2042 search results for "Regression"

A Bayesian Model for a Function Increasing by Chi-Squared Jumps (in Stan)

October 16, 2013
By
A Bayesian Model for a Function Increasing by Chi-Squared Jumps (in Stan)

(This article was first published on David Chudzicki's Blog, and kindly contributed to R-bloggers) This post will describe a way I came up with of fitting a function that’s constrained to be increasing, using Stan. If you want practical help, standard statistical approaches, or expert research, this isn’t the place for you (look up “isotonic regression” or “Bayesian isotonic...

Read more »

That’s Smooth

October 10, 2013
By
That’s Smooth

I had someone ask me the other day how to take a scatterplot and draw something other than a straight line through the graph using Excel.  Yes, it can be done in Excel and it’s really quite simple, but there are some limitations when using the stock Excel dialog screens. So it is probably in

Read more »

Please, never use my codes without checking twice (at least)!

October 9, 2013
By
Please, never use my codes without checking twice (at least)!

I wanted to get back on some interesting experience, following a discussion I had with Carlos after my class, this morning. Let me simplify the problem, and change also the dataset. Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db2.txt",header=TRUE,sep=";") Let me change also one little thing (in the course, we use the age of people as explanatory variables, so...

Read more »

Fast Bayesian Inference with INLA

October 9, 2013
By
Fast Bayesian Inference with INLA

I am currently a research fellow and 4th year PhD candidate within the INLA group.  If you deal with Bayesian models and have never heard about INLA, I sincerely think you should spend a small portion of your time to at least know what it is. If you have heard about it before, you know how nice

Read more »

Some heuristics about spline smoothing

October 8, 2013
By
Some heuristics about spline smoothing

Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that  is continuous, that exists, and is continuous, that  exists and is also continuous, etc. If  is smooth enough, Taylor’s expansion can be used. Hence, for which can also be writen as for...

Read more »

Too crude to be true?

October 8, 2013
By
Too crude to be true?

The key to programming is being lazy; it has actually been called a virtue by some. When I discovered the update() function it blew me away. Within short I had created a monster based upon this tiny function, allowing quick and easy output of regression tables that contain crude and adjusted estimates. In this post I’ll show...

Read more »

Sensitivity analysis for neural networks

October 7, 2013
By
Sensitivity analysis for neural networks

I’ve made quite a few blog posts about neural networks and some of the diagnostic tools that can be used to ‘demystify’ the information contained in these models. Frankly, I’m kind of sick of writing about neural networks but I wanted to share one last tool I’ve implemented in R. I’m a strong believer that

Read more »

Questions on my online forecasting course

October 3, 2013
By

I’ve been getting emails asking questions about my upcoming course on Forecasting using R. Here are some answers. Do I need to use the Revolution Enterprise version of R, or can I use open-source R? Open source R is fine. Revolution Analytics is organizing the course, but there is no requirement to use their software. I will be using...

Read more »

R and Data Week 2013

October 3, 2013
By
R and Data Week 2013

by Joseph Rickert Data Week 2013 is being held this week in sunny San Francisco at the Fort Mason conference center overlooking the Bay. Holding a Bay Area R User Group Meeting (BARUG) at Data Week helped to raise the R consciousness among the hip conference crowd attracted by the intoxicating mix of blue skies, big data hype, startups...

Read more »

R PMML Support: BetteR than EveR

October 2, 2013
By
R PMML Support: BetteR than EveR

How does it work? Simple! Once you build your model in R using any of the PMML supported model types, pass the model object as an input parameter to the pmml package as shown in the figure below.The pmml package offers export for a variety of model types, including:   •   ksvm (kernlab): Support Vector Machines    •   nnet: Neural Networks    •   rpart: C&RT Decision Trees    •   lm & glm (stats):...

Read more »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training

datasociety

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)