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From: Andres Martin <andres.mrtn@gm...>  20110630 13:17:42

Hi all! I've installed psignifit3 in a Dell xps 501 running under Ubuntu 11.04. When I ran "sudo make test" in the command line, the output said: "73 tests failed" (the details are below). On the other hand, when I ran "test_psignifit" within matlab (64bit 7.10.0 R2010a), the output was a fatal error: >> test_psignifit testing 2afc setting MapEstimate ??? Error: Unexpected MATLAB operator. Error in ==> MapEstimate at 156 eval ( output ); Error in ==> test_psignifit at 9 mapest = MapEstimate ( data2afc, priors ); Any help? Thanks in advance!!! Make test output [ OK ] PsiGumbelL 1st derivative value: 0.00456628 [ OK ] PsiGumbelL 2nd derivative value: 0.0291742 [ OK ] PsiGumbelR 1st derivative value: 0.118205 [ OK ] PsiGumbelR 2nd derivative value: 0.102208 [ OK ] PsiLogistic 1st derivative value: 0.104994 [ OK ] PsiLogistic 2nd derivative value: 0.0799625 [ OK ] Psychometric function 1st derivative w.r.t. prm 0 value: 0.0528291 [ OK ] Psychometric function 1st derivative w.r.t. prm 1 value: 0.0704388 [ OK ] Psychometric function 1st derivative w.r.t. prm 2 value: 0.208609 [ OK ] Psychometric function 2nd derivative w.r.t. prm 0 and 0 value: 0.0205253 [ OK ] Psychometric function 2nd derivative w.r.t. prm 0 and 1 value: 0.00785238 [ OK ] Psychometric function 2nd derivative w.r.t. prm 0 and 2 value: 0.110061 [ OK ] Psychometric function 2nd derivative w.r.t. prm 1 and 0 value: 0.00785238 [ OK ] Psychometric function 2nd derivative w.r.t. prm 1 and 1 value: 0.0574291 [ OK ] Psychometric function 2nd derivative w.r.t. prm 1 and 2 value: 0.146748 [ OK ] Psychometric function 2nd derivative w.r.t. prm 2 and 0 value: 0.110061 [ OK ] Psychometric function 2nd derivative w.r.t. prm 2 and 1 value: 0.146748 [ OK ] Psychometric function 2nd derivative w.r.t. prm 2 and 2 value: 0 [ OK ] psi function at x=0.5 value: 1.96351 [ OK ] psi function at x=5.5 value: 1.61109 [ OK ] psi function at x=10.5 value: 2.303 [ OK ] psi function at x=15.5 value: 2.70824 [ OK ] psi function at x=20.5 value: 2.99584 [ OK ] psi function at x=25.5 value: 3.21894 [ OK ] psi function at x=30.5 value: 3.40124 [ OK ] psi function at x=35.5 value: 3.55538 [ OK ] psi function at x=40.5 value: 3.68891 [ OK ] psi function at x=45.5 value: 3.80668 [ OK ] psi function at x=50.5 value: 3.91204 [ OK ] digamma function at x=0.5 value: 4.9348 [ OK ] digamma function at x=5.5 value: 0.199342 [ OK ] digamma function at x=10.5 value: 0.099917 [ OK ] digamma function at x=15.5 value: 0.066642 [ OK ] digamma function at x=20.5 value: 0.0499896 [ OK ] digamma function at x=25.5 value: 0.0399947 [ OK ] digamma function at x=30.5 value: 0.0333302 [ OK ] digamma function at x=35.5 value: 0.0285695 [ OK ] digamma function at x=40.5 value: 0.0249987 [ OK ] digamma function at x=45.5 value: 0.0222213 [ OK ] digamma function at x=50.5 value: 0.0199993 =============> Solutions of optimizer <============ [ OK ] OptimizerSolution 2AFC alpha value: 3.29751 [ OK ] OptimizerSolution 2AFC beta value: 0.959018 [ OK ] OptimizerSolution 2AFC lambda value: 0.0191278 [ OK ] OptimizerSolution 2AFC deviance value: 3.9847 [ OK ] OptimizerSolution 2AFC deviance sum value: 3.9847 [ OK ] OptimizerSolution 2AFC Rpd value: 0.155845 [ OK ] OptimizerSolution 2AFC Rkd value: 0.319545 [FAIL] OptimizerSolution Y/N alpha value: 3.42566 should be: 3.43942 [FAIL] OptimizerSolution Y/N beta value: 1.02624 should be: 0.988357 [ OK ] OptimizerSolution Y/N lambda value: 0.000437318 [ OK ] OptimizerSolution Y/N gamma value: 0.0240525 [FAIL] OptimizerSolution Y/N deviance value: 2.21098 should be: 2.08172 [ OK ] OptimizerSolution Y/N deviance sum value: 2.21098 [FAIL] OptimizerSolution Y/N Rpd value: 0.339664 should be: 0.217146 [FAIL] OptimizerSolution Y/N Rkd value: 0.350166 should be: 0.477967 [ OK ] Optimizer Solution gamma=lambda, alpha value: 3.32555 [ OK ] Optimizer Solution gamma=lambda, beta value: 1.06769 [FAIL] Optimizer Solution gamma=lambda, lambda value: 0.000327229 should be: 0.000506492 =============> Bootstrap properties <============ [ OK ] Acceleration constant (threshold) value: 0.00033244 < 0.018662 [FAIL] Bias (threshold) value: 0.0677307 should be: 0.118085 [FAIL] th(.1) value: 2.65266 should be: 2.58437 [FAIL] th(.9) value: 3.89757 should be: 3.83427 [ OK ] Acceleration constant (slope) value: 0.00033244 [ OK ] Bias (slope) value: 0.0401168 [ OK ] sl(.1) value: 0.172577 [FAIL] sl(.9) value: 0.497512 should be: 0.545639 [FAIL] Deviance limits value: 9.67016 should be: 8.95713 [ OK ] Rpd( 2.5%) value: 0.532793 [FAIL] Rpd(97.5%) value: 0.632072 should be: 0.495472 [ OK ] Rkd( 2.5%) value: 0.922931 [ OK ] Rkd(97.5%) value: 0.6258 [ OK ] influential 0 good [ OK ] outliers 0 good [ OK ] influential 1 good [ OK ] outliers 1 good [ OK ] influential 2 good [ OK ] outliers 2 good [ OK ] influential 3 good [ OK ] outliers 3 good [ OK ] influential 4 good [ OK ] outliers 4 good [ OK ] influential 5 good [ OK ] outliers 5 good =============> Properties of sigmoids <============ [ OK ] Phi(0) value: 0.5 [ OK ] invPhi(0.5) value: 0 [ OK ] invPhi(Phi(0.3)) value: 0.3 [ OK ] Phi(invPhi(0.3)) value: 0.3 [ OK ] PsiLogistic>f( 0) value: 0.5 [ OK ] PsiLogistic>f(3) value: 0.0474259 < 0.05 [ OK ] PsiLogistic>f( 3) value: 0.952574 > 0.95 [ OK ] PsiLogistic( 3)PsiLogistic(3) value: 0.952574 [ OK ] PsiLogistic>df(3) value: 0.0451767 < 0.05 [ OK ] PsiLogistic>df(3) value: 0.0451767 < 0.05 [ OK ] PsiLogistic monotonically increasing value: 0.00664806 > 0 [ OK ] PsiLogistic>ddf(3) value: 0.0408916 < 0 [ OK ] PsiLogistic>ddf(3) value: 0.0408916 > 0 [ OK ] PsiGauss>f( 0) value: 0.5 [ OK ] PsiGauss>f(3) value: 0.0013499 < 0.01 [ OK ] PsiGauss>f( 3) value: 0.99865 > 0.99 [ OK ] PsiGauss( 3)PsiGauss(3) value: 0.99865 [ OK ] PsiGaussian monotonically increasing value: 1.48672e06 > 0 [ OK ] PsiGauss>df(3) value: 0.00443185 < 0.01 [ OK ] PsiGauss>df(3) value: 0.00443185 < 0.01 [ OK ] PsiGauss>ddf(3) value: 0.0132955 < 0 [ OK ] PsiGauss>ddf(3) value: 0.0132955 > 0 [ OK ] PsiGumbelL>f(0) value: 0.632121 [ OK ] PsiGumbelL>f(0) value: 0.048568 [ OK ] PsiGumbelL>f(0) value: 1 [ OK ] PsiGumbelL( 3 )PsiGumbelL (3 ) value: 1 > 0.951432 [ OK ] PsiGumbelL monotonically increasing value: 5.20543e63 > 0 [ OK ] PsiGumbelL>df(3) value: 3.80054e08 < 0.01 [ OK ] PsiGumbelL>df(3) value: 0.047369 < 0.05 [ OK ] PsiGumbelL>ddf(3) value: 7.25354e07 < 0 [ OK ] PsiGumbelL>ddf(3) value: 0.0450106 > 0 [ OK ] PsiGumbelR>f(0) value: 0.367879 [ OK ] PsiGumbelR>f(0) value: 1.89218e09 [ OK ] PsiGumbelR>f(0) value: 0.951432 [ OK ] PsiGumbelR( 3 )PsiGumbelR (3 ) value: 1 > 0.951432 [ OK ] PsiGumbelR monotonically increasing value: 5.20543e63 > 0 [ OK ] PsiGumbelR>df(3) value: 0.047369 < 0.05 [ OK ] PsiGumbelR>df(3) value: 3.80054e08 < 0.01 [ OK ] PsiGumbelR>ddf(3) value: 0.0450106 < 0 [ OK ] PsiGumbelR>ddf(3) value: 7.25354e07 > 0 [ OK ] PsiCauchy>f(0) value: 0.5 [ OK ] PsiCauchy>f(z(0.1)*(.5) ) value: 0.1 [ OK ] PsiCauchy>f( z(0.9)*.5 ) value: 0.9 [ OK ] PsiCauchy monotonically increasing value: 0.0122427 > 0 [ OK ] PsiCauchy>df(3) value: 0.031831 < 0.05 [ OK ] PsiCauchy>df(3) value: 0.031831 < 0.05 [ OK ] PsiCauchy>ddf(3) value: 0.0190986 < 0 [ OK ] PsiCauchy>ddf(3) value: 0.0190986 > 0 [ OK ] PsiId>f(1) value: 1 [ OK ] PsiId>f(0) value: 0 [ OK ] PsiId>f( 1) value: 1 [ OK ] PsiId>df(1) value: 1 [ OK ] PsiId>df( 0) value: 1 [ OK ] PsiId>df( 1) value: 1 [ OK ] PsiId>ddf(1) value: 0 [ OK ] PsiId>ddf( 0) value: 0 [ OK ] PsiId>ddf( 1) value: 0 [ OK ] PsiId>inv(PsiId>f(1)) value: 1 [ OK ] PsiId>inv(PsiId>f( 0)) value: 0 [ OK ] PsiId>inv(PsiId>f( 1)) value: 1 =============> Tests of core objects <============ [ OK ] abCore at threshold value: 0 [ OK ] abCore derivative stimulus value: 0.5 [ OK ] abCore derivative 0 value: 0.5 [ OK ] abCore derivative 1 value: 