2355 search results for "Time series"

Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

May 2, 2015
By
Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

In the third part of the series on survival analysis with GAMs we will review the use of the baseline hazard estimates provided by this regression model. In contrast to the Cox mode, the log-baseline hazard is estimated along with other quantities (e.g. the log hazard ratios) by the Poisson GAM (PGAM) as: In the

Read more »

Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

May 2, 2015
By
Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

In the third part of the series on survival analysis with GAMs we will review the use of the baseline hazard estimates provided by this regression model. In contrast to the Cox mode, the log-baseline hazard is estimated along with other quantities (e.g. the log hazard ratios) by the Poisson GAM (PGAM) as: In the

Read more »

Should I use premium Diesel? Result: No

May 2, 2015
By
Should I use premium Diesel? Result: No

A while ago I had a post: 'Should I use premium Diesel? Setup. Since that time the data has been acquired. This post describes the results.DataData is registered by me in 2014 and 2015. 2014 has standard Diesel, while 2015 has premium. Both are fr...

Read more »

Survival Analysis With Generalized Additive Models : Part I (background and rationale)

May 1, 2015
By
Survival Analysis With Generalized Additive Models : Part I (background and rationale)

After a really long break, I’d will resume my blogging activity. It is actually a full circle for me, since one of the first posts that kick started this blog, matured enough to be published in a peer-reviewed journal last week. In the next few posts I will use the R code included to demonstrate the

Read more »

Survival Analysis With Generalized Additive Models : Part I (background and rationale)

May 1, 2015
By
Survival Analysis With Generalized Additive Models : Part I (background and rationale)

After a really long break, I’d will resume my blogging activity. It is actually a full circle for me, since one of the first posts that kick started this blog, matured enough to be published in a peer-reviewed journal last week. In the next few posts I will use the R code included to demonstrate the

Read more »

Wakefield: Random Data Set (Part II)

April 29, 2015
By
Wakefield: Random Data Set (Part II)

This post is part II of a series detailing the GitHub package, wakefield, for generating random data sets. The First Post (part I) was a test run to gauge user interest. I received positive feedback and some ideas for improvements, … Continue reading →

Read more »

Wakefield: Random Data Set (Part II)

April 29, 2015
By
Wakefield: Random Data Set (Part II)

This post is part II of a series detailing the GitHub package, wakefield, for generating random data sets. The First Post (part I) was a test run to gauge user interest. I received positive feedback and some ideas for improvements, … Continue reading →

Read more »

Twelve Graphs & Dashboards You Should See On Climate Change, Science, & Public Opinion

April 28, 2015
By
Twelve Graphs & Dashboards You Should See On Climate Change, Science, & Public Opinion

Plotly has teamed up with The White House on President Obama’s Climate Data Initiative to explore and explain climate trends. This post is our first contribution. You’ll see interactive graphs about: temperature and CO2 (4), climate change & environmental impact (4), attitudes about global warming (3), and a population graph. If you like this post, please share...

Read more »

Situational Baseball: Analyzing Runs Potential Statistics

April 28, 2015
By
Situational Baseball: Analyzing Runs Potential Statistics

by Mark Malter After reading the book, Analyzing Baseball with R, by Max Marchi and Jim Albert, I decided to expand on some of their ideas relating to runs created and put them into an R shiny app . The Server and UI code are linked at the bottom of the Introduction tab. I downloaded the Retrosheet play-by-play data...

Read more »

Using R and lme/lmer to fit different two- and three-level longitudinal models

April 21, 2015
By
Using R and lme/lmer to fit different two- and three-level longitudinal models

I often get asked how to fit different multilevel models (or individual growth models, hierarchical linear models or linear mixed-models, etc.) in R. In this guide I have compiled some of the more common and/or useful models (at least common in clinical psychology), and how to fit them using nlme::lme() and lme4::lmer(). I will cover the common two-level random...

Read more »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Dommino data lab

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)