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Articles 1 - 18 of 18
Full-Text Articles in Entire DC Network
Msis-Glenn: Natural Selection In Wolves Leads To Domesticated Dogs Predicted By Agent-Based Model Simulations, Alex Capaldi, David C. Elzinga
Msis-Glenn: Natural Selection In Wolves Leads To Domesticated Dogs Predicted By Agent-Based Model Simulations, Alex Capaldi, David C. Elzinga
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
A Course In Data Science: R And Prediction Modeling, Adam Kapelner
A Course In Data Science: R And Prediction Modeling, Adam Kapelner
Open Educational Resources
This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.
Supplementary Files For "Creating A Universal Depth-To-Load Conversion Technique For The Conterminous United States Using Random Forests", Jesse Wheeler, Brennan Bean, Marc Maguire
Supplementary Files For "Creating A Universal Depth-To-Load Conversion Technique For The Conterminous United States Using Random Forests", Jesse Wheeler, Brennan Bean, Marc Maguire
Browse all Datasets
As part of an ongoing effort to update the ground snow load maps in the United States, this paper presents an investigation into snow densities for the purpose of predicting ground snow loads for structural engineering design with ASCE 7. Despite their importance, direct measurements of snow load are sparse when compared to measurements of snow depth. As a result, it is often necessary to estimate snow load using snow depth and other readily accessible climate variables. Existing depth-to-load conversion methods, each of varying complexity, are well suited for snow load estimation for a particular region or station network, but …
Where On Ice? Algorithmically Deconstructing Nhl Shot Locations As A Method For Player Classification, Devan Becker, Douglas G. Woolford, Charmaine B. Dean
Where On Ice? Algorithmically Deconstructing Nhl Shot Locations As A Method For Player Classification, Devan Becker, Douglas G. Woolford, Charmaine B. Dean
Western Research Forum
Where do hockey players shoot from? How does this vary from player to player? We present the results of a study that uses data-driven statistical methods to investigate these questions. The locations of shots by National Hockey League (NHL) players from 2011 to 2017 are analyzed using a combination of an image recognition algorithm and spatial statistical methodology. An unsupervised classifier is applied to output from a spatial point process model in order to determine which shot locations best characterize a given player. We define the number of regions a priori, but the image recognition algorithm chooses the shape …
Comparing Methods Of Measuring Chaos In The Symbolic Dynamics Of Strange Attractors, James J. Scully
Comparing Methods Of Measuring Chaos In The Symbolic Dynamics Of Strange Attractors, James J. Scully
Georgia State Undergraduate Research Conference
No abstract provided.
Pglr-Sas Data, Joseph M. Hilbe
Pglr-Sas Data, Joseph M. Hilbe
Joseph M Hilbe
SAS data files for Practical Guide to Logistic Regression
R Code For Practical Guide To Logistic Regression, Joseph M. Hilbe
R Code For Practical Guide To Logistic Regression, Joseph M. Hilbe
Joseph M Hilbe
R code for Practical Guide to Logistic Regression
Pglr-Stata Data Files, Joseph M. Hilbe
Pglr-Stata Data Files, Joseph M. Hilbe
Joseph M Hilbe
Stata data files for Practical Guide to Logistic Regression
Demonstration Databases (Supplemental To Psychology & Health Article), Blair T. Johnson
Demonstration Databases (Supplemental To Psychology & Health Article), Blair T. Johnson
CHIP Documents
Here is a database (in Stata, R, SAS, SPSS formats) that was used to demonstrate simple slopes analysis in meta-regression in an online supplement to the article, "Panning for the gold in health research: Incorporating studies’ methodological quality in meta-analysis," published in the journal Psychology & Health in 2014. It is an archive (zip) file that also contains the Stata syntax used in the demonstrations.
Sas Macro: Weighted Kappa Statistic For Clustered Matched-Pair Ordinal Data, Zhao Yang
Sas Macro: Weighted Kappa Statistic For Clustered Matched-Pair Ordinal Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
This SAS macro calculate the weighted kappa statistic and its corresponding non-parametric variance estimator for the clustered matched-pair ordinal data.
Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang
Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
This SAS macro calculate the kappa statistic and its semi-parametric variance estimator for the clustered physician-patients polytomous data. The proposed method depends on the assumption of conditional independence for the clustered physician-patients data structure.
Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara
Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara
George McNamara
Hamamatsu FLASH4.0 scientific cMOS camera exposure time series are pairs of images of:
1 millisecond (00,001ms series)
10 millisecond (00,010ms series)
100 millisecond (00,100ms series)
1,000 millisecond (01,000ms series)
4,000 millisecond (04,000ms series)
10,000 millisecond (10,000ms series)
I also included:
* difference images (exposure 2 minus exposure 1 plus 100 intensity values).
* a series of eleven 1 second (1,000 ms) exposure time images in a multi-plane TIFF file (different images than the pair of 1,000ms images above).
* Stack Arithmetic: Median, Average, Minimum, Maximum, of the eleven plane series (Stack Arithmetic is a MetaMorph command).
These images were acquired …
Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang
Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
The SAS macro was developed to calculate the kappa statistic for the clustered matched-pair data.
Glme3_Ado_Do_Files, Joseph Hilbe
R Code: A Non-Iterative Implementation Of Tango's Score Confidence Interval For A Paired Difference Of Proportions, Zhao Yang
Zhao (Tony) Yang, Ph.D.
For matched-pair binary data, a variety of approaches have been proposed for the construction of a confidence interval (CI) for the difference of marginal probabilities between two procedures. The score-based approximate CI has been shown to outperform other asymptotic CIs. Tango’s method provides a score CI by inverting a score test statistic using an iterative procedure. In the developed R code, we propose an efficient non-iterative method with closed-form expression to calculate Tango’s CIs. Examples illustrate the practical application of the new approach.
Nbr2 Stata Ado-Do Files, Joseph Hilbe
Windows Executable For Gaussian Copula With Nbd Margins, Michael S. Smith
Windows Executable For Gaussian Copula With Nbd Margins, Michael S. Smith
Michael Stanley Smith
This is an example Windows 32bit program to estimate a Gaussian copula model with NBD margins. The margins are estimated first using MLE, and the copula second using Bayesian MCMC. The model was discussed in Danaher & Smith (2011; Marketing Science) as example 4 (section 4.2).
Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston
Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston
Joseph M Hilbe
SAS Macro to estimate censored Poisson data, using method of Hilbe. See Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed (Cambridge University Press)