+++ /dev/null
-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(toxicity_calibrated)
-qplot(labelr$toxic,type='hist')
-names(labelr)
-labelr$n
-labelr
-names(labelr)
-fbyg
-gghist(fbyg$weight)
-hist(fbyg$weight)
-hist(log(fbyg$weight))
-fbyg$weight==1
-all(fbyg$weight==1)
-fbyg$weight[fbyg$weight != 1]
-fbyg[fbyg$weight != 1]
-fbyg[,fbyg$weight != 1]
-fbyg[[fbyg$weight != 1]]
-fbyg[fbyg$weight != 1,]
-names(labelr)
-summary(toxicity_calibrated)
-toxicity_calibrated
-val.data
-names(labelr)
-labelr$data
-labelr
-labelr[data]
-labelr[data=='yg']
-labelr[,data=='yg']
-labelr[data=='yg',]
-labelr[labelr$data=='yg']
-labelr[,labelr$data=='yg']
-labelr[labelr$data=='yg',]
-toxicity_calibrated
-summary(toxicity_calibrated)
-yg3
-yg3[,['toxic','toxic_pred']]
-yg3 %>% select('toxic','toxic_pred')
-yg3 |> select('toxic','toxic_pred')
-names(yg3)
-yg3[,c('toxic_pred','toxic')]
-corr(yg3[,c('toxic_pred','toxic')])
-cor(yg3[,c('toxic_pred','toxic')])
-cor(yg3[,c('toxic_pred','toxic')],na.rm=T)
-cor(yg3[,c('toxic_pred','toxic')],rm.na=T)
-?cor(yg3[,c('toxic_pred','toxic')],use=
-?cor
-cor(yg3[,c('toxic_pred','toxic')],use='all.obs')
-?cor
-cor(yg3[,c('toxic_pred','toxic')],use='complete.obs')
-cor(yg3[,c('toxic_pred','toxic')],use='complete.obs',method='spearman')
-?predict
-yg3$toxic_pred
-names(preds)
-preds
-preds
-preds$error
-preds
-preds
-summary(errormod)
-summary(errormod)
-summary(preds)
-names(preds)
-preds
-resids
-qplot(resids)
-resids
-?predict.lm
-dnorm(1)
-dnorm(2)
-dnorm(1)
-pnorm(1)
-preds
-p1 + p2
-p1 + p2
-p1
-p2
-preds
-preds1 <- preds
-preds1$diff - preds$diff
-preds1$diff
-preds1$diff - preds1$diff
-preds1$diff - preds$diff
-preds1$diff - preds$diff
-preds1$diff - preds$diff
-preds1$diff - preds$diff
-preds1
-preds
-dnorm(-1)
-dnorm(1)
-pnorm(1)
-pnorm(-1)
-pnorm(2)
-pnorm(9)
-pnorm(6)
-pnorm(2)
-dnorm(0.95)
-qnorm(0.95)
-qnorm(0.841)
-fulldata_preds
-names(yg3)
-yg3$toxic_feature_1
-yg3$toxic_feature_2
-yg3
-yg3[,.(toxic_pred,toxic_var)]
-yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma)]
-yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,cov(toxicity_2_pred,toxicity_1_pred))]
-cov(1,2)
-cov(c(1),c(3))
-cov(c(1),c(3,2))
-cov(c(1,1),c(3,2))
-cov(c(1,2),c(3,2))
-covterm
-covterm
-?cov
-covterm
-yg3
-yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,cov(toxicity_2_pred,toxicity_1_pred))]
-yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,toxic_var)]
-yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,toxic_var,toxic_sd)]
-yg3
-names(yg3)
-print(sg)
-print(sg)
-1+1
-library(stargazer)
-
-stargazer(w1,w2,w3,w4,w5,t1,t2,t3,t4,t5, type="text",
- keep = c("cond1","meantox","cond1:meantox","Constant"),
- keep.stat=c("n","adj.rsq"),
- model.numbers = F,
- dep.var.labels = c("DV = Willingness to comment","DV = Toxicity of YG respondent comments"),
- covariate.labels = c("Treatment (top comments shown)",
- "Average toxicity of top comments",
- "Treatment $\times$ top comments toxicity",
