Blog Archives

Example 9.34: Bland-Altman type plot

June 5, 2012
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Example 9.34: Bland-Altman type plot

The Bland-Altman plot is a visual aid for assessing differences between two ways of measuring something. For example, one might compare two scales this way, or two devices for measuring particulate matter. The plot simply displays the difference between the measures against their average. Rather than a statistical test, it is intended...

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Example 9.33: Multiple imputation, rounding, and bias

May 29, 2012
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Example 9.33: Multiple imputation, rounding, and bias

Nick has a paper in the American Statistician warning about bias in multiple imputation arising from rounding data imputed under a normal assumption. One example where you might run afoul of this is if the data are truly dichotomous or count variables, but you model it as normal (either because your software is unable to model dichotomous...

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Example 9.32: Multiple testing simulation

May 21, 2012
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Example 9.32: Multiple testing simulation

In examples 9.30 and 9.31 we explored corrections for multiple testing and then extracting p-values adjusted by the Benjamini and Hochberg (or FDR) procedure. In this post we'll develop a simulation to explore the impact of "strong" and "weak" control of the family-wise error rate offered in multiple comparison corrections. Loosely put, weak control procedures...

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Example 9.31: Exploring multiple testing procedures

May 14, 2012
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Example 9.31: Exploring multiple testing procedures

In example 9.30 we explored the effects of adjusting for multiple testing using the Bonferroni and Benjamini-Hochberg (or false discovery rate, FDR) procedures. At the time we claimed that it would probably be inappropriate to extract the adjusted p-values from the FDR method from their context. In this entry we attempt to explain our misgivings about...

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Example 9.27: Baseball and shrinkage

April 16, 2012
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Example 9.27: Baseball and shrinkage

To celebrate the beginning of the professional baseball season here in the US and Canada, we revisit a famous example of using baseball data to demonstrate statistical properties. In 1977, Bradley Efron and Carl Morris published a paper about the Jame...

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Example 9.26: More circular plotting

April 9, 2012
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Example 9.26: More circular plotting

SAS's Rick Wicklin showed a simple loess smoother for the temperature data we showed here. Then he came back with a better approach that does away with edge effects. Rick's smoothing was calculated and plotted on a cartesian plane. In this entry we'll explore another option or two...

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Example 9.25: It’s been a mighty warm winter? (Plot on a circular axis)

April 2, 2012
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Example 9.25: It’s been a mighty warm winter? (Plot on a circular axis)

Updated (see below)People here in the northeast US consider this to have been an unusually warm winter. Was it?The University of Dayton and the US Environmental Protection Agency maintain an archive of daily average temperatures that's reasonably current. In the case of Albany, NY (the most similar of their records to our...

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Example 9.24: Changing the parameterization for categorical predictors

March 22, 2012
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Example 9.24: Changing the parameterization for categorical predictors

In our book, we discuss the important question of how to assign different parameterizations to categorical variables when fitting models (section 3.1.3). We show code in R for use in the lm() function, as follows:lm(y ~ x, contrasts=list(x,"contr.trea...

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Example 9.23: Demonstrating proportional hazards

March 13, 2012
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Example 9.23: Demonstrating proportional hazards

A colleague recently asked after a slide suitable for explaining proportional hazards. In particular, she was concerned that her audience not focus on the time to event or probability of the event. An initial thought was to display the cumulative haz...

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Example 9.21: The birthday "problem" re-examined

February 23, 2012
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Example 9.21: The birthday "problem" re-examined

The so-called birthday paradox or birthday problem is simply the counter-intutitive discovery that the probability of (at least) two people in a group sharing a birthday goes up surprisingly fast as the group size increases. If the group is only 23 peo...

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