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Philip E. Higuera - One of the best experts on this subject based on the ideXlab platform.
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Matlab code for a Weibull likelihood-ratio test
2014Co-Authors: Philip E. HigueraAbstract:Matlab code for a Weibull likelihood-ratio test The Matlab function wbl_LRT.m compares n distributions of data using a likelihood-ratio test, based on the maximum-likelihood values of a Weibull distribution fit to the input data. Details on the test are in Appendix B in Higuera et al. (2009), and details on how the function works are below. Philip E. Higuera, Linda B. Brubaker, Patricia M. Anderson, Feng Sheng Hu, and Thomas A. Brown. 2009. Vegetation mediated the impacts of postglacial climate change on fire regimes in the south-central Brooks Range, Alaska. Ecological Monographs 79:201–219. README: To use the function wbl_LRT.m, save the file in the Working Directory or in the search path of Matlab. You must pass the function three variables: 1. FRI_data: observed (fire) return intervals organized in columns, where each column (j) represents one population that will be compared to all other populations, and each row (i) is an observed return interval. Empty cells must contain “NaN’, such that the final matrix has no blank values. 2. alpha: specifies the significance level for the test, e.g. 0.05. 3. n_perm: specifies the number of permutations to use in the permutation test to estimate theprobability of Type I error, p. If sample sizes are large (e.g. > 30), you can set n_perm equal to 0, in which case p is calculated from a Chi-squared distribution. For example, in Matlab, you would define these variables, and then enter in the command line: [H,P,N] = wbl_LRT (FRI_data,alpha,n_perm) After the function has run (which can take a few minutes, depending on the value of n_perm), the program returns two matrices, H and P, where each row (i) corresponds to populations 1 through n-1, where n is the total number of populations being compared (i.e. columns in FRI_data), and each column (j) corresponds to populations 2 through n. The values in the matrix are the results of comparing population i to population j, and therefore half of the matrix will be blank (NaN). The variable P contains the probability of Type I error, and thematrix H contains a “1” where the probability of Type I error is < alpha, and a 0 otherwise. The variable N contains the number of return intervals in each population (column) in FRI_data. The symbol “%” signifies code that is commented out. Text following “%” is to be read to help understand the function.
José Miguel Ponciano - One of the best experts on this subject based on the ideXlab platform.
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Supplement 1. R scripts for calculating maximum likelihood estimates, restricted maximum likelihood estimates, and parametric bootstrap likelihood ratio tests of density dependence, for the Ornstein-Uhlenbeck state-space model, using population-abund
2016Co-Authors: Brian Dennis, José Miguel PoncianoAbstract:File List RUNNING-ROUSS.R (MD5: c07b061d09a71a54d0a0f70844ef9d77) RUNNING-PBLRT.R (MD5: f786fefc7684bd3bd099a1f682babaa5) ROUSSE-1.0.R (MD5: d3ced6b948020ce1231da2262e3f0648) Description This supplement contains three R scripts: (1) RUNNING-ROUSS.R calculates maximum likelihood and restricted maximum likelihood estimates for the Ornstein Uhlenbeck state space model (stationary and non-stationary), using population-abundance data having possibly unequal observation time intervals. (2) RUNNING-PBLRT.R performs parametric bootstrap likelihood ratio test of exponential growth state space model vs. Ornstein-Uhlenbeck state space model. (3) ROUSSE-1.0.R contains the functions used in statistical inferences for the Ornstein-Uhlenbeck state space model. ROUSSE-1.0.R is called by both RUNNING-ROUSS.R and RUNNING-PBLRT.R and must be present (as a file by that name) in the Working Directory of R.
Bruce Becker - One of the best experts on this subject based on the ideXlab platform.
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AAROC/CODE-RADE-container: DevOps for CODE-RADE - Build Container role
2017Co-Authors: Bruce BeckerAbstract:CODE-RADE Build Container A somewhat OS-independent role to build CODE-RADE slaves for use in a continuous integration environment, using Ansible-Container. Adds a CODE-RADE build container service to your Ansible Container project. To be used in conjunction with CODE-RADE Build Containers. For galaxy info, see meta/main.yml Run the following commands to install the service: # Set the Working Directory to your Ansible Container project root $ cd myproject # Install the service $ ansible-container install AAROC.code-rade-build-containers Requirements Ansible Container An existing Ansible Container project. To create a project, simply run the following: # Create an empty project Directory $ mkdir myproject # Set the Working Directory to the new Directory $ cd myproject # Initialize the project $ ansible-contiainer init Role Variables Variables are all in vars/main.yml : modules_path - The OS-dependent path where environment-modules keeps its base configuration by default. modules - CODE-RADE specific modulefiles ci and deploy which set up the build, test and deploy shells. These contain OS-specific variables, using anisble_os_family module_domains - The domain-specific modulefile paths to which CODE-RADE can write application modulefiles. Contain : astronomy bioinformatics compilers languages libraries physical_sciences hep prerequisites: OS-specific dependencies that need to be in the build environment in order to execute compilation and tests. Intentionally kept small. Dependencies None License Apache-2.0 Author Information Bruce Becker | bbecker@csir.co.za | C.S.I.R. Meraka Institute Africa-Arabia Regional Operations Centre Citing If you use this role in an academic or research context, please cite : Bruce Becker. (2017). AAROC/CODE-RADE-container: DevOps for CODE-RADE - Build Container role [Data set]. Zenodo. http://doi.org/10.5281/zenodo.57227
A. D. Owen - One of the best experts on this subject based on the ideXlab platform.
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RFC 775 Directory oriented FTP commands Page 1 Directory ORIENTED FTP COMMANDS
2014Co-Authors: David Mankins, Dan Franklin, A. D. OwenAbstract:BBN has installed and maintains the software of several DEC PDP-11s running the Unix operating system. Since Unix has a tree-like Directory structure, in which directories are as easy to manipulate as ordinary files, we have found it convenient to expand the FTP servers on these machines to include commands which deal with the creation of directories. Since there are other hosts on the ARPA net which have tree-like directories, including Tops-20 and Multics, we have tried to make these commands as general as possible. We have added four commands to our server: XMKD child Make a Directory with the name "child". XRMD child Remove the Directory with the name "child". XPWD Print the current Working Directory
Matthew D. Holland - One of the best experts on this subject based on the ideXlab platform.
