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Monthly R jobs on r-bloggers

Monthly R jobs on r-bloggers | R evolution | Scoop.it
R-bloggers is now offering a new “R jobs” post, that will be published (around) once a month.
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Showing results from Cox Proportional Hazard Models in R with simPH

Showing results from Cox Proportional Hazard Models in R with simPH | R evolution | Scoop.it
(This article was first published on Christopher Gandrud (간드루드 크리스토파), and kindly contributed to R-bloggers) Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear...
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Fun With Just-In-Time Compiling: Julia, Python, R and pqR

Fun With Just-In-Time Compiling: Julia, Python, R and pqR | R evolution | Scoop.it
(This article was first published on randyzwitch.com » R, and kindly contributed to R-bloggers)
Recently I’ve been spending a lot of time trying to learn Julia by doing the problems at Project Euler.
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Plot Weekly or Monthly Totals in R

Plot Weekly or Monthly Totals in R | R evolution | Scoop.it
(This article was first published on Mollie's Research Blog, and kindly contributed to R-bloggers)
When plotting time series data, you might want to bin the values so that each data point corresponds to the sum for a given month or week.
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My Significance Magazine Contribution

My Significance Magazine Contribution | R evolution | Scoop.it
(This article was first published on Kevin Davenport » R, and kindly contributed to R-bloggers)
I am excited to have the opportunity to be a regular contributor to Significance Magazine.
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analyze the national longitudinal study of adolescent health (addhealth) with r

analyze the national longitudinal study of adolescent health (addhealth) with r | R evolution | Scoop.it
(This article was first published on asdfree by anthony damico, and kindly contributed to R-bloggers) the national longitudinal survey of adolescent health (addhealth) is to the health and retirement study what teen people is to aarp...
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From SVG to probability distributions [with R package]

From SVG to probability distributions [with R package] | R evolution | Scoop.it
(This article was first published on Statisfaction » R, and kindly contributed to R-bloggers) Hey, To illustrate generally complex probability density functions on continuous spaces, researchers always use the same examples, for instance...
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Exporting Results of Linear Regression with Robust Standard Errors

Exporting Results of Linear Regression with Robust Standard Errors | R evolution | Scoop.it
(This article was first published on R for Public Health, and kindly contributed to R-bloggers)

To leave a comment for the author, please follow the link and comment on his blog: R for Public Health.
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BMC Bioinformatics | Abstract | RCircos: an R package for Circos 2D track plots

Background

Circos is a Perl language based software package for visualizing similarities and differences of genome structure and positional relationships between genomic intervals. Running Circos requires extra data processing procedures to prepare plot data files and configure files from datasets, which limits its capability of integrating directly with other software tools such as R. Recently published R Bioconductor package ggbio provides a function to display genomic data in circular layout based on multiple other packages, which increases its complexity of usage and decreased the flexibility in integrating with other R pipelines.

Results

We implemented an R package, RCircos, using only R packages that come with R base installation. The package supports Circos 2D data track plots such as scatter, line, histogram, heatmap, tile, connectors, links, and text labels. Each plot is implemented with a specific function and input data for all functions are data frames which can be objects read from text files or generated with other R pipelines.

Conclusion

RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.


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BMC Bioinformatics | Abstract | svapls: an R package to correct for hidden factors of variability in gene expression studies

Background

Hidden variability is a fundamentally important issue in the context of gene expression studies. Collected tissue samples may have a wide variety of hidden effects that may alter their transcriptional landscape significantly. As a result their actual differential expression pattern can be potentially distorted, leading to inaccurate results from a genome-wide testing for the important transcripts.

Results

We present an R package svapls that can be used to identify several types of unknown sample-specific sources of heterogeneity in a gene expression study and adjust for them in order to provide a more accurate inference on the original expression pattern of the genes over different varieties of samples. The proposed method implements Partial Least Squares regression to extract the hidden signals of sample-specific heterogeneity in the data and uses them to find the genes that are actually correlated with the phenotype of interest. We also compare our package with three other popular softwares for testing differential gene expression along with a detailed illustration on the widely popular Golub dataset. Results from the sensitivity analyes on simulated data with widely different hidden variation patterns reveal the improved detection power of our R package compared to the other softwares along with reasonably smaller error rates. Application on the real-life dataset exhibits the efficacy of the R package in detecting potential batch effects from the dataset.

Conclusions

Overall, Our R package provides the user with a simplified framework for analyzing gene expression data with a wide range of hidden variation patterns and delivering a differential gene expression analysis with substantially improved power and accuracy. The R package svapls is freely available at http://cran.r-project.org/web/packages/svapls/index.html.