- "Constant"),
- add.lines = list(c("Article fixed effects","No","No","No","Yes","Yes","No","No","No","Yes","Yes")),
- star.cutoffs = c(0.05,0.01,0.005),
- notes = "Standard errors are clustered at the respondent level.",
- column.labels = c("(1)","(2)","(3)","(4)","(5)","(6)","(7)","(8)","(9)","(10)"),
- style = "apsr")
-
-q()
-n
-yglabels
-labelr
-names(labelr)
-fb
-names(fb
-)
-fb.comment_id
-fb['comment_id']
-fb[,'comment_id']
-labelr[,'comment_id']
-names(fb)
-fb.labeled
-names(fb.labeled)
-names(yg)
-?amelia
-yg
-names(yg)
-names(yg3)
-?rbind
-nrow(yg3)
-nrow(yg)
-yg3[,.(.N),by=.(toxic,fb)]
-yg3.toimpute
-names(yg3.toimpute)
-yg3.toimpute
-names(yg3.toimpute)
-names(labelr)
-nrow(yg3)
-nrow(labelr)
-?merge.data.table
-labelr
-is.data.table(labelr)
-yg3.toimpute
-overimp.grid
-overimp.grid
-?amelia
-q()
-n
-setwd("presentations/ica_hackathon_2022/")
-ls()
-attach(r)
-example_2_B.plot.df
-library(ggplot2)
-example_2_B.plot.df[(variable=='x') && (m < 1000)]
-example_2_B.plot.df[(variable=='x') && (m < 1000)]
-theme_set(theme_default())
-theme_set(theme_minimal())
-theme_set(theme_classic())
-example_2_B.plot.df[(variable=='x') && (m < 1000)]
-example_2_B.plot.df[(variable=='x') && (m < 1000),unique(method)]
-as.factor
-update.packages()
-update.packages()
-update.packages()
-cancel
-plot.df
-example_2_B.plot.df
-plot.df
-example_2_B.plot.df
-example_2_B.plot.df$method %>% unique
-example_2_B.plot.df$method |> unique
-example_2_B.plot.df$method |> uniq
-unique(example_2_B.plot.df$method)
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-example_2_B.plot.df <- r$example_2_B.plot.df
-q()
-n
-setwd("presentations/ica_hackathon_2022/')
-setwd("presentations/ica_hackathon_2022/")
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-q()
-n
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-q()
-n
-example_2_B.plot.df$method
-example_2_B.plot.df$method
-q()
-n
-q()
-n
-plot.df
-plot.df
-plot.df[,.N,by=.(N,m)]
-plot.df[,.N,by=.(N,m,method)]
-plot.df[variable=='x',.N,by=.(N,m,method)]
-plot.df
-plot.df[(variable=='x') & (m < 1000) & (!is.na(p.true.in.ci))]
-plot.df[(variable=='x') & (m != 1000) & (!is.na(p.true.in.ci))]
-plot.df
-?label_wrap_gen
-install.packages("ggplot2")
-devtools::install_github("tidyverse/ggplot2")
-2
-library(ggplot2)
-ggplot2::version
-sessioninfo()
-sessionInfo()
-q()
-n
-sessionInfo()
-?scale_x_discrete
-?facet_grid
-plot.df
-plot.df
-plot.df[method="2SLS+gmm"]
-plot.df[method=="2SLS+gmm"]
-df <- example_2_B.plot.df
-df
-q()
-n
-plot.df
-plot.df[m=50]
-plot.df[m==50]
-plot.df.example.2[m==50][method=2SLS+gmm]
-plot.df.example.2[m==50][method==2SLS+gmm]
-plot.df.example.2[(m==50) & (method==2SLS+gmm)]
-plot.df.example.2[(m==50) & (method=="2SLS+gmm")]
-plot.df[m==50]
-plot.df.example.3
-plot.df.example.3
-plot.df.example.3[N=25000]
-plot.df.example.3[N==25000]
-plot.df
-plot.df
-plot.df
-q()
-n
projects / ml_measurement_error_public.git / blobdiff