--
You received this message because you are subscribed to the Google Groups "Stan users mailing list" group.
To unsubscribe from this group and stop receiving emails from it, send an email to stan-users+...@googlegroups.com.
To post to this group, send email to stan-...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.
Hi, Lee!
I confirmed the rhat, but it does not seem to converge.
I do not know what the modes, so I probably can not check it.
How can I check the modes?
I am using MatlabStan.
The data generating script and the output are as follows.
The data generating script is:
f01 = 0.1;
f11 = 0.3;
f02 = 0.5;
f12 = 0.8;
g = 10;
c = 0.5;
sig = 1;
TT = 100;
y = zeros(TT,1);
G = zeros(TT,1);
y(1) = 1;
G(1) = (1 + exp(-g*( 1/TT -c))).^(-1);
for t=2:TT
st = t/TT;
G(t) = (1 + exp(-g*(st-c))).^(-1);
y(t) = (0.1 + 0.3*y(t-1))*(1 - G(t)) + (0.5 + 0.8*y(t-1))*G(t) + sig * normrnd(0,1);
end;
y
r = y;
rats_dat = struct('T',size(r,1),'r',r,'G1',size(r,1));
rats_fit1 = stan('file','ex2stan.stan','data',rats_dat,'iter',1000,'warmup',20,'thin',1,'chains',4);
rats_fit1.block()
print(rats_fit1);
g = rats_fit1.extract('permuted',true).g;
mean(g)
c = rats_fit1.extract('permuted',true).c;
mean(c)
sigma = rats_fit1.extract('permuted',true).sigma;
mean(sigma)
The output is:
Inference for Stan model: ex2stan_model
4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
Warmup took (0.020, 0.0090, 1.0, 1.0) seconds, 2.1 seconds total
Sampling took (0.62, 0.87, 0.60, 0.71) seconds, 2.8 seconds total
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ 6.2e+002 2.2e+001 1.3e+002 3.1e+002 6.5e+002 6.5e+002 38 14 1.1e+000
accept_stat__ 8.5e-001 4.0e-002 1.6e-001 5.0e-001 9.1e-001 1.0e+000 16 5.7 1.1e+000
stepsize__ 7.5e-002 1.2e-002 1.7e-002 5.2e-002 8.7e-002 9.5e-002 2.0 0.71 1.8e+014
treedepth__ 3.3e+000 1.8e-001 1.5e+000 1.0e+000 3.0e+000 6.0e+000 70 25 1.0e+000
n_leapfrog__ 2.3e+001 4.1e+000 3.5e+001 2.0e+000 1.5e+001 6.7e+001 71 25 1.0e+000
divergent__ 7.2e-001 3.4e-002 4.5e-001 0.0e+000 1.0e+000 1.0e+000 177 63 1.0e+000
energy__ -6.2e+002 2.2e+001 1.4e+002 -6.5e+002 -6.5e+002 -3.1e+002 38 14 1.1e+000
g 8.2e+307 inf inf 2.0e+162 8.6e+307 1.7e+308 4000 1427 nan
c -3.9e+001 1.8e+001 2.8e+001 -8.7e+001 -2.6e+001 -6.6e+000 2.4 0.85 3.1e+000
sigma 1.1e+000 1.3e-003 7.7e-002 9.5e-001 1.1e+000 1.2e+000 3424 1221 1.0e+000
G[1] 1.0e+002 2.2e-014 1.4e-012 1.0e+002 1.0e+002 1.0e+002 4000 1427 1.0e+000
G[2] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[3] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[4] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[5] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[6] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[7] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[8] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[9] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[10] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[11] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[12] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[13] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[14] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[15] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[16] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[17] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[18] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[19] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[20] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[21] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[22] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[23] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[24] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[25] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[26] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[27] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[28] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[29] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[30] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[31] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[32] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[33] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[34] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[35] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[36] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[37] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[38] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[39] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[40] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[41] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[42] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[43] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[44] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[45] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[46] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[47] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[48] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[49] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[50] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[51] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[52] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[53] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[54] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[55] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[56] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[57] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[58] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[59] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[60] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[61] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[62] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[63] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[64] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[65] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[66] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[67] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[68] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[69] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[70] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[71] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[72] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[73] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[74] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[75] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[76] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[77] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[78] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[79] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[80] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[81] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[82] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[83] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[84] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[85] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[86] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[87] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[88] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[89] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[90] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[91] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[92] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[93] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[94] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[95] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[96] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[97] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[98] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[99] 1.0e+000 6.2e-004 3.9e-002 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
G[100] 1.0e+000 1.2e-004 7.4e-003 1.0e+000 1.0e+000 1.0e+000 4000 1427 1.0e+000
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).
