112 lines
4.7 KiB
R
112 lines
4.7 KiB
R
library(scales)
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library(data.table)
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library(ggplot2)
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iv.example <- readRDS("iv_perspective_example.RDS")
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dv.example <- readRDS("dv_perspective_example.RDS")
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iv.sample.prop <- iv.example$cc_ex_tox.likes.race_disclosed.medsampsample.prop
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dv.sample.prop <- dv.example$cc_ex_tox.likes.race_disclosed.largesampsample.prop
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iv.sample.count <- iv.example$cc_ex_tox.likes.race_disclosed.medsampsample.count
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dv.sample.count <- dv.example$cc_ex_tox.likes.race_disclosed.largesampsample.count
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plot.cc.example <- function(datalist, name, varnames=NULL, varorder=NULL, include.models=c("Automated Classifications", "Manual Annotations")){
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model.names <- c("Automated Classifications", "Manual Annotations", "Annotation Sample", "Error Correction")
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glm.par.names <- paste0(name,"coef_",c("pred", "coder", "sample"), "_model")
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measerr.par.name <- paste0(name,"measerr_model_par")
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glm.pars <- datalist[glm.par.names]
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n.pars <- length(glm.pars[[1]])
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all.pars <- append(glm.pars, list("corrected"=datalist[[measerr.par.name]][1:n.pars]))
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names(all.pars) <- model.names
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df.pars <- as.data.table(data.frame(all.pars),keep.rownames=TRUE)
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if(!is.null(varnames)){
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df.pars[, rn := varnames]
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}
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setnames(df.pars, old="rn", new="variable")
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glm.stderr.names <- paste0(name,"se_",c("pred", "coder", "sample"), "_model")
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glm.stderr <- datalist[glm.stderr.names]
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measerr.stderr.name <- paste0(name,"measerr_model_stderr")
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all.stderr <- append(glm.stderr, list("corrected"=datalist[[measerr.stderr.name]][1:n.pars]))
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names(all.stderr) <- model.names
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df.stderr <- as.data.table(data.frame(all.stderr), keep.rownames=TRUE)
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if(!is.null(varnames)){
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df.stderr[, rn := varnames]
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}
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setnames(df.stderr, old="rn", new="variable")
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df.pars <- melt(df.pars, id.vars = "variable", variable.name = "Model", value.name = "Estimate")
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df.stderr <- melt(df.stderr, id.vars = "variable",variable.name = "Model", value.name = "StdErr")
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df <- df.pars[df.stderr, on = c("variable", "Model")]
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df[,":="(UpperCI = Estimate + 1.96*sqrt(StdErr),
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LowerCI = Estimate - 1.96*sqrt(StdErr))]
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if(!is.null(varorder)){
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df[,variable:=factor(variable,levels=varorder)]
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}
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df[,Model:= factor(gsub('\\.',' ', Model), levels=rev(model.names))]
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df <- df[Model %in% include.models]
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p <- ggplot(df[variable != "Intercept"], aes(y = Estimate, x=Model, ymax=LowerCI, ymin=UpperCI, group=variable))
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p <- p + geom_pointrange(shape=1) + facet_wrap('variable',scales='free_x',nrow=1,as.table=F) + geom_hline(aes(yintercept=0),linetype='dashed',color='gray40') + coord_flip() + xlab("")
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p <- p + scale_y_continuous(breaks=breaks_extended(4))
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return(p)
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}
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plot.civilcomments.dv.example <- function(include.models=c("Automatic Classification", "All Annotations")){
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return(plot.cc.example(dv.example, "cc_ex_tox.likes.race_disclosed.medsamp", varnames=c("Intercept", "Likes", "Identity Disclosure", "Likes:Identity Disclosure"),varorder=c("Intercept", "Likes", "Identity Disclosure", "Likes:Identity Disclosure"), include.models=include.models) + ylab("Coefficients and 95% Confidence Intervals") + ggtitle("Logistic Regression Predicting Toxicity"))
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}
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plot.civilcomments.iv.example <- function(include.models=c("Automatic Classification", "All Annotations")){
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plot.cc.example(iv.example, "cc_ex_tox.likes.race_disclosed.medsamp", varnames=c("Intercept", "Likes", "Likes:Toxicity", "Toxicity"),varorder=c("Intercept", "Likes", "Toxicity", "Likes:Toxicity"), include.models=include.models) + ylab("Coefficients and 95% Confidence Intervals") + ggtitle("Logistic Regression Predicting Racial/Ethnic Identity Disclosure")
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}
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plot.civilcomments.iv.example.2 <- function(){
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attach(iv.example)
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df.pars <- rbind(cc_ex_tox.likes.race_disclosedcoef_pred_model,
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cc_ex_tox.likes.race_disclosedcoef_coder_model,
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cc_ex_tox.likes.race_disclosedcoef_sample_model,
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cc_ex_tox.likes.race_disclosedmeaserr_model_par[1:3]
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)
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rownames(df.pars) <- c('predictions', 'coders', 'sample', 'corrected')
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df.stderr <- rbind(cc_ex_tox.likes.race_disclosedse_pred_model,
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cc_ex_tox.likes.race_disclosedse_coder_model,
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cc_ex_tox.likes.race_disclosedse_sample_model,
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cc_ex_tox.likes.race_disclosedmeaserr_model_stderr[1:3]
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)
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rownames(df.pars) <- c('predictions', 'coders', 'sample', 'corrected')
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ci.upper <- df.pars + 1.96 * sqrt(df.stderr)
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ci.lower <- df.pars - 1.96 * sqrt(df.stderr)
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return(plot.cc.example(df.pars, ci.lower, ci.upper))
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}
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