simulations/.Rhistory

   1 ls()
   2 weight
   3 weight
   4 lablr
   5 labelr
   6 nrow(labelr)
   7 names(labelr)
   8 names(labelr$data)
   9 labelr$data
  10 labelr
  11 names(labelr)
  12 labelr$labelr
  13 labelr$toxic
  14 setwd("..")
  15 q()
  16 n
  17 summary(w2)
  18 summary(w2)
  19 q()
  20 n
  21 summary(fit1)
  22 0.5*(dat$x1 + sapply(dat$sdx, function(sd) rnorm(1,0,sd)))
  23 summary(fit1)
  24 summary(fit2)
  25 summary(fit2)
  26 conditional_effects(fit2,resp='y')
  27 plot(conditional_effects(fit2,resp='y'))
  28 stancode(fit2)
  29 stancode(fit1)
  30 sessionInfo()
  31 q()
  32 y
  33 p.y
  34 range(p.y)
  35 rbinom
  36 df2
  37 df2
  38 df2
  39 brms.corrected.logit
  40 q()
  41 n
  42 summary(brms.corrected.logit)
  43 summary(brms.corrected.logit)
  44 p.y
  45 q()
  46 n
  47 mw
  48 summary(mw)
  49 )
  50 summary(true.model)
  51 true.model
  52 true.model$R
  53 true.model$null.deviance
  54 true.model$deviance
  55 getwd()
  56 setwd("../../)
  57 setwd("../../)
  58
  59 setwd("../../partitioning_reddit")
  60 ls
  61 getwd()
  62 list.files()
  63 install.packages("filelock")
  64 q()
  65 n
  66 df
  67 df
  68 outcome_formula <- y ~ x + z
  69 outcome_family=gaussian()
  70 proxy_formula <- w_pred ~ x
  71 truth_formula <- x ~ z
  72 params <- start
  73 ll.y.obs.x0
  74 ll.y.obs.x1
  75 rater_formula <- x.obs ~ x
  76 rater_formula
  77 rater.modle.matrix.obs.x0
  78 rater.model.matrix.obs.x0
  79 names(rater.model.matrix.obs.x0)
  80 head(rater.model.matrix.obs.x0)
  81 df.obs
  82 ll.x.obs.0
  83 rater.params
  84 rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$xobs.0==1])
  85 df.obs$xobs.0==1
  86 df.obs$x.obs.0==1
  87 ll.x.obs.0[df.obs$x.obs.0==1]
  88 rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
  89 df.obs$x.obs.0==1
  90 n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2]
  91         rater.params <- params[param.idx:n.rater.model.covars]
  92 rater.params
  93         ll.x.obs.0[df.obs$x.obs.0==1] <- plogis(rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE)
  94 t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
  95 )
  96 dimt(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,])
  97 dim(t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]))
  98 dim(ll.x.obs.0[df.obs$x.obs.0==1])
  99 rater.params
 100 rater.params
 101 rater.params
 102 rater_formula
 103 rater.params
 104 )
 105 1+1
 106 q()
 107 n
 108 outcome_formula <- y ~ x + z
 109 proxy_formula <- w_pred ~ x + z + y
 110 truth_formula <- x ~ z
 111 proxy_formula
 112 eyboardio Model 01 - Kaleidoscope locally built
 113 df <- df.triple.proxy.mle
 114 outcome_family='gaussian'
 115 outcome_family=gaussian()
 116 proxy_formulas=list(proxy_formula,x.obs.0~x, x.obs.1~x)
 117 proxy_formulas
 118 proxy_familites <- rep(binomial(link='logit'),3)
 119 proxy_families = rep(binomial(link='logit'),3)
 120 proxy_families
 121 proxy_families = list(binomial(link='logit'),binomial(link='logit'),binomial(link='logit'))
 122 proxy_families
 123 proxy_families[[1]]
 124 proxy.params
 125 i
 126 proxy_params
 127 proxy.params
 128 params
 129 params <- start
 130 df.triple.proxy.mle
 131 df
 132 coder.formulas <- c(x.obs.0 ~ x, x.obs.1 ~x)
 133 outcome.formula
 134 outcome_formula
 135 depvar(outcome_formula
 136 )
 137 outcome_formula$terms
 138 terms(outcome_formula)
 139 q()
 140 n
 141 df.triple.proxy.mle
 142 triple.proxy.mle
 143 df
 144 df <- df.triple.proxy
 145 outcome_family <- binomial(link='logit')
 146 outcome_formula <- y ~x+z
 147 proxy_formula <- w_pred ~ y
 148 coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit'))
 149 coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit')
 150 coder_formulas=list(y.obs.0~y,y.obs.1~y)
 151 traceback()
 152 df
 153 df
 154 outcome.model.matrix
 155 q()
 156 n