeHyundai Scoupe NA NA NA NA
## MakeHyundai Sonata NA NA NA NA
## MakeInfiniti Q45 NA NA NA NA
## MakeLexus ES300 NA NA NA NA
## MakeLexus SC300 NA NA NA NA
## MakeLincoln Continental NA NA NA NA
## MakeLincoln Town_Car NA NA NA NA
## MakeMazda 323 NA NA NA NA
## MakeMazda 626 NA NA NA NA
## MakeMazda Protege NA NA NA NA
## MakeMercedes-Benz 190E NA NA NA NA
## MakeMercedes-Benz 300E NA NA NA NA
## MakeMercury Capri NA NA NA NA
## MakeMercury Cougar NA NA NA NA
## MakeMitsubishi Diamante NA NA NA NA
## MakeMitsubishi Mirage NA NA NA NA
## MakeNissan Altima NA NA NA NA
## MakeNissan Maxima NA NA NA NA
## MakeNissan Sentra NA NA NA NA
## MakeOldsmobile Achieva NA NA NA NA
## MakeOldsmobile Cutlass_Ciera NA NA NA NA
## MakeOldsmobile Eighty-Eight NA NA NA NA
## MakePlymouth Laser NA NA NA NA
## MakePontiac Bonneville NA NA NA NA
## MakePontiac Firebird NA NA NA NA
## MakePontiac Grand_Prix NA NA NA NA
## MakePontiac LeMans NA NA NA NA
## MakePontiac Sunbird NA NA NA NA
## MakeSaab 900 NA NA NA NA
## MakeSaturn SL NA NA NA NA
## MakeSubaru Justy NA NA NA NA
## MakeSubaru Legacy NA NA NA NA
## MakeSubaru Loyale NA NA NA NA
## MakeSuzuki Swift NA NA NA NA
## MakeToyota Camry NA NA NA NA
## MakeToyota Celica NA NA NA NA
## MakeToyota Tercel NA NA NA NA
## MakeVolkswagen Corrado NA NA NA NA
## MakeVolkswagen Fox NA NA NA NA
## MakeVolkswagen Passat NA NA NA NA
## MakeVolvo 240 NA NA NA NA
## MakeVolvo 850 NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 81 and 0 DF, p-value: NA
lm.fit2 <- lm(Width ~ Price + Weight + Length, data = Cars93)
summary(lm.fit2)
##
## Call:
## lm(formula = Width ~ Price + Weight + Length, data = Cars93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6593 -1.0706 -0.0884 0.9439 4.8393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.0116630 2.5470574 16.102 < 2e-16 ***
## Price -0.0702370 0.0228935 -3.068 0.00286 **
## Weight 0.0046752 0.0005475 8.539 3.37e-13 ***
## Length 0.0838872 0.0195165 4.298 4.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.616 on 89 degrees of freedom
## Multiple R-squared: 0.8232, Adjusted R-squared: 0.8172
## F-statistic: 138.1 on 3 and 89 DF, p-value: < 2.2e-16
coef(lm.fit2)
## (Intercept) Price Weight Length
## 41.011662954 -0.070236973 0.004675217 0.083887180
summary(lm.fit2)
##
## Call:
## lm(formula = Width ~ Price + Weight + Length, data = Cars93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6593 -1.0706 -0.0884 0.9439 4.8393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.0116630 2.5470574 16.102 < 2e-16 ***
## Price -0.0702370 0.0228935 -3.068 0.00286 **
## Weight 0.0046752 0.0005475 8.539 3.37e-13 ***
## Length 0.0838872 0.0195165 4.298 4.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.616 on 89 degrees of freedom
## Multiple R-squared: 0.8232, Adjusted R-squared: 0.8172
## F-statistic: 138.1 on 3 and 89 DF, p-value: < 2.2e-16
library(MASS)
lm.fit3 <- lm(Width ~ Length, data = Cars93)
qqnorm(residuals(lm.fit3))
qqline(residuals(lm.fit3))
lm.fit3 <- lm(Width ~ Length, data = Cars93)
summary(lm.fit3)
##
## Call:
## lm(formula = Width ~ Length, data = Cars93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5882 -1.4180 -0.3967 1.0501 6.3267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.39676 2.83824 10.71 <2e-16 ***
## Length 0.21277 0.01544 13.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.163 on 91 degrees of freedom
## Multiple R-squared: 0.6759, Adjusted R-squared: 0.6724
## F-statistic: 189.8 on 1 and 91 DF, p-value: < 2.2e-16
lm.fit4 <- lm(Width ~ Weight, data = Cars93)
summary(lm.fit4)
