Hello, readers new and old!We started adding examples a year ago, in advance of the book's publication. To mark the occasion, we're closing chapter 7 and starting chapter 8 next week. We've crafted a listing of all entries from the first year and mad...
Blog Archives
Example 7.41: hazard function plotting
As we continue with our series on survival analysis, we demonstrate how to plot estimated (smoothed) hazard functions. RWe will utilize the routines available in the muhaz package. Background information on the methods can be found in K.R. Hess, D.M....
Example 7.40: Nelson-Aalen plotting
In our previous entry, we described how to calculate the Nelson-Aalen estimate of cumulative hazard. In this entry, we display the estimates for the time to linkage to primary care for both the treatment and control groups in the HELP study.RWe use the...
Example 7.39: Nelson-Aalen estimate of cumulative hazard
In our previous example, we demonstrated how to calculate the Kaplan-Meier estimate of the survival function for time to event data. A related quantity is the Nelson-Aalen estimate of cumulative hazard. In addition to summarizing the hazard incurred ...
Example 7.38: Kaplan-Meier survival estimates
In example 7.30 we demonstrated how to simulate data from a Cox proportional hazards model.In this and the next few entries, we expand upon support in R and SAS for survival (time-to-event) models. We'll start with a small, artificial dataset of 19 su...
Example 7.37: calculation of Hotelling’s T^2
Hotelling's T^2 is a multivariate statistic used to compare two groups, where multiple outcomes are observed for each subject. Here we demonstrate how to calculate Hotelling's T^2 using R and SAS, and test the code using a simulation study then apply ...
Example 7.32: Add reference lines to a plot; fine control of tick marks
Sometimes it's useful to plot regular reference lines along with the data. For a time-series plot, this can show when critical values are reached in a clearer way than simple tick marks.As an example, we revisit the empirical CDF plot shown in Example...
Augmented support for complex survey designs in R
We'll get back to code examples later this week, but wanted to let you know about an R package with updated functionality in the meantime.The appropriate analysis of sample surveys requires incorporation of complex design features, including stratification, clustering, weights, and finite population correction. These can be address in SAS and R for many common models. Section...
Example 7.24: Sampling from a pathological distribution
Evans and Rosenthal consider ways to sample from a distribution with density given by:f(y) = c e^(-y^4)(1+|y|)^3where c is a normalizing constant and y is defined on the whole real line.Use of the probability integral transform (section 1.10.8) is not feasible in this setting, given the complexity of inverting the cumulative density function.The Metropolis--Hastings algorithm is a Markov...
Example 7.22: the Knapsack problem
The website http://rosettacode.org/wiki/Knapsack_Problem describes a fanciful trip by a traveler to Shangri La. They can take as many as they want of three valuable items, as long as they fit in a knapsack. The knapsack will hold no more than 25 weight units, and no more than 25 volume units. The problem is to maximize the...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
