by Erik B. Erhardt, 2010.03.04

Indecent (inactives cross)

[Pulses Call]

A1:
4 (Old neighbors) Left-hand star 1/2
6 (New, current neighbors) Right-hand Star 3/4
6 Neighbor Swing (quick)
A2:
8 Balance ring, Petrunella turn (to the right)
4 Circle Right 1/2
4 (with Gents in lead) couples (with partner) weave Right around neighbors, then Left to connect with next neighbors
B1:
8 (Next neighbors) Circle Left 3/4
8 Pass the ocean wave (ladies turn 1/2 by left, gents cross set to connect right hands with partner to form a wavy line), Balance the wave
B2:
8 Ladies left-hand turn 1 time around, while Gents orbit 1/2
8 Pass up and down the line to Mad Robin (with current neighbors, ladies in first)

Dance Notes:

• This dance starts with old neighbors, moves to current neighbors, previews next neighbors, and ends with current neighbors.  Can call them the “old guard”, “current crop”, “up-and-comers”.
• No partner swing, one quick neighbor swing, so make the mad robin count!
• Jig about 110bpm would be fine slow and smooth tempo.
• This dance is for Laurel Wilson.
• Not yet danced.

by Erik B. Erhardt, 2010.03.03

Proper

[Pulses Call]

A1:
8 Symmetric Do-si-do, Actives together, then Inactives
8 Actives Figure-8 down
A2:
8 (Left shoulder) Pass through (to maintain eye-contact), hands-off Courtesy turn (as in Money Musk)
8 Mad robin, Gents in first
B1:
8 Gents begin Right-shoulder Hay 1/2
2 Gents cross by Left shoulder
6 Left-shoulder (partner) Gypsy 1 time
B2:
8 Single-file Promenade 3/4, Gents in lead (to Left) [Gents, draw the Lady to you with your eyes]
8 Actives Swing, face down (stay connected for do-si-do), while Inactives 2-hand turn 1/2 (left)

Dance Notes:

• This dance is eyes-strong and you don’t get to touch anyone until B2.2, and then, only your partner.
• This dance is for Karina Wilson.
• Not yet danced.

by Erik B. Erhardt, 2010.02.18

Becket, double progression to the right (reverse Becket progression)
Music: slow tempo, flowing through B1, can have punctuation on beat 4 in four measures in B2

[Pulses Call]

A1:
4 (with Neighbors across,) Box the Gnat
6 (into) Right-hand (wrist-grip) Star 3/4
6 (Gents turn over right shoulder,) Neighbor swing
A2: (catch)
8 (with Partner across, Ladies pass left shoulder taking right hand with Partner)
Gents Lasso Ladies [Partner], high then low (Ladies walk around twice, letting hands go behind the Gent’s back on second time around)
8 (into) Partner gypsy 1-1/2 (lades end on left, facing in)
B1: (release and catch)
8 1/2 Poussette (Ladies join both hands with Neighbor Gent, then push and pull the Gents as the Ladies move as a do-si-do relative to each other)
8 (while Ladies are backing up, gents catch partner with right hand and) Partner Swing
B2:
16 Zipper (across right-hand pull-by (no courtesy turn), turn (or hop) alone to right and with same-gender diagonal one person left pull-by, face in and repeat; end effect is to not pull by if no one on is on diagonal)

Dance Notes:

• Many dancers may be unfamiliar with a Lasso (Richard Wilson), Poussette (English country dance), and Zipper.  Therefore, this dance may take a while to teach.
• Teaching Zipper:  It is a series of pull-bys with four people.  The person straight across is #1, to their immediate right is #2, and #3 and #4 are the next two to the right.  Now pull-by across and use the held hand as a pivot, turn (or hop) to right and look on the close left diagonal for that next person with the left hand.
• The first three quarters of the dance turns to the right, so the zipper is a welcome relief to stop turning.
• I wrote this dance in the car driving to Stellar Days and Nights, 2010 talking about dance with Richard Wilson who mentioned Lasso and Poussette as rare moves.  I called the dance there two days later and I thank Mary Devlin for providing calling feedback.
• This dance is for Tara Schneider who I befriended at Berea Christmas Country Dance School, Dec 2009.

I met Laurel at SIRFER 2008 where we enjoyed a wide range of stable isotope lectures and lab experience. She first used my software, SISUS, to estimate the proportion of C3 vs CAM photosynthesis of epiphytes. Our work and friendship led to the collaboration where we thought about and developed a model for the environmental factors affecting the phothsynthetic pathways of the species studied.

