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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | xyza11808/MATLAB-master | writeNPY.m | .m | MATLAB-master/npy-matlab/writeNPY.m | 470 | utf_8 | 561adf7ce2f8b0bdf0e611a5b1aeabe1 |
function writeNPY(var, filename)
% function writeNPY(var, filename)
%
% Only writes little endian, fortran (column-major) ordering; only writes
% with NPY version number 1.0.
%
% Always outputs a shape according to matlab's convention, e.g. (10, 1)
% rather than (10,).
shape = size(var);
dataType = class(var);
hea... |
github | xyza11808/MATLAB-master | readNPYheader.m | .m | MATLAB-master/npy-matlab/readNPYheader.m | 2,089 | utf_8 | b7f1965e929221519e65ec7b8e74ebde |
function [arrayShape, dataType, fortranOrder, littleEndian, totalHeaderLength, npyVersion] = readNPYheader(filename)
% function [arrayShape, dataType, fortranOrder, littleEndian, ...
% totalHeaderLength, npyVersion] = readNPYheader(filename)
%
% parse the header of a .npy file and return all the info contained
... |
github | xyza11808/MATLAB-master | readNPY.m | .m | MATLAB-master/npy-matlab/readNPY.m | 878 | utf_8 | f674d6ff7e59396e231b3d2e93919f97 |
function data = readNPY(filename)
% Function to read NPY files into matlab.
% *** Only reads a subset of all possible NPY files, specifically N-D arrays of certain data types.
% See https://github.com/kwikteam/npy-matlab/blob/master/tests/npy.ipynb for
% more.
%
[shape, dataType, fortranOrder, littleEndian, totalHea... |
github | xyza11808/MATLAB-master | tsdata_to_autocov_debias.m | .m | MATLAB-master/mvgc_v1.0/experimental/tsdata_to_autocov_debias.m | 2,317 | utf_8 | e2ed3a0b91a56111389800c831e0fe06 | %% tsdata_to_autocov_debias (EXPERIMENTAL)
%
% Calculate sample autocovariance sequence from time series data with debias
%
% <matlab:open('tsdata_to_autocov_debias.m') code>
%
%% Syntax
%
% G = tsdata_to_autocov_debias(X,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
... |
github | xyza11808/MATLAB-master | mvgc_kpss.m | .m | MATLAB-master/mvgc_v1.0/experimental/mvgc_kpss.m | 2,279 | utf_8 | 76d75955e542218c5fbb8fe5b76837ae | %% mvgc_kpss (EXPERIMENTAL)
%
% KPSS unit root stationarity test
%
% <matlab:open('mvgc_kpss.m') code>
%
%% Syntax
%
% [ksstat,cval] = mvgc_kpss(X,alpha,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% alpha... |
github | xyza11808/MATLAB-master | mvgc_adf.m | .m | MATLAB-master/mvgc_v1.0/experimental/mvgc_adf.m | 8,867 | utf_8 | 8f8156d7dd0b85549607698b5a8d6da2 | %% mvgc_adf (EXPERIMENTAL)
%
% Augmented Dickey-Fuller unit root stationarity test
%
% <matlab:open('mvgc_adf.m') code>
%
%% Syntax
%
% [tstat,cval] = mvgc_adf(X,alpha,q,pdeg)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time ser... |
github | xyza11808/MATLAB-master | var5_test.m | .m | MATLAB-master/mvgc_v1.0/demo/var5_test.m | 814 | utf_8 | 16c2518e911adc36597882c9024e93b8 | %% var5_test
%
% Create VAR coefficients for 5-node test network
%
% <matlab:open('var5_test.m') code>
%
%% Syntax
%
% A = var5_test
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _output_
%
% A VAR coefficients matrix for 5-node test network
%
%% Descripti... |
github | xyza11808/MATLAB-master | var9_test.m | .m | MATLAB-master/mvgc_v1.0/demo/var9_test.m | 3,157 | utf_8 | 403814f0a6fc9ea6dc2b7e7956399c8e | %% var9_test
%
% Create VAR coefficients for 9-node test network
%
% <matlab:open('var9_test.m') code>
%
%% Syntax
%
% A = var9_test
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _output_
%
% A VAR coefficients matrix for 9-node test network
%
%% Descripti... |
github | xyza11808/MATLAB-master | isbad.m | .m | MATLAB-master/mvgc_v1.0/utils/isbad.m | 1,174 | utf_8 | 20c8291c71e30e810b3967cbea37a794 | %% isbad
%
% Determine whether array is "bad"
%
% <matlab:open('isbad.m') code>
%
%% Syntax
%
% b = isbad(x)
%
%% Arguments
%
% _input_
%
% x an array
% demand_allfinite true (default) if "bad" means at least one NaN or Inf
%
% _output_
%
% b logical scalar
%
%% Descrip... |
github | xyza11808/MATLAB-master | get_urand.m | .m | MATLAB-master/mvgc_v1.0/utils/get_urand.m | 1,189 | utf_8 | 4557748490c1a532aeb5a25ee0cd6c5e | %% get_urand
%
% Read high-entropy random numbers from from |/dev/urandom| (Unix and Mac only)
%
% <matlab:open('get_urand.m') code>
%
%% Syntax
%
% u = get_urand(n)
%
%% Arguments
%
% _input_
%
% n number of random numbers (default: 1)
%
% _output_
%
% u random numbers (double)
%
%% Descr... |
github | xyza11808/MATLAB-master | secs2hms.m | .m | MATLAB-master/mvgc_v1.0/utils/secs2hms.m | 701 | utf_8 | 727ddd09786d5efae57d8e1854b202eb | %% secs2hms
%
% Convert time in seconds to hours/minutes/seconds
%
% <matlab:open('secs2hms.m') code>
%
%% Syntax
%
% [h,m,s] = secs2hms(t)
%
%% Arguments
%
% _input_
%
% t time in seconds
%
% _output_
%
% h hours
% m minutes
% s seconds
%
%% Description
%
% Retur... |
github | xyza11808/MATLAB-master | var2trfun.m | .m | MATLAB-master/mvgc_v1.0/utils/var2trfun.m | 1,480 | utf_8 | 1209a5a72c45dc6568e089b0ade66bfe | %% var2trfun
%
% Calculate VAR transfer function from VAR coefficients
%
% <matlab:open('var2trfun.m') code>
%
%% Syntax
%
% H = var2trfun(A,fres)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% fres frequenc... |
github | xyza11808/MATLAB-master | plot_confints.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_confints.m | 2,371 | utf_8 | c5951f00bed55ff36fb75921a80d7741 | %% plot_confints
%
% Confidence intervals plotting utility for pairwise-conditional causalities
%
% <matlab:open('plot_confints.m') code>
%
%% Syntax
%
% plot_confints(F,FUP,FLO,FCRIT)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% F matrix of p... |
github | xyza11808/MATLAB-master | helpon.m | .m | MATLAB-master/mvgc_v1.0/utils/helpon.m | 1,186 | utf_8 | bdaf6e2cc80554f39d40858056323845 | %% helpon
%
% Display the <mvgcfuncref.html Function Reference> page for an MVGC function or
% script in the Matlab Help Browser.
