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@Famondir
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Hello everyone,
I faced odd results from sim() using it with (ulam) models that have simplex predictors in it. I tested it with a modified version of model m12.6:
data(Trolley)
d <- Trolley %>% sample_n(1000)
edu_levels <- c( 6 , 1 , 8 , 4 , 7 , 2 , 5 , 3 )
d$edu_new <- edu_levels[ d$edu ]
dat <- list(
R = d$response ,
action = d$action,
intention = d$intention,
contact = d$contact,
E = as.integer( d$edu_new ), # edu_new as an index
alpha = rep( 2 , 8 ) ) # delta prior
m12.6 <- ulam(
alist(
R ~ ordered_logistic( phi , kappa ),
phi <- bE*sum( delta[1:E] ) + bA*action + bI*intention + bC*contact,
kappa ~ normal( 0 , 1.5 ),
c(bA,bI,bC,bE) ~ normal( 0 , 1 ),
simplex[8]: delta ~ dirichlet( alpha )
), data=dat , chains=4 , cores=4 )
summary(m12.6)
temp <- sim(m12.6) %>% as_tibble()
d$response %>% hist()
temp %>% pivot_longer(everything()) %>% pull(value) %>% hist()
I had to estimate the first parameter of the simplex model as well instead setting it zero to get sim() working.
The original data looks like:
grafik
The prediction of the model with simplex looks like:
grafik
Using the model m12.7 works just fine:
dat$edu_norm <- normalize( d$edu_new )
m12.7 <- ulam(
alist(
R ~ ordered_logistic( mu , cutpoints ),
mu <- bE*edu_norm + bA*action + bI*intention + bC*contact,
c(bA,bI,bC,bE) ~ normal( 0 , 1 ),
cutpoints ~ normal( 0 , 1.5 )
), data=dat , chains=4 , cores=4 )
temp2 <- sim(m12.7) %>% as_tibble()
temp2 %>% pivot_longer(everything()) %>% pull(value) %>% hist()
The prediction looks well:
grafik
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