2015-05-11
## Loading required package: splines
## Loading required package: RcmdrMisc
## Loading required package: car
## Loading required package: sandwich
## The Commander GUI is launched only in interactive sessions
> summary(Wheat)
Year Wheat Wages
Min. :1565 Min. :26.0 Min. : 5.00
1st Qu.:1630 1st Qu.:33.0 1st Qu.: 6.14
Median :1695 Median :41.0 Median : 7.80
Mean :1695 Mean :43.3 Mean :11.58
3rd Qu.:1760 3rd Qu.:47.0 3rd Qu.:14.88
Max. :1821 Max. :99.0 Max. :30.00
NA's :3
> library(abind, pos=16)
> with(Wheat, Hist(Wages, scale="frequency", breaks="Sturges",
+ col="darkgray"))
> with(Wheat, Hist(Wheat, scale="frequency", breaks="Sturges",
+ col="darkgray"))
> with(Wheat, Hist(Year, scale="frequency", breaks="Sturges", col="darkgray"))
> densityPlot( ~ Wages, data=Wheat, bw="SJ", adjust=1, kernel="gaussian")
> densityPlot( ~ Wheat, data=Wheat, bw="SJ", adjust=1, kernel="gaussian")
> Boxplot( ~ Wages, data=Wheat, id.method="y")
[1] "48" "49" "50"
> Boxplot( ~ Wheat, data=Wheat, id.method="y")
[1] "47" "48" "49" "50" "51"
> cor(Wheat[,c("Wages","Wheat")], use="complete")
Wages Wheat
Wages 1.0000 0.5805
Wheat 0.5805 1.0000
> scatterplot(Wages~Wheat, reg.line=lm, smooth=TRUE, spread=TRUE,
+ id.method='mahal', id.n = 2, boxplots='xy', span=0.5, data=Wheat)
49 50
49 50
> RegModel.1 <- lm(Wages~Wheat, data=Wheat)
> summary(RegModel.1)
Call:
lm(formula = Wages ~ Wheat, data = Wheat)
Residuals:
Min 1Q Median 3Q Max
-12.30 -4.75 -1.76 5.96 12.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4790 2.5868 -0.19 0.85
Wheat 0.2862 0.0579 4.94 9.9e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.04 on 48 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.337, Adjusted R-squared: 0.323
F-statistic: 24.4 on 1 and 48 DF, p-value: 9.92e-06
> summary(RegModel.1)
Call:
lm(formula = Wages ~ Wheat, data = Wheat)
Residuals:
Min 1Q Median 3Q Max
-12.30 -4.75 -1.76 5.96 12.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4790 2.5868 -0.19 0.85
Wheat 0.2862 0.0579 4.94 9.9e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.04 on 48 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.337, Adjusted R-squared: 0.323
F-statistic: 24.4 on 1 and 48 DF, p-value: 9.92e-06
> AIC(RegModel.1)
[1] 325.6
> BIC(RegModel.1)
[1] 331.4
> library(MASS, pos=17)
> Confint(RegModel.1, level=0.95)
Estimate 2.5 % 97.5 %
(Intercept) -0.4790 -5.6802 4.7222
Wheat 0.2862 0.1697 0.4027
> Anova(RegModel.1, type="II")
Anova Table (Type II tests)
Response: Wages
Sum Sq Df F value Pr(>F)
Wheat 889 1 24.4 9.9e-06 ***
Residuals 1749 48
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> library(zoo, pos=18)
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> library(lmtest, pos=18)
> bptest(Wages ~ Wheat, varformula = ~ fitted.values(RegModel.1),
+ studentize=FALSE, data=Wheat)
Breusch-Pagan test
data: Wages ~ Wheat
BP = 1.7, df = 1, p-value = 0.2
> dwtest(Wages ~ Wheat, alternative="greater", data=Wheat)
Durbin-Watson test
data: Wages ~ Wheat
DW = 0.25, p-value <2e-16
alternative hypothesis: true autocorrelation is greater than 0
> outlierTest(RegModel.1)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
rstudent unadjusted p-value Bonferonni p
7 -2.188 0.03371 NA
> oldpar <- par(oma=c(0,0,3,0), mfrow=c(2,2))
> plot(RegModel.1)
> par(oldpar)
> qqPlot(RegModel.1, simulate=TRUE, id.method="y", id.n=2)
7 46
1 50
> stripchart(Wheat$Wages, method="stack", xlab="Wages")
> stripchart(Wheat$Wages, method="jitter", xlab="Wages")