%
% <matlab:open('get_hostname.m') code>
%
%% Syntax
%
% helpon mname
% helpon(mname)
%
%% Arguments
%
% _input_
%
% mname string; the name of an MVGC function or script
%
%... |
github | xyza11808/MATLAB-master | rng_seed.m | .m | MATLAB-master/mvgc_v1.0/utils/rng_seed.m | 1,846 | utf_8 | 167e1671f2d5d95276fc8c5c97b879b8 | %% rng_seed
%
% Seed the Matlab default (global) random number generator
%
% <matlab:open('rng_seed.m') code>
%
%% Syntax
%
% state = rng_seed(seed)
%
%% Arguments
%
% _input_
%
% seed random seed: an integer in the range [0, 2^32 ? 1], or a negative number
%
% _output_
%
% state previous rng sta... |
github | xyza11808/MATLAB-master | mvgc_makemex.m | .m | MATLAB-master/mvgc_v1.0/utils/mvgc_makemex.m | 5,619 | utf_8 | ba049fcf7af2a9ab59d4988e5205e80a | %% mvgc_makemex
%
% Build MVGC |mex| files
%
% <matlab:open('mvgc_makemex.m') code>
%
%% Syntax
%
% mvgc_makemex(force_recompile,verbose)
%
%% Arguments
%
% _input_
%
% force_recompile forced recompilation flag (default: false)
% verbose verbosity flag (default: false)
%
%% Description
%
% Builds ... |
github | xyza11808/MATLAB-master | plot_pw.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_pw.m | 1,363 | utf_8 | 0c32f8b90d2acddce778cef18d1d25c4 | %% plot_pw
%
% Plot pairwise quantities on a colourmapped grid
%
% <matlab:open('plot_pw.m') code>
%
%% Syntax
%
% plot_pw(P,cm)
%
%% Arguments
%
% _input_
%
% P square matrix of pairwise quantities
% cm colour map (default: something soothing)
%
%% Description
%
% Plot pairwise quantities ... |
github | xyza11808/MATLAB-master | plot_tsdata.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_tsdata.m | 1,845 | utf_8 | a2f78babed8ebea9e7a3b984765ed940 | %% plot_tsdata
%
% Time series data plotting utility
%
% <matlab:open('plot_tsdata.m') code>
%
%% Syntax
%
% plot_tsdata(X,leg,dt,trange)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% leg cell vector ... |
github | xyza11808/MATLAB-master | get_hostname.m | .m | MATLAB-master/mvgc_v1.0/utils/get_hostname.m | 1,765 | utf_8 | dd8e5a621e977a98763a02cb97c46968 | %% get_hostname
%
% Get system host name
%
% <matlab:open('get_hostname.m') code>
%
%% Syntax
%
% hostname = get_hostname
%
%% Arguments
%
% _output_
%
% hostname string; system short host name
%
%% Description
%
% Returns system short hostname for Unix, Mac and Windows machines. This is
% handy if you run Ma... |
github | xyza11808/MATLAB-master | ptoc.m | .m | MATLAB-master/mvgc_v1.0/utils/ptoc.m | 1,183 | utf_8 | 8814e9347b0fbc5f270014986ccf79fb | %% ptoc
%
% Stop timer and print message
%
% <matlab:open('ptoc.m') code>
%
%% Syntax
%
% ptoc(s1,s2,inhms)
%
%% Arguments
%
% _input_
%
% s1 pre-time message string
% 21 post-time message string
% inhms use `timestr' time formatting utility to print time
%
%% Description
%
% Simple... |
github | xyza11808/MATLAB-master | plot_spw.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_spw.m | 1,999 | utf_8 | fb93c530222f47cdadbeb449feb870c7 | %% plot_spw
%
% Plot spectral pairwise quantities on a grid
%
% <matlab:open('plot_spw.m') code>
%
%% Syntax
%
% plot_spw(P,fs)
%
%% Arguments
%
% _input_
%
% P matrix of spectral pairwise quantities
% fs sample rate in Hz (default: normalised freq as per routine 'sfreqs')
% frange ... |
github | xyza11808/MATLAB-master | isposdef.m | .m | MATLAB-master/mvgc_v1.0/utils/isposdef.m | 803 | utf_8 | 7dfee3b229746de91f739995fce098e9 | %% isposdef
%
% Determine whether (symmetric) matrix is positive-definite
%
% <matlab:open('isposdef.m') code>
%
%% Syntax
%
% pd = isposdef(A)
%
%% Arguments
%
% _input_
%
% A a symmetric matrix
%
% _output_
%
% pd logical true if A is positive-definite
%
%% Description
%
% Returns true if... |
github | xyza11808/MATLAB-master | var_specrad.m | .m | MATLAB-master/mvgc_v1.0/utils/var_specrad.m | 1,785 | utf_8 | a80363616e72db468d0154a1d1a94d5b | %% var_specrad
%
% Calculate VAR spectral radius
%
% <matlab:open('var_specrad.m') code>
%
%% Syntax
%
% rho = var_specrad(A)
% [A,rho]= var_specrad(A,newrho)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% ... |
github | xyza11808/MATLAB-master | warn_supp.m | .m | MATLAB-master/mvgc_v1.0/utils/warn_supp.m | 1,682 | utf_8 | dfdb8f623ad9bd090f258c8d1790ac35 | %% warn_supp
%
% Suppress printing of Matlab warning message
%
% <matlab:open('warn_supp.m') code>
%
%% Syntax
%
% oldstate = warn_supp(warnid)
%
%% Arguments
%
% _input_
%
% warnid Matlab warning identifier (default: 'all', for all warnings)
%
% _output_
%
% oldstate previous state of warning corresp... |
github | xyza11808/MATLAB-master | bifft.m | .m | MATLAB-master/mvgc_v1.0/utils/bifft.m | 1,106 | utf_8 | 157dfe4b4a4ea47a73f660ba75227408 | %% bifft
%
% "Block" Inverse Fast Fourier Transform (IFFT)
%
% <matlab:open('bifft.m') code>
%
%% Syntax
%
% AF = bifft(A,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A n1 x n2 x p matrix
% q frequency resolution (default: p)
... |
github | xyza11808/MATLAB-master | genvar.m | .m | MATLAB-master/mvgc_v1.0/utils/genvar.m | 2,125 | utf_8 | ec96ff04ad6c586aacfd1187d143eb43 | %% genvar
%
% Generate VAR time series data
%
% <matlab:open('genvar.m') code>
%
%% Syntax
%
% [X,E] = genvar(A,E,trunc)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% E residuals (single-trial) time seri... |
github | xyza11808/MATLAB-master | cov2corr.m | .m | MATLAB-master/mvgc_v1.0/utils/cov2corr.m | 889 | utf_8 | d3376a5657b68d81a6db2c731f5aa10e | %% cov2corr
