157 lines
3.0 KiB
R
157 lines
3.0 KiB
R
ls()
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weight
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weight
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lablr
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labelr
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nrow(labelr)
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names(labelr)
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names(labelr$data)
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labelr$data
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labelr
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names(labelr)
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labelr$labelr
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labelr$toxic
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setwd("..")
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q()
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n
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summary(w2)
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summary(w2)
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q()
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n
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summary(fit1)
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0.5*(dat$x1 + sapply(dat$sdx, function(sd) rnorm(1,0,sd)))
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summary(fit1)
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summary(fit2)
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summary(fit2)
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conditional_effects(fit2,resp='y')
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plot(conditional_effects(fit2,resp='y'))
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stancode(fit2)
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stancode(fit1)
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sessionInfo()
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q()
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y
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p.y
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range(p.y)
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rbinom
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df2
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df2
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df2
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brms.corrected.logit
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q()
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n
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summary(brms.corrected.logit)
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summary(brms.corrected.logit)
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p.y
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q()
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n
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mw
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summary(mw)
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)
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summary(true.model)
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true.model
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true.model$R
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true.model$null.deviance
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true.model$deviance
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getwd()
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setwd("../../)
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setwd("../../)
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setwd("../../partitioning_reddit")
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ls
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getwd()
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list.files()
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install.packages("filelock")
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q()
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n
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df
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df
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outcome_formula <- y ~ x + z
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outcome_family=gaussian()
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proxy_formula <- w_pred ~ x
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truth_formula <- x ~ z
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params <- start
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ll.y.obs.x0
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ll.y.obs.x1
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rater_formula <- x.obs ~ x
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rater_formula
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rater.modle.matrix.obs.x0
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rater.model.matrix.obs.x0
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names(rater.model.matrix.obs.x0)
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head(rater.model.matrix.obs.x0)
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df.obs
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ll.x.obs.0
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rater.params
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rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$xobs.0==1])
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df.obs$xobs.0==1
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df.obs$x.obs.0==1
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ll.x.obs.0[df.obs$x.obs.0==1]
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rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
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df.obs$x.obs.0==1
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n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2]
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rater.params <- params[param.idx:n.rater.model.covars]
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rater.params
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ll.x.obs.0[df.obs$x.obs.0==1] <- plogis(rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE)
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t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
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)
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dimt(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,])
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dim(t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]))
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dim(ll.x.obs.0[df.obs$x.obs.0==1])
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rater.params
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rater.params
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rater.params
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rater_formula
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rater.params
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)
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1+1
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q()
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n
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outcome_formula <- y ~ x + z
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proxy_formula <- w_pred ~ x + z + y
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truth_formula <- x ~ z
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proxy_formula
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eyboardio Model 01 - Kaleidoscope locally built
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df <- df.triple.proxy.mle
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outcome_family='gaussian'
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outcome_family=gaussian()
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proxy_formulas=list(proxy_formula,x.obs.0~x, x.obs.1~x)
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proxy_formulas
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proxy_familites <- rep(binomial(link='logit'),3)
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proxy_families = rep(binomial(link='logit'),3)
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proxy_families
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proxy_families = list(binomial(link='logit'),binomial(link='logit'),binomial(link='logit'))
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proxy_families
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proxy_families[[1]]
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proxy.params
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i
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proxy_params
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proxy.params
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params
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params <- start
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df.triple.proxy.mle
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df
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coder.formulas <- c(x.obs.0 ~ x, x.obs.1 ~x)
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outcome.formula
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outcome_formula
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depvar(outcome_formula
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)
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outcome_formula$terms
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terms(outcome_formula)
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q()
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n
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df.triple.proxy.mle
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triple.proxy.mle
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df
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df <- df.triple.proxy
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outcome_family <- binomial(link='logit')
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outcome_formula <- y ~x+z
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proxy_formula <- w_pred ~ y
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coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit'))
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coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit')
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coder_formulas=list(y.obs.0~y,y.obs.1~y)
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traceback()
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df
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df
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outcome.model.matrix
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q()
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n
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