0 [ OK ] abCore 2nd derivative 0,0 value: 0 [ OK ] abCore 2nd derivative 0,1 value: 0.25 [ OK ] abCore 2nd derivative 1,1 value: 0 [ OK ] abCore inversion g(inv(2)) value: 2 [ OK ] abCore inversion inv(g(2)) value: 2 [ OK ] abCore inversion dinv(2,0) value: 1 [ OK ] abCore inversion dinv(2,1) value: 2 [ OK ] mwCore at threshold value: 0 [ OK ] mwCore derivative stimulus value: 2.19722 [ OK ] mwCore derivative 0 value: 2.19722 [ OK ] mwCore derivative 1 value: 0 [ OK ] mwCore 2nd derivative 0,0 value: 0 [ OK ] mwCore 2nd derivative 0,1 value: 1.09861 [ OK ] mwCore 2nd derivative 1,1 value: 0 [ OK ] mwCore inversion g(inv(2)) value: 2 [ OK ] mwCore inversion inv(g(2)) value: 2 [ OK ] mwCore inversion dinv(2,0) value: 1 [ OK ] mwCore inversion dinv(2,1) value: 0.45512 [ OK ] mwCore (m) for PsiLogistic value: 0.5 [ OK ] mwCore (w) for PsiLogistic value: 2 [ OK ] mwCore (m) for PsiGauss value: 0.5 [ OK ] mwCore (w) for PsiGauss value: 2 [ OK ] mwCore (m) for PsiGumbelL value: 0.5 [ OK ] mwCore (w) for PsiGumbelL value: 2 [ OK ] mwCore (m) for PsiCauchy value: 0.5 [ OK ] mwCore (w) for PsiCauchy value: 2 [ OK ] mwCore (m) for PsiExponential value: 0.5 [ OK ] mwCore (w) for PsiExponential value: 2 [ OK ] mwCore (m) for PsiGumbelR value: 0.5 [ OK ] mwCore (w) for PsiGumbelR value: 2 [ OK ] linearCore at threshold value: 0 [ OK ] linearCore derivative stimulus value: 3 [ OK ] linearCore derivative(0) at threshold value: 0.666667 [ OK ] linearCore derivative(1) at threshold value: 1 [ OK ] linearCore derivative(0,0) at threshold value: 0 [ OK ] linearCore derivative(0,0) at threshold value: 0 [ OK ] linearCore derivative(0,0) at threshold value: 0 [ OK ] linearCore 2nd derivative symmetry at threshold value: 0 [ OK ] linearCore inverse threshold value: 0.666667 [ OK ] linearCore inverse g(inv(2)) value: 2 [ OK ] linearCore inverse inv(g(2)) value: 2 [ OK ] linearCore inverse derivative(0) value: 0 [ OK ] linearCore inverse derivative(1) value: 0.333333 [ OK ] logCore at threshold value: 0 [ OK ] linearCore derivative stimulus value: 1 [ OK ] logCore derivative(0) at threshold value: 0.666667 [ OK ] logCore derivative(1) at threshold value: 1 [ OK ] logCore derivative(0,0) at threshold value: 0 [ OK ] logCore derivative(1,0) at threshold value: 0 [ OK ] logCore derivative(1,1) at threshold value: 0 [ OK ] logCore 2nd derivative symmetry at threshold value: 0 [ OK ] logCore inverse value: 0.513417 [ OK ] logCore inversion g(inv(2)) value: 2 [ OK ] logCore inversion inv(g(2)) value: 2 [ OK ] logCore inversion dinv(2,0) value: 0 [ OK ] logCore inversion dinv(2,1) value: 0.333333 [ OK ] NakaRushton at threshold value: 0.5 [ OK ] NakaRushton derivative stimulus value: 0.24 [ OK ] NakaRushton inverse value: 4 [ OK ] NakaRushton inversion inv(g(2)) value: 2 [ OK ] NakaRushton inversion g(inv(0.5)) value: 0.5 [ OK ] NakaRushton inversion dinv(2,0) value: 0.5 [ OK ] NakaRushton inversion dinv(2,1) value: 0.693147 =============> MCMC <============ [ OK ] Hybrid MCMC alpha value: 3.5093 [ OK ] Hybrid MCMC beta value: 0.941225 [ OK ] Hybrid MCMC lambda value: 0.0242945 [ OK ] Metropolis Hastings alpha value: 3.13955 [ OK ] Metropolis Hastings beta value: 1.03687 [ OK ] Metropolis Hastings lambda value: 0.0199283 [ OK ] Generic Metropolis MCMC alpha value: 3.2443 [ OK ] Generic Metropolis MCMC beta value: 0.980343 [ OK ] Generic Metropolis MCMC lambda value: 0.0276917 =============> Priors <============ [ OK ] Flat prior at 0 value: 1 [ OK ] Flat prior derivative at 0 value: 0 [ OK ] Uniform prior at 0.5 value: 0 [ OK ] Uniform prior at 0.5 value: 1 [ OK ] Uniform prior at 1.5 value: 0 [ OK ] Uniform prior derivative at 0.5 value: 0 [ OK ] Uniform prior derivative at 0.5 value: 0 [ OK ] Uniform prior derivative at 1.5 value: 0 [ OK ] Gaussian prior at 1 value: 0.241971 [ OK ] Gaussian prior at 0 value: 0.398942 [ OK ] Gaussian prior at 1 value: 0.241971 [ OK ] Gaussian prior derivative at 1 value: 0.241971 [ OK ] Gaussian prior derivative at 0 value: 0 [ OK ] Gaussian prior derivative at 1 value: 0.241971 [ OK ] BetaPrior at 0 value: 0 [ OK ] BetaPrior at 0.1 value: 1.68095 [ OK ] BetaPrior at 0.5 value: 1.1601 [ OK ] BetaPrior at 1.1 value: 0 [ OK ] BetaPrior derivative at 0 value: 0 [ OK ] BetaPrior derivative at 0.1 value: 12.1402 [ OK ] BetaPrior derivative at 0.5 value: 5.80049 [ OK ] BetaPrior derivative at 1.1 value: 0 [ OK ] GammaPrior at 0.5 value: 0 [ OK ] GammaPrior at 0.5 value: 0.12998 [ OK ] GammaPrior at 1.0 value: 0.1556 [ OK ] GammaPrior at 1.5 value: 0.161314 [ OK ] GammaPrior derivative at 0.5 value: 0 [ OK ] GammaPrior derivative at 0.5 value: 0.0866532 [ OK ] GammaPrior derivative at 1.0 value: 0.0259333 [ OK ] GammaPrior derivative at 1.5 value: 0 [ OK ] nGammaPrior at 0.5 value: 0 [ OK ] nGammaPrior at 0.5 value: 0.12998 [ OK ] nGammaPrior at 1.0 value: 0.1556 [ OK ] nGammaPrior at 1.5 value: 0.161314 [ OK ] nGammaPrior derivative at 0.5 value: 0 [FAIL] nGammaPrior derivative at 0.5 value: 0.0866532 should be: 0.0866532 [FAIL] nGammaPrior derivative at 1.0 value: 0.0259333 should be: 0.0259333 [ OK ] nGammaPrior derivative at 1.5 value: 0 =============> Linear algebra routines <============ [ OK ] Inverse (0,0) value: 1.80489 [ OK ] Inverse (1,0) value: 0.677728 [ OK ] Inverse (2,0) value: 0.00780493 [ OK ] Inverse (0,1) value: 0.677728 [ OK ] Inverse (1,1) value: 1.20944 [ OK ] Inverse (2,1) value: 0.753826 [ OK ] Inverse (0,2) value: 0.00780493 [ OK ] Inverse (1,2) value: 0.753826 [ OK ] Inverse (2,2) value: 2.48652 [ OK ] solving Ax=b, x[0] value: 1.46603 [ OK ] solving Ax=b, x[1] value: 0.0730086 [ OK ] solving Ax=b, x[2] value: 0.384718 [ OK ] Cholesky (0,0) value: 0.866025 [ OK ] Cholesky (1,0) value: 0.600444 [ OK ] Cholesky (2,0) value: 0.184752 [ OK ] Cholesky (0,1) value: 0 [ OK ] Cholesky (1,1) value: 1.00969 [ OK ] Cholesky (2,1) value: 0.306102 [ OK ] Cholesky (0,2) value: 0 [ OK ] Cholesky (1,2) value: 0 [ OK ] Cholesky (2,2) value: 0.634168 [ OK ] LU (0,0) value: 0.75 [ OK ] LU (1,0) value: 0.693333 [ OK ] LU (2,0) value: 0.213333 [ OK ] LU (0,1) value: 0.52 [ OK ] LU (1,1) value: 1.01947 [ OK ] LU (2,1) value: 0.303165 [ OK ] LU (0,2) value: 0.16 [ OK ] LU (1,2) value: 0.309067 [ OK ] LU (2,2) value: 0.402168 [ OK ] Ax=b, b[0] value: 1 [ OK ] Ax=b, b[1] value: 0.5 [ OK ] Ax=b, b[2] value: 2.77556e17 [ OK ] M should be symmetric value: 1 [ OK ] matrix scaling value: 1.5 [ OK ] matrix scaling value: 1.04 [ OK ] matrix scaling value: 0.32 [ OK ] matrix scaling value: 1.04 [ OK ] matrix scaling value: 2.76 [ OK ] matrix scaling value: 0.84 [ OK ] matrix scaling value: 0.32 [ OK ] matrix scaling value: 0.84 [ OK ] matrix scaling value: 1.06 [ OK ] pivot Ax=b, x[0] value: 5.24051 [ OK ] pivot Ax=b, x[1] value: 1.32526 [ OK ] pivot Ax=b, x[2] value: 0.334347 =============> Testing return bug in jackknifedata <============ [ OK ] optimizer hung value: 0 =============> Initial parameter heuristics <============ [ OK ] PsiPsychometric>getStart() for increasing data value: 0.608 > 0 [ OK ] PsiPsychometric>getStart() for decreasing data value: 0.202335 < 0 =============> Finding good starting values <============ [FAIL] yesno: Starting value for alpha value: 3.33333 should be: 3.49558 [FAIL] yesno: Starting value for beta value: 1.03704 should be: 0.898865 [FAIL] yesno: Starting value for lambda value: 0.00185185 should be: 0.00555556 [FAIL] yesno: Starting value for gamma value: 0.0166667 should be: 0.0444444 [ OK ] yesno: minimum of alpha range value: 0 [ OK ] yesno: maximum of alpha range value: 10 [ OK ] yesno: minimum of alpha range value: 0 [ OK ] yesno: maximum of alpha range value: 10 [FAIL] yesno: minimum of beta range value: 2 should be: 0.45512 [FAIL] yesno: maximum of beta range value: 10 should be: 2.2756 [FAIL] yesno: minimum of beta range value: 2 should be: 0.45512 [FAIL] yesno: maximum of beta range value: 10 