%
% Convert (auto)covariance to (auto)correlation
%
% <matlab:open('cov2corr.m') code>
%
%% Syntax
%
% R = cov2corr(G)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G a covariance matrix or autocovariance sequence
%
% _output_
%
% ... |
github | xyza11808/MATLAB-master | plot_autocov.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_autocov.m | 3,554 | utf_8 | 93d9c52fa5fc435f71789e5ad1fe6589 | %% plot_autocov
%
% Autocovariance plotting utility
%
% <matlab:open('plot_autocov.m') code>
%
%% Syntax
%
% plot_autocov(G,leg,dt,trange,auto,acorr)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G matrix of autocovariance matrices
% leg ... |
github | xyza11808/MATLAB-master | rng_restore.m | .m | MATLAB-master/mvgc_v1.0/utils/rng_restore.m | 910 | utf_8 | eb3ebcaf71c14488d58c82500fefdcd1 | %% rng_restore
%
% Restore the Matlab default (global) random number generator state
%
% <matlab:open('rng_restore.m') code>
%
%% Syntax
%
% rng_restore(state)
%
%% Arguments
%
% _input_
%
% state previous rng state
%
%% Description
%
% Restore the Matlab default (global) <matlab:doc('rng') |random number
... |
github | xyza11808/MATLAB-master | var_normalise.m | .m | MATLAB-master/mvgc_v1.0/utils/var_normalise.m | 1,141 | utf_8 | ab5c767038b835c82422df1beaedf4ff | %% var_normalise
%
% Normalise VAR coefficients
%
% <matlab:open('var_normalise.m') code>
%
%% Syntax
%
% [A,SIG] = var_normalise(A,SIG)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% SIG residuals covarian... |
github | xyza11808/MATLAB-master | plot_varcoeffs.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_varcoeffs.m | 971 | utf_8 | 5752697b57111d9561e25baf90fa0694 | %% plot_varcoeffs
%
% VAR coefficients plotting utility
%
% <matlab:open('plot_varcoeffs.m') code>
%
%% Syntax
%
% plot_varcoeffs(A)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
%
%% Description
%
% Plots VAR coeffici... |
github | xyza11808/MATLAB-master | timestr.m | .m | MATLAB-master/mvgc_v1.0/utils/timestr.m | 829 | utf_8 | c9294425a9f581c18563544d5815b26e | %% timestr
%
% Format time string in hours/minutes/seconds
%
% <matlab:open('timestr.m') code>
%
%% Syntax
%
% tstr = timestr(t)
%
%% Arguments
%
% _input_
%
% t time (seconds)
%
% _output_
%
% tstr formatted time string
%
%% Description
%
% Return formatted string |tstr| of time |t| (assumed... |
github | xyza11808/MATLAB-master | isint.m | .m | MATLAB-master/mvgc_v1.0/utils/isint.m | 589 | utf_8 | 3e0f8ec8c22d51e8017a4fe7e3070051 | %% isint
%
% Determine whether numerical values are integers
%
% <matlab:open('isint.m') code>
%
%% Syntax
%
% I = isint(x)
%
%% Arguments
%
% _input_
%
% x a numeric array
%
% _output_
%
% I a logical array
%
%% Description
%
% Simple routine that returns a logical array with logical |tru... |
github | xyza11808/MATLAB-master | warn_test.m | .m | MATLAB-master/mvgc_v1.0/utils/warn_test.m | 3,767 | utf_8 | 8be1cd9c82e8938ceb1089644e6f1308 | %% warn_test
%
% Test for (suppressed) Matlab warning message
%
% <matlab:open('warn_test.m') code>
%
%% Syntax
%
% [waswarn,msgstr,warnid] = warn_test(oldstate, false)
% [waswarn,msgstr,warnid] = warn_test(oldstate, warnmsg, warnfunc,rwarnid)
%
%% Arguments
%
% _input_
%
% oldstate old warning state to r... |
github | xyza11808/MATLAB-master | warn_if.m | .m | MATLAB-master/mvgc_v1.0/utils/warn_if.m | 1,538 | utf_8 | 1c974238ea3c926060c6d5a60ce195de | %% warn_if
%
% Test a condition and warn if true
%
% <matlab:open('warn_if.m') code>
%
%% Syntax
%
% wcond = warn_if(wcond,msgstr,warnfunc)
%
%% Arguments
%
% _input_
%
% wcond condition to test
% msgstr string specifying warning message
% warnfunc string specifying which function issued the ... |
github | xyza11808/MATLAB-master | mvdetrend.m | .m | MATLAB-master/mvgc_v1.0/utils/mvdetrend.m | 2,736 | utf_8 | 5e6e961dd57be7ef62d06914aaba553a | %% mvdetrend
%
% Multivariate polynomial detrend of time series data
%
% <matlab:open('mvdetrend.m') code>
%
%% Syntax
%
% [Y,P,p,x] = mvdetrend(X,pdeg,x)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% pdeg ... |
github | xyza11808/MATLAB-master | trfun2var.m | .m | MATLAB-master/mvgc_v1.0/utils/trfun2var.m | 1,625 | utf_8 | 1a58b228ad41ba0355eadc4691bbf4c2 | %% trfun2var
%
% Calculate coefficients from VAR transfer function
%
% <matlab:open('trfun2var.m') code>
%
%% Syntax
%
% [A,p] = trfun2var(H,p)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% H VAR transfer function matrix
% p VAR mo... |
github | xyza11808/MATLAB-master | quads.m | .m | MATLAB-master/mvgc_v1.0/utils/quads.m | 1,045 | utf_8 | 9e747bca832eb464417b852552d97853 | %% quads
%
% Trapezoidal rule quadrature (numerical integration)
%
% <matlab:open('quads.m') code>
%
%% Syntax
%
% q = quads(x,Y)
%
%% Arguments
%
% _input_
%
% x vector of evaluation points
% Y matrix of values corresponding to x
%
% _output_
%
% q vector of quadratures
%
%% ... |
github | xyza11808/MATLAB-master | var_info.m | .m | MATLAB-master/mvgc_v1.0/utils/var_info.m | 1,900 | utf_8 | e032ee6f98f6b8ca1540b2e0b832b525 | %% var_info
%
% Display VAR information as returned by |var_to_autocov|
%
% <matlab:open('var_info.m') code>
%
%% Syntax
%
% acerr = var_info(info,abort_on_error)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% info info structure returned by v... |
