Diff for "HLPMiniCourseSession2"

Differences between revisions 6 and 7

Session 2: Issues in linear regression

June 3 2008

G&H07	Chapter 4 (pp. 53-74)	Linear regression: before and after fitting the model
Baa08	Sections 6.2.2-6.2.4 (pp. 198-212)	Collinearity, Model criticism, and Validation
	Section 6.4 (pp. 234-239)	Regression with breakpoints

Feel free to add terms you want clarified in class:

If you have any material that you would like to cover that isn't included in the list below, please make note of it here.

Send your solutions to Andrew Watts, who will upload them here. Please send them by Wednesday 3:30pm or upload them yourself to this page by 10pm.

G&H07	Section 4.9 (p.76)	Exercise 4
Baa08	Section 6.7 (p. 260)	Exercise 1, 8

In addition to the book problems, we will distribute a data set from the ongoing ngrams project.

More on outliers
- detect outliers
  - boxplot(), scatterplots plot(), identify()BR
- dealing with outliers
  - exclusion subset()BR robust regression (based on t-distribution): tlm() (in package hatt)BR
Overly influential cases (can be, but don't have to be outliers)
- lm.influence(), also library(Rcmdr)BR
Collinearity:
- tests: vif(), kappa(), summary of correlations between fixed effects in lmer() BR
- countermeasures:
  - centering and/or standardizing scale()} BR
  - use of residuals resid(lm(x1 ~ x2, data)) BR
  - principal component analysis (PCA) princomp() BR
Model evaluation: Where is the model off?
- case-by-case: residuals(), predict()BR
- based on predictor: residuals() against predictors, calibrate()BR
- overall: validate()BR
Corrections:
- correcting for clusters (violation of assumption of independence): bootcov()BR

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-       robust regression (based on t-distribution): {{{tlm()}}} (in package {{{hatt}}}))[[BR]]
+       robust regression (based on t-distribution): {{{tlm()}}} (in package {{{hatt}}})[[BR]]
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+ * Collinearity:
   * tests: {{{vif(), kappa()}}}, summary of correlations between fixed effects in {{{lmer()}}} [[BR]]
   * countermeasures: 
     * centering and/or standardizing {{{scale()}}}} [[BR]]
     * use of residuals {{{resid(lm(x1 ~ x2, data))}}} [[BR]]
     * principal component analysis (PCA) {{{princomp()}}} [[BR]]
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-   * overall: {{{validate()))[[BR]]
+   * overall: {{{validate()}}}[[BR]]