should be: 2.2756 [FAIL] yesno: minimum of lambda range value: 2 should be: 0 [ OK ] yesno: maximum of lambda range value: 0.1 [FAIL] yesno: minimum of lambda range value: 2 should be: 0 [ OK ] yesno: maximum of lambda range value: 0.1 [FAIL] yesno: minimum of gamma range value: 2 should be: 0 [ OK ] yesno: maximum of gamma range value: 0.1 [FAIL] yesno: minimum of gamma range value: 2 should be: 0 [ OK ] yesno: maximum of gamma range value: 0.1 [FAIL] 2afc: Starting value for alpha value: 3.33333 should be: 3.29584 [FAIL] 2afc: Starting value for beta value: 0.888889 should be: 0.988751 [FAIL] 2afc: Starting value for lambda value: 0.0203704 should be: 0.0185185 [ OK ] 2afc: minimum of alpha range value: 0 [ OK ] 2afc: maximum of alpha range value: 10 [ OK ] 2afc: minimum of alpha range value: 0 [ OK ] 2afc: maximum of alpha range value: 10 [FAIL] 2afc: minimum of beta range value: 2 should be: 0.45512 [FAIL] 2afc: maximum of beta range value: 10 should be: 2.2756 [FAIL] 2afc: minimum of beta range value: 2 should be: 0.45512 [FAIL] 2afc: maximum of beta range value: 10 should be: 2.2756 [FAIL] 2afc: minimum of lambda range value: 2 should be: 0 [ OK ] 2afc: maximum of lambda range value: 0.1 [FAIL] 2afc: minimum of lambda range value: 2 should be: 0 [ OK ] 2afc: maximum of lambda range value: 0.1 [ OK ] grid parameter 0 lower limit value: 0 [ OK ] grid parameter 0 upper limit value: 0.5 [ OK ] grid parameter 1 lower limit value: 0 [ OK ] grid parameter 1 upper limit value: 0.5 [ OK ] grid parameter 2 lower limit value: 0 [ OK ] grid parameter 2 upper limit value: 0.05 [ OK ] grid.empty() on nonempty grid value: 0 [ OK ] grid.empty() on empty grid value: 1 [ OK ] grid.dimension() on 3d grid value: 3 [ OK ] grid.get_gridsize() on small grid value: 5 [ OK ] grid.front()[0] value: 0 [ OK ] grid.front()[1] value: 0.125 [ OK ] grid.front()[2] value: 0.25 [ OK ] grid.front()[3] value: 0.375 [ OK ] grid.front()[4] value: 0.5 [ OK ] shifted grid.front()[0] value: 0.125 [ OK ] shifted grid.front()[1] value: 0 [ OK ] shifted grid.front()[2] value: 0.125 [ OK ] shifted grid.front()[3] value: 0.25 [ OK ] shifted grid.front()[4] value: 0.375 [ OK ] shrunken grid.front()[0] value: 0 [ OK ] shrunken grid.front()[1] value: 0.0625 [ OK ] shrunken grid.front()[2] value: 0.125 [ OK ] shrunken grid.front()[3] value: 0.1875 [ OK ] shrunken grid.front()[4] value: 0.25 [ OK ] subgrid dimension value: 2 [ OK ] linspace 0 value: 0 [ OK ] linspace 1 value: 0.5 [ OK ] linspace 2 value: 1 [ OK ] Number of generated gridpoints from 2x2x2 grid value: 8 [ OK ] gridpoint 0 first param value: 0 [ OK ] gridpoint 0 second param value: 0 [ OK ] gridpoint 0 third param value: 0 [ OK ] gridpoint 1 first param value: 0 [ OK ] gridpoint 1 second param value: 0 [ OK ] gridpoint 1 third param value: 0.05 [ OK ] gridpoint 2 first param value: 0 [ OK ] gridpoint 2 second param value: 0.5 [ OK ] gridpoint 2 third param value: 0 [ OK ] gridpoint 3 first param value: 0 [ OK ] gridpoint 3 second param value: 0.5 [ OK ] gridpoint 3 third param value: 0.05 [ OK ] gridpoint 4 first param value: 0.5 [ OK ] gridpoint 4 second param value: 0 [ OK ] gridpoint 4 third param value: 0 [ OK ] gridpoint 5 first param value: 0.5 [ OK ] gridpoint 5 second param value: 0 [ OK ] gridpoint 5 third param value: 0.05 [ OK ] gridpoint 6 first param value: 0.5 [ OK ] gridpoint 6 second param value: 0.5 [ OK ] gridpoint 6 third param value: 0 [ OK ] gridpoint 7 first param value: 0.5 [ OK ] gridpoint 7 second param value: 0.5 [ OK ] gridpoint 7 third param value: 0.05 [FAIL] Best fit on grid value: 35.4381 should be: 23.8999 [FAIL] Second best fit on grid value: 43.346 should be: 32.0023 [FAIL] Best fitting alpha on grid value: 0.5 should be: 0 [ OK ] Best fitting beta on grid value: 0.5 [ OK ] Best fitting lambda on grid value: 0.05 [FAIL] Second best fitting alpha on grid value: 0 should be: 0.5 [ OK ] Second best fitting beta on grid value: 0.5 [ OK ] Second best fitting lambda on grid value: 0.05 [FAIL] gridpoint 0 first param value: 0.25 should be: 0.25 [ OK ] gridpoint 0 second param value: 0.25 [ OK ] gridpoint 0 third param value: 0.025 [FAIL] gridpoint 1 first param value: 0.25 should be: 0.25 [ OK ] gridpoint 1 second param value: 0.25 [ OK ] gridpoint 1 third param value: 0.075 [FAIL] gridpoint 2 first param value: 0.25 should be: 0.25 [ OK ] gridpoint 2 second param value: 0.75 [ OK ] gridpoint 2 third param value: 0.025 [FAIL] gridpoint 3 first param value: 0.25 should be: 0.25 [ OK ] gridpoint 3 second param value: 0.75 [ OK ] gridpoint 3 third param value: 0.075 [FAIL] gridpoint 4 first param value: 0.75 should be: 0.25 [ OK ] gridpoint 4 second param value: 0.25 [ OK ] gridpoint 4 third param value: 0.025 [FAIL] gridpoint 5 first param value: 0.75 should be: 0.25 [ OK ] gridpoint 5 second param value: 0.25 [ OK ] gridpoint 5 third param value: 0.075 [FAIL] gridpoint 6 first param value: 0.75 should be: 0.25 [ OK ] gridpoint 6 second param value: 0.75 [ OK ] gridpoint 6 third param value: 0.025 [FAIL] gridpoint 7 first param value: 0.75 should be: 0.25 [ OK ] gridpoint 7 second param value: 0.75 [ OK ] gridpoint 7 third param value: 0.075 =============> Approximate numerical integration <============ [FAIL] Lower bound of m value: 2.22222 should be: 0 [FAIL] Upper bound of m value: 8.88889 should be: 10 [ OK ] Length of m grid value: 7 [FAIL] Lower bound of w value: 0.538267 should be: 2 [FAIL] Upper bound of w value: 8.35062 should be: 10 [ OK ] Length of w grid value: 7 [FAIL] Lower bound of lm value: 0.0351852 should be: 0 [FAIL] Upper bound of lm value: 0.0759259 should be: 0.1 [ OK ] Length of lm grid value: 7 [ OK ] cdf_grid Gauss value: 1.28155 [ OK ] cdf_grid Gauss value: 0.727913 [ OK ] cdf_grid Gauss value: 0.340695 [ OK ] cdf_grid Gauss value: 4.27646e18 [ OK ] cdf_grid Gauss value: 0.340695 [ OK ] cdf_grid Gauss value: 0.727913 [ OK ] cdf_grid Gauss value: 1.28155 [FAIL] fit_posterior Gauss mean value: 0.00547325 should be: 0 [FAIL] fit_posterior Gauss std value: 0.00115231 should be: 1 [FAIL] cdf_grid Gamma value: inf should be: 1.74477 [FAIL] cdf_grid Gamma value: nan should be: 2.45729 [FAIL] cdf_grid Gamma value: nan should be: 3.06205 [FAIL] cdf_grid Gamma value: nan should be: 3.67206 [FAIL] cdf_grid Gamma value: nan should be: 4.35885 [FAIL] cdf_grid Gamma value: nan should be: 5.23689 [FAIL] cdf_grid Gamma value: nan should be: 6.68078 [FAIL] fit_posterior Gamma shape value: 2 should be: 4 [FAIL] fit_posterior Gamma scale value: 1.815 should be: 1 [ OK ] cdf_grid Beta value: 0.0256173 [ OK ] cdf_grid Beta value: 0.0437104 [ OK ] cdf_grid Beta value: 0.0606308 [ OK ] cdf_grid Beta value: 0.0786438 [ OK ] cdf_grid Beta value: 0.0996593 [ OK ] cdf_grid Beta value: 0.127175 [ OK ] cdf_grid Beta value: 0.172935 [FAIL] fit_posterior Beta alpha value: 1.99882 should be: 2 [FAIL] fit_posterior Beta beta value: 19.9891 should be: 20 [FAIL] Posterior for m  mu value: 3.39015 should be: 3.28 [FAIL] Posterior for m  sg value: 0.451554 should be: 0.436 [FAIL] Posterior for w  k value: 10.4441 should be: 12.732 [FAIL] Posterior for w  th value: 0.440721 should be: 0.362 [FAIL] Posterior for lm  al value: 2.54935 should be: 3.17 [FAIL] Posterior for lm  bt value: 106.59 should be: 111.26 H = 0.963892 [ OK ] Sampled and fitted posterior mean for m value: 3.21063 [FAIL] Sampled and fitted posterior mean for w value: 4.58142 should be: 4.60294 [ OK ] Sampled and fitted posterior mean for lm value: 0.0270066 [ OK ] number of duplicates in SIR value: 0.04 < 0.1 [ OK ] Rkd is set value: 1 [ OK ] Rpd is set value: 1 [ OK ] logratio is set value: 1 [ OK ] ppRkd is set value: 1 [ OK ] ppRpd is set value: 1 [ OK ] ppData is set value: 6 [ OK ] Deviance is set value: 1 [ OK ] ppDeviance is set value: 1 ==> 73 tests failed  Andres 
From: Ingo Fründ <ingo.fruend@tu...>  20110616 14:47:21

