ggplot2 2.0.0

ggplot2 2.0.0


I have used the ggplot2 package for a while and really like this package. It's happy to see that Hadley Wickham has officially updated the ggplot2 to version 2.0.0. On the RStudio Blog, Hadley highlights several important changes:

• ggplot2 now has an official extension mechanism.
• There are a handful of new geoms, and updates to existing geoms.
• The default appearance has been thoroughly tweaked so most plots should look better.
• Facets have a much richer set of labelling options.
• The documentation has been overhauled to be more helpful, and require less integration across multiple pages.
• A number of older and less used features have been deprecated.

You can find the document/manua on the project website. Many times, I go to the dev website to find the latest document/vignettes (extension, aesthetic specifications, themes).

The R Graphics Cookbook by Winston Chang is a must-have book to learn and become an expert of ggplot2 user. You can find the codes here from the Cookbook-R, and the Google book here.

general linear models vs. generalized linear models

General linear models vs. generalized linear models

• Wikipedia: Comparison of general and generalized linear models (modified)

	General linear model	Generalized linear model
Typical estimation method	Least squares, best linear unbiased prediction	Maximum likelihood or Bayesian
Special cases	ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, mixed model, t-test, F-test	linear regression, logistic regression, Poisson regression, gamma regression
Function in R	lm()	glm()
Function in Matlab	mvregress()	glmfit()
Procedure in SAS	PROC GLM, PROC MIXED	PROC GENMOD (PROC LOGISTIC for logistic regression only), PROC GLIMMIX Comparing the MIXED and GLIMMIX
Command in Stata	regress	Stata Generalized linear models
Function in Mathematica	LinearModelFit	GeneralizedLinearModelFit
Command in EViews	ls

• Generalized linear models have the flexiblility for response variables that have other than a normal distribution. If a generalized linear model uses an identity link function and a normal family distribution, then this model is equivalent to a general linear model.
• Generalized linear mixed models have the flexibility to model random effects and correlated errors for nonmormal data.

non-probability sample

Non-Probability Sample


Definition

• Sage Publication (pdf)
• Wikipedia
• Laerd

Reflection

• Wretman (2010) Reflections on Probability vs Nonprobability Sampling (pdf)
• Doherty   (1994) Probability versus Non-Probability Sampling in Sample Surveys (pdf)

Video

• Washington Statistical Society Mini-Conference Presentations (9/9/2015, Vimeo)