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Computes inverse-link linear model values for map
and map2stan
samples.
link( fit , data , n=1000 , ... )
# S4 method for map2stan
link( fit , data , n=1000 , post , refresh=0.1 ,
replace=list() , flatten=TRUE , ... )
Object of class map
or map2stan
Optional list of data to compute predictions over. When missing, uses data found inside fit object.
Number of samples to use
Optional samples from posterior. When missing, link
extracts the samples using extract.samples
.
Refresh interval for progress display. Set to refresh=0
to suppress display.
Optional named list of samples to replace inside posterior samples. See examples.
When TRUE
, removes linear model names from result
Other parameters to pass to someone
This function computes the value of each linear model at each sample for each case in the data. Inverse link functions are applied, so that for example a logit link linear model produces probabilities, using the logistic transform.
This function is used internally by WAIC
, sim
, postcheck
, and ensemble
.
It is possible to replace components of the posterior distribution with simulated values. The replace
argument should be a named list
with replacement values. This is useful for marginalizing over varying effects. See the examples below for an example in which varying intercepts are marginalized this way.
It is easy to substitute zero values for any varying effect parameters in a model. In the data
passed to link
, either omit the relevant index variable or set the index variable to value zero. This will cause link
to replace with zero all samples for any parameters corresponding the index variable in a prior. For example, the prior declaration aid[id] ~ dmvnorm2(0,sigma,Rho)
defines a vector of parameters aid
that are indexed by the variable id
. If id
is absent from data
when calling link
, or set to value zero, then link
will replace all samples for aid
with value zero. This effectively removes the varying effect from posterior predictions. If the prior were instead c(aid,bid)[id] ~ dmvnorm(0,sigma,Rho)
, then both aid
and bid
would be set to zero in all samples.
# NOT RUN {
library(rethinking)
data(chimpanzees)
d <- chimpanzees
d$recipient <- NULL # get rid of NAs
# model 4 from chapter 12 of the book
m12.4 <- map2stan(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a + a_actor[actor] + (bp + bpC*condition)*prosoc_left ,
a_actor[actor] ~ dnorm( 0 , sigma_actor ),
a ~ dnorm(0,10),
bp ~ dnorm(0,10),
bpC ~ dnorm(0,10),
sigma_actor ~ dcauchy(0,1)
) ,
data=d , warmup=1000 , iter=4000 , chains=4 )
# posterior predictions for a particular actor
chimp <- 2
d.pred <- list(
prosoc_left = c(0,1,0,1), # right/left/right/left
condition = c(0,0,1,1), # control/control/partner/partner
actor = rep(chimp,4)
)
link.m12.4 <- link( m12.4 , data=d.pred )
apply( link.m12.4 , 2 , mean )
apply( link.m12.4 , 2 , PI )
# posterior predictions marginal of actor
# here we replace the varying intercepts samples
# with simulated values
# replace varying intercept samples with simulations
post <- extract.samples(m12.4)
a_actor_sims <- rnorm(7000,0,post$sigma_actor)
a_actor_sims <- matrix(a_actor_sims,1000,7)
# fire up link
# note use of replace list
link.m12.4 <- link( m12.4 , n=1000 , data=d.pred ,
replace=list(a_actor=a_actor_sims) )
# summarize
apply( link.m12.4 , 2 , mean )
apply( link.m12.4 , 2 , PI )
# }
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