Blog Archives

Example 8.35: Grab true (not pseudo) random numbers; passing API URLs to functions or macros

April 19, 2011
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Example 8.35: Grab true (not pseudo) random numbers; passing API URLs to functions or macros

Usually, we're content to use a pseudo-random number generator. But sometimes we may want numbers that are actually random-- an example might be for randomizing treatment status in a randomized controlled trial.The site Random.org provides truly rando...

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Example 8.33: Merging data sets one-to-many

April 5, 2011
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Example 8.33: Merging data sets one-to-many

It's often necessary to combine data from two data sets for further analysis. Such merging can be one-to-one, many-to-one, and many-to-many. The most common form is the one-to-one match, which we cover in section 1.5.7. Today we look at a one-to-man...

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Example 8.32: The HistData package, sunflower plots, and getting data from R into SAS

March 29, 2011
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Example 8.32: The HistData package, sunflower plots, and getting data from R into SAS

This entry is mainly a promotion of the fascinating HistData R package. The package, compiled by the psychologist, statistician, and graphics innovator Michael Friendly, contains a number of small data sets of historical interest. These include data ...

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Example 8.31: Choropleth maps

March 22, 2011
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Example 8.31: Choropleth maps

In our book, we show a simple example of a map (section 6.4.2) where we read the boundary files as data sets and use SAS and R to plot them. But both SAS and R have complex functionality for using pre-compiled map data. To demonstrate them, we'll sho...

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Example 8.30: Compare Poisson and negative binomial count models

March 15, 2011
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Example 8.30:  Compare Poisson and negative binomial count models

How similar can a negative binomial distribution get to a Poisson distribution?When confronted with modeling count data, our first instinct is to use Poisson regression. But in practice, count data is often overdispersed. We can fit the overdispersio...

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Example 8.29: Risk ratios and odds ratios

March 7, 2011
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Example 8.29: Risk ratios and odds ratios

When can you safely think of an odds ratio as being similar to a risk ratio?Many people find odds ratios hard to interpret, and thus would prefer to have risk ratios. In response to this, you can find several papers that purport to convert an odds rat...

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Plug for RStudio: powerful, free, and easy to use interactive development environment for R

February 28, 2011
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Plug for RStudio: powerful, free, and easy to use interactive development environment for R

(click for a bigger picture)As a longtime SAS user, one obstacle for me in using R professionally has been figuring out a process for saving and testing code across several work sessions and integrating code composition and execution. There are a coup...

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Example 8.20: Referencing lists of variables, part 2

January 10, 2011
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Example 8.20: Referencing lists of variables, part 2

In Example 8.19, we discussed how to refer to a group of variables with sequential names, such as varname1, varname2, varname3. This is trivial in SAS and can be done in R as we showed.It's also sometimes useful to refer to all variables which begin w...

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Example 8.18: A Monte Carlo experiment

December 13, 2010
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Example 8.18: A Monte Carlo experiment

In recent weeks, we've explored methods to fit logistic regression models when a state of quasi-complete separation exists. We considered Firth's penalized likelihood approach, exact logistic regression, and Bayesian models using Markov chain Monte Ca...

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Example 8.17: Logistic regression via MCMC

December 6, 2010
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Example 8.17: Logistic regression via MCMC

In examples 8.15 and 8.16 we considered Firth logistic regression and exact logistic regression as ways around the problem of separation, often encountered in logistic regression. (Re-cap: Separation happens when all the observations in a category sha...

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