github | xyza11808/MATLAB-master | plot_cpsd.m | .m | MATLAB-master/mvgc_v1.0/utils/plot_cpsd.m | 3,019 | utf_8 | f7a329aabde6016458cd485ae39557ea | %% plot_cpsd
%
% Cross-power spectral density plotting utility
%
% <matlab:open('plot_cpsd.m') code>
%
%% Syntax
%
% plot_cpsd(S,leg,fs,frange,auto)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% S matrix of cpsd matrices
% leg cell v... |
github | xyza11808/MATLAB-master | var_decay.m | .m | MATLAB-master/mvgc_v1.0/utils/var_decay.m | 1,208 | utf_8 | 2137dcf74625237f27fd47ef67226565 | %% var_decay
%
% Decay VAR coefficients
%
% <matlab:open('var_decay.m') code>
%
%% Syntax
%
% A = var_decay(A,dfac)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% dfac decay factor
%
% _output_
%
% A ... |
github | xyza11808/MATLAB-master | mvdiff.m | .m | MATLAB-master/mvgc_v1.0/utils/mvdiff.m | 1,547 | utf_8 | 1078056add9e3027431ad437a055b560 | %% mvdiff
%
% Multivariate differencing
%
% <matlab:open('mvdiff.m') code>
%
%% Syntax
%
% Y = mvdiff(X,dff)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% dff vector of number of differencing steps (d... |
github | xyza11808/MATLAB-master | quadsr.m | .m | MATLAB-master/mvgc_v1.0/utils/quadsr.m | 2,863 | utf_8 | a7b7ec5b5bac16a276d9573b12e74cb7 | %% quadsr
%
% Trapezoidal rule quadrature (numerical integration) with sub-range specification
%
% <matlab:open('quadsr.m') code>
%
%% Syntax
%
% [q,xrsupp] = quadsr(x,y,xrange)
%
%% Arguments
%
% _input_
%
% x vector of evaluation points
% y vector of values corresponding to x
% xrang... |
github | xyza11808/MATLAB-master | get_crand.m | .m | MATLAB-master/mvgc_v1.0/utils/get_crand.m | 1,185 | utf_8 | 6da0973e1722dad430208878f8031d75 | %% get_crand
%
% Generate a high-entropy random number from clock time
%
% <matlab:open('get_crand.m') code>
%
%% Syntax
%
% u = get_crand
%
%% Arguments
%
% _output_
%
% u a random number (double)
%
%% Description
%
% Generates a 32-bit integer from clock time via an
% <http://www.mathworks.com/matlab... |
github | xyza11808/MATLAB-master | bfft.m | .m | MATLAB-master/mvgc_v1.0/utils/bfft.m | 1,074 | utf_8 | a80ff25ab617114a397d047a5de58f4c | %% bfft
%
% "Block" Fast Fourier Transform (FFT)
%
% <matlab:open('bfft.m') code>
%
%% Syntax
%
% AF = bfft(A,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A n1 x n2 x p matrix
% q frequency resolution (default: p)
%
% _output_... |
github | xyza11808/MATLAB-master | rng_save.m | .m | MATLAB-master/mvgc_v1.0/utils/rng_save.m | 684 | utf_8 | 0d20df50e5c4e3b535f9e9460d1b45bc | %% rng_save
%
% Save the Matlab default (global) random number generator state
%
% <matlab:open('rng_save.m') code>
%
%% Syntax
%
% state = rng_save
%
%% Arguments
%
% _output_
%
% state current rng state
%
%% Description
%
% Save the Matlab default (global) <matlab:doc('rng') |random number generator|>
% ... |
github | xyza11808/MATLAB-master | maxabs.m | .m | MATLAB-master/mvgc_v1.0/utils/maxabs.m | 544 | utf_8 | 95c78563dfdf474b816c6065663e6f2e | %% maxabs
%
% Calculate maximum absolute value of all entries in an array
%
% <matlab:open('maxabs.m') code>
%
%% Syntax
%
% d = maxabs(X)
%
%% Arguments
%
% _input_
%
% X an array
%
% _output_
%
% d maximum absolute value of all entries in X
%
%% Description
%
% Returns the maximum absolu... |
github | xyza11808/MATLAB-master | sfreqs.m | .m | MATLAB-master/mvgc_v1.0/utils/sfreqs.m | 940 | utf_8 | e226700d8ef5455ac178f89190c00a86 | %% sfreqs
%
% Return vector of frequencies
%
% <matlab:open('sfreqs.m') code>
%
%% Syntax
%
% freqs = sfreqs(fres,fs)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% fres frequency resolution
% fs sample rate (default: 2*pi)
%
% _output_... |
github | xyza11808/MATLAB-master | dlyap_aitr.m | .m | MATLAB-master/mvgc_v1.0/utils/dlyap_aitr.m | 2,826 | utf_8 | 83d7a513eb1410e203512fc238ed7279 | %% dlyap_aitr
%
% Solve discrete-time Lyapunov equation by Smith's accelerated iterative method
%
% <matlab:open('dlyap_itr.m') code>
%
%% Syntax
%
% [X,iters] = dlyap_itr(A,Q,maxiters,maxrelerr)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A ... |
github | xyza11808/MATLAB-master | ptic.m | .m | MATLAB-master/mvgc_v1.0/utils/ptic.m | 641 | utf_8 | 0a2558b88b83533a6ac42ba96362f3d4 | %% ptic
%
% Print message and start timer
%
% <matlab:open('ptic.m') code>
%
%% Syntax
%
% ptic(s)
%
%% Arguments
%
% _input_
%
% s message string
%
%% Description
%
% Simple wrapper for Matlab <matlab:doc('tic') |tic|> timer function. Print
% message string |s| and start a timer. Stop timer with <ptoc... |
github | xyza11808/MATLAB-master | dlyap.m | .m | MATLAB-master/mvgc_v1.0/utils/control/dlyap.m | 1,840 | utf_8 | 63f1be73cfcfbd93b0d40c5e31f44a4e | %% dlyap
%
% Solve discrete-time Lyapunov equation by Schur decomposition
%
% <matlab:open('dlyap.m') code>
%
%% Syntax
%
% X = dlyap(A,Q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A square matrix with spectral radius < 1
% Q s... |
github | xyza11808/MATLAB-master | make_legacy.m | .m | MATLAB-master/mvgc_v1.0/utils/legacy/make_legacy.m | 2,040 | utf_8 | 2d2fafb7084ce6142038ea91efc521ef | function mdir = make_legacy(tdir)
global mvgc_root;
if nargin < 1 || isempty(tdir), tdir = [tempdir 'mvgc_legacy']; end
fprintf('Populating target directory...');
syscmd = ['cp -r ' mvgc_root filesep '* ' tdir];
status = system(syscmd,'-echo');
if status == 0
fprintf(' done\n');
else
fprintf(2,' failed\n');
... |
