> # R analysis for the manuscript
> # Schmid, D., Hesse, C., & Schenk, T. (in review). Blindsight and residual vision: How reliable is the redundant target effect as a
> #  tool? Cortex
> 
> 
> # using R version 4.2.0 (2022-04-22 ucrt) -- "Vigorous Calisthenics"
> # calculate 95% confidence intervals using the Wilson score method
> 
> # use binomial package
> # https://www.rdocumentation.org/packages/binom/versions/1.1-1.1/topics/binom.confint
> # install.packages("binom")   # run once
> require(binom)
> require(tidyverse)
> 
> # set current directory
> # current directory has to contain the following files
> # RTP Review.R
> # RTP Review Data.xlsx
> setwd("XXX") # insert your directory here!
> 
> # load data from excel file
> Data <- read.csv2("RTP Review Data.csv")
> 
> # prevalence of RVCs in patients combining single-case and group results: 21/74; see table 1
> X = sum(Data$`RVC-Prevalence N Clean`, na.rm = TRUE)
> N = sum(Data$`HVFD Patients N Clean`, na.rm = TRUE)
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # prevalence of RVCs in patients based on single-case results: 15/68
> X = sum(Data$`RVC-Prevalence SC N Clean`, na.rm = TRUE)
> N = sum(Data$`RVC SC-Test N Clean`, na.rm = TRUE)
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # sensitivity is assessed by the RTE measured for a unilateral-redundant condition where targets are presented within the sighted visual field
> # sensitivity in patients based on single-case results: 5/12
> X = sum(Data$`RTE Unilateral SC N`, na.rm = TRUE)
> N = sum(Data$`Unilateral SC N`, na.rm = TRUE)
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # sensitivity in patients combining single-case and group results: 34/53
> X = sum(Data$`RTE Unilateral N`, na.rm = TRUE)
> N = sum(Data$`Unilateral Test N`, na.rm = TRUE)
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # sensitivity in healthy participants: 17/22
> # derived from Schärli et al., 1999
> X = sum(Data[Data$Study == "Schärli et al., 1999", "Healthy Participants RTE-Prevalence"])
> N = sum(Data[Data$Study == "Schärli et al., 1999", "Healthy Participants SC N"])
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # specificity in healthy participants: 17/19 do NOT show the effect
> # derived from Schmid et al., 2022
> X = sum(Data[Data$Study == "Schmid et al., 2022", "Healthy Participants RTE-Absence"])
> N = sum(Data[Data$Study == "Schmid et al., 2022", "Healthy Participants SC N"])
> result <- binom.confint(X, N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # reliability in patients: 4/11; see Table 3
> X = sum(Data$`RTE Reliable N Clean`, na.rm = TRUE)
> N = sum(Data$`Repeated Testing N Clean`, na.rm = TRUE)
> result <- binom.confint(x = X, n = N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> # unreliable results in patients: 7/11; see Table 3
> X = sum(Data$`Repeated Testing N Clean`, na.rm = TRUE) - sum(Data$`RTE Reliable N Clean`, na.rm = TRUE)
> N = sum(Data$`Repeated Testing N Clean`, na.rm = TRUE)
> result <- binom.confint(x = X, n = N, methods = "wilson")
> print(result)
  method x n mean lower upper
1 wilson 0 0  NaN   NaN   NaN
> paste("value = ",round(result$mean*100, digits = 1), "% (95% CI [", round(result$lower*100),"%, ",round(result$upper*100),"%])", sep = "")
[1] "value = NaN% (95% CI [NaN%, NaN%])"
> 
> 
> # -------------------------------------------------------------
> # create figure for positive and negative predictive values
> 
> # load packages
> library(tidyverse)
> library(ggpattern)
> 
> # define folder to save figures
> # in this folder, there should also be the PlotSytleApa.R file
> setwd("XXX")
> 
> # load default plot style
> source("PlotStyleApa.R")
> 
> # prevalence estimate based on Marzi et al., 1986: 4/20 = 20%
> Prevalence = 20 # 20 of 100 patients possess RVCs
> Sensitivity = 0.42
> Specificity = 0.89
> TruePos20 = round(Prevalence*Sensitivity) # RVC cases correctly detected
> FalseNeg20 = Prevalence-TruePos20           # RVC cases missed
> TrueNeg20 = round((100-Prevalence)*Specificity) # No-RVC cases correctly detected
> FalsePos20 = (100-Prevalence) - TrueNeg20
> 
> AllPos20 = TruePos20 + FalsePos20
> AllNeg20 = TrueNeg20 + FalseNeg20
> 
> # prevalence estimate: 60%
> Prevalence = 60 # 60 of 100 patients possess RVCs
> Sensitivity = 0.42
> Specificity = 0.89
> TruePos60 = round(Prevalence*Sensitivity) # RVC cases correctly detected
> FalseNeg60 = Prevalence-TruePos60           # RVC cases missed
> TrueNeg60 = round((100-Prevalence)*Specificity) # No-RVC cases correctly detected
> FalsePos60 = (100-Prevalence) - TrueNeg60
> 
> AllPos60 = TruePos60 + FalsePos60
> AllNeg60 = TrueNeg60 + FalseNeg60
> 
> # define data
> Values <- data.frame(Prevalence = c("Prevalence 20%","Prevalence 20%", "Prevalence 20%","Prevalence 20%", "Prevalence 60%", "Prevalence 60%", "Prevalence 60%", "Prevalence 60%"),
+                      Outcome = c('Positive','Negative','Positive','Negative','Positive','Negative','Positive','Negative'),
+                      ConditionNumber = c(AllPos20, AllNeg20, AllPos20, AllNeg20, AllPos60, AllNeg60, AllPos60, AllNeg60),
+                      Correct = c('Correct','Correct','False','False','Correct','Correct','False','False'),
+                      Count = c(TruePos20, TrueNeg20, FalsePos20, FalseNeg20, TruePos60, TrueNeg60, FalsePos60,FalseNeg60))
> 
> Values$Percent <- round(Values$Count/Values$ConditionNumber*100)
> Values$PercentStr <- paste(Values$Percent,"%", sep= "")
> Values$PercentY <- (Values$ConditionNumber-(Values$ConditionNumber-Values$Count))/2 +(Values$ConditionNumber-Values$Count)
> Values$PercentY[Values$Correct == "False"] = Values$Count[Values$Correct == "False"]/2
> 
> Values$Outcome <- factor(Values$Outcome, levels = c("Positive","Negative"))
> 
> # define figure name for png
> figurename_png = paste("RTP_Figure_1.png", sep = "")
> 
> # define size of png-figure
> # 1 column: 9.14 cm; 1.5 column: 14.09 cm; 2 columns: 19.05 cm
> width_cm = 14.09
> height_cm = 9.14
> 
> print(ggplot(data = Values, aes(x=Outcome, y = Count, fill = Correct))+              
+         geom_col(color = "black")+
+         scale_y_continuous(limits = c(0,100), breaks = seq(0, 100, by = 25))+
+         scale_fill_manual(values = c("white","grey"))+
+         geom_text(data = Values, aes(x = Outcome, y = PercentY, label = Count), color = "black", size = 2.6)+
+         ylab("Number of Cases")+
+         xlab("Test Outcome")+
+         facet_grid(cols = vars(Prevalence))+
+         PlotStyle+
+         labs(fill = NULL))         
> 
> # save figure as png
> ggsave(filename = figurename_png, width = width_cm, height = height_cm, units = "cm", dpi = 300)