DATA MINING
Desktop Survival Guide by Graham Williams |
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Compare two linear models
> wine = read.csv("wine.csv") > lm1 = lm(Type ~ ., data=wine) > lm1 Call: lm(formula = Type ~ ., data = wine) Coefficients: (Intercept) Alcohol Malic Ash 4.4732853 -0.1170038 0.0301710 -0.1485522 Alcalinity Magnesium Phenols Flavanoids 0.0398543 -0.0004898 0.1443201 -0.3723914 Nonflavanoids Proanthocyanins Color Hue -0.3034743 0.0393565 0.0756239 -0.1492451 Dilution Proline -0.2700542 -0.0007011 > lm2 = lm(Type ~ Alcalinity + Magnesium, data=wine) > lm2 Call: lm(formula = Type ~ Alcalinity + Magnesium, data = wine) Coefficients: (Intercept) Alcalinity Magnesium 0.563157 0.116950 -0.009072 > anova(lm1, lm2) Analysis of Variance Table Model 1: Type ~ Alcohol + Malic + Ash + Alcalinity + Magnesium + Phenols + Flavanoids + Nonflavanoids + Proanthocyanins + Color + Hue + Dilution + Proline Model 2: Type ~ Alcalinity + Magnesium Res.Df RSS Df Sum of Sq F Pr(>F) 1 164 10.623 2 175 74.856 -11 -64.234 90.154 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 |