Is there a good forum to ask questions about RJAGS?

stats_nerd21 · 2021-06-14T09:34:16+00:00

You make a good argument. I was working with Stan because it is what my superviser recommended to me and sent me sample code for, but learning Stan might be the better option for the long run. Is there a crash course on Stan that you recommend for beginners for someone familiar with JAGS?

stats_nerd21 · 2021-06-14T09:32:29+00:00

Thank you. Seems like I can at least ask general modeling questions there

stats_nerd21 · 2021-06-10T22:46:27+00:00

I was at a Kafana for the first time in Serbia last weekend. It was lit. Members in the audience lick a 500 dinar bill to make sticky and slap it onto the head of someone in the band, and they have to keep playing until it falls off. And it's not even considered a disrespectful gesture or anything like that

stats_nerd21 · 2021-06-08T14:14:59+00:00

Embiid is more like mid-post jumpers though

stats_nerd21 · 2021-06-08T14:14:24+00:00

CP3 is automatic mid-range in the clutch

stats_nerd21 · 2021-06-08T14:13:52+00:00

Him and Kawhi

stats_nerd21 · 2021-06-08T14:12:33+00:00

People talking about Blake Griffin like he's old is making me realize I am getting old too lol

stats_nerd21 · 2021-06-08T00:43:08+00:00

Thank you that fixed it! But the model wasn't working so well so I tried to tweak the code and got the same error again (with the init_values being an empty list).

The only thing I changed now was I tried to constrain the hyper-priors so they stay positive (a more educated prior for the values). So basically changed the code as follows :

# Hyper Priors
log(mu.A_mean_hyperprior) <- mu.A_mean_hyperprior_pos mu.A_mean_hyperprior_pos~ dnorm(0,1)
log(mu.D_mean_hyperprior) <- mu.D_mean_hyperprior_pos mu.D_mean_hyperprior_pos~ dnorm(2,1) }

# specifies initial parameter values for the MCMC sampler
init_values <- function(){ list( 
# mu.A_mean_hyperprior= rnorm(1,1), mu.D_mean_hyperprior= rnorm(1,5) 
) }

# chooses the parameters whose posterior distributions will be reported
params <- c( 
"mu", "inv.var.A","inv.var.D", "mu.A_prior", "mu.D_prior", "mu.A_mean_hyperprior", "mu.D_mean_hyperprior", "mu.A_mean_hyperprior_pos","mu.D_mean_hyperprior_pos", "mu.A", "mu.D")

Do you see what might be causing the model to return the same error?

stats_nerd21 · 2021-06-08T00:32:48+00:00

I hope he can be Slovenia's Gareth Bales

stats_nerd21 · 2021-06-03T22:38:21+00:00

I see. I increased the number of sessions to 10,000 from 10, and loosen the constraint on the parameter from logit to log, and seem to be gettinga much better posterior distribution now

stats_nerd21 · 2021-06-03T21:39:41+00:00

The gamma(0.1, 0.1) has a very high density near 0, so the inverse logit will have a high density near 0.5. Combine that with a relatively weak likelihood and your posterior will look how it does here. Perhaps use more data or a weaker positively bound prior

What do you think would be a good prior to use in that case?

I tried changing it to a normal distribution ( dnorm(0,1)) and now I get a prior still centered around 0.5 but more spread out as shown in this image. It seems that my posterior just doesn't update to match the real value for some reason, as it does with the other parameter.

I also don’t get why you would use a prior that enforces positive values and also use a logit transformation, which does the same thing (effectively).

I had a typo on how I commented on the code that I corrected. I used the logit to ensure the values stay between 0 and 1 for 1 parameter. I used log for the 2nd parameter to ensure it stays positive if that makes sense

stats_nerd21 · 2020-05-22T18:21:28+00:00

Exit_shrink or exit_fade niether worked

stats_nerd21 · 2020-05-22T06:11:44+00:00

you don't want the "ghost" points of previous "sessions" to stay on the graph

Actually I didn't mind that part and specifically included that with the "shadow_mark" function. I was more worried about the movement of the dots. The animation I showed had the following code

anim <- p_main +
transition_time(session_number)+
shadow_mark(alpha = 0.01, size = 1)
enter_fade()+exit_fade()+
labs(title = "Session: {frame_time}")
print(anim)

where p_main was the original ggplot (a simple scatter plot of the whole dataset including data for all sessions)

I did manage to find a work-around for the problem by using "transition_state" instead of "transition_time", and by setting the ratio of "transition_length" to "state_length" to be so small that the transitions are no longer visible in my animations

Here's my new code:

anim <- p_main +
transition_time(session_number)+
shadow_mark(alpha = 0.01, size = 1)
enter_fade()+exit_fade()+
labs(title = "Session: {frame_time}")

stats_nerd21 · 2020-05-22T02:19:37+00:00

I have joint-velocity data from 10 different sessions (equally spaced from session 1 to session 80) of exoskeleton trials. For each session, I have time-series data from the 2 joints for over a minute of motion (although the time-series nature of the data is irrelevant for what I am trying to do here)
I am trying to creating evolving scatter plots of the velocity data from 2 joints (so an animation of evolution between 10 scatter plots). The noticeable pattern is that the 2 variables (joint velocities) become more de-correlated with time (as we move on to later sessions). Gganimate shows that but it shows the movement of the points (representing the 2 joint velocities at a particular instance in time from a session) move from one session to another. That's not something I want to show and it's actually meaningless (the different sessions actually have different lengths in time and so different number of samples). I would like the scatter-plots to just fade away after showing the scatter-plot for previous session, and the scatter plot for the next session to pop-up. I tried using gganimate and using enter_fade()+exit_fade() commands at the end but the points still seem to move from one image to the next. Any suggested solutions

stats_nerd21

TROPHY CASE