R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i386-pc-mingw32/i386 (32-bit) R -- ýòî ñâîáîäíîå ÏÎ, è îíî ïîñòàâëÿåòñÿ áåçî âñÿêèõ ãàðàíòèé. Âû âîëüíû ðàñïðîñòðàíÿòü åãî ïðè ñîáëþäåíèè íåêîòîðûõ óñëîâèé. Ââåäèòå 'license()' äëÿ ïîëó÷åíèÿ áîëåå ïîäðîáíîé èíôîðìàöèè. R -- ýòî ïðîåêò, â êîòîðîì ñîòðóäíè÷àåò ìíîæåñòâî ðàçðàáîò÷èêîâ. Ââåäèòå 'contributors()' äëÿ ïîëó÷åíèÿ äîïîëíèòåëüíîé èíôîðìàöèè è 'citation()' äëÿ îçíàêîìëåíèÿ ñ ïðàâèëàìè óïîìèíàíèÿ R è åãî ïàêåòîâ â ïóáëèêàöèÿõ. Ââåäèòå 'demo()' äëÿ çàïóñêà äåìîíñòðàöèîííûõ ïðîãðàìì, 'help()' -- äëÿ ïîëó÷åíèÿ ñïðàâêè, 'help.start()' -- äëÿ äîñòóïà ê ñïðàâêå ÷åðåç áðàóçåð. Ââåäèòå 'q()', ÷òîáû âûéòè èç R. [Çàãðóæåíî ðàíåå ñîõðàíåííîå ðàáî÷åå ïðîñòðàíñòâî] > income Îøèáêà: îáúåêò 'income' íå íàéäåí > savings Îøèáêà: îáúåêò 'savings' íå íàéäåí > ?savings No documentation for 'savings' in specified packages and libraries: you could try '??savings' > data(savings) Ïðåäóïðåæäåíèå In data(savings) : äàííûå 'savings' íå íàéäåíû > library(dataset) Îøèáêà â library(dataset) : íåò ïàêåòà ïîä íàçâàíèåì 'dataset' > library(datasets) > ?savings No documentation for 'savings' in specified packages and libraries: you could try '??savings' > library(help=datasets) > ?mtcars starting httpd help server ... done > l <- lm(mpg ~ cyl + disp + hp +wt, data = mtcars) > summary(l) Call: lm(formula = mpg ~ cyl + disp + hp + wt, data = mtcars) Residuals: Min 1Q Median 3Q Max -4.0562 -1.4636 -0.4281 1.2854 5.8269 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 40.82854 2.75747 14.807 1.76e-14 *** cyl -1.29332 0.65588 -1.972 0.058947 . disp 0.01160 0.01173 0.989 0.331386 hp -0.02054 0.01215 -1.691 0.102379 wt -3.85390 1.01547 -3.795 0.000759 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.513 on 27 degrees of freedom Multiple R-squared: 0.8486, Adjusted R-squared: 0.8262 F-statistic: 37.84 on 4 and 27 DF, p-value: 1.061e-10 > coef(l) (Intercept) cyl disp hp wt 40.82853674 -1.29331972 0.01159924 -0.02053838 -3.85390352 > residuals(l) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant -1.56804806 -0.58530266 -3.25685052 -0.01170091 0.89393888 -2.08741154 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE -1.56736731 0.61046532 -0.39748889 -0.02900250 -1.42900250 2.10124593 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 1.69091873 -0.21638609 -1.11345925 -0.09830540 4.43729665 5.66600261 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 1.15872950 5.82694367 -4.05623180 -2.02404020 -2.48923264 -1.41082097 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 2.49080024 -0.45876160 -0.93430055 1.79344705 -1.11430642 -0.78097948 Maserati Bora Volvo 142E 1.66544168 -2.70623099 > fitted(y) Îøèáêà â fitted(y) : îáúåêò 'y' íå íàéäåí > fitted(l) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant 22.56805 21.58530 26.05685 21.41170 17.80606 20.18741 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE 15.86737 23.78953 23.19749 19.22900 19.22900 14.29875 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 15.60908 15.41639 11.51346 10.49831 10.26270 26.73400 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 29.24127 28.07306 25.55623 17.52404 17.68923 14.71082 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 