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First we define a figure hook:

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First we define a figure hook:

> options(SweaveHooks = list(fig = function() par(mfrow=c(2,2)))) Then we setup variable definitions without actually evaluating them

> x <- 1:10

> y <- rnorm(x)

Then we put the pieces together:

> x <- 1:10

> y <- rnorm(x)

> lm1 <- lm(y~x)

> summary(lm1) Call:

lm(formula = y ~ x) Residuals:

Min 1Q Median 3Q Max

-0.7125 -0.6122 -0.1006 0.4825 0.9848 Coefficients:

Estimate Std. Error t value Pr(>|t|) (Intercept) 0.08009 0.46293 0.173 0.867

x -0.09574 0.07461 -1.283 0.235

Residual standard error: 0.6777 on 8 degrees of freedom

Multiple R-squared: 0.1707, Adjusted R-squared: 0.06703 F-statistic: 1.647 on 1 and 8 DF, p-value: 0.2353

> plot(lm1)

1

(2)

−0.2 0.0 0.2 0.4 0.6

−1.0−0.50.00.5

Fitted values

Residuals

Residuals vs Fitted

7

3 8

−1.5 −0.5 0.5 1.5

−2.0−0.50.5

Theoretical Quantiles

Standardized residuals

Normal Q−Q

7

3 8

−0.2 0.0 0.2 0.4 0.6

0.00.51.01.5

Fitted values

Standardized residuals

Scale−Location

7

3 8

0.00 0.10 0.20 0.30

−2−101

Leverage

Standardized residuals

Cook's distance

1 0.5 0.5

Residuals vs Leverage

7

1 3

2

Références

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