Distributed Solar: The Democratizaton of Energy
Category Archives: R
On odds of storms, and extreme precipitation
People talk about “thousand year storms”. Rather than being a storm having a recurrence time of once in a thousand years, these are storms which have a 0.001 chance per year of occurring. Storms aren’t the only weather events of … Continue reading
Posted in American Meteorological Association, American Statistical Association, AMETSOC, catastrophe modeling, climate disruption, climate economics, climate education, ecopragmatism, evidence, extreme events, extreme value distribution, flooding, floods, games of chance, global warming, global weirding, insurance, meteorological models, meteorology, R, R statistical programming language, real estate values, risk, Risky Business, riverine flooding, science, Significance
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Macros in R
via Macros in R See also: The gtools package of R which enables these. There’s a description and motivation beginninng on page 11 of an (old: 2001) R News issue. They have been around a long time, but I haven’t … Continue reading
On bag bans and sampling plans
Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading
Posted in bag bans, citizen data, citizen science, Commonwealth of Massachusetts, Ecology Action, evidence, Google, Google Earth, Google Maps, goverance, lifestyle changes, microplastics, municipal solid waste, oceans, open data, planning, plastics, politics, pollution, public health, quantitative ecology, R, R statistical programming language, reasonableness, recycling, rhetorical statistics, sampling, sampling networks, statistics, surveys, sustainability
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Bayesian blocks via PELT in R
The Bayesian blocks algorithm of Scargle, Jackson, Norris, and Chiang has an enthusiastic user community in astrostatistics, in data mining, and among some in machine learning. It is a dynamic programming algorithm (see VanderPlas referenced below) and, so, exhibits optimality … Continue reading
Posted in American Statistical Association, AMETSOC, anomaly detection, astrophysics, Cauchy distribution, changepoint detection, engineering, geophysics, multivariate statistics, numerical analysis, numerical software, numerics, oceanography, population biology, population dynamics, Python 3, quantitative biology, quantitative ecology, R, Scargle, spatial statistics, square wave approximation, statistics, stepwise approximation, time series, Woods Hole Oceanographic Institution
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Rushing the +2 degree Celsius boundary
I made a comment on Google+ pertaining to a report of a recent NOAA finding. Enjoy. But remember that COP21 boundary is equivalent to 450 ppm CO2.
Posted in adaptation, AMETSOC, Anthropocene, atmosphere, Bill Nye, bridge to nowhere, carbon dioxide, Carbon Tax, Carbon Worshipers, citizenship, civilization, clean disruption, climate, climate disruption, COP21, corporate litigation on damage from fossil fuel emissions, differential equations, disruption, distributed generation, Donald Trump, ecology, El Nina, El Nino, energy, energy reduction, engineering, environment, environmental law, Epcot, explosive methane, forecasting, fossil fuel divestment, fossil fuels, geophysics, global warming, greenhouse gases, greenwashing, Hyper Anthropocene, investment in wind and solar energy, IPCC, local generation, Mark Jacobson, Martyn Plummer, microgrids, Miguel Altieri, philosophy, physical materialism, R, resiliency, Ricky Rood, risk, Sankey diagram
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data.table
R provides a helpful data structure called the “data frame” that gives the user an intuitive way to organize, view, and access data. Many of the functions that you would us… Source: Intro to The data.table Package
Gavin Simpson updates his temperature analysis
See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.
Posted in AMETSOC, Anthropocene, astrophysics, Berkeley Earth Surface Temperature project, carbon dioxide, changepoint detection, climate, climate change, climate data, climate disruption, climate models, ecology, environment, evidence, Gavin Simpson, Generalize Additive Models, geophysics, global warming, HadCRUT4, hiatus, Hyper Anthropocene, information theoretic statistics, Kalman filter, maths, meteorology, numerical analysis, R, rationality, reasonableness, splines, time series
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R and “big data”
On 2nd November 2015, Wes McKinney, the developer of the highly useful Python pandas module (and other things, including books), wrote an amusing blog post, “The problem with the data science language wars“. I by no means disagree with him. … Continue reading
STUFF IN PROGRESS
It’s a good time to reconnoiter and review the things I have in progress and are planned, both as a teaser, and as a promise. I am currently working the following technical projects, entirely on my personal time outside of … Continue reading
Posted in numerical analysis, planning, R, rationality, reasonableness, state-space models, statistics
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Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS
A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading
Posted in Bayes, Bayesian, dynamic linear models, dynamical systems, forecasting, Kalman filter, mathematics, maths, multivariate statistics, numerical software, open source scientific software, prediction, R, Rauch-Tung-Striebel, state-space models, statistics, stochastic algorithms, SVD, time series
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Earth Day, my hope
Posted in carbon dioxide, Carl Sagan, Charles Darwin, citizen science, citizenship, civilization, clean disruption, climate, climate change, climate education, compassion, conservation, Darwin Day, demand-side solutions, ecology, economics, education, efficiency, energy reduction, environment, ethics, forecasting, fossil fuel divestment, geophysics, history, humanism, investing, investment in wind and solar energy, IPCC, mathematics, maths, meteorology, NCAR, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, privacy, probit regression, R, rationality, Ray Pierrehumbert, reasonableness, reproducible research, risk, science, science education, scientific publishing, Scripps Institution of Oceanography, sociology, the right to know, Unitarian Universalism, UU Humanists, WHOI, wind power
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We are trying. And the bitterest result is to have so-called colleagues align themselves with the Koch brothers
I attended a 350.org meeting tonight. One group A group presenting there called “Fighting Against Natural Gas” applauded themselves for assailing Senator Whitehouse of Rhode Island for his supportive position on natural gas pipelines. Now, I am no friend of … Continue reading
Posted in Anthropocene, astrophysics, Boston Ethical Society, bridge to nowhere, carbon dioxide, carbon dioxide sequestration, Carbon Tax, chemistry, citizenship, climate, climate change, climate education, consumption, decentralized electric power generation, demand-side solutions, ecology, economics, energy reduction, engineering, forecasting, fossil fuel divestment, investment in wind and solar energy, IPCC, JAGS, meteorology, methane, model comparison, NASA, natural gas, NCAR, Neill deGrasse Tyson, oceanography, open data, physics, politics, population biology, Principles of Planetary Climate, Python 3, R, rationality, reasonableness, reproducible research, risk, science, science education, Scripps Institution of Oceanography
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Dynamic Linear Models package, dlmodeler
I’m checking out the dlmodeler package in R for a work project. It is accompanied by textbooks, G. Petris, S. Petrone, P. Campagnoli, Dynamic Linear Models with R, Springer, 2009 and J. Durbin, S. J. Koopman, Time Series Analysis by … Continue reading
Markov Chain Monte Carlo methods and logistic regression
This post could also be subtitled “Residual deviance isn’t the whole story.” My favorite book on logistic regression is by Dr Joseph Hilbe, Logistic Regression Models, CRC Press, 2009, Chapman & Hill. It is a solidly frequentist text, but its … Continue reading
Posted in Bayes, Bayesian, logistic regression, MCMC, notes, R, statistics, stochastic algorithms, stochastic search
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R vs Python: Practical Data Analysis
R vs Python: Practical Data Analysis (Nonlinear Regression).