github | xyza11808/MATLAB-master | mvgc_cval.m | .m | MATLAB-master/mvgc_v1.0/stats/mvgc_cval.m | 1,997 | utf_8 | e82823288af2ba9bdaa5e8c114ff8ecf | %% mvgc_cval
%
% Critical values for sample MVGC based on theoretical asymptotic null distribution
%
% <matlab:open('mvgc_cval.m') code>
%
%% Syntax
%
% x = mvgc_cval(alpha,p,m,N,nx,ny,nz,tstat)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% alpha v... |
github | xyza11808/MATLAB-master | mvgc_cdfi.m | .m | MATLAB-master/mvgc_v1.0/stats/mvgc_cdfi.m | 4,470 | utf_8 | adb384db88a84ea51ce4e60e6cdaef69 | %% mvgc_cdfi
%
% Sample MVGC thoretical asymptotic inverse cumulative distribution function
%
% <matlab:open('mvgc_cdfi.m') code>
%
%% Syntax
%
% x = mvgc_cdfi(P,X,p,m,N,nx,ny,nz,tstat)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% P vector of ... |
github | xyza11808/MATLAB-master | empirical_confint.m | .m | MATLAB-master/mvgc_v1.0/stats/empirical_confint.m | 2,155 | utf_8 | ca816310300fff45f86366dd66fb40d8 | %% empirical_confint
%
% Confidence intervals for sample statistics based on estimated empirical null distribution
%
% <matlab:open('empirical_confint.m') code>
%
%% Syntax
%
% [xup,xlo] = empirical_confint(alpha,X,ptails,ksmooth)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structure... |
github | xyza11808/MATLAB-master | mvgc_cdf.m | .m | MATLAB-master/mvgc_v1.0/stats/mvgc_cdf.m | 5,847 | utf_8 | 8a4482e659e81d1b2afee4e87e4a86d7 | %% mvgc_cdf
%
% Sample MVGC thoretical asymptotic cumulative distribution function
%
% <matlab:open('mvgc_cdf.m') code>
%
%% Syntax
%
% P = mvgc_cdf(x,X,p,m,N,nx,ny,nz,tstat)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% x vector of MVGC values... |
github | xyza11808/MATLAB-master | empirical_pval.m | .m | MATLAB-master/mvgc_v1.0/stats/empirical_pval.m | 2,642 | utf_8 | e4dfe25cf85ed18e805693eff1b7f65c | %% empirical_pval
%
% p-values for sample statistics based on estimated empirical null distribution
%
% <matlab:open('empirical_pval.m') code>
%
%% Syntax
%
% pval = empirical_pval(x,XNULL,ptails,ksmooth)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% x ... |
github | xyza11808/MATLAB-master | empirical_cdfi.m | .m | MATLAB-master/mvgc_v1.0/stats/empirical_cdfi.m | 2,936 | utf_8 | 38ee7409b9304ed93c14a5f53118ef0a | %% empirical_cdfi
%
% Empirical inverse cumulative distribution function
%
% <matlab:open('empirical_cdfi.m') code>
%
%% Syntax
%
% x = empirical_cdfi(P,X,ptails,ksmooth)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% P vector of cumulative dist... |
github | xyza11808/MATLAB-master | demean.m | .m | MATLAB-master/mvgc_v1.0/stats/demean.m | 1,600 | utf_8 | 5d1fbdb6275ec4a75c3362c91301a2f0 | %% demean
%
% Temporal demean of time series data
%
% <matlab:open('demean.m') code>
%
%% Syntax
%
% Y = demean(X,normalise)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% normalise normalise (temporal) vari... |
github | xyza11808/MATLAB-master | infocrit.m | .m | MATLAB-master/mvgc_v1.0/stats/infocrit.m | 1,379 | utf_8 | a4f0924e8997c01af30f7cecb65ba167 | %% infocrit
%
% Calculate Akaike and Bayesian information criteria
%
% <matlab:open('infocrit.m') code>
%
%% Syntax
%
% [aic,bic] = infocrit(L,k,m)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% L maximum log-likelihood
% k number o... |
github | xyza11808/MATLAB-master | mvgc_pval.m | .m | MATLAB-master/mvgc_v1.0/stats/mvgc_pval.m | 2,195 | utf_8 | 5b0620cedd9649505aaf7bd2a799fc04 | %% mvgc_pval
%
% p-values for sample MVGC based on theoretical asymptotic null distribution
%
% <matlab:open('mvgc_pval.m') code>
%
%% Syntax
%
% pval = mvgc_pval(x,p,m,N,nx,ny,nz,tstat)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% x matrix of... |
github | xyza11808/MATLAB-master | consistency.m | .m | MATLAB-master/mvgc_v1.0/stats/consistency.m | 2,057 | utf_8 | 011aa3d3210389a3f05b61f750aa96d8 | %% consistency
%
% Calculate VAR model consistency
%
% <matlab:open('consistency.m') code>
%
%% Syntax
%
% cons = consistency(X,E)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% E residuals time seri... |
github | xyza11808/MATLAB-master | whiteness.m | .m | MATLAB-master/mvgc_v1.0/stats/whiteness.m | 2,596 | utf_8 | e7ecfd90b3ab506b71d493fbf81c6e39 | %% whiteness
%
% Durbin-Watson test for whiteness (no serial correlation) of VAR residuals
%
% <matlab:open('whiteness.m') code>
%
%% Syntax
%
% [dw,pval] = whiteness(X,E)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series ... |
github | xyza11808/MATLAB-master | empirical_cval.m | .m | MATLAB-master/mvgc_v1.0/stats/empirical_cval.m | 1,957 | utf_8 | 126e85729db7dc3c844516b9c979be55 | %% empirical_cval
%
% Critical values for sample statistics based on estimated empirical null distribution
%
% <matlab:open('empirical_cval.m') code>
%
%% Syntax
%
% x = empirical_cval(alpha,XNULL,ptails,ksmooth)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
... |
github | xyza11808/MATLAB-master | rsquared.m | .m | MATLAB-master/mvgc_v1.0/stats/rsquared.m | 1,596 | utf_8 | 456ad51415fed453d272c9df6007e7c0 | %% rsquared
%
% [R^2] and adjusted [R^2] statistics
%
% <matlab:open('rsquared.m') code>
%
%% Syntax
%
% [RSQ,RSQADJ] = rsquared(X,E)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% E residuals time s... |
github | xyza11808/MATLAB-master | significance.m | .m | MATLAB-master/mvgc_v1.0/stats/significance.m | 6,604 | utf_8 | 8b62617f58bc8af288e5838edf02c3c8 | %% significance
%