Dear Anne, in the meantime, I have reanalyzed your data in both matlab and python. You are right. It takes about 30 seconds to fit a psychometric function (including bootstrap) from within matlab. It takes about 4 seconds to do the same job in python. Although 30 seconds is a very large difference, we knew that the call from python was faster. We were not aware however, that the difference was as big as it is. There might be two reasons for this. First, the matlab interface is less current than the python interface. We really came to like the fast development cycles of python. So we usually have prototyes in python and transfer the parts that work well to the matlab interface. This also means that the matlab interface typically lags behind the python interface. Second, there is a lot of ascii code that is handed around between matlab and the psignifit command line interface. This might slow down processing even more. However, it is not clear where a difference of 26 seconds could come from. Thank you also for providing installation instructions for psignifit 3 on your website. I think it is a very good idea to have such a detailed step by step installation guide. It might be even better to have a copy of these instructions on the psignifit website, too. What do you think? I feel that you somehow got trapped by the rather complicated installation instructions: You do a lot of steps that are not necessary. We recently had a programming sprint during which the installation instructions were restructured. It seems like some things have come out of order there. My apologies for that. In your instructions, you can omit ALL references to python. No need for numpy, scipy, or matplotlib. You don't need python at all. Psignifit is essentially written in C++. Python is just one way to interactively use the C++ library. The other way is the command line interface which is entirely written in C++. This second way is what matlab uses. So what you really need is the commandline interface and the mpsignifit folder somewhere in your matlab path. Typically, the command line interface should be moved to the central folder you specified in CLI_INSTALL by just typing make cliinstall Most probably, you only have to perform step 8 in your instructions if you did *not* change the CLI_INSTALL variable in step 7. A good way to check this is by typing echo $PATH and seeing whether the path you set via CLI_INSTALL is in the list. If you now open a *new* terminal, you should be able to call psignifitmcmc h from anywhere on your computer. In addition, if I remember correctly, the main problem with xcode 4 was that you had to pay for it whereas xcode 3 comes for free. In fact, I don't think it's a problem with the gcc version. I myself use gcc4.4.5 to compile psignifit locally. Best, Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Ashwani Jha <a.jha@io...>  20110615 21:05:26