Posted in Bayes, Bayesian, biology, climate change, ecology, environment, Python 3, R, statistics, Wordpress
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The designers of our climate
Originally posted on …and Then There's Physics:
Okay, I finally succumbed and actually waded through some of the new paper by Monckton, Soon, Legates & Briggs called Why models run hot: results from an irreducibly simple climate model. I…
Posted in astrophysics, bridge to nowhere, carbon dioxide, carbon dioxide capture, carbon dioxide sequestration, Carbon Tax, Carl Sagan, citizenship, civilization, climate, climate change, climate education, differential equations, ecology, economics, engineering, environment, ethics, forecasting, fossil fuel divestment, geoengineering, geophysics, humanism, IPCC, mathematics, mathematics education, maths, meteorology, methane, NASA, NCAR, Neill deGrasse Tyson, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, probabilistic programming, R, rationality, reasonableness, reproducible research, risk, science, science education, scientific publishing, sociology, solar power, statistics, testing, the right to know
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On nested equivalence classes of climate models, ordered by computational complexity
I’m digging into the internals of ABC, for professional and scientific reasons. I’ve linked a great tutorial elsewhere, and argued that this framework, advanced by Wood, and Wilkinson (Robert), and Wilkinson (Darren), and Hartig and colleagues, and Robert and colleagues, … Continue reading
Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, environment, forecasting, geophysics, IPCC, mathematics, maths, MCMC, meteorology, NCAR, NOAA, oceanography, optimization, population biology, Principles of Planetary Climate, probabilistic programming, R, science, stochastic algorithms, stochastic search
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illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle
Introduction Modern treatments of Bayesian integration to obtain posterior densities often use some form of Markov Chain Monte Carlo (“MCMC”), typically Gibbs sampling. Gibbs works well with many Bayesian hierarchical models. The standard problem-solving situation with these is that a … Continue reading
Bayesian deconvolution of stick lengths
Consider trying to determine the length of a straight stick. Instead of the measurement errors being clustered about zero, suppose the errors are known to be always positive, that is, no measurement ever underestimates the length of the stick. Such … Continue reading
The dp-means algorithm of Kulis and Jordan in R and Python
dp-means algorithm. Think k-means but with the number of clusters calculated. By John Myles White, in R. (Github link off that page.) By Scott Hendrickson, in Python. (Github link off that page.)
Posted in Bayesian, Gibbs Sampling, JAGS, mathematics, maths, R, statistics, stochastic algorithms, stochastic search
Tagged dp-means
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Blind Bayesian recovery of components of residential solid waste tonnage from totals data
This is a sketch of how maths and statistics can do something called blind source separation, meaning to estimate the components of data given only their totals. Here, I use Bayesian techniques for the purpose, sometimes called Bayesian inversion, using … Continue reading
“The joy and martyrdom of trying to be a Bayesian”
Bayesians have all been there. Some of us don’t depend upon producing publications to assure our pay, so we less have the pressure of pleasing peer reviewers. Nonetheless, it’s all reacting to “What the hell are you doing? I don’t … Continue reading
How fast is JAGS?
How fast is JAGS?.
The zero-crossings trick for JAGS: Finding roots stochastically
BUGS has a “zeros trick” (Lund, Jackson, Best, Thomas, Spiegelhalter, 2013, pages 204-206; see also an online illustration) for specifying a new distribution which is not in the standard set. The idea is to couple an invented-for-the-moment Poisson density to … Continue reading
Posted in Bayesian, BUGS, education, forecasting, Gibbs Sampling, JAGS, mathematics, MCMC, probabilistic programming, R, statistics, stochastic search
Tagged error-in-variables problem, optimization, zeros trick
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HadCRUT4 version “HadCRUT.4.2.0.0” available as .RData R workspace or image
I’m happy to announce that I have made available the HadCRUT4 observational ensemble data as an .RData image for use with R. These were downloaded from the MetOffice Hadley Observations Web site. Detailed documentation is available on this page, with the … Continue reading
