In case of continuously collected data, e.g. observations from a monitoring network, spatial interpolation of this data cannot be done manually. Instead, the interpolation should be done automatically. To achieve this goal, I developed the automap package. automap builds on… See more ›
This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If 
Version 1.0 of multilevelPSA has been released to CRAN. The multilevelPSA package provides functions to estimate and visualize propensity score models with multilevel, or clustered, data. The graphics are an extension of PSAgraphics package by Helmreich and Pruzek. The example below will investigate the differences between private and public school internationally using the Programme of International Student Assessment...
During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...
Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: , the number of claims on the period is unobserved the number of...
As a celebration of Florence Henderson’s 79th birthday (on February 14), I have created this scatterplot to use in my regression course. The plot depicts the relationship between time spent on mathematics homework outside of school (expressed as z-scores) and … Continue reading →
Inspired by Preis et al.’s article Quantifying the advantage of looking forward, recently published in Scientific Reports (one of Nature publishing group’s journals), I wondered if similar big-data web-based research methods might address a question even bigger than how much different countries wonder about next year. How about the meaning of life. Who is searching
In the past, I wrote about robust regression. This is an important tool which handles outliers in the data. Roger Koenker is a substantial contributor in this area. His website is full of useful information and code so visit when … Continue reading →
This is the second of a two part reexamination of Florence Nightingale's data visualization based on her innovative cam diagrams (my term) shown in Figure 1.
Figure 1. Nightingale's original cam diagrams (click to enlarge)
We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