Dear Valentin, Thanks, it works great now! The link I tried was: http://psignifit.sourceforge.net/INSTALL_WINDOWS.html 'Download the file psignifit_cli_3_beta_installer.exe from sourceforge and run it.' best wishes, Ashwani Jha On 15 June 2011 10:42, Valentin Haenel <valentin.haenel@...> wrote: > Daer Ashwani Jha, > > * Ashwani Jha <a.jha@...> [110614]: >> I am trying to install Psignifit 3 on Windows 7 64bit, as a Matlab >> 2010a toolbox. >> I have downloaded and extracted 'psignifit3.0_beta_07062011', and >> placed 'mpsignifit' in my Matlab path. >> >> I notice that on your website you suggest also to download the command >> line installer 'psignifit_cli_3_beta_installer.exe'. However the link >> on the website doesn't direct me to this. Where can I get this from / >> how would I install it? > > Here is a link to the most recent version. > > http://sourceforge.net/projects/psignifit/files/psignifit3.0_beta_07062011/ > > This is windows installer and can be installed as usual. > > Also, it sounds like you detected a broken link on the website. Please > write back and tell us where it is. > > thanks for trying Psignifit3.x > > V > > 
From: Valentin Haenel <valentin.haenel@gm...>  20110615 09:42:40

Daer Ashwani Jha, * Ashwani Jha <a.jha@...> [110614]: > I am trying to install Psignifit 3 on Windows 7 64bit, as a Matlab > 2010a toolbox. > I have downloaded and extracted 'psignifit3.0_beta_07062011', and > placed 'mpsignifit' in my Matlab path. > > I notice that on your website you suggest also to download the command > line installer 'psignifit_cli_3_beta_installer.exe'. However the link > on the website doesn't direct me to this. Where can I get this from / > how would I install it? Here is a link to the most recent version. http://sourceforge.net/projects/psignifit/files/psignifit3.0_beta_07062011/ This is windows installer and can be installed as usual. Also, it sounds like you detected a broken link on the website. Please write back and tell us where it is. thanks for trying Psignifit3.x V 
From: Ingo Fründ <ingo.fruend@tu...>  20110615 09:28:52

