# 1201 search results for "latex"

## Creating a Quick Report with knitr, xtable, R Markdown, Pandoc (and some OpenBLAS Benchmark Results)

August 15, 2013
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To cut a long story short, I always wanted to write professional-looking documents (technical reports and potentially my thesis) with R codes. No more copy and paste. No more Microsoft Word. At the same time, I don't feel comfortable with LaTeX. Somehow I found a workaround with knitr, xtable, R Markdown...

## predictNLS (Part 1, Monte Carlo simulation): confidence intervals for ‘nls’ models

August 14, 2013
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$predictNLS (Part 1, Monte Carlo simulation): confidence intervals for ‘nls’ models$

Those that do a lot of nonlinear fitting with the nls function may have noticed that predict.nls does not have a way to calculate a confidence interval for the fitted value. Using confint you can obtain the error of the fit parameters, but how about the error in fitted values? ?predict.nls says: “At present se.fit

## Exposure as a possible explanatory variable

August 13, 2013
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$Y_i$

Iin insurance pricing, the exposure is usually used as an offset variable to model claims frequency. As explained many times on this blog (e.g. here), and in my notes, if we have to identical drivers, but one with an exposure of 6 months, and the other one of one year, it should be natural to assume that, on average,...

## When Discussing Confidence Level With Others…

August 13, 2013
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This post spawned from a discussion I had the other day. Confidence intervals are notoriously a difficult topic for those unfamiliar with statistics. I can’t really think of another statistical topic that is so widely published in newspaper articles, television, and elsewhere that so few people really understand. It’s been this way since the moment

## Exploratory Data Analysis: The 5-Number Summary – Two Different Methods in R

$Exploratory Data Analysis: The 5-Number Summary – Two Different Methods in R$

Introduction Continuing my recent series on exploratory data analysis (EDA), today’s post focuses on 5-number summaries, which were previously mentioned in the post on descriptive statistics in this series.  I will define and calculate the 5-number summary in 2 different ways that are commonly used in R.  (It turns out that different methods arise from

## Enhanced meboot package, simulating regression standard errors

August 11, 2013
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In my June 25 post I described R- (i) code to change scale without changing the mean, and (ii) code to make a probability distribution symmetric by modifying order statistics.  Both are commonly encountered problems by R programmers.  My coauthor Javier Lopez-de-Lacalle of Spain has incorporated an efficient version of my code inside the maximum entropy bootstrap (meboot) package in R See the package...

## R-Squared for a VBGM

August 9, 2013
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$R-Squared for a VBGM$

Recently, a fishR user asked me the following question: After fitting the age-length data into VBGM, I overviewed the results. But I can’t find the coefficient of determination () for the VBGM fitting. Because some reviewer want the the coefficient … Continue reading →

## Bio7 1.7 for Windows Released!

August 8, 2013
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07.08.2013 A new Windows version of Bio7 is available. This version comes with a lot of new features and improvements for Java, R and ImageJ. One highlight is that you can now interpret Jython (Python) code with Bio7. In addition a new console implementation is available which offers access to a native shell, different Java

## Model Scale Parameterization for MCMC Efficiency

August 1, 2013
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$Model Scale Parameterization for MCMC Efficiency$

I recently came across a very interesting paper by Y. Yu and X. Meng who present an interweaving strategy between different model parameterizations to improve mixing. It is well known that different model parameterizations can perform better than others under certain conditions. Papaspiliopoulos, Roberts and Sköld present a general framework for how to parameterize The post Model...

## Measuring Bias in Published Work

July 31, 2013
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$Measuring Bias in Published Work$

In a series of previous posts, I’ve spent some time looking at the idea that the review and publication process in political science—and specifically, the requirement that a result must be statistically significant in order to be scientifically notable or publishable—produces a very misleading scientific literature. In short, published studies of some relationship will tend