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rat/EA-predictions/Analysis.md
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@ -159,11 +159,22 @@ You can find [Predictions.csv](https://nunosempere.github.io/rat/EA-predictions/
+ return(sum2(as.vector((answers>=DataFrameProcessed[x,y-1])) & as.vector(answers<=DataFrameProcessed[x,y])) ) + return(sum2(as.vector((answers>=DataFrameProcessed[x,y-1])) & as.vector(answers<=DataFrameProcessed[x,y])) )
+ } + }
> >
> ### We can get the Brier scores:
> Brierscore <- function(x){ return( (x*(1-0.8)^2 + (24-x)*(.8)^2)/24) }
>
> ### vapply applies a function to every member of a vector. > ### vapply applies a function to every member of a vector.
> vapply(c(1:35),total,numeric(1))->DataFrameProcessed$totalcorrect > vapply(c(1:35),total,numeric(1))->DataFrameProcessed$totalcorrect
>
> vapply(DataFrameProcessed$totalcorrect,Brierscore,numeric(1))->DataFrameProcessed$Brierscores > vapply(DataFrameProcessed$totalcorrect,Brierscore,numeric(1))->DataFrameProcessed$Brierscores
> >
> ### We can also aggregate stuff by question:
> totalperquestion <-function(x){
+ z=c(1:35)
+ return(sum2(as.vector((answers[x]>=DataFrameProcessed[z,2*x-1])) & as.vector(answers[x]<=DataFrameProcessed[z,2*x])) )
+ }
> vapply(c(1:26), totalperquestion, numeric(1)) -> TotalCorrect
> percentageperquestion <-function(x){return( totalperquestion(x)*100/35)}
> png("Scatterplot-questions.png", units="px", width=3200, height=3200, res=500)
>
> ### And you can get graphics using > ### And you can get graphics using
> png("Scatterplot3.png", units="px", width=3200, height=3200, res=500) > png("Scatterplot3.png", units="px", width=3200, height=3200, res=500)
> plot(DataFrameProcessed$totalcorrect*100/24, xlab= "Persons, from 1 to 35", ylab="% of questions they got right", main="Scatterplot!") > plot(DataFrameProcessed$totalcorrect*100/24, xlab= "Persons, from 1 to 35", ylab="% of questions they got right", main="Scatterplot!")
@ -174,4 +185,12 @@ You can find [Predictions.csv](https://nunosempere.github.io/rat/EA-predictions/
> dev.off() > dev.off()
> ### As well as with the function hist(), whose parameter break = number allows you to control the granularity of the histogram. > ### As well as with the function hist(), whose parameter break = number allows you to control the granularity of the histogram.
> png("Scatterplot-questions.png", units="px", width=3200, height=3200, res=500)
> plot(vapply(p, percentageperquestion, numeric(1)), ylim=c(0,100), main="Results aggregated per question", xlab="Questions, from 1 to 24", ylab= "% of participants who got that question right")
> abline(h=80, col="blue")
> abline(h=55.14286, col="red")
> text(x=12.5, y=80+4, "Target average % of right-guessers per question", col="blue")
> text(x=12.5, y=55.14286-4, "Actual average % of right-guessers per question", col="red")
> dev.off()
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