Dear Johahn, dear Anne, would any of you mind sending me the code you use along with an example dataset so that I can try to do exactly the same thing you did and see wether it takes that long for me too? Thank you very much, Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Ingo Fründ <ingo.fruend@tu...>  20110615 09:26:15

Dear Jim, > This appears to be the answer? Is there anything I can do to fix this? > While updating older MEXfiles, you could find calls to unsupported > functions, such as mxCreateFull, mxGetName, or mxIsString. MATLAB > removed support for these functions in Version 7.1 (R14SP3). You cannot > use unsupported functions with 64bit array dimensions. For the list of > unsupported functions and the recommended replacements, see *Obsolete > Functions No Longer Documented*. > Update your code to use an equivalent function, if available. For example, > use > mxCreateDoubleMatrix instead of mxCreateFull. Yes, this appears to be the answer. As I wrote in my previous mail. However, the psignifit version you are trying to install is psignifit 2.5.6 that was written by Jeremy Hill and Felix Wichmann. It is no longer maintained. So if you are willing to perform all the necessary changes to get it running on a recent matlab, you are more than welcome to do so. Be aware though, that there is a more recent version of psignifit (psignifit 3). This more recent version is working well, but is slightly slower than the old version. There are however, some differences in the way you fit psychometric functions from 2.5.6 to 3. So if you want to run psignifit on your computer, I see two options: 1. Modify the old version to run with more recent versions of matlab. 2. Use the new version with either matlab or if you are familiar with python, that's even better. I would strongly advise you against option 1  it is a lot of work and probably not worth it. If you need any help with option 2, please write to this mailing list (psignifitusers@...). You will most probably find someone to help you (for example me). Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Ingo Fründ <ingo.fruend@tu...>  20110615 09:16:05

Hi Jim, > but in the mean time, I cannot run the old psignfit engine and I would > like to get it running so I can compare a single data set using psignifit, > the new 3.0 version, and also the python version. (all are giving me > different results). That is not really a problem. The results of psignifit depend of random number generation. This has the effect, that your results will also be subject to randomness. So there should be small differences between every call you make. However, these differences should be small. If they are not, it typically means that the number of random numbers that were generated was not sufficiently large. > I am on linux 64 bit (ubuntu). I use matla R2009b. I get the following > error when trying to use the pfit function: > Invalid Mex File: psignifit.mexa64: symbol mxGetName, version v7.0 not > defined > in file libmx.so with link time reference. > Any ideas? Yes. Matworks (the company that produces matlab) has changed some of the internals of matlab a couple of years ago. As a result many people report problems running psignifit on recent versions of matlab ("recent" here means a couple of years). To overcome this problem, we changed a lot in the internal organization of psignifit. One thing is that we mainly develop psignifit on python. That means that only those things that have proven good are eventually moved to the matlab interface. The other thing is that the real work in the matlab version is not actually done by matlab but by an external program that is merely called from within matlab. So, to cut a long story short: There does not seem a solution to your problem but other people have the same problem. So if you happen to get psignifit 2.5.6 running on a recent matlab (e.g. R2009a), please send an eMail to this mailing list. Besides that, thanks a lot for trying out the "new" psignifit and helping us improve on what we have so far. Best, Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Johahn Leung <johahn.leung@gm...>  20110615 01:26:00

Hi Ingo, Just to further this discussion, 2050secs is around the time it takes for a 2000 bootstrap running on my machines here too, averaging around 40secs. This is using the Matlab interface. Running on Mac OSX or Windows didn't seem to affect the speed too much. Not having run psignifit 2, i did not know this was on the slow side. In the case of the Mac, I compiled the command line using GCC. With Windows, I just used the downloaded version. We have tried fitting 40 trial staircases (fast) to Quest (slow). cheers /j ___________________________ Johahn Leung Auditory Neuroscience Laboratory Department of Physiology University of Sydney T +612 93517615 F +612 93518259 On Wednesday, 15 June 2011 at 2:06 AM, Ingo Fründ wrote: > Dear Anne, > > this sounds like very strange behavior. Psignifit 3 is definitely slower > than psignifit 2. We try to improve on that. However, 32 seconds for > 2000 samples is more than a magnitude more than I would expect. Would > you mind posting a little code example that helps us reproduce your > result? That would be very nice. > > Thanks a lot for trying psignifit, > > Ingo > > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > Ingo Fründ, Dr. rer. nat. > Modelling of Cognitive Processes > TUBerlin > Sekr 64 > Franklinstrasse 28/29 > 10587 Berlin > > phone: (+490)3031478962 > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >  > EditLive Enterprise is the world's most technically advanced content > authoring tool. Experience the power of Track Changes, Inline Image > Editing and ensure content is compliant with Accessibility Checking. > http://p.sf.net/sfu/ephoxdev2dev > _______________________________________________ > Psignifitusers mailing list > Psignifitusers@... (mailto:Psignifitusers@...) > https://lists.sourceforge.net/lists/listinfo/psignifitusers 
From: Ingo Fründ <ingo.fruend@tu...>  20110614 16:06:24