##
## Call:
## lm(formula = Width ~ Weight, data = Cars93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0696 -1.2378 0.1367 1.0246 4.6871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.215e+01 1.017e+00 51.27 <2e-16 ***
## Weight 5.605e-03 3.252e-04 17.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.84 on 91 degrees of freedom
## Multiple R-squared: 0.7656, Adjusted R-squared: 0.763
## F-statistic: 297.2 on 1 and 91 DF, p-value: < 2.2e-16
lm.fit5 <- lm(Width ~ Price, data = Cars93)
summary(lm.fit5)
##
## Call:
## lm(formula = Width ~ Price, data = Cars93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9391 -2.1587 -0.7314 1.6066 9.1428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.8957 0.7937 83.020 < 2e-16 ***
## Price 0.1784 0.0365 4.888 4.35e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.382 on 91 degrees of freedom
## Multiple R-squared: 0.208, Adjusted R-squared: 0.1993
## F-statistic: 23.89 on 1 and 91 DF, p-value: 4.354e-06
summary(Smarket)
## Year Lag1 Lag2 Lag3
## Min. :2001 Min. :-4.922000 Min. :-4.922000 Min. :-4.922000
## 1st Qu.:2002 1st Qu.:-0.639500 1st Qu.:-0.639500 1st Qu.:-0.640000
## Median :2003 Median : 0.039000 Median : 0.039000 Median : 0.038500
## Mean :2003 Mean : 0.003834 Mean : 0.003919 Mean : 0.001716
## 3rd Qu.:2004 3rd Qu.: 0.596750 3rd Qu.: 0.596750 3rd Qu.: 0.596750
## Max. :2005 Max. : 5.733000 Max. : 5.733000 Max. : 5.733000
## Lag4 Lag5 Volume Today
## Min. :-4.922000 Min. :-4.92200 Min. :0.3561 Min. :-4.922000
## 1st Qu.:-0.640000 1st Qu.:-0.64000 1st Qu.:1.2574 1st Qu.:-0.639500
## Median : 0.038500 Median : 0.03850 Median :1.4229 Median : 0.038500
## Mean : 0.001636 Mean : 0.00561 Mean :1.4783 Mean : 0.003138
## 3rd Qu.: 0.596750 3rd Qu.: 0.59700 3rd Qu.:1.6417 3rd Qu.: 0.596750
## Max. : 5.733000 Max. : 5.73300 Max. :3.1525 Max. : 5.733000
## Direction
## Down:602
## Up :648
##
##
##
##
train <- (Smarket$Year < 2005)
Smarket.2005 <- Smarket[!train, ]
Direction.2005 <- Smarket$Direction[!train]
lda.fit <- lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train)
lda.pred <- predict(lda.fit, Smarket.2005)
lda.class <- lda.pred$class
table(lda.class, Direction.2005)
## Direction.2005
## lda.class Down Up
## Down 35 35
## Up 76 106
mean(lda.class == Direction.2005)
## [1] 0.5595238
lda.pred$posterior[11:12, ]
## Down Up
## 1009 0.4906963 0.5093037
## 1010 0.5119988 0.4880012
lda.pred$class[11:12]
## [1] Up Down
## Levels: Down Up
summary(Smarket)
## Year Lag1 Lag2 Lag3
## Min. :2001 Min. :-4.922000 Min. :-4.922000 Min. :-4.922000
## 1st Qu.:2002 1st Qu.:-0.639500 1st Qu.:-0.639500 1st Qu.:-0.640000
## Median :2003 Median : 0.039000 Median : 0.039000 Median : 0.038500
## Mean :2003 Mean : 0.003834 Mean : 0.003919 Mean : 0.001716
## 3rd Qu.:2004 3rd Qu.: 0.596750 3rd Qu.: 0.596750 3rd Qu.: 0.596750
## Max. :2005 Max. : 5.733000 Max. : 5.733000 Max. : 5.733000
## Lag4 Lag5 Volume Today
## Min. :-4.922000 Min. :-4.92200 Min. :0.3561 Min. :-4.922000
## 1st Qu.:-0.640000 1st Qu.:-0.64000 1st Qu.:1.2574 1st Qu.:-0.639500
## Median : 0.038500 Median : 0.03850 Median :1.4229 Median : 0.038500
## Mean : 0.001636 Mean : 0.00561 Mean :1.4783 Mean : 0.003138
## 3rd Qu.: 0.596750 3rd Qu.: 0.59700 3rd Qu.:1.6417 3rd Qu.: 0.596750
## Max. : 5.733000 Max. : 5.73300 Max. :3.1525 Max. : 5.733000
## Direction
## Down:602
## Up :648
##
##
##
##
train <- (Smarket$Year < 2005)
test<- !train
Smarket.2005 <- Smarket[!train, ]
Direction.2005 <- Smarket$Direction[!train]
lda.fit <- lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = test)
