鈻讹笍 Tematyka
- Model liniowy z 1 zmienn膮 obja艣niaj膮c膮
- Model liniowy z 2 zmiennymi obja艣niaj膮cymi
#Wykorzystywany zbi贸r danych:
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## uruchamianie serwera httpd dla pomocy ... wykonano
馃摌 Model liniowy z 1 zmienn膮 obja艣niaj膮c膮
Zmienna obja艣niana: spalanie paliwa, zmienna obja艣niaj膮ca: waga
samochodu.
model1<-lm(mpg~wt,data=mtcars)
#Podstawowe informacje.
model1
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344
#Rozszerzony widok informacji o modelu.
summary(model1)
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5432 -2.3647 -0.1252 1.4096 6.8727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
## wt -5.3445 0.5591 -9.559 1.29e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
## F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
Wy艣wietlenie wynik贸w
Wsp贸艂czynniki
## (Intercept) wt
## 37.285126 -5.344472
Warto艣膰 wsp贸艂czynnika kierunkowego
summary(model1)$coefficients[2,1]
## [1] -5.344472
summary(model1)$coef[2,1]
## [1] -5.344472
Warto艣膰 wyrazu wolnego
summary(model1)$coefficients[1,1]
## [1] 37.28513
Wsp贸艂czynnik R-kwadrat
summary(model1)$r.squared
## [1] 0.7528328
Reszty
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## -2.2826106 -0.9197704 -2.0859521 1.2973499
## Hornet Sportabout Valiant Duster 360 Merc 240D
## -0.2001440 -0.6932545 -3.9053627 4.1637381
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 2.3499593 0.2998560 -1.1001440 0.8668731
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## -0.0502472 -1.8830236 1.1733496 2.1032876
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 5.9810744 6.8727113 1.7461954 6.4219792
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## -2.6110037 -2.9725862 -3.7268663 -3.4623553
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 2.4643670 0.3564263 0.1520430 1.2010593
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## -4.5431513 -2.7809399 -3.2053627 -1.0274952
summary(model1)$residuals
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## -2.2826106 -0.9197704 -2.0859521 1.2973499
## Hornet Sportabout Valiant Duster 360 Merc 240D
## -0.2001440 -0.6932545 -3.9053627 4.1637381
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 2.3499593 0.2998560 -1.1001440 0.8668731
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## -0.0502472 -1.8830236 1.1733496 2.1032876
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 5.9810744 6.8727113 1.7461954 6.4219792
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## -2.6110037 -2.9725862 -3.7268663 -3.4623553
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 2.4643670 0.3564263 0.1520430 1.2010593
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## -4.5431513 -2.7809399 -3.2053627 -1.0274952
Warto艣ci teoretyczne
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 23.282611 21.919770 24.885952 20.102650
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 18.900144 18.793255 18.205363 20.236262
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 20.450041 18.900144 18.900144 15.533127
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 17.350247 17.083024 9.226650 8.296712
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 8.718926 25.527289 28.653805 27.478021
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 24.111004 18.472586 18.926866 16.762355
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 16.735633 26.943574 25.847957 29.198941
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 20.343151 22.480940 18.205363 22.427495
# Uwaga! Funkcja poni偶ej nie zwr贸ci wynik贸w.
# summary(model1)$fitted.values
plot(mtcars$mpg,mtcars$wt)
馃摌 Model liniowy z 2 zmiennymi obja艣niaj膮cymi
Zmienna zale偶na: spalanie, dwie zmienne niezale偶ne: waga samochodu i
przespieszenie
model2<-lm(mpg~wt+qsec,data=mtcars)
model2
##
## Call:
## lm(formula = mpg ~ wt + qsec, data = mtcars)
##
## Coefficients:
## (Intercept) wt qsec
## 19.7462 -5.0480 0.9292
##
## Call:
## lm(formula = mpg ~ wt + qsec, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3962 -2.1431 -0.2129 1.4915 5.7486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.7462 5.2521 3.760 0.000765 ***
## wt -5.0480 0.4840 -10.430 2.52e-11 ***
## qsec 0.9292 0.2650 3.506 0.001500 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.596 on 29 degrees of freedom
## Multiple R-squared: 0.8264, Adjusted R-squared: 0.8144
## F-statistic: 69.03 on 2 and 29 DF, p-value: 9.395e-12