Many if not most data clean up, tidying, wrangling, and joining can be done directly in R. There are many advantages to this approach – i.e. read in data in whatever format (from excel to json to zip) and then do your tidying – including transparency, a record of what you ... [Read more...]

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PreambleColleagues and I had some sweet telemetry data, we did some simple models (& some relatively more complex ones too), we drew maps, and we wrote a paper. However, I thought it would be great to also provide stakeholders with the capacity to engage with the models, data, and maps. I ... [Read more...]

PreambleColleagues and I had some sweet telemetry data, we did some simple models (& some relatively more complex ones too), we drew maps, and we wrote a paper. However, I thought it would be great to also provide stakeholders with the capacity to engage with the models, data, and maps. I ... [Read more...]

In a previous post, I discussed the key elements that really stood out for me in recent workshops associated with open science, data science, and ecology. Summer workshop season is upon us, and here are some principles to consider that can be used to hack a workshop. These hacks can ... [Read more...]

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A summary note on recent set of #rstats discoveries in estimating AIC scores to better understand a quasipoisson family in GLMS relative to treating data as poisson. Conceptual GLM workflow rules/guidelines Data are best untransformed. Fit better model to data. Select your data structure to match purpose with statistical ... [Read more...]

A brief note on overdispersion Assumptions Poisson distribution assume variance is equal to the mean. Quasi-poisson model assumes variance is a linear function of mean. Negative binomial model assumes variance is a quadratic function of the mean. rstats implementation #to test you need to fit a poisson GLM then apply ... [Read more...]

What makes an open science workshop effective or successful*? Over the last 15 years, I have had the good fortune to participate in workshops as a student and sometimes as an instructor. Consistently, there were beneficial discovery experiences, and at times, some of the processes highlighted have been transformative. Last year, ... [Read more...]

Purpose I recently completed a set of data science for biostatistics training exercises for graduate students. I extensively used R for Data Science and Efficient R programming to develop a set of Adventure Time R-statistics slide decks. Whilst I recognize that they are very minimal in terms of text, I ... [Read more...]

Data science Data science is a critical component of many domains of research including the domain I primarily function – ecology. However, in teaching biostatistics within the university context, we have typically focussed on the statistics and less on the science of data (i.e. handling, understanding, and manipulating data). This ... [Read more...]

Preamble Every few months, I try to do a clean install on my machine. I know that OS X Sierra is due out in September, but I elected to do a wipe and clean install now for the remainder of summer. Wipe, reinstall OSX from usb, brief minor hack/tweaks, ... [Read more...]

Rationale This Fall, I am teaching graduate-level biostatistics. I have not had the good fortune of teaching many graduate-level offerings, and I am really excited to do so. A team of top-notch big data scientists are hosted at NCEAS. They have recently formed a really exciting collaborative-learning collective entitled ecodatascience. ... [Read more...]

Cohen is power. Inferential statistics primarily invoke the following four key concepts: sample size, significance criterion, effect size, and statistical power. Cohen elegantly developed the maths, benchmarks, and key semantics associated with statistical power.   Statistical power is the long-term probability of rejecting the null hypothesis (typically assumed to be no ... [Read more...]