% Statistical significance adjusted for multiple hypotheses
%
% <matlab:open('significance.m') code>
%
%% Syntax
%
% sig = significance(pval,alpha,correction)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% pval multi-trial time ... |
github | xyza11808/MATLAB-master | empirical_cdf.m | .m | MATLAB-master/mvgc_v1.0/stats/empirical_cdf.m | 2,867 | utf_8 | 7878035ca468041acf6fcbee0ef9a875 | %% empirical_cdf
%
% Empirical cumulative distribution function
%
% <matlab:open('empirical_cdf.m') code>
%
%% Syntax
%
% P = empirical_cdf(x,X,ptails,ksmooth)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% x vector of statistic values
% X ... |
github | xyza11808/MATLAB-master | mvgc_confint.m | .m | MATLAB-master/mvgc_v1.0/stats/mvgc_confint.m | 2,362 | utf_8 | 6b44c63a08ea43ddffc53f550375e023 | %% mvgc_confint
%
% Confidence intervals for sample MVGC based on theoretical asymptotic distribution
%
% <matlab:open('mvgc_confint.m') code>
%
%% Syntax
%
% [xup,xlo] = mvgc_confint(alpha,x,p,m,N,nx,ny,nz,tstat)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%... |
github | xyza11808/MATLAB-master | smvgc_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/smvgc_to_mvgc.m | 2,582 | utf_8 | d72ff84f1c6da361be14f5662f652d28 | %% smvgc_to_mvgc
%
% Average (integrate) frequency-domain causality over specified frequency range
%
% <matlab:open('smvgc_to_mvgc.m') code>
%
%% Syntax
%
% F = smvgc_to_mvgc(f,B)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% f spectral (freque... |
github | xyza11808/MATLAB-master | autocov_to_pwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/autocov_to_pwcgc.m | 2,467 | utf_8 | 0e6625851668341ae20002fd38e441ff | %% autocov_to_pwcgc
%
% Calculate pairwise-conditional time-domain MVGCs (multivariate Granger causalities)
%
% <matlab:open('autocov_to_pwcgc.m') code>
%
%% Syntax
%
% F = autocov_to_pwcgc(G)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G aut... |
github | xyza11808/MATLAB-master | autocov_to_smvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/autocov_to_smvgc.m | 6,365 | utf_8 | 4427b515e784b1154f2070651b8ad16b | %% autocov_to_smvgc
%
% Calculate conditional frequency-domain MVGC (spectral multivariate Granger causality)
%
% <matlab:open('autocov_to_smvgc.m') code>
%
%% Syntax
%
% [f,fres] = autocov_to_smvgc(G,x,y,fres,useFFT)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _inpu... |
github | xyza11808/MATLAB-master | autocov_to_spwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/autocov_to_spwcgc.m | 4,510 | utf_8 | 6fa5e55968d93893d9b4f3cc5075de0c | %% autocov_to_spwcgc
%
% Calculate pairwise-conditional frequency-domain MVGCs (spectral multivariate Granger causalites)
%
% <matlab:open('autocov_to_spwcgc.m') code>
%
%% Syntax
%
% [f,fres] = autocov_to_spwcgc(G,fres,useFFT)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.... |
github | xyza11808/MATLAB-master | autocov_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/autocov_to_mvgc.m | 2,568 | utf_8 | 652bff1d61d3c20c44572a30bed0b449 | %% autocov_to_mvgc
%
% Calculate conditional time-domain MVGC (multivariate Granger causality)
%
% <matlab:open('autocov_to_mvgc.m') code>
%
%% Syntax
%
% F = autocov_to_mvgc(G,x,y)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G autocovariance... |
github | xyza11808/MATLAB-master | permtest_tsdata_to_smvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/permtest_tsdata_to_smvgc.m | 4,834 | utf_8 | c9574265656c85db7c85a4559eb0f439 | %% permtest_tsdata_to_smvgc
%
% Calculate null distribution for conditional frequency-domain MVGC from time series
% data, based on a permutation test
%
% <matlab:open('permtest_tsdata_to_smvgc.m') code>
%
%% Syntax
%
% fP = permtest_tsdata_to_smvgc(U,x,y,p,fres,bsize,nsamps,regmode,acmaxlags,acdectol)
%
%% Argument... |
github | xyza11808/MATLAB-master | empirical_var_to_pwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/empirical_var_to_pwcgc.m | 5,413 | utf_8 | 5ec8a0f774b97f2c2956d4253081b798 | %% empirical_var_to_pwcgc
%
% Calculate sampling distribution for pairwise-conditional time-domain MVGCs
% from generated time series data for a specified VAR model
%
% <matlab:open('empirical_var_to_pwcgc.m') code>
%
%% Syntax
%
% FE = empirical_var_to_pwcgc(A,SIG,m,N,H0,nsamps,mtrunc,decayfac,regmode,acmaxlags,ac... |
github | xyza11808/MATLAB-master | bootstrap_tsdata_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/bootstrap_tsdata_to_mvgc.m | 4,654 | utf_8 | 1d1076f00e7b46caf7bcc6be2f10d546 | %% bootstrap_tsdata_to_mvgc
%
% Calculate sampling distribution for conditional time-domain MVGC from time
% series data, based on a nonparametric bootstrap
%
% <matlab:open('bootstrap_tsdata_to_mvgc.m') code>
%
%% Syntax
%
% FB = bootstrap_tsdata_to_mvgc(U,x,y,p,nsamps,acmaxlags,acdectol)
%
%% Arguments
%
% See al... |
github | xyza11808/MATLAB-master | permtest_tsdata_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/permtest_tsdata_to_mvgc.m | 4,254 | utf_8 | 892e60f4d730e189e216ed7885a31b23 | %% permtest_tsdata_to_mvgc
%
% Calculate null distribution for conditional time-domain MVGC from time series
% data, based on a permutation test
%
% <matlab:open('permtest_tsdata_to_mvgc.m') code>
%
%% Syntax
%
% FP = permtest_tsdata_to_mvgc(U,x,y,p,bsize,nsamps,regmode,acmaxlags,acdectol)
%
%% Arguments
%
% See al... |