Dear Anne, this sounds like very strange behavior. Psignifit 3 is definitely slower than psignifit 2. We try to improve on that. However, 32 seconds for 2000 samples is more than a magnitude more than I would expect. Would you mind posting a little code example that helps us reproduce your result? That would be very nice. Thanks a lot for trying psignifit, Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Ingo Fründ <ingo.fruend@tu...>  20110614 16:00:53

Hi Jim, I'm sorry to hear that you had problems to get psignifit3 working with a recent matlab version. With our test setup, it worked more or less out of the box. What operatings system/ linux distribution are you using? On what architecture? Which version of psignifit are you using? This might help us improve the installation process and make sure psignifit takes the "correct" libraries in the first place. Now concerning your questions: > 1) The first is regarding the new "Baysian Inference." I have 5 stimulus > levels and 16 subjects. Is it better that I use the Bayesian model > instead of the contrained ML and Bootstrap? There are reasons to use Bayes and reasons to use Bootstrap. We are still investigating which framework provides more "honest" confidence regions. A first result that might be related to that question is that Bayesian Inference seems to be more robust with respect to nonstationarities (such as learning, fluctuating attention, changing strategies and the like). This has recently been published in the journal of vision if you need more details about it: doi: 10.1167/11.6.16 Besides that, we have some (preliminary) evidence that Bayesian inference at least does not perform worse than the constrained ML + Bootstrap approach. However, this is (i) preliminary and does (ii) depend on the MCMC procedure converging. We have an alternative way of doing Bayesian Inference implemented but that is not yet available from matlab and has not been tested in large scale simulations. So we can't really guarantee for that. The main advantage of the bootstrap procedure is that you don't have to worry about converging Markov Chains. Unfortunately, bootstrap confidence intervals are a bit biased. The bias can be reduced by performing a sensitivity analysis as described in the original paper by Wichmann & Hill (2001). Unfortunately, sensitivity analysis is not implemented in matlab either (but is available from python). Sensitivity analysis is one of our main todo points for the matlab interface. But right now that won't help you much. Sorry. > Any reason why it should hang like that? The hanging issue is not new. We had a fix for something that sounded very similar a while ago. Do you use the most recent version? Why had a couple of bugfixes in new downloads recently. > 2) My second question is this: I did a previous analysis using the old > psignifit toolbox (on a different computer). I've been unable to > duplicate and get a working mex file on my 64 bit linux machine, so I'm > using the 3.0 version now. I have gotten that working, but I obtain > different results using 3.0 than I did with the 2.x.x version. Most > likely, I'm just doing something different and I don't realize it. Yes, you probably do something different. If I remember correctly, psignifit 2.x.x does the above sensitivity analysis by default. Also, psignifit uses random numbers to generate its confidence regions. So there will always be some random fluctations in psignifit's output. > The figures produced by pfit in 2.x.x showed the deviance and correlation > and changed text color so it was easy to tell when one of them failed. > The 3.0 doesn't see to do this, so I'm not sure what the result is when > the data plots. Psignifit 3 is still in a development status. That means (amoung other things) that we don't show all possible plots by default. You can however request the plots by calling the GoodnessOfFit function. You are right, the matlab version you are using does not yet perform the color change. Most people actually didn't notice the color change or did not know how to interpret it. In the python version we therefore decided to write an explicite warning on top of the plots that tells you that something went wrong (and a guess what might have gone wrong). That's not in the matlab interface though. But you can easily see that by yourself. It is described in more detail here http://psignifit.sourceforge.net/TUTORIAL_BOOTSTRAP.html#goodnessoffitassessment > Also, I'm wondering what parameters I would need to pass in 3.0 psignifit > matlab to equal the results I obtained in 2.x.x above. > Currently, For 3.0, I'm doing: > boots = BootstrapInference( data, priors, 'nafc', 1, 'sigmoid', > 'logistic', 'core', 'ab', 'cuts', [0.25 0.5 0.75]) > GoodnessOfFit( boots ); Your parameters seem fine for me. How far apart are you the values you get? > a) Is conf 'v1.0' in 2.x.x default in 3.0? How do I change conf in the > 3.0 version to match? No its not. I'm not aware of the 'v1.0' switch in neither 2.x.x nor psignifit 3. But there are differences in the default confidence limits in 2.x.x and in 3.0: 2.x.x: [0.023, 0.159, 0.841, 0.977] 3.0 : [0.025, 0.975] > b) I don't remember specifically setting priors in 2.x.x. How do I match > this in 3.0? ("","","Uniform(0,0.05)","Uniform(0,0.05)") You find that in psycho_options.m > c) is 'gamma=lambda' option default in 2.x.x? No, it's not. It is not available in 2.x.x. Please write again, in case anything was unclear or if you psignifit does not perform the way you expected it to perform. Best, Ingo ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Ashwani Jha <a.jha@io...>  20110614 15:43:52

Dear Psignifit team, I am trying to install Psignifit 3 on Windows 7 64bit, as a Matlab 2010a toolbox. I have downloaded and extracted 'psignifit3.0_beta_07062011', and placed 'mpsignifit' in my Matlab path. I notice that on your website you suggest also to download the command line installer 'psignifit_cli_3_beta_installer.exe'. However the link on the website doesn't direct me to this. Where can I get this from / how would I install it? best wishes, Ashwani Jha 
From: Churchland, Anne <churchland@cs...>  20110614 15:32:19

Hi All, This is my first post to this group I have got psignifit 3.0 to work with matlab but am finding things to be very slow compared with the psignifit 2. The line that is slowing things down is in BootStrapInference.m; it is the line that calls "psignifit bootstrap". The funny thing is that the slowness doesn't seem to have to do with the number of samples. It takes 25 seconds to do the bootstrap when I have 20 samples, and only 32 seconds when I have 2000 samples! So there is some strange thing that takes 25 seconds that I can't seem to pin down. Does anyone else have this problem? I am using python 2.6.6. Thanks! Anne Anne Churchland Assistant Professor Cold Spring Harbor Laboratories churchland@...<mailto:churchland@...> mobile: 206 853 7536 http://churchlandlab.cshl.edu 
From: Ingo Fründ <ingo.fruend@tu...>  20110614 14:01:46

