R for Researchers: Regression inference solutions
This article contains solutions to exercises for an article in the series R for Researchers. For a list of topics covered by this series, see the Introduction article. If you're new to R we highly recommend reading the articles in order.
There is often more than one approach to the exercises. Do not be concerned if your approach is different than the solution provided.
These solutions require the solutions from the prior lesson be run in your R session.
These exercises use the alfalfa dataset and the work you started on the alfAnalysis script. Open the script and run all the commands in the script to prepare your session for these problems.
Note, we will use the shade and irrig variable as continuous variables for these exercise. They could also be considered as factor variables. Since both represent increasing levels we first try to use them as scale.
Find the confidence interval for the model coefficients.
2.5 % 97.5 % (Intercept) 24.4862967 29.48970330 irrig -1.0167775 -0.07122252 inocA 4.4856748 8.71432519 inocB 3.7656748 7.99432519 inocC 4.4056748 8.63432519 inocD 3.6256748 7.85432519 shade 0.7952225 1.74077748
Test if inoculant A equals inoculant D.
linearHypothesis(out5, c("inocA-inocD") )
Linear hypothesis test Hypothesis: inocA - inocD = 0 Model 1: restricted model Model 2: yield ~ irrig + inoc + shade Res.Df RSS Df Sum of Sq F Pr(>F) 1 19 47.425 2 18 45.576 1 1.849 0.7303 0.404
This data set does not provide evidence that inoculant A and D are different, when considered at the same level of irrigation and shade.
Predict the confidence interval for the mean yield for a plot which has irrigation level 3, shade level 5, and inoculant C.
newAlfObs <- data.frame(irrig=c(3), inoc=c("C"), shade=c(5) ) predict(out5, newAlfObs, interval="confidence")
fit lwr upr 1 38.216 36.44703 39.98497
Plot the observered verse fitted values for your model
ggplot(out5Diag, aes(x=fit)) + geom_point(aes(y=yield, col=inoc)) + geom_line(aes(y=fit)) + theme_bw() + ggtitle("Alfalfa Yields Vs Predicted") + theme( plot.title=element_text(vjust=1.0) ) + xlab("Predicted Yield") + theme( axis.title.x = element_text(vjust=-.5) ) + ylab("Observed yield") + theme( axis.title.y = element_text(vjust=1.0) ) + theme(legend.position = "bottom")
Commit your changes to AlfAnalysis.
Return to the Regression inference article.
Last Revised: 3/9/2015