*** print is deprecated and will be removed in v3.0;
*** use stansummary instead
ans =
Inf
ans =
-39.1619
ans =
1.0670
ans stands for g, c and sigma in order from the top.
The values I really want to estimate are 10, 0.5 and 1.
If you need RStan data generating script, create it and attach it.
I changed warmup from 20 to 1000, but the value does not converge as usual.
However,
since it is still displayed as warmup=(0,0,0,0), there is a
possibility that the value of warmup could not be changed.
I
will try out various methods of change so please give me some time.
Inference for Stan model: ex2stan_model
4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
Warmup took (1.1, 0.19, 0.20, 0.21) seconds, 1.7 seconds total
Sampling took (0.50, 0.38, 0.42, 0.50) seconds, 1.8 seconds total
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ 6.4e+002 5.4e-002 1.3e+000 6.4e+002 6.4e+002 6.4e+002 543 302 1.0e+000
accept_stat__ 8.4e-001 4.0e-002 1.4e-001 5.0e-001 8.7e-001 1.0e+000 12 6.7 1.1e+000
stepsize__ 2.9e-001 1.1e-001 1.5e-001 1.2e-001 3.5e-001 5.0e-001 2.0 1.1 2.4e+014
treedepth__ 2.3e+000 4.2e-001 1.2e+000 0.0e+000 2.0e+000 4.0e+000 8.5 4.7 1.1e+000
n_leapfrog__ 8.8e+000 2.6e+000 7.4e+000 1.0e+000 7.0e+000 2.4e+001 7.8 4.4 1.1e+000
divergent__ 8.9e-001 7.1e-003 3.1e-001 0.0e+000 1.0e+000 1.0e+000 1881 1045 1.0e+000
energy__ -6.4e+002 6.2e-002 1.8e+000 -6.4e+002 -6.4e+002 -6.3e+002 809 450 1.0e+000
g 8.8e+307 inf inf 9.4e+306 8.8e+307 1.7e+308 4000 2223 nan
c -1.3e+003 1.4e+003 2.4e+003 -7.1e+003 -1.8e+002 -4.1e+001 2.9 1.6 2.7e+000
sigma 1.2e+000 3.4e-003 9.0e-002 1.1e+000 1.2e+000 1.4e+000 718 399 1.0e+000
G[1] 1.0e+002 2.2e-014 1.4e-012 1.0e+002 1.0e+002 1.0e+002 4000 2223 1.0e+000
G[2] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[3] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[4] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[5] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[6] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[7] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[8] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[9] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[10] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[11] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[12] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[13] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[14] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[15] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[16] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[17] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[18] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[19] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[20] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[21] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[22] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[23] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[24] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[25] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[26] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[27] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[28] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[29] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[30] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[31] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[32] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[33] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[34] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[35] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[36] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[37] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[38] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[39] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[40] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[41] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[42] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[43] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[44] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[45] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[46] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[47] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[48] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[49] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[50] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[51] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[52] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[53] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[54] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[55] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[56] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[57] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[58] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[59] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[60] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[61] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[62] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[63] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[64] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[65] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[66] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[67] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[68] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[69] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[70] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[71] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[72] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[73] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[74] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[75] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[76] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[77] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[78] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[79] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[80] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[81] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[82] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[83] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[84] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[85] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[86] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[87] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[88] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[89] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[90] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[91] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[92] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[93] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[94] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[95] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[96] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[97] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[98] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[99] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
G[100] 1.0e+000 1.1e-017 6.7e-016 1.0e+000 1.0e+000 1.0e+000 4000 2223 1.0e+000
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).
*** print is deprecated and will be removed in v3.0;
*** use stansummary instead
ans =
Inf
ans =
-1.3135e+03
ans =
1.2287
Since I was receiving advice, I tried to give g and c various priors.
Then, the value of g successfully converged to the value I wanted.
Thank you very much.
Although the value of c has converged to an incorrect value, I would like to try giving various priors.
2017年1月31日火曜日 5時30分44秒 UTC+9 Bob Carpenter:
That report is unfortunate, but I think Daniel's right. You're
getting the 1000 warmup iterations by default, but they're not
being saved by MatlabStan, so the summary has zero warmup.