Abstract

We studied carbon stable isotopic composition (δ13C) of bulk leaf tissue and extracted sugars of four epiphytic Tillandsia species to investigate flexibility in the use of crassulacean acid metabolism (CAM) and C3 photosynthetic pathways. Plants growing in two seasonally-dry tropical forest reserves in Mexico that differ in annual precipitation were measured during wet and dry seasons, and among secondary, mature, and wetland forest types within each site. Dry season sugars were more enriched in 13C than wet season sugars, but there was no seasonal difference in bulk tissues. Bulk tissue δ13C differed by species and by forest type, with values from open-canopied wetlands more enriched in 13C than mature or secondary forest types. The shifts within forest habitat were related to temporal and spatial changes in vapour pressure deficits (VPD). Modeling results estimate a possible 4% increase in the proportional contribution of the C3 pathway during the wet season, emphasizing that any seasonal or habitat-mediated variation in photosynthetic pathway appears to be quite moderate and within the range of isotopic effects caused by variation in stomatal conductance during assimilation through the C3 pathway and environmental variation in VPD. Carbon isotopic analysis of sugars together with bulk leaf tissue offer a useful approach for incorporating short- and long-term measurements of carbon isotope discrimination during photosynthesis.

My advisor, Edward Bedrick, provided a wonderful introduction that reminded me of the many ways I’ve connected with the UNM community: teaching (and awards), research assistantships (and publications), statistics department consultant, collaborations, and unicycling around campus those first few years.

I was generally happy with my presentation and was grateful that Ed reminded me occasionally of points that I wanted to make but had forgotten to mention.  He was also my champion whenever I tried to sell myself short, interjecting and saying that a particular point I was glossing over is nontrivial in theory and application.  I had some positive feedback and am learning how to give better talks.

My committee’s questions really helped me feel more like a peer, asking questions with the expectation that I might provide insight on a topic.  They never asked a question with the goal to stump me or make me feel small.  I felt supported, respected, encouraged, and welcomed into PhD family.

What next?  I’m now a postdoc at the Mind Research Network on the UNM campus in Albuquerque doing modeling for fMRI brain imaging data, see previous post.  I’m completing my dissertation to submit to the university, writing papers from the dissertation, and organizing work on my other collaborations.  These next couple years are going to challenging and exciting, frustrating and daunting, engaging and inspiring, and I’d better also say peaceful and tranquil so my mom thinks I’m getting some rest, too.  Carpe data!

Title: Stable Isotope Sourcing using Sampling

Abstract:

Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets, plant nutrient use, geochemistry, pollution, and forensics. We focus on animal ecology because of the particular complexities due to the process of digestion and assimilation. Parameter estimation has been a challenge because there are often many sources and few isotopes leading to an underconstrained linear system for the diet probability vector. This dissertation off ers three primary contributions to the mixing model community. (1) We detail and provide an R implementation of a better algorithm (SISUS) for representing possible solutions in the underconstrained case (many sources, few isotopes) when no variance is considered (Phillips and Gregg, 2003). (2) We provide general methods for performing frequentist estimation in the perfectly-constrained case using the delta method and the bootstrap, which extends previous work applying the delta method to two- and three-source problems (Phillips and Gregg, 2001). (3) We propose two Bayesian models, the implicit representation model estimating the population mean diet through the mean mixture isotope ratio, and the explicit representation model estimating the population mean diet through mixture-specific diets given individual isotope ratios. Secondary contributions include (4) estimation using summaries from the literature in lieu of observation-level data, (5) multiple methods for incorporating isotope ratio discrimination (fractionation) in the analysis, (6) the use of measurement error to account for and partition more uncertainty, (7) estimation improvements by pooling multiple estimates, and (8) detailing scenarios when one model is preferred over another. We show that the Bayesian explicit representation model provides more precise diet estimates than other models when measurement error is small and informed by the necessary calibration measurements.

The Wishart distribution commonly arises in the context of the Bayesian multivariate normal model, so first I detail that model. Let -dimensional vector , , be independent with , where is a (column) vector of length and is a covariance matrix, which is symmetric and positive definite. The conjugate priors for the parameters are , where is the prior mean and is the number of prior measurements on the scale, and , where is the prior covariance matrix with degrees of freedom.

I’ll follow the definition of the Wishart and inverse-Wishart distributions used in BDA and Wikipedia. if , with expected value (notation: indicates the trace of the transpose of matrix ) . The integral is finite when and the density is finite if . In this definition and are on the same scale. and are both covariance matrices when modeling a sample covariance matrix. Specifically, the scatter matrix , where , has , where is the population covariance and is the sample size informing . and are both precision matrices when modeling the inverse covariance (precision) matrix () of the normal model above. That is , where is the inverse of the prior population covariance and can be thought of as the prior sample size. If (where is a covariance matrix and and are precision matrices) then .  Now, with and as covariance matricies, if , with expected value .