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Supplement 1. MATLAB code used in simulations and analysis.
2016Co-Authors: Wilson J. White, Louis W. Botsford, Alan Hastings, Matthew D. HollandAbstract:File List pacSalmonIterator.m extendVectorToLength.m get_ts_stats.m salmon_ts_SR_comparison.m short_forcing_scenarios.m pacSalmonIterator_single_figures.m read_agedistribution_data_Bristol.m read_agedistribution_data_Fraser.m salmon_ts_diagnostics.m BristolBay_Agedist_Jan2010.mat Fraser_AgeDist_May2010.mat ageStructuredSRSim.m ageStructuredSREq.m checkPersist.m BevertonHolt.m pacSalmonF.m pacSalmonF_fixed.m semelparousFluctuatingLogSurvival.m semelparousFluctuatingLogSurvival_fixed.m Ricker.m wavelet.m chisquare_inv.m chisquare_solve.m coif.m contwt.m invcwt.m wave_bases.m wave_signif.m xcorr_simple.m Description The Matlab script pacSalmonIterator.m is a wrapper that defines parameters and runs the simulations. All of the simulation functions are in the Directory ageStructuredSimulator, which must be a sub-Directory to the Working Directory where pacSalmonIterator.m resides. The range of forcing values and all other parameters used in the paper are given in the manuscript. The mean age at spawning was 4 years, corresponding to Fraser R. stocks. Other files used in simulations, extracting data, etc. The Matlab script extendVectorToLength.m is a helper file when assembling the age-structured population. The Matlab script get_ts_stats.m calculates time series metrics. Given a time series and wavelet, this will calculate: CV_PowerDom_Scale - A measure of cyclic consistency, called 'C' in the ms Mean_maxmin_ratio - A measure of cyclic dominance. Called 'D' in the ms The Matlab script salmon_ts_SR_comparison.m compares time series statistics to position on stock-recruit curve for actual sockeye data (Fig. 6 in the ms). The Matlab script short_forcing_scenarios.m creates the parameters needed to run some short, non-stochastic forcings of different types to see how/if they set up cycles (Fig. 5 in the ms). The Matlab script pacSalmonIterator_single_figures.m runs the simulations specified by short_forcing_scenarios.m and plots the results. The Matlab script read_agedistribution_data_Bristol.m reads in Bristol Bay data and formats it. The Matlab script read_agedistribution_data_Fraser.m reads in Fraser River data and formats it. The Matlab script salmon_ts_diagnostics.m calculates time series metrics for actual sockeye data by calling get_ts_stats.m. Data Files: The Matlab data file BristolBay_Agedist_Jan2010.mat contains age structure data as of Jan 2010 from Randall Peterman. The Matlab data file Fraser_AgeDist_May2010.mat containes age structure data as of May 2010 from Mike LaPointe. Files in ageStructuredSimulator: The Matlab script ageStructuredSRSim.m models the age-structured population dynamics. This is the code that was used for most of the simulations in the paper. This code requires inputting various function handles to define the fecundity and survival functions. These are explained in the text of the m-file. In addition to running the simulations, this file calls wavelet.m to perform wavelet analysis. The Matlab script ageStructuredSREq.m calculates the equilibrium of an age-structured model with density-dependent recruitment. A simplified version of ageStructuredSRSim.m The Matlab script checkPersist.m determines whether a given parameter set has a nonzero equilibrium (i.e., deterministic persistence). Functions that define the demography of the species being modeled These functions are specified as function handles (@function) when calling ageStructuredSRSim.m in pacSalmonIterator.m The Matlab script BevertonHolt.m calculates a stock-recruit curve, given parameters. The Matlab script pacSalmonF.m specifies a fecundity function for salmon. It returns the probability of spawning at each age, not fecundity per se. The Matlab script pacSalmonF_fixed.m specifies a fecundity function, just like pacSalmonF.m. This one allows for user-specified variation at specific times, rather than stochastic variation. The Matlab script semelparousFluctuatingLogSurvival.m calculates survival of an iteroparous species, which varies on the log scale. So S = exp(log(S)+sigma), where sigma is random normal error. The Matlab script semelparousFluctuatingLogSurvival_fixed.m is just like semelparousFluctuatingLogSurvival.m, but allows user-specified variation at specific times, rather than stochastic variation. The Matlab script Ricker.m calculates a stock-recruit curve, given parameters. The Matlab script wavelet.m calculates the wavelet spectrum. Originally written by Terrence and Compo, with some minor modifications. One key modification is to calculate the lag-1 autocorrelation directly from the data set (this is done in ageStructuredSRSim.m) rather than just use the default value, which seems to be from the NINO3 data set. The wavelet.m code uses a number of helper files: chisquare_inv.m chisquare_solve.m coif.m contwt.m invcwt.m wave_bases.m wave_signif.m xcorr_simple.m (if you don't have the signal processing toolbox, this calculates the cross-correlation for you)
