# Blog Archives

## Example 7.31: Contour plot of BMI by weight and height

April 5, 2010
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A contour plot is a simple way to plot a surface in two dimensions. Lines with a constant Z value are plotted on the X-Y plane.Typical uses include weather maps displaying "isobars" (lines of constant pressure), and maps displaying lines of constant e...

## Example 7.30: Simulate censored survival data

March 30, 2010
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To simulate survival data with censoring, we need to model the hazard functions for both time to event and time to censoring. We simulate both event times from a Weibull distribution with a scale parameter of 1 (this is equivalent to an exponential ra...

## Example 7.29: Bubble plots colored by a fourth variable

March 27, 2010
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In Example 7.28, we generated a bubble plot showing the relationship among CESD, age, and number of drinks, for women. An anonymous commenter asked whether it would be possible to color the circles according to gender. In the comments, we showed simp...

## Example 7.28: Bubble plots

March 22, 2010
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A bubble plot is a means of displaying 3 variables in a scatterplot. The z dimension is presented in the size of the plot symbol, typically a circle. The area or radius of the circle plotted is proportional to the value of the third variable. This c...

## Example 7.27: probability question reconsidered

March 15, 2010
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In Example 7.26, we considered a problem, from the xkcd blog:Suppose I choose two (different) real numbers, by any process I choose. Then I select one at random (p= .5) to show Nick. Nick must guess whether the other is smaller or larger. Being righ...

## Example 7.26: probability question

March 8, 2010
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Here's a surprising problem, from the xkcd blog.Suppose I choose two (different) real numbers, by any process I choose. Then I select one at random (p= .5) to show Nick. Nick must guess whether the other is smaller or larger. Being right 50% of the ...

## Example 7.25: compare draws with distribution

March 5, 2010
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In example 7.24, we demonstrated a Metropolis-Hastings algorithm for generating observations from awkward distributions. In such settings it is desirable to assess the quality of draws by comparing them with the target distribution.Recall that the dis...

## Example 7.23: the Monty Hall problem

January 20, 2010
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The Monty Hall problem illustrates a simple setting where intuition often leads to a solution different from formal reasoning. The situation is based on the game show Let's Make a Deal. First, Monty puts a prize behind one of three doors. Then the player chooses a door. Next, (without moving the pize) Monty opens an...

## Example 7.21: Write a function to simulate categorical data

January 8, 2010
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In example 7.20, we showed how to simulate categorical data. But we might anticipate needing to do that frequently. If a SAS function weren't built in and an equivalent R function not available in a package, we could build them from scratch.SASThe SAS code is particularly tortured, since we must parse the parameter string to extract the...

## Example 7.20: Simulate categorical data

January 4, 2010
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Both SAS and R provide means of simulating categorical data (see section 1.10.4). Alternatively, it is trivial to write code to do this directly. In this entry, we show how to do it once. In a future entry, we'll demonstrate writing a SAS Macro (section A.8.1) and a function in R (section B.5.2) to do it...