Dear Johahn, you are right, there is not sensitivity analysis yet in the matlab code. I added that as a todo. With respect to the deviance, you should be able to also get deviances for all the bootstrap samples. If you use the GoodnessOfFit plot, you will see the deviance of the MLestimate along with a histogram of the deviances from the bootstrap samples. That should help you interpret the deviance value you get. Best, Ingo On Fri, Jun 10, 2011 at 04:57:31PM +1000, Johahn Leung wrote: > Hi Ingo, > Thanks, I just downloaded the newest version (07_06) and the code runs. > However, I note that the Matlab interface does not have a Sensitivity > analysis, or is that incorporated in the getCI function? > I am getting really small CI and have no way of telling if it's correct. > Using GoodnessofFit, i can get a value of deviance, but again, need a way > to interpret it. > cheers > /j > ___________________________ > Johahn Leung > Auditory Neuroscience Laboratory > Department of Physiology > University of Sydney > T +612 93517615 > F +612 93518259 > > On Tuesday, 7 June 2011 at 8:01 PM, Ingo FrA 1/4nd wrote: > > Dear Johahn, > > the fix has been uploaded. The other bug you report below should have > been fixed > some time ago. See commit: > > 833a8e9095aebb4ed907729f9fa0d3bf245a4653 > > and in particular: > f99cd7e169c87f2821a336714e69162c78742969 > > Which version are you using? If you are experienceing these problems > with a recent snapshot of psignifit3 could you please write me a short > note? What you report should not happen. We have implemented another > strategy for sampling from the posterior: The ASIRInference object in > python tries to find a reasonable parametric approximation to the > posterior marginals and then uses sampling importance resampling. This > way, there are no issues about chain convergence anymore. However, ASIR > inference is not yet available from matlab. If you are interested in > trying that out, please drop me a note. > > Thanks a lot, > > Ingo > > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > Ingo FrA 1/4nd, Dr. rer. nat. > Modelling of Cognitive Processes > TUBerlin > Sekr 64 > Franklinstrasse 28/29 > 10587 Berlin > > phone: (+490)3031478962 > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ingo Fründ, Dr. rer. nat. Modelling of Cognitive Processes TUBerlin Sekr 64 Franklinstrasse 28/29 10587 Berlin phone: (+490)3031478962 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
From: Jim Gwilliam <jim.gwilliam@jh...>  20110614 03:49:53

Hi, I've spent most the day getting the psignifit 3.0 to work wiht my matlab. Installing the matlab path is easy, but getting the command line installed was a bit trickier. It turned out that matlab was trying to use some of it's own libstd++ libraries instead of the newer linux libraries. Once I removed those matlab libraries to an "old" folder, it worked better. I have two questions I hope I can receive a timely response to; 1) The first is regarding the new "Baysian Inference." I have 5 stimulus levels and 16 subjects. Is it better that I use the Bayesian model instead of the contrained ML and Bootstrap? Also, more importantly, when I run the "test_psignifit" in matlab, the MapEstimate, BootstrapInference, BayesInference:explicite stepwidths all run fine (and quickly), but then when it gets to BayesInference: pilot sample, it just says "busy" in matlab. My CPU shows 100% and I've left it for up to 10 minutes with no return (and I have a powerful quadcore machine). Any reason why it should hang like that? 2) My second question is this: I did a previous analysis using the old psignifit toolbox (on a different computer). I've been unable to duplicate and get a working mex file on my 64 bit linux machine, so I'm using the 3.0 version now. I have gotten that working, but I obtain different results using 3.0 than I did with the 2.x.x version. Most likely, I'm just doing something different and I don't realize it. *Previously, I used for 2.x.x:* pfit( data, 'n_intervals', 1 ); PSE = findthreshold('logistic', data.params.est, 0.5); JND_u = findthreshold('logistic', data.params.est, 0.25); JND_l = findthreshold('logistic', data.params.est, 0.75); The figures produced by pfit in 2.x.x showed the deviance and correlation and changed text color so it was easy to tell when one of them failed. The 3.0 doesn't see to do this, so I'm not sure what the result is when the data plots. Also, I'm wondering what parameters I would need to pass in 3.0 psignifit matlab to equal the results I obtained in 2.x.x above. *Currently, For 3.0, I'm doing:* boots = BootstrapInference( data, priors, 'nafc', 1, 'sigmoid', 'logistic', 'core', 'ab', 'cuts', [0.25 0.5 0.75]) GoodnessOfFit( boots ); but I am not getting the same results. I have a few questions about the options: a) Is conf 'v1.0' in 2.x.x default in 3.0? How do I change conf in the 3.0 version to match? b) I don't remember specifically setting priors in 2.x.x. How do I match this in 3.0? c) is 'gamma=lambda' option default in 2.x.x? Finally, How do I know if the Goodness of fit has passed in 3.0? It was clear by text color change in 2.x.x. I'd appreciate help trying to make 3.0 match the results I had previously obtained in 2.x.x. Thanks, Jim  *Jim Gwilliam* PhD Candidate Johns Hopkins University School of Medicine Dept. of Biomedical Engineering *Laboratory for Computational Sensing and Robotics<https://haptics.lcsr.jhu.edu/Jim_Gwilliam>; 137 Hackerman Hall (formerly CSEB)* *3400 N. Charles St., Baltimore, MD 21218* *Calendar <http://bit.ly/gwilliam_calendar>;* Office: 4105164184 Mobile: 8019998546 * * 
From: Ingo Fründ <ingo.fruend@tu...>  20110607 09:44:57

Hi Johan, I'm sorry for this rather stupid bug. "Hi my name is Metropolis Hastings" is debugging output to make sure that the code reaches the Metropolis Hastings object at some point. Unfortunately, Matlab combines the standard output channel with the standard error channel such that there is no way to have any status messages from the external programs. The bug has been fixed in the current master branch. I will now prepare a new download snapshot that you can expect in about two hours. Best, Ingo 
From: Johahn Leung <johahn.leung@gm...>  20110607 05:03:54

Hi, Ran test_psignifit and there's a bug in the Bayesinference.m code specifically, the output of line 220: [status,output] = system ( cmd ); the first line of OUTPUT is "Hi my name is MetropolisHastings results.mcdata = [ 26, 33, 44, 48, 50, 46; ..." Obviously it crashes with the "Hi my name is MetropolisHastings" /j ___________________________ Johahn Leung Auditory Neuroscience Laboratory Department of Physiology University of Sydney T +612 93517615 F +612 93518259 
From: Ingo Fründ <ingo.fruend@tu...>  20110606 09:59:20

Hi Uli, thanks for reporting this problem with psignifit3. It seems like your problem is related to swig, though. The file swignifit_raw.cxx is automatically generated by swig. Also, you could help us by sending your distribution. I did not find gcc4.6.1 on the gcc website. Best, Ingo 
From: Wannek, Uli <wannek@un...>  20110605 16:13:50

Hi, i stumbled about an error while > python setup.py install Compiling "swignifit_raw.cxx" with gcc4.6.1 raised some errors: ptrdiff_t does not name a type. Searching for that error, i found a solution in http://bugs.gentoo.org/show_bug.cgi?id=362905: #include <cstddef> After adding that single line, compiling the whole psignifit suite went well. Thanks a lot for psignifit! Best regards, Uli 