2209 search results for "regression"

Predicting optimal of iterations and completion time for GBM

November 20, 2013
By
Predicting optimal of iterations and completion time for GBM

When choosing the hyperparameters for Generalized Boosted Regression Models, two important choices are shrinkage and the number of trees. Generally a smaller shrinkage with more trees produces a better model, but the modeling time significantly increases. Building a model with too many trees that are heavily cut back by cross validation wastes time, while building a model...

Read more »

Art of Statistical Inference

November 20, 2013
By
Art of Statistical Inference

(This article was first published on MATHEMATICS IN MEDICINE, and kindly contributed to R-bloggers) Art of Statistical Inference Art of Statistical Inference This post was written by me a few years ago, when I started learning the art and science of data analysis. It will be a good starter for the amateur data analysts. Introduction What is statistics? There...

Read more »

On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

November 19, 2013
By
On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

Perrakis, Ntzoufras, and Tsionas just arXived a paper on marginal likelihood (evidence) approximation (with the above title). The idea behind the paper is to base importance sampling for the evidence on simulations from the product of the (block) marginal posterior distributions. Those simulations can be directly derived from an MCMC output by randomly permuting the

Read more »

Simulation (is where it’s happening)

November 18, 2013
By
Simulation (is where it’s happening)

Jim Silverton wrote to the Allstat mailing list recently: “Hi, Anyone up for a challenge? Suppose we have random variables that are random points on the surface of a sphere. What is the probability that the tetrahedron made by joining these … Continue reading →

Read more »

Some Options for Testing Tables

November 18, 2013
By
Some Options for Testing Tables

Contingency tables are a very good way to summarize discrete data.  They are quite easy to construct and reasonably easy to understand. However, there are many nuances with tables and care should be taken when making conclusions related to the data. Here are just a few thoughts on the topic. Dealing with sparse data On

Read more »

Visualizing neural networks in R – update

November 14, 2013
By
Visualizing neural networks in R – update

In my last post I said I wasn’t going to write anymore about neural networks (i.e., multilayer feedforward perceptron, supervised ANN, etc.). That was a lie. I’ve received several requests to update the neural network plotting function described in the original post. As previously explained, R does not provide a lot of options for visualizing

Read more »

Calibration of p-value under variable selection: an example

November 14, 2013
By
Calibration of p-value under variable selection: an example

Very often people report p-values for linear regression estimates after performing variable selection step. Here is a simple simulation that shows that such a procedure might lead to wrong calibration of such tests.Consider a simple data generating pro...

Read more »

A slightly different introduction to R, part V: plotting and simulating linear models

November 11, 2013
By
A slightly different introduction to R, part V: plotting and simulating linear models

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a

Read more »

A statistical review of ‘Thinking, Fast and Slow’ by Daniel Kahneman

November 11, 2013
By
A statistical review of ‘Thinking, Fast and Slow’ by Daniel Kahneman

I failed to find Kahneman’s book in the economics section of the bookshop, so I had to ask where it was.  ”Oh, that’s in the psychology section.”  It should have also been in the statistics section. He states that his collaboration with Amos Tversky started with the question: Are humans good intuitive statisticians? The wrong The post A...

Read more »

Key Driver vs. Network Analysis in R

November 8, 2013
By
Key Driver vs. Network Analysis in R

When marketing researchers speak of driver analysis, they are referring to an input-output model with overall satisfaction as the output and performance ratings of specific product and service components as the inputs. The causal model is straightforwa...

Read more »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



http://www.eoda.de







ODSC

ODSC

CRC R books series





Six Sigma Online Training





Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)