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Data Recovery Software R-Studio

R-Studio is a comprehensive data recovery and undelete program renowned for its ability to recover mission-critical data lost to viruses, malicious attacks, ...
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Monthly R jobs on r-bloggers

Monthly R jobs on r-bloggers | R evolution | Scoop.it
R-bloggers is now offering a new “R jobs” post, that will be published (around) once a month.
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Poll: R top language for data science three years running

Poll: R top language for data science three years running | R evolution | Scoop.it
(This article was first published on Revolutions, and kindly contributed to R-bloggers) KDDNuggets has completed its annual poll of top languages for analytics, data mining and data science, and just as in the prior two years the R language...
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R and Linear Algebra

R and Linear Algebra | R evolution | Scoop.it
(This article was first published on Revolutions, and kindly contributed to R-bloggers) by Joseph Rickert I was recently looking through upcoming Coursera offerings and came across the course Coding the Matrix: Linear Algebra through...
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Plot Weekly or Monthly Totals in R

Plot Weekly or Monthly Totals in R | R evolution | Scoop.it
(This article was first published on Mollie's Research Blog, and kindly contributed to R-bloggers)
When plotting time series data, you might want to bin the values so that each data point corresponds to the sum for a given month or week.
more...
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RSiteCatalyst Version 1.1 Release Notes

RSiteCatalyst Version 1.1 Release Notes | R evolution | Scoop.it
(This article was first published on randyzwitch.com » R, and kindly contributed to R-bloggers)
RSiteCatalyst version 1.1 is now available on CRAN.
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From SVG to probability distributions [with R package]

From SVG to probability distributions [with R package] | R evolution | Scoop.it
(This article was first published on Statisfaction » R, and kindly contributed to R-bloggers) Hey, To illustrate generally complex probability density functions on continuous spaces, researchers always use the same examples, for instance...
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New Webinar: High Performance Predictive Analytics in R and Hadoop

New Webinar: High Performance Predictive Analytics in R and Hadoop | R evolution | Scoop.it
(This article was first published on Revolutions, and kindly contributed to R-bloggers) This coming Tuesday, August 27, our US Chief Scientist Mario Inchosa will reveal some details of the forthcoming in-Hadoop predictive analytics...
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Research Methods and Statistics, 3rd Edition - Free eBook Share

Research Methods and Statistics, 3rd Edition - Free eBook Share | R evolution | Scoop.it
eBook Free Download: Research Methods and Statistics, 3rd Edition | PDF, EPUB | ISBN: 0495510017 | 2008-03-17 | English | PutLocker

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Fox eBook's curator insight, August 18, 2013 9:03 PM

Research Methods and Statistics: A Critical Thinking Approach

RESEARCH METHODS AND STATISTICS: A CRITICAL THINKING APPROACH, 3e, successfully illustrates theintegration between statistics and research methods by demonstrating the ways to use statistics in analyzing data collected during research. Jackson’s combined text adopts an inviting narrative style that draws you into the material, helping you overcome the initial apprehension you may feel at having to learn both subject areas at once. She incorporates a student-friendly critical-thinking approach and presents examples and exercises to which you can relate. Jackson focuses on the logic of the process and the methodology aspect of research.

Table of Contents

1 Thinking Like a Scientist
2 Getting Started: Ideas, Resources, and Ethics
3 Defining, Measuring, and Manipulating Variables
4 Descriptive Methods
5 Data Organization and Descriptive Statistics
6 Correlational Methods and Statistics
7 Hypothesis Testing and Inferential Statistics
8 The Logic of Experimental Design
9 Inferential Statistics: Two-Group Designs
10 Experimental Designs with More Than Two Levels of an Independent Variable
11 Complex Experimental Designs
12 Quasi-Experimental and Single-Case Designs
13 APA Communication Guidelines
14 APA Sample Manuscript
APPENDIX A: Statistical Tables
APPENDIX B: Computational Formulas for ANOVAs
APPENDIX C: Answers to Odd-Numbered Chapter Exercises and All Review Exercises

 

Free Download Here: http://www.foxebook.net/research-methods-and-statistics-3rd-edition/

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Learning Statistics with R - Fox eBook

Learning Statistics with R - Fox eBook | R evolution | Scoop.it
“Learning Statistics with R” covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software.

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Fox eBook's curator insight, July 2, 2013 9:44 PM

“Learning Statistics with R” covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. Please be aware that this is version 0.3 of a work in progress. It has been ‘published’ at Lulu primarily for teaching purposes at the University of Adelaide: this book allows students to buy hard copies of the lecture notes that are more sturdy than the readers that the university printer produces, at a price that is only slightly higher than the readers, and less than half the price that a traditional textbook company would have charged for the sameservice. If you are not one of my students, and are nevertheless considering buying a hard copy of this book, I would suggest downloading the soft copy from ua.edu.au/ccs/teaching/lsr (available for free) to check that it is suitable for your purposes.

Table of Contents

I Background 1
1 Why do we learn statistics? 3
2 A brief introduction to research design 11
II An introduction to R 33
3 Getting started with R 35
4 Additional R concepts 67
III Working with data 103
5 Descriptive statistics 105
6 Drawing graphs 147
7 Pragmatic matters 185
8 Basic programming 243
IV Statistical theory 259
9 Introduction to probability 261
10 Estimating population parameters from a sample 285
11 Hypothesis testing 303
V Statistical tools 327
12 Categorical data analysis 329
13 Comparing two means 353
14 Comparing several means (one-way ANOVA) 395
15 Linear regression 427
16 Factorial ANOVA 465
17 Epilogue 521

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Best Data Recovery Apps for Mac: Disk Drill, R-Studio, TechTool, VirtualLab, Disk Doctors. Episode 1

Episode #1 : Disk Drill http://www.cleverfiles.com/ The best Mac data recovery software products -- Disk Drill, R-Studio, TechTool, VirtualLab, Boomerang, Di...
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