lda.pred <- predict(lda.fit, Smarket.2005)
lda.class <- lda.pred$class
table(lda.class, Direction.2005)
## Direction.2005
## lda.class Down Up
## Down 12 18
## Up 99 123
mean(lda.class == Direction.2005)
## [1] 0.5357143
library(MASS)
attach(Cars93)
## The following objects are masked from Cars93 (pos = 3):
##
## AirBags, Cylinders, DriveTrain, EngineSize, Fuel.tank.capacity,
## Horsepower, Length, Luggage.room, Make, Man.trans.avail,
## Manufacturer, Max.Price, Min.Price, Model, MPG.city, MPG.highway,
## Origin, Passengers, Price, Rear.seat.room, Rev.per.mile, RPM,
## Turn.circle, Type, Weight, Wheelbase, Width
summary(Cars93)
## Manufacturer Model Type Min.Price Price
## Chevrolet: 8 100 : 1 Compact:16 Min. : 6.70 Min. : 7.40
## Ford : 8 190E : 1 Large :11 1st Qu.:10.80 1st Qu.:12.20
## Dodge : 6 240 : 1 Midsize:22 Median :14.70 Median :17.70
## Mazda : 5 300E : 1 Small :21 Mean :17.13 Mean :19.51
## Pontiac : 5 323 : 1 Sporty :14 3rd Qu.:20.30 3rd Qu.:23.30
## Buick : 4 535i : 1 Van : 9 Max. :45.40 Max. :61.90
## (Other) :57 (Other):87
## Max.Price MPG.city MPG.highway AirBags
## Min. : 7.9 Min. :15.00 Min. :20.00 Driver & Passenger:16
## 1st Qu.:14.7 1st Qu.:18.00 1st Qu.:26.00 Driver only :43
## Median :19.6 Median :21.00 Median :28.00 None :34
## Mean :21.9 Mean :22.37 Mean :29.09
## 3rd Qu.:25.3 3rd Qu.:25.00 3rd Qu.:31.00
## Max. :80.0 Max. :46.00 Max. :50.00
##
## DriveTrain Cylinders EngineSize Horsepower RPM
## 4WD :10 3 : 3 Min. :1.000 Min. : 55.0 Min. :3800
## Front:67 4 :49 1st Qu.:1.800 1st Qu.:103.0 1st Qu.:4800
## Rear :16 5 : 2 Median :2.400 Median :140.0 Median :5200
## 6 :31 Mean :2.668 Mean :143.8 Mean :5281
## 8 : 7 3rd Qu.:3.300 3rd Qu.:170.0 3rd Qu.:5750
## rotary: 1 Max. :5.700 Max. :300.0 Max. :6500
##
## Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers
## Min. :1320 No :32 Min. : 9.20 Min. :2.000
## 1st Qu.:1985 Yes:61 1st Qu.:14.50 1st Qu.:4.000
## Median :2340 Median :16.40 Median :5.000
## Mean :2332 Mean :16.66 Mean :5.086
## 3rd Qu.:2565 3rd Qu.:18.80 3rd Qu.:6.000
## Max. :3755 Max. :27.00 Max. :8.000
##
## Length Wheelbase Width Turn.circle
## Min. :141.0 Min. : 90.0 Min. :60.00 Min. :32.00
## 1st Qu.:174.0 1st Qu.: 98.0 1st Qu.:67.00 1st Qu.:37.00
## Median :183.0 Median :103.0 Median :69.00 Median :39.00
## Mean :183.2 Mean :103.9 Mean :69.38 Mean :38.96
## 3rd Qu.:192.0 3rd Qu.:110.0 3rd Qu.:72.00 3rd Qu.:41.00
## Max. :219.0 Max. :119.0 Max. :78.00 Max. :45.00
##
## Rear.seat.room Luggage.room Weight Origin Make
## Min. :19.00 Min. : 6.00 Min. :1695 USA :48 Acura Integra: 1
## 1st Qu.:26.00 1st Qu.:12.00 1st Qu.:2620 non-USA:45 Acura Legend : 1
## Median :27.50 Median :14.00 Median :3040 Audi 100 : 1
## Mean :27.83 Mean :13.89 Mean :3073 Audi 90 : 1
## 3rd Qu.:30.00 3rd Qu.:15.00 3rd Qu.:3525 BMW 535i : 1
## Max. :36.00 Max. :22.00 Max. :4105 Buick Century: 1
## NA's :2 NA's :11 (Other) :87
lm.fit <- lm(Width ~ Length + EngineSize + Turn.circle)
summary(lm.fit)
##
## Call:
## lm(formula = Width ~ Length + EngineSize + Turn.circle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4168 -0.9121 0.0262 1.1312 4.2728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.49370 3.34764 11.797 < 2e-16 ***
## Length 0.07294 0.01912 3.814 0.000252 ***
## EngineSize 1.59635 0.28894 5.525 3.24e-07 ***
## Turn.circle 0.31474 0.08632 3.646 0.000448 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.578 on 89 degrees of freedom
## Multiple R-squared: 0.8313, Adjusted R-squared: 0.8256
## F-statistic: 146.1 on 3 and 89 DF, p-value: < 2.2e-16
par(mfrow = c(2,2))
plot(lm.fit)
