ls()
weight
weight
lablr
labelr
nrow(labelr)
names(labelr)
names(labelr$data)
labelr$data
labelr
names(labelr)
labelr$labelr
labelr$toxic
setwd("..")
q()
n
summary(w2)
summary(w2)
q()
n
summary(fit1)
0.5*(dat$x1 + sapply(dat$sdx, function(sd) rnorm(1,0,sd)))
summary(fit1)
summary(fit2)
summary(fit2)
conditional_effects(fit2,resp='y')
plot(conditional_effects(fit2,resp='y'))
stancode(fit2)
stancode(fit1)
sessionInfo()
q()
y
p.y
range(p.y)
rbinom
df2
df2
df2
brms.corrected.logit
q()
n
summary(brms.corrected.logit)
summary(brms.corrected.logit)
p.y
q()
n
mw
summary(mw)
)
summary(true.model)
true.model
true.model$R
true.model$null.deviance
true.model$deviance
getwd()
setwd("../../)
setwd("../../)

setwd("../../partitioning_reddit")
ls
getwd()
list.files()
install.packages("filelock")
q()
n
df
df
outcome_formula <- y ~ x + z
outcome_family=gaussian()
proxy_formula <- w_pred ~ x
truth_formula <- x ~ z
params <- start
ll.y.obs.x0
ll.y.obs.x1
rater_formula <- x.obs ~ x
rater_formula
rater.modle.matrix.obs.x0
rater.model.matrix.obs.x0
names(rater.model.matrix.obs.x0)
head(rater.model.matrix.obs.x0)
df.obs
ll.x.obs.0
rater.params
rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$xobs.0==1])
df.obs$xobs.0==1
df.obs$x.obs.0==1
ll.x.obs.0[df.obs$x.obs.0==1]
rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
df.obs$x.obs.0==1
n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2]
        rater.params <- params[param.idx:n.rater.model.covars]
rater.params
        ll.x.obs.0[df.obs$x.obs.0==1] <- plogis(rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE)
t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
)
dimt(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,])
dim(t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]))
dim(ll.x.obs.0[df.obs$x.obs.0==1])
rater.params
rater.params
rater.params
rater_formula
rater.params
)
1+1
q()
n
outcome_formula <- y ~ x + z
proxy_formula <- w_pred ~ x + z + y
truth_formula <- x ~ z
proxy_formula
eyboardio Model 01 - Kaleidoscope locally built
df <- df.triple.proxy.mle
outcome_family='gaussian'
outcome_family=gaussian()
proxy_formulas=list(proxy_formula,x.obs.0~x, x.obs.1~x)
proxy_formulas
proxy_familites <- rep(binomial(link='logit'),3)
proxy_families = rep(binomial(link='logit'),3)
proxy_families
proxy_families = list(binomial(link='logit'),binomial(link='logit'),binomial(link='logit'))
proxy_families
proxy_families[[1]]
proxy.params
i
proxy_params
proxy.params
params
params <- start
df.triple.proxy.mle
df
coder.formulas <- c(x.obs.0 ~ x, x.obs.1 ~x)
outcome.formula
outcome_formula
depvar(outcome_formula
)
outcome_formula$terms
terms(outcome_formula)
q()
n
df.triple.proxy.mle
triple.proxy.mle
df
df <- df.triple.proxy
outcome_family <- binomial(link='logit')
outcome_formula <- y ~x+z
proxy_formula <- w_pred ~ y
coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit'))
coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit')
coder_formulas=list(y.obs.0~y,y.obs.1~y)
traceback()
df
df
outcome.model.matrix
q()
n