359 search results for "pCA"

IP string to integer conversion with Rcpp

May 19, 2016
By

IP address conversion At work I recently had to match data on IP addresses and some fuzzy timestamp matching – a mess, to say the least. But before I could even tackle that problem, one dataset had the IPs stored as a character (e.g. 10.0.0.0), while the other dataset had the IP addresses converted as integers (e.g. 167772160). Storing IPs as...

Read more »

Principal Components Regression in R, an operational tutorial

May 17, 2016
By
Principal Components Regression in R, an operational tutorial

John Mount Ph. D. Data Scientist at Win-Vector LLC Win-Vector LLC's Dr. Nina Zumel has just started a two part series on Principal Components Regression that we think is well worth your time. You can read her article here. Principal Components Regression (PCR) is the use of Principal Components Analysis (PCA) as a dimension reduction step prior to linear...

Read more »

Principal Components Regression, Pt.1: The Standard Method

May 16, 2016
By

In this note, we discuss principal components regression and some of the issues with it: The need for scaling. The need for pruning. The lack of “y-awareness” of the standard dimensionality reduction step. The purpose of this article is to set the stage for presenting dimensionality reduction techniques appropriate for predictive modeling, such as y-aware … Continue reading...

Read more »

“Data Mining with R” Course | May 17-18

May 9, 2016
By

Discover Data Mining with R: find patterns in large data sets using the R tools for Dimensionality Reduction, Clustering, Classification and Prediction. The post "Data Mining with R" Course | May 17-18 appeared first on MilanoR.

Read more »

RTCGA factory of R packages – Quick Guide

May 3, 2016
By

Yesterday we have been delivered with the new version of R - R 3.3.0 (codename Supposedly Educational). This enabled Bioconductor (yes, not all packages are distributed on CRAN) to release it’s new version 3.3. This means that all packages held on Bioconductor, that were under rapid and vivid development, have been moved to...

Read more »

R benchmark for High-Performance Analytics and Computing (I)

April 14, 2016
By
R benchmark for High-Performance Analytics and Computing (I)

  Objectives of Experiments R is more and more popular in various fields, including the high-performance analytics and computing (HPAC) fields. Nowadays, the architecture of HPC system can be classified as pure CPU system, CPU + Accelerators (GPGPU/FPGA) heterogeneous system, CPU + Coprocessors system. In software side, high performance scientific libraries, such as basic linear

Read more »

Perform co-operations with the coop package

April 6, 2016
By

About The coop package does co-operations: covariance, correlation, and cosine, and it does them quickly. The package is available on CRAN and GitHub, and has two vignettes: Introducing coop: Fast Covariance, Correlation, and Cosine Operations Algorithms and Benchmarks for the coop Package Incidentally, the vignettes don't render correctly on CRAN's end for some reason; if any of you rmarkdown...

Read more »

Are you doing parallel computations in R? Then use BiocParallel

March 6, 2016
By
Are you doing parallel computations in R? Then use BiocParallel

It’s the morning of the first day of oral conferences at #ENAR2016. I feel like I have a spidey sense since I woke up 3 min after an email from Jeff Leek; just a funny coincidence. Anyhow, I promised Valerie Obenchain at #Bioc2014 that I would write a post about one of my favorite Bioconductor packages:...

Read more »

Nairobi Data Science Meetup: Paradigm Shift in Research with Samuel Kamande

March 1, 2016
By

Samuel Kamande is a Data Scientist at Nielsen and his presentation will focus on “Paradigm Shift in Research”. We caught up with him and he shared a lot about his work at Nielsen, some of the projects he has worked on like “Digital Divide project in Trinidad and Tobago in 2013”,thoughts on the future of Data Science and something...

Read more »

Principal Component Analysis using R

February 27, 2016
By
Principal Component Analysis using R

Curse of Dimensionality:One of the most commonly faced problems while dealing with data analytics problem such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios,...

Read more »

Sponsors

Mango solutions



plotly webpage

dominolab webpage



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)