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] > ?prcomp starting httpd help server ... done > plot(prcomp(USArrests)) > plot(prcomp(USArrests, scale = TRUE)) > summary(prcomp(USArrests, scale = TRUE)) Importance of components: PC1 PC2 PC3 PC4 Standard deviation 1.5749 0.9949 0.59713 0.41645 Proportion of Variance 0.6201 0.2474 0.08914 0.04336 Cumulative Proportion 0.6201 0.8675 0.95664 1.00000 > biplot(prcomp(USArrests, scale = TRUE)) > (pc.cr <- princomp(USArrests, cor = TRUE)) # inappropriate Call: princomp(x = USArrests, cor = TRUE) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 1.5748783 0.9948694 0.5971291 0.4164494 4 variables and 50 observations. > screeplot(pc.cr) > ANOVA Îøèáêà: îáúåêò 'ANOVA' íå íàéäåí > library(faraway) > coagulation coag diet 1 62 A 2 60 A 3 63 A 4 59 A 5 63 B 6 67 B 7 71 B 8 64 B 9 65 B 10 66 B 11 68 C 12 66 C 13 71 C 14 67 C 15 68 C 16 68 C 17 56 D 18 62 D 19 60 D 20 61 D 21 63 D 22 64 D 23 63 D 24 59 D > ?coagulation > summary(coagulation) coag diet Min. :56.00 A:4 1st Qu.:61.75 B:6 Median :63.50 C:6 Mean :64.00 D:8 3rd Qu.:67.00 Max. :71.00 > plot(coag ~ diet, data = coagulation) > ?boxplot > stripchar(coag ~ diet, data = coagulation) Îøèáêà: íå ìîãó íàéòè ôóíêöèþ "stripchar" > stripchart(coag ~ diet, data = coagulation) > stripchart(coag ~ diet, data = coagulation, vertical = TRUE) > stripchart(coag ~ diet, data = coagulation, vertical = TRUE, method = "stack") > g <- lm(coag ~ diet, data = coagulation) > summary(g) Call: lm(formula = coag ~ diet, data = coagulation) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.100e+01 1.183e+00 51.554 < 2e-16 *** dietB 5.000e+00 1.528e+00 3.273 0.003803 ** dietC 7.000e+00 1.528e+00 4.583 0.000181 *** dietD -3.333e-15 1.449e+00 0.000 1.000000 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.6706, Adjusted R-squared: 0.6212 F-statistic: 13.57 on 3 and 20 DF, p-value: 4.658e-05 > g <- lm(coag ~ diet - 1, data = coagulation) > summary(g) Call: lm(formula = coag ~ diet - 1, data = coagulation) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) dietA 61.0000 1.1832 51.55 <2e-16 *** dietB 66.0000 0.9661 68.32 <2e-16 *** dietC 68.0000 0.9661 70.39 <2e-16 *** dietD 61.0000 0.8367 72.91 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.9989, Adjusted R-squared: 0.9986 F-statistic: 4399 on 4 and 20 DF, p-value: < 2.2e-16 > g1 <- lm(coag ~ 1, data = coagulation) > summary(g1) Call: lm(formula = coag ~ 1, data = coagulation) Residuals: Min 1Q Median 3Q Max -8.00 -2.25 -0.50 3.00 7.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 64.0000 0.7848 81.55 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.845 on 23 degrees of freedom > anova(g, g1) Analysis of Variance Table Model 1: coag ~ diet - 1 Model 2: coag ~ 1 Res.Df RSS Df Sum of Sq F Pr(>F) 1 20 112 2 23 340 -3 -228 13.571 4.658e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > summary(g2 <- lm(coag ~ diet, data = coagulation)) Call: lm(formula = coag ~ diet, data = coagulation) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.100e+01 1.183e+00 51.554 < 2e-16 *** dietB 5.000e+00 1.528e+00 3.273 0.003803 ** dietC 7.000e+00 1.528e+00 4.583 0.000181 *** dietD -3.333e-15 1.449e+00 0.000 1.000000 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.6706, Adjusted R-squared: 0.6212 F-statistic: 13.57 on 3 and 20 DF, p-value: 4.658e-05 > aov(g2) Call: aov(formula = g2) Terms: diet Residuals Sum of Squares 228 112 Deg. of Freedom 3 20 Residual standard error: 2.366432 Estimated effects may be unbalanced > options(consts = c("constr.sum", "constr.poly")) > summary(g2 <- lm(coag ~ diet, data = coagulation)) Call: lm(formula = coag ~ diet, data = coagulation) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.100e+01 1.183e+00 51.554 < 2e-16 *** dietB 5.000e+00 1.528e+00 3.273 0.003803 ** dietC 7.000e+00 1.528e+00 4.583 0.000181 *** dietD -3.333e-15 1.449e+00 0.000 1.000000 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.6706, Adjusted R-squared: 0.6212 F-statistic: 13.57 on 3 and 20 DF, p-value: 4.658e-05 > options(contrasts = c("constr.sum", "constr.poly")) > summary(g2 <- lm(coag ~ diet, data = coagulation)) Îøèáêà â get(ctr, mode = "function", envir = parent.frame()) : ïåðåìåííàÿ 'constr.sum' òèïà 'function' íå íàéäåíà > options(contrasts = c("contr.sum", "contr.poly")) > summary(g2 <- lm(coag ~ diet, data = coagulation)) Call: lm(formula = coag ~ diet, data = coagulation) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 64.0000 0.4979 128.537 < 2e-16 *** diet1 -3.0000 0.9736 -3.081 0.005889 ** diet2 2.0000 0.8453 2.366 0.028195 * diet3 4.0000 0.8453 4.732 0.000128 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.6706, Adjusted R-squared: 0.6212 F-statistic: 13.57 on 3 and 20 DF, p-value: 4.658e-05 > TukeyHSD(aov(coag ~ diet, data = coagulation)) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = coag ~ diet, data = coagulation) $diet diff lwr upr p adj B-A 5.000000e+00 0.7245544 9.275446 0.0183283 C-A 7.000000e+00 2.7245544 11.275446 0.0009577 D-A -7.105427e-15 -4.0560438 4.056044 1.0000000 C-B 2.000000e+00 -1.8240748 5.824075 0.4766005 D-B -5.000000e+00 -8.5770944 -1.422906 0.0044114 D-C -7.000000e+00 -10.5770944 -3.422906 0.0001268 > hsd <- TukeyHSD(aov(coag ~ diet, data = coagulation)) > plot(hsd) > g2 Call: lm(formula = coag ~ diet, data = coagulation) Coefficients: (Intercept) diet1 diet2 diet3 64 -3 2 4 > residuals(g2) 1 2 3 4 5 6 7 8 1.000000e+00 -1.000000e+00 2.000000e+00 -2.000000e+00 -3.000000e+00 1.000000e+00 5.000000e+00 -2.000000e+00 9 10 11 12 13 14 15 16 -1.000000e+00 -8.185726e-18 -3.967638e-16 -2.000000e+00 3.000000e+00 -1.000000e+00 -3.967638e-16 -3.967638e-16 17 18 19 20 21 22 23 24 -5.000000e+00 1.000000e+00 -1.000000e+00 -6.369688e-17 2.000000e+00 3.000000e+00 2.000000e+00 -2.000000e+00 > summary(g3 <- lm(abs(residuals(g2)) ~ coagulation$diet))) Îøèáêà: íåîæèäàííûé ')' â "summary(g3 <- lm(abs(residuals(g2)) ~ coagulation$diet)))" > summary(g3 <- lm(abs(residuals(g2)) ~ coagulation$diet)) Call: lm(formula = abs(residuals(g2)) ~ coagulation$diet) Residuals: Min 1Q Median 3Q Max -2.000 -1.000 0.000 0.625 3.000 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6250 0.3013 5.394 2.8e-05 *** coagulation$diet1 -0.1250 0.5891 -0.212 0.834 coagulation$diet2 0.3750 0.5115 0.733 0.472 coagulation$diet3 -0.6250 0.5115 -1.222 0.236 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.432 on 20 degrees of freedom Multiple R-squared: 0.09559, Adjusted R-squared: -0.04007 F-statistic: 0.7046 on 3 and 20 DF, p-value: 0.5604 > med <- aggregate(coagulation$coag, coagulation$diet, median) Îøèáêà â aggregate.data.frame(as.data.frame(x), ...) : 'by' äîëæåí áûòü ñïèñêîì > med <- aggregate(coagulation$coag, by = list(coagulation$diet), median) > med Group.1 x 1 A 61.0 2 B 65.5 3 C 68.0 4 D 61.5 > med[coagulation$diet] Îøèáêà â `[.data.frame`(med, coagulation$diet) : undefined columns selected > med$x[coagulation$diet] [1] 61.0 61.0 61.0 61.0 65.5 65.5 65.5 65.5 65.5 65.5 68.0 68.0 68.0 68.0 68.0 68.0 61.5 61.5 61.5 61.5 61.5 61.5 61.5 61.5 > med <- med$x[coagulation$diet] > med [1] 61.0 61.0 61.0 61.0 65.5 65.5 65.5 65.5 65.5 65.5 68.0 68.0 68.0 68.0 68.0 68.0 61.5 61.5 61.5 61.5 61.5 61.5 61.5 61.5 > summary(g3 <- lm(abs(residuals(g2)-med) ~ coagulation$diet)) Call: lm(formula = abs(residuals(g2) - med) ~ coagulation$diet) Residuals: Min 1Q Median 3Q Max -5.00 -1.25 0.00 1.25 5.