16.70920 27.75876 26.93430 28.60655 16.91431 20.48098 Maserati Bora Volvo 142E 13.33456 24.10623 > predict(l) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant 22.56805 21.58530 26.05685 21.41170 17.80606 20.18741 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE 15.86737 23.78953 23.19749 19.22900 19.22900 14.29875 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 15.60908 15.41639 11.51346 10.49831 10.26270 26.73400 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 29.24127 28.07306 25.55623 17.52404 17.68923 14.71082 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 16.70920 27.75876 26.93430 28.60655 16.91431 20.48098 Maserati Bora Volvo 142E 13.33456 24.10623 > predict(l, mtcars) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant 22.56805 21.58530 26.05685 21.41170 17.80606 20.18741 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE 15.86737 23.78953 23.19749 19.22900 19.22900 14.29875 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 15.60908 15.41639 11.51346 10.49831 10.26270 26.73400 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 29.24127 28.07306 25.55623 17.52404 17.68923 14.71082 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 16.70920 27.75876 26.93430 28.60655 16.91431 20.48098 Maserati Bora Volvo 142E 13.33456 24.10623 > l1 <- lm(mpg ~ wt, data = mtcars) > plot(mpg ~ wt, data = mtcars) > abline(l1) > cooks.distance(l) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant 7.938590e-03 1.144503e-03 3.514101e-02 4.712632e-07 6.550961e-03 1.126508e-02 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE 1.476927e-02 2.620321e-03 1.073082e-03 5.065437e-06 1.229737e-02 3.686924e-02 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 1.598090e-02 2.728914e-04 1.909245e-02 1.472228e-04 2.553628e-01 1.020489e-01 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 8.531287e-03 1.335216e-01 5.983084e-02 2.774361e-02 4.337988e-02 8.192328e-03 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 5.946208e-02 7.607494e-04 3.748536e-03 3.058690e-02 1.694121e-02 3.935760e-03 Maserati Bora Volvo 142E 1.866417e-01 3.811640e-02 > plot(cooks.distance(l)) > residuals(l) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant -1.56804806 -0.58530266 -3.25685052 -0.01170091 0.89393888 -2.08741154 Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE -1.56736731 0.61046532 -0.39748889 -0.02900250 -1.42900250 2.10124593 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 1.69091873 -0.21638609 -1.11345925 -0.09830540 4.43729665 5.66600261 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 1.15872950 5.82694367 -4.05623180 -2.02404020 -2.48923264 -1.41082097 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino 2.49080024 -0.45876160 -0.93430055 1.79344705 -1.11430642 -0.78097948 Maserati Bora Volvo 142E 1.66544168 -2.70623099 > summary(l) Call: lm(formula = mpg ~ cyl + disp + hp + wt, data = mtcars) Residuals: Min 1Q Median 3Q Max -4.0562 -1.4636 -0.4281 1.2854 5.8269 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 40.82854 2.75747 14.807 1.76e-14 *** cyl -1.29332 0.65588 -1.972 0.058947 . disp 0.01160 0.01173 0.989 0.331386 hp -0.02054 0.01215 -1.691 0.102379 wt -3.85390 1.01547 -3.795 0.000759 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.513 on 27 degrees of freedom Multiple R-squared: 0.8486, Adjusted R-squared: 0.8262 F-statistic: 37.84 on 4 and 27 DF, p-value: 1.061e-10 > l$df.residual [1] 27 > l2 <- lm(mpg ~ cyl + disp + hp, data = mtcars) > anova(l, l2) Analysis of Variance Table Model 1: mpg ~ cyl + disp + hp + wt Model 2: mpg ~ cyl + disp + hp Res.Df RSS Df Sum of Sq F Pr(>F) 1 27 170.44 2 28 261.37 -1 -90.925 14.403 0.0007589 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > l2 <- update(l, . ~ . - wt) > l2 Call: lm(formula = mpg ~ cyl + disp + hp, data = mtcars) Coefficients: (Intercept) cyl disp hp 34.18492 -1.22742 -0.01884 -0.01468 > l2 <- update(l, . ~ . - 0) > summary(l2) Call: lm(formula = mpg ~ cyl + disp + hp + wt, data = mtcars) Residuals: Min 1Q Median 3Q Max -4.0562 -1.4636 -0.4281 1.2854 5.8269 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 40.82854 2.75747 14.807 1.76e-14 *** cyl -1.29332 0.65588 -1.972 0.058947 . disp 0.01160 0.01173 0.989 0.331386 hp -0.02054 0.01215 -1.691 0.102379 wt -3.85390 1.01547 -3.795 0.000759 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.513 on 27 degrees of freedom Multiple R-squared: 0.8486, Adjusted R-squared: 0.8262 F-statistic: 37.84 on 4 and 27 DF, p-value: 1.061e-10 > l2 <- update(l, . ~ . - 1) > l2 Call: lm(formula = mpg ~ cyl + disp + hp + wt - 1, data = mtcars) Coefficients: cyl disp hp wt 5.35601 -0.12061 -0.03131 5.69127 > anova(l, l2) Analysis of Variance Table Model 1: mpg ~ cyl + disp + hp + wt Model 2: mpg ~ cyl + disp + hp + wt - 1 Res.Df RSS Df Sum of Sq F Pr(>F) 1 27 170.44 2 28 1554.41 -1 -1384 219.23 1.761e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > l <- lm(mpg ~ cyl + disp + hw + I(wt/3) + I(wt*2/3), data = mtcars) Îøèáêà â eval(expr, envir, enclos) : îáúåêò 'hw' íå íàéäåí > l <- lm(mpg ~ cyl + disp + hp + I(wt/3) + I(wt*2/3), data = mtcars) > l Call: lm(formula = mpg ~ cyl + disp + hp + I(wt/3) + I(wt * 2/3), data = mtcars) Coefficients: (Intercept) cyl disp hp I(wt/3) I(wt * 2/3) 40.82854 -1.29332 0.01160 -0.02054 -11.56171 NA > summary(l) Call: lm(formula = mpg ~ cyl + disp + hp + I(wt/3) + I(wt * 2/3), data = mtcars) Residuals: Min 1Q Median 3Q Max -4.0562 -1.4636 -0.4281 1.2854 5.8269 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 40.82854 2.75747 14.807 1.76e-14 *** cyl -1.29332 0.65588 -1.972 0.058947 . disp 0.01160 0.01173 0.989 0.331386 hp -0.02054 0.01215 -1.691 0.102379 I(wt/3) -11.56171 3.04642 -3.795 0.000759 *** I(wt * 2/3) NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.513 on 27 degrees of freedom Multiple R-squared: 0.8486, Adjusted R-squared: 0.8262 F-statistic: 37.84 on 4 and 27 DF, p-value: 1.061e-10 > l <- lm(mpg ~ cyl + disp + hp + wt, data = mtcars) > l <- lm(mpg ~ cyl + disp + I(hp + wt), data = mtcars) > l2 <- lm(mpg ~ cyl + disp + I(hp + wt), data = mtcars) > l <- lm(mpg ~ cyl + disp + hp + wt, data = mtcars) > anova(l, l2) Analysis of Variance Table Model 1: mpg ~ cyl + disp + hp + wt Model 2: mpg ~ cyl + disp + I(hp + wt) Res.Df RSS Df Sum of Sq F Pr(>F) 1 27 170.44 2 28 260.66 -1 -90.22 14.292 0.0007888 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 >