github | xyza11808/MATLAB-master | bootstrap_tsdata_to_smvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/bootstrap_tsdata_to_smvgc.m | 5,231 | utf_8 | b9a3cf5a9d849de1139e3ac484fb5c86 | %% bootstrap_tsdata_to_smvgc
%
% Calculate sampling distribution for conditional frequency-domain MVGC from
% time series data, based on a nonparametric bootstrap
%
% <matlab:open('bootstrap_tsdata_to_smvgc.m') code>
%
%% Syntax
%
% fB = bootstrap_tsdata_to_smvgc(U,x,y,p,fres,nsamps,acmaxlags,acdectol)
%
%% Argumen... |
github | xyza11808/MATLAB-master | empirical_var_to_spwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/empirical_var_to_spwcgc.m | 5,938 | utf_8 | 2eb70b1ad148bdf1f4fe94f0c5562011 | %% empirical_var_to_spwcgc
%
% Calculate sampling distribution for pairwise-conditional frequency-domain
% MVGCs from generated time series data for a specified VAR model
%
% <matlab:open('empirical_var_to_spwcgc.m') code>
%
%% Syntax
%
% fE = empirical_var_to_spwcgc(A,SIG,m,N,fres,H0,nsamps,mtrunc,decayfac,regmode... |
github | xyza11808/MATLAB-master | bootstrap_tsdata_to_pwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/bootstrap_tsdata_to_pwcgc.m | 4,430 | utf_8 | d1172b67762b10120805e6ff66e5d685 | %% bootstrap_tsdata_to_pwcgc
%
% Calculate sampling distribution for pairwise-conditional time-domain MVGCs
% from time series data, based on a nonparametric bootstrap
%
% <matlab:open('bootstrap_tsdata_to_pwcgc.m') code>
%
%% Syntax
%
% FB = bootstrap_tsdata_to_pwcgc(U,p,nsamps,acmaxlags,acdectol)
%
%% Arguments
... |
github | xyza11808/MATLAB-master | bootstrap_tsdata_to_spwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/bootstrap_tsdata_to_spwcgc.m | 4,823 | utf_8 | 25125e4b0475901bd6fd9d99ef968b6d | %% bootstrap_tsdata_to_spwcgc
%
% Calculate sampling distribution for pairwise-conditional frequency-domain MVGCs
% from time series data, based on a nonparametric bootstrap
%
% <matlab:open('bootstrap_tsdata_to_spwcgc.m') code>
%
%% Syntax
%
% fB = bootstrap_tsdata_to_spwcgc(U,p,fres,nsamps,acmaxlags,acdectol)
%
%... |
github | xyza11808/MATLAB-master | empirical_var_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/empirical_var_to_mvgc.m | 4,538 | utf_8 | 85f0c56cf987d12e2d68707cf264acec | %% empirical_var_to_mvgc
%
% Calculate sampling distribution for conditional time-domain MVGC from
% generated time series data for a specified VAR model
%
% <matlab:open('empirical_var_to_mvgc.m') code>
%
%% Syntax
%
% FE = empirical_var_to_mvgc(A,SIG,m,N,x,y,H0,nsamps,mtrunc,decayfac,regmode,acmaxlags,acdectol)
%... |
github | xyza11808/MATLAB-master | permtest_tsdata_to_pwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/permtest_tsdata_to_pwcgc.m | 4,143 | utf_8 | b1775535bb8baab28488bcdb01935fa7 | %% permtest_tsdata_to_pwcgc
%
% Calculate null distribution for pairwise-conditional time-domain MVGCs from time series
% data, based on a permutation test
%
% <matlab:open('permtest_tsdata_to_pwcgc.m') code>
%
%% Syntax
%
% FP = permtest_tsdata_to_pwcgc(U,p,bsize,nsamps,regmode,acmaxlags,acdectol)
%
%% Arguments
%
... |
github | xyza11808/MATLAB-master | empirical_var_to_smvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/empirical_var_to_smvgc.m | 5,115 | utf_8 | 27c300cc46f97199b0d706043c6fd8f9 | %% empirical_var_to_smvgc
%
% Calculate sampling distribution for conditional frequency-domain MVGC from
% generated time series data for a specified VAR model
%
% <matlab:open('empirical_var_to_smvgc.m') code>
%
%% Syntax
%
% fE = empirical_var_to_smvgc(A,SIG,m,N,x,y,fres,H0,nsamps,mtrunc,decayfac,regmode,acmaxlag... |
github | xyza11808/MATLAB-master | permtest_tsdata_to_spwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/subsample/permtest_tsdata_to_spwcgc.m | 5,253 | utf_8 | e80167f9417e81424731ab83862943a2 | %% permtest_tsdata_to_spwcgc
%
% Calculate null distribution for pairwise-conditional frequency-domain MVGCs
% from time series data, based on a permutation test
%
% <matlab:open('permtest_tsdata_to_spwcgc.m') code>
%
%% Syntax
%
% fP = permtest_tsdata_to_spwcgc(U,p,fres,bsize,nsamps,regmode,acmaxlags,acdectol)
%
%%... |
github | xyza11808/MATLAB-master | GCCA_tsdata_to_pwcgc.m | .m | MATLAB-master/mvgc_v1.0/gc/GCCA_compat/GCCA_tsdata_to_pwcgc.m | 5,013 | utf_8 | bb348489d5be46266852fb1d43c4ca1a | %% GCCA_tsdata_to_pwcgc
%
% Calculate pairwise-conditional time-domain MVGCs (multivariate Granger
% causality) from time series data by "traditional" method (as e.g. in GCCA
% toolbox)
%
% <matlab:open('GCCA_tsdata_to_pwcgc.m') code>
%
%% Syntax
%
% [F,A,SIG,E] = GCCA_tsdata_to_pwcgc(X,p,regmode)
%
%% Arguments
%
... |
github | xyza11808/MATLAB-master | GCCA_tsdata_to_mvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/GCCA_compat/GCCA_tsdata_to_mvgc.m | 4,176 | utf_8 | 66480c6ac0df124c44d56b7a156b76ee | %% GCCA_tsdata_to_mvgc
%
% Calculate conditional time-domain MVGC (multivariate Granger causality) from time series data by
% "traditional" method (as e.g. in GCCA toolbox)
%
% <matlab:open('GCCA_tsdata_to_mvgc.m') code>
%
%% Syntax
%
% F = GCCA_tsdata_to_mvgc(U,x,y,p,regmode)
%
%% Arguments
%
% See also <mvgchelp.... |
github | xyza11808/MATLAB-master | GCCA_tsdata_to_smvgc.m | .m | MATLAB-master/mvgc_v1.0/gc/GCCA_compat/GCCA_tsdata_to_smvgc.m | 5,164 | utf_8 | ce9332887bc2e89552ad796b86d1a893 | %% GCCA_tsdata_to_smvgc
%
% Calculate _unconditional_ frequency-domain MVGC (spectral multivariate
% Granger causality) from time series data by "traditional" method (as e.g.