One point of confusion was when using WinBUGS and OpenBUGS to fit the Bayesian multivariate normal model. I’ll call the WinBUGS parameterization of the Wishart the “BUGS-Wishart” with a covariance parameter () and a precision random matrix () (these are on inverse scales from one another, where our standard definition above had the parameter and random matrix on the same scale). if , with expected value . By itself, this was straightforward for the parameter to be on the covariance scale rather than the precision scale, however it becomes misleading when incorporated into the normal model. For example, I expected a non-informative prior to have covariance matrix with large diagonal elements. But the following example shows the need for a Bayesian to write down the full posterior to see how the prior and likelihoods combine in the posterior.

The second point of confusion was in the prior distributional specification for precision matrix in the OpenBUGS Camel example. This example has replicated structured missing bivariate data at , but four complete-data points at the corners of a square () centered at zero. The camel example is named so because the posterior for (rho) has two humps; two of the complete observations support correlation and two support correlation . The data has a normal distribution parameterized with precision matrix, , as , and precision matrix , where . The correlation is calculated from by inverting to get the covariance matrix and taking the off-diagonal covariance divided by the square root of the product of the on-diagonal variances. Because the BUGS-Wishart distribution has a covariance parameter , I was surprised at their use of a noninformative prior parameter matrix with diagonal values of 0.001 (instead of variance values of 1000). Hal Stern helped clarify why the prior they use is the correct one by writing out the posterior distribution based on the BUGS-Wishart density and the normal likelihood (which is parameterized in terms of precision ). Looking at the exponential kernel of the BUGS-Wishart distribution, we have . The kernel of the likelihood is , where is the “sums of squares” relative to . Thus, when the prior and likelihood are combined in the posterior, the posterior for is now , which is BUGS-Wishart with parameter . This is the correct noninformative analysis since the near-zero matrix from the prior contributes little to the sum with informed by the data. However, it requires looking ahead to the form of the posterior to see that the parameter for the BUGS-Wishart needs to be specified as a precision matrix INSTEAD of a covariance matrix! Dave Lunn (first author of the reference for WinBUGS) has written to me that he will try to clarify matters in the next version. I would like to see a second version of the Wishart implemented in WinBUGS and OpenBUGS with the parameter and the random matrix on the same scale, as in BDA, to avoid this awkward inverse specification of the parameter for the BUGS-Wishart distribution.

This is the first in a series of papers that will be the result of my research assistantship Fall 2005 — Spring 2008 with Seymour Grufferman, M.D., Dr. P.H. and Deirdre A. Hill, Ph.D., M.P.H. at the Cancer Research and Treatment Center, University of New Mexico, Albuquerque, NM 87131, USA.  More results are likely to come from this rich dataset.

Vince Calhoun, Edward Bedrick (my stat advisor), Jeremy Bockholt, and I compose the Biostatistics & NeuroInformatics Core (STATNI) on UNM’s Center of Biomedical Research Excellence (COBRE) grant funded by the National Center for Research Resources (NCRR), a part of the National Institutes of Health (NIH).  STATNI serves as a centralized resource for biostatistical consulting for a number of scientific projects. My role will be in the development of statistical methods and programming numerical and statistical methods to address the aims of the projects.  Specifically, the development of a Bayesian ICA model for fMRI data.

There are five aims to the project that will ultimately extend the ability to incorporate prior information to move beyond the semi-blind ICA approach. [from the project summary] First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner.

This is an ideal position for me because the modeling is similar to work I have done in my dissertation, I continue to work closely with my advisor, Ed, who I continue to learn so much from, I get to learn and work with Vince who has many ideas and is very prolific, and all of this in the hot area of fMRI.  I also have family and friends in Albuquerque who I want to stay close with for a little longer and this position allows me to stay put.

Chris Bickford, PhD candidate UNM Biology, and I met when we attended Iso-Camp at Jim Ehleringer’s lab at U Utah Summer 2008.  On the flight home we started discussing a challenge he was facing in his first of three dissertation papers. He studies details of plant photosynthesis.  He had complicated expressions for leaf carbon isotope discrimination and internal conductance based on CO concentrations of CO isotopologues and . He needed to propigate the variation of the CO measurements into his variables of interest, and .  He also needed to compare his accurate and precise measurements using tunable diode laser spectroscopy (TDL) to predictions from three models.

There were a number of statistical issues.  One was how to make model and observation comparisons.  I suggested using RMSE since it includes both variance and bias in the single measurement.  The main issue was the incorporation of variation from the CO measurements into the quantities of interest.  The bootstrap allowed us to do this.  There were a number of programming sessions in R to write functions and scripts to do all the calculations, create plots, output spreadsheets of results, and so on.  Chris has become a convert from Excel to R over the course of this project.  These methods implemented on this paper will likely flow into later pubs for both Chris and Dave.

Chris has taken a postdoc in New Zealand, where he and his wife, Karen, will spend the next two years with their dog.  He defends his dissertation on April 13th.