867 search results for "how to import image file to R"

Visiting FHCRC, JHSPH and Meeting Xi’an

October 29, 2012
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Visiting FHCRC, JHSPH and Meeting Xi’an

I have been traveling during the last two weeks. I visited Fred Hutchinson Cancer Research Center on Oct 16 and the Department of Biostatistics at Johns Hopkins at the invitation of Simply Statistics on Oct 23. Today Christian Robert was visiting our department at Iowa State, and I also talked to him. It is really cool...

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Bayes for President!

October 23, 2012
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Bayes for President!

I couldn't resist getting sucked into the hype associated with the US election and debates, and so I thought I had a little fun of my own and played around a bit with the numbers. [OK: you may disagree with the definition of "fun" $-$ but then again, i...

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Getting data in and out of R

October 22, 2012
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Getting data in and out of R

One of the great advantages of R is that it recognizes almost any data format that you can throw at it. There are a myriad of different possible file formats but I'll concentrate on the four files that we see almost exclusively in public health: Excel ...

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Predict User’s Return Visit within a day part-3

October 22, 2012
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Predict User’s Return Visit within a day part-3

Welcome to the last part of the series on predicting user’s revisit to the website. In the  first part of series, I generated the logistic regression model for prediction problem whether a user will come back on  website in next 24 hours. In the second part, I discussed about model improvement and seen the model accuracy.

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The consequence of merging insurance companies – Risk simulation and probability of ruin

October 17, 2012
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The consequence of merging insurance companies – Risk simulation and probability of ruin

The merge of two insurance companies enables to curb the probability of ruin by sharing the risk and the capital of the two companies. For example, we can consider two insurance companies, A and B. A is a well known insurance company with a big capital and is dealing with a risk with a low variance. We will assume...

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9 reasons to use RStudio

October 16, 2012
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9 reasons to use RStudio

In no particular order, here are nine reasons why I really like the RStudio IDE for the R statistical programming language. 1) R benefits from an IDE – I accept that in some languages an IDE is unnecessary—Perl is the first example … Continue reading →

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S&P 500 sector strengths

October 10, 2012
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S&P 500 sector strengths

Which sectors are coherent, and which aren’t? Previously The post “S&P 500 correlations up to date” looked at rolling mean correlations among stocks.  In particular it looked at rolling mean correlations of stocks within sectors. Of importance to this post is that the sectors used are taken from Wikipedia. Relative correlations The thought is that … Continue reading...

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City Size and SUHI

October 2, 2012
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City Size and SUHI

In the course of putting together data for my kriging project with the CRN stations, I got another idea related to a small but potentially important corner of the concerns over UHI in the global temperature index. For clarity I suppose I should make it clear that my position is that the UHI bias is

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Using R in Political Controversies: Unemployment Reduction Prowess Under Bush versus Obama Years

September 27, 2012
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Using R in Political Controversies: Unemployment Reduction Prowess Under Bush versus Obama Years

Editor’s note: R-bloggers does not take a political side. Since this is an important topic, this post has the comments turned on. Also, If you wish to write a reply post (which includes an R context), you are welcome to contact me to have it published. This post was written by Prof. H. D. Vinod. Fordham University, New York.

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Continuous dispersal on a discrete lattice

September 27, 2012
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Continuous dispersal on a discrete lattice

Dispersal is a key process in many domains, and particularly in ecology. Individuals move in space, and this movement can be modelled as a random process following some kernel. The dispersal kernel is simply a probability distribution describing the distance travelled in a given time frame. Since space is continuous, it is natural to use

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