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 64.0000 0.4979 128.537 < 2e-16 *** coagulation$diet1 -3.0000 0.9736 -3.081 0.005889 ** coagulation$diet2 1.5000 0.8453 1.774 0.091213 . coagulation$diet3 4.0000 0.8453 4.732 0.000128 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.366 on 20 degrees of freedom Multiple R-squared: 0.6357, Adjusted R-squared: 0.5811 F-statistic: 11.63 on 3 and 20 DF, p-value: 0.0001245 > speedo h d l b j f n a i e m c k g o y 1 - - + - + + - - + + - + - - + 0.4850 2 + - - - - + + - - + + + + - - 0.5750 3 - + - - + - + - + - + + - + - 0.0875 4 + + + - - - - - - - - + + + + 0.1750 5 - - + + - - + - + + - - + + - 0.1950 6 + - - + + - - - - + + - - + + 0.1450 7 - + - + - + - - + - + - + - + 0.2250 8 + + + + + + + - - - - - - - - 0.1750 9 - - + - + + - + - - + - + + - 0.1250 10 + - - - - + + + + - - - - + + 0.1200 11 - + - - + - + + - + - - + - + 0.4550 12 + + + - - - - + + + + - - - - 0.5350 13 - - + + - - + + - - + + - - + 0.1700 14 + - - + + - - + + - - + + - - 0.2750 15 - + - + - + - + - + - + - + - 0.3425 16 + + + + + + + + + + + + + + + 0.5825 > ?speedo > g <- lm(y ~ ., data = speedo) > summary(g) Call: lm(formula = y ~ ., data = speedo) Residuals: ALL 16 residuals are 0: no residual degrees of freedom! Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2917187 NA NA NA h1 0.0310938 NA NA NA d1 0.0304688 NA NA NA l1 0.0135938 NA NA NA b1 -0.0279687 NA NA NA j1 -0.0004688 NA NA NA f1 0.0370312 NA NA NA n1 0.0032812 NA NA NA a1 0.0339063 NA NA NA i1 0.0214063 NA NA NA e1 0.1226562 NA NA NA m1 0.0139062 NA NA NA c1 0.0448437 NA NA NA k1 0.0342187 NA NA NA g1 -0.0701562 NA NA NA o1 0.0029687 NA NA NA Residual standard error: NaN on 0 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: NaN F-statistic: NaN on 15 and 0 DF, p-value: NA > model.matrix(g) (Intercept) h1 d1 l1 b1 j1 f1 n1 a1 i1 e1 m1 c1 k1 g1 o1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 2 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 3 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 4 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 5 1 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 6 1 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 7 1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 8 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 9 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 10 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 11 1 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 12 1 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 13 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 14 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 15 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 attr(,"contrasts") attr(,"contrasts")$h [1] "contr.sum" attr(,"contrasts")$d [1] "contr.sum" attr(,"contrasts")$l [1] "contr.sum" attr(,"contrasts")$b [1] "contr.sum" attr(,"contrasts")$j [1] "contr.sum" attr(,"contrasts")$f [1] "contr.sum" attr(,"contrasts")$n [1] "contr.sum" attr(,"contrasts")$a [1] "contr.sum" attr(,"contrasts")$i [1] "contr.sum" attr(,"contrasts")$e [1] "contr.sum" attr(,"contrasts")$m [1] "contr.sum" attr(,"contrasts")$c [1] "contr.sum" attr(,"contrasts")$k [1] "contr.sum" attr(,"contrasts")$g [1] "contr.sum" attr(,"contrasts")$o [1] "contr.sum" >