% in GCCA toolbox)
%
% <matlab:open('GCCA_tsdata_to_smvgc.m') code>
%
%% Syntax
%
% f = GCCA_tsdata_to_smvgc(U,x,y,p,fres,regmode)
%
%% Argum... |
github | xyza11808/MATLAB-master | var_to_tsdata.m | .m | MATLAB-master/mvgc_v1.0/core/var_to_tsdata.m | 3,078 | utf_8 | d7125126b3eece6088f577a762bd3f91 | %% var_to_tsdata
%
% Generate random multi-trial Gaussian VAR time series
%
% <matlab:open('var_to_tsdata.m') code>
%
%% Syntax
%
% [X,E,mtrunc] = var_to_tsdata(A,SIG,m,N,mtrunc,decayfac)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coef... |
github | xyza11808/MATLAB-master | var_to_cpsd.m | .m | MATLAB-master/mvgc_v1.0/core/var_to_cpsd.m | 1,577 | utf_8 | e8220776b5cbc0ce28f6079e2af5b993 | %% var_to_cpsd
%
% Calculate cross-power spectral density and transfer function from VAR
% parameters
%
% <matlab:open('var_to_cpsd.m') code>
%
%% Syntax
%
% [S,H] = var_to_cpsd(A,SIG,fres)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR co... |
github | xyza11808/MATLAB-master | cpsd_xform.m | .m | MATLAB-master/mvgc_v1.0/core/cpsd_xform.m | 2,048 | utf_8 | 9274dc19aa7d715839cfe89649c76a85 | %% cpsd_xform
%
% Transform cross-power spectral density for reduced regression
%
% <matlab:open('cpsd_xform.m') code>
%
%% Syntax
%
% SR = cpsd_xform(S,AR,SIGR)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% S cross-power spectral density (cpsd... |
github | xyza11808/MATLAB-master | var_to_autocov.m | .m | MATLAB-master/mvgc_v1.0/core/var_to_autocov.m | 9,607 | utf_8 | 98bfe42271bd9ebbdd640bf9b1bf3323 | %% var_to_autocov
%
% Return autocovariance sequence for a VAR model
%
% <matlab:open('var_to_autocov.m') code>
%
%% Syntax
%
% [G,info] = var_to_autocov(A,SIG,acmaxlags,acdectol,aitr,maxiters,maxrelerr)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A ... |
github | xyza11808/MATLAB-master | autocov_to_cpsd.m | .m | MATLAB-master/mvgc_v1.0/core/autocov_to_cpsd.m | 1,985 | utf_8 | d1495f6040d151c87924fc830234b6b9 | %% autocov_to_cpsd
%
% Calculate cross-power spectral density from autocovariance sequence
%
% <matlab:open('autocov_to_cpsd.m') code>
%
%% Syntax
%
% [S,fres] = autocov_to_cpsd(G,fres)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G autocovari... |
github | xyza11808/MATLAB-master | tsdata_to_var.m | .m | MATLAB-master/mvgc_v1.0/core/tsdata_to_var.m | 6,173 | utf_8 | 9c66b353a3f55b2be292aa6d2746a43e | %% tsdata_to_var
%
% Fit VAR model to multi-trial, multivariate time series data
%
% <matlab:open('tsdata_to_var.m') code>
%
%% Syntax
%
% [A,SIG,E] = tsdata_to_var(X,p,regmode)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time s... |
github | xyza11808/MATLAB-master | autocov_to_var.m | .m | MATLAB-master/mvgc_v1.0/core/autocov_to_var.m | 3,934 | utf_8 | 26e3510317cd1b12a4516a576e13c984 | %% autocov_to_var
%
% Calculate VAR parameters from autocovariance sequence
%
% <matlab:open('autocov_to_var.m') code>
%
%% Syntax
%
% [A,SIG] = autocov_to_var(G)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G autocovariance sequence
%
% _outp... |
github | xyza11808/MATLAB-master | cpsd_to_var.m | .m | MATLAB-master/mvgc_v1.0/core/cpsd_to_var.m | 4,331 | utf_8 | 0b6cb5657ba557fe521922e0701dfe5c | %% cpsd_to_var
%
% Spectral factorisation: calculate VAR parameters from cross-power spectral density
%
% <matlab:open('cpsd_to_var.m') code>
%
%% Syntax
%
% [H,SIG,iters] = cpsd_to_var(S,G0,maxiters,numtol)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% ... |
github | xyza11808/MATLAB-master | autocov_xform.m | .m | MATLAB-master/mvgc_v1.0/core/autocov_xform.m | 4,841 | utf_8 | d7bc98b9dbd87d83f88bf3bbda9c082d | %% autocov_xform
%
% Transform autocovariance sequence for reduced regression
%
% <matlab:open('autocov_xform.m') code>
%
%% Syntax
%
% G = autocov_xform(G,AR,SIGR,useFFT)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% G autocovariance sequence
... |
github | xyza11808/MATLAB-master | cpsd_to_autocov.m | .m | MATLAB-master/mvgc_v1.0/core/cpsd_to_autocov.m | 2,175 | utf_8 | fe135cfe5793e6957bfcd6e511bd6d63 | %% cpsd_to_autocov
%
% Calculate autocovariance sequence from cross-power spectral density
%
% <matlab:open('cpsd_to_autocov.m') code>
%
%% Syntax
%
% [G,q] = cpsd_to_autocov(S,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% S cross-power spec... |
github | xyza11808/MATLAB-master | tsdata_to_autocov.m | .m | MATLAB-master/mvgc_v1.0/core/tsdata_to_autocov.m | 2,514 | utf_8 | 469ce99c45a2b73123dc241dac8ebe93 | %% tsdata_to_autocov
%
% Calculate sample autocovariance sequence from time series data
%
% <matlab:open('tsdata_to_autocov.m') code>
%
%% Syntax
%
% G = tsdata_to_autocov(X,q)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time se... |
github | xyza11808/MATLAB-master | tsdata_to_infocrit.m | .m | MATLAB-master/mvgc_v1.0/core/tsdata_to_infocrit.m | 9,519 | utf_8 | 3e8be3633a761d30e95745b75d65e50a | %% tsdata_to_infocrit
%
% Calculate Akaike and Bayesian information criteria for VAR models from time series
% data
%
% <matlab:open('tsdata_to_infocrit.m') code>
%
%% Syntax
%
% [aic,bic,moaic,mobic] = tsdata_to_infocrit(X,morder,regmode,verb)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and ... |
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