2297 search results for "Time Series"

Introduction to bootstrap with applications to mixed-effect models

November 25, 2015
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Introduction to bootstrap with applications to mixed-effect models

Bootstrap is one of the most famous resampling technique and is very useful to get confidence intervals in situations where classical approach (t- or z- tests) would fail. What is bootstrap Instead of writing down some equations let’s directly see how one may perform bootstrap. Below we will show a simple bootstrap example using the

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Modeling gene expression with broom: a case study in tidy analysis

November 25, 2015
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Modeling gene expression with broom: a case study in tidy analysis

Previously in this series Cleaning and visualizing genomic data: a case study in tidy analysis In the last post, we examined an available genomic dataset from Brauer et al 2008 about yeast gene expression under nutrient starvation. We learned to tidy it with the dplyr and tidyr packages, and saw how useful this tidied form is for visualizing...

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Fitting linear mixed models for QTL mapping

November 24, 2015
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Fitting linear mixed models for QTL mapping

Linear mixed models (LMMs) have become widely used for dealing with population structure in human GWAS, and they’re becoming increasing important for QTL mapping in model organisms, particularly for the analysis of advanced intercross lines (AIL), which often exhibit variation in the relationships among individuals. In my efforts on R/qtl2, a reimplementation R/qtl to better

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Are some seasons warming more than others?

November 23, 2015
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Are some seasons warming more than others?

I ended the last post with some pretty plots of air temperature change within and between years in the Central England Temperature series. The elephant in the room1 at the end of that post was is the change in the within year (seasonal) effect over time statistically significant? This is the question I’ll try to answer,...

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Using Apache SparkR to Power Shiny Applications: Part I

November 22, 2015
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Using Apache SparkR to Power Shiny Applications: Part I

Introduction The objective of this blog post is demonstrate how to use Apache SparkR to power Shiny applications. I have been curious about what the use cases for a “Shiny-SparkR” application would be and how to develop and deploy such an app. SparkR is an R package that provides a light-weight frontend to use Apache…

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Cleaning and visualizing genomic data: a case study in tidy analysis

November 19, 2015
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Cleaning and visualizing genomic data: a case study in tidy analysis

I recently ran into a question looking for a case study in genomics, particularly for teaching ggplot2, dplyr, and the tidy data framework developed by Hadley Wickham. There exist many great resources for learning how to analyze genomic data using Bioconductor tools, including these workflows and package vignettes. But case studies for teaching the...

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Creating nice tables using R Markdown

November 17, 2015
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Creating nice tables using R Markdown

One of the neat tools available via a variety of packages in R is the creation of beautiful tables using data frames stored in R. In what follows, I’ll discuss these different options using data on departing flights from Seattle … Continue reading →

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PubMed search Shiny App using RISmed

November 17, 2015
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PubMed search Shiny App using RISmed

In part one of a series of tutorials, we will develop a Shiny App for performing analysis of academic text from PubMed. There’s no shortage of great tutorials for developing a Shiny App using R, including Shiny’s own tutorial. Here at datascience+ we have a perfect introduction by Teja Kodali and a more in-depth development

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Aggregate – A Powerful Tool for Data Frame in R

November 16, 2015
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Aggregate – A Powerful Tool for Data Frame in R

This post gives a short review of the aggregate function as used for data.frames and presents some interesting uses: from the trivial but handy to the most complicated problems I have solved with aggregate. Aggregate (data.frame): Technical Overview Aggregate is a function in base R which can, as the name suggests, aggregate the inputted data.frame

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Trading Autocorrelation?

November 15, 2015
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Trading Autocorrelation?

Markets are very smart in absorbing and reflecting information. If you think otherwise, try making money by trading. If you are new to it, make sure you don’t bet the house. In other words, markets are efficient. At least most of the time. So then why people trade? The general believe is that there are The post

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