In LIKANblk/eyelinesOnline: Eyelines Online

knitr::opts_chunk$set(echo = TRUE)
library(eyelinesOnline)
library(knitr)
load(params$path)

Experiment path

my_path <- paste0(str_filter(experiment[[1]]$file_data$filename_edf, '(^.+/[[:digit:]]+)/[[:digit:]]+.edf')[[1]][2], '/')
cat(my_path)

experiment_date <- paste0('Experiment date: ', experiment[[1]]$file_data$date)
report_date <- paste0('Report date: ', format(Sys.Date(), format="%d.%m.%Y"))
cat(paste0(experiment_date, '\n', report_date))

Average scores

cat(paste0('Scores = ', format(mean(
  sapply(
    Filter(
      function(x) x$file_data$score && x$file_data$record_type %in% c('train','test') ,
      experiment
    ),
    function(x) x$file_data$score
  )
), digits = 2)))

Classifier parameters

filename <- file.path(dirname(params$path), 'dump.txt')
eval(parse(text = paste0(readLines(filename, warn = F), collapse = '\n')))

cat(sprintf('Target epochs:    %i
Nontarget epochs: %i

Specifity:
   Train: %.3f ± %.3f
   Test:  %.3f ± %.3f
Sensivity:
   Train: %.3f ± %.3f
   Test:  %.3f ± %.3f
Accuracy:
   Train: %.3f ± %.3f
   Test:  %.3f ± %.3f
AUC:
   Train: %.3f ± %.3f
   Test:  %.3f ± %.3f
   All:   %.3f ± %.3f

',
res$target_epochs, res$nontarget_epochs,
res$quality$spec$tr[1],  res$quality$spec$tr[2],
res$quality$spec$tst[1], res$quality$spec$tst[2],

res$quality$sens$tr[1],  res$quality$sens$tr[2],
res$quality$sens$tst[1], res$quality$sens$tst[2],

res$quality$acc$tr[1],  res$quality$acc$tr[2],
res$quality$acc$tst[1], res$quality$acc$tst[2],

res$quality$auc$tr[1],  res$quality$auc$tr[2],
res$quality$auc$tst[1], res$quality$auc$tst[2],
res$quality$auc$all[1], res$quality$auc$all[2]))

events <- data.frame()
for ( i in 1:length(experiment)) {
  if(experiment[[i]]$file_data$record_type == 'test') {
    events <- rbind(events, experiment[[i]]$events)
  }
}
TP_ball <- length(which(events$quick_fixation == T & events$activation == T 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
TP_cell <- length(which(events$quick_fixation == T & events$activation == T 
                        & (events$field_type == 'field' | events$field_type == 'field_nT') & events$changed_selection == F))
TN_ball <- length(which(events$quick_fixation == T & events$activation == F 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
TN_cell <- length(which(events$quick_fixation == T & events$activation == F 
                        & (events$field_type == 'field' | events$field_type == 'field_nT')  & events$changed_selection == F))
FP_ball <- length(which((events$false_alarm == T | events$changed_selection == T) & 
                          (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$quick_fixation == TRUE))
FP_cell <- length(which((events$false_alarm == T | events$changed_selection == T) & 
                         (events$field_type == 'field' | events$field_type == 'field_nT') & events$quick_fixation == TRUE))
FN_ball <- length(which(events$quick_fixation == F & events$activation == T 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
FN_cell <- length(which(events$quick_fixation == F & events$activation == T 
                        & (events$field_type == 'field' | events$field_type == 'field_nT') & events$changed_selection == F))


df_for_table <- data.frame(type = c('ball', 'cell'),
                           TP = c(TP_ball, TP_cell),
                           TN = c(TN_ball, TN_cell),
                           FP = c(FP_ball, FP_cell),
                           FN = c(FN_ball, FN_cell),
                           INTENT = c(TP_ball + FN_ball, TP_cell + FN_cell),
                           SPONT = c(TN_ball + FP_ball, TN_cell + FP_cell),
                           SENS = c((TP_ball / (TP_ball + FN_ball)), (TP_cell / (TP_cell + FN_cell))),
                           SPEC = c((1 - (FP_ball / (TN_ball + FP_ball))), (1 - (FP_cell / (TN_cell + FP_cell)))))

Classifier Response table

``` {r, echo=FALSE} kable(df_for_table, format = 'markdown', align = 'l', digits = 3, padding = 4) remove(df_for_table)

```r
events <- data.frame()
for ( i in 1:length(experiment)) {
  if(experiment[[i]]$file_data$record_type == 'random') {
    events <- rbind(events, experiment[[i]]$events)
  }
}
if(length(events)){
TP_ball <- length(which(events$quick_fixation == T & events$activation == T 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
TP_cell <- length(which(events$quick_fixation == T & events$activation == T 
                        & (events$field_type == 'field' | events$field_type == 'field_nT') & events$changed_selection == F))
TN_ball <- length(which(events$quick_fixation == T & events$activation == F 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
TN_cell <- length(which(events$quick_fixation == T & events$activation == F 
                        & (events$field_type == 'field' | events$field_type == 'field_nT')  & events$changed_selection == F))
FP_ball <- length(which((events$false_alarm == T | events$changed_selection == T) & 
                          (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$quick_fixation == TRUE))
FP_cell <- length(which((events$false_alarm == T | events$changed_selection == T) & 
                         (events$field_type == 'field' | events$field_type == 'field_nT') & events$quick_fixation == TRUE))
FN_ball <- length(which(events$quick_fixation == F & events$activation == T 
                        & (events$field_type == 'ball' |  events$field_type == 'ball_nT') & events$changed_selection == F))
FN_cell <- length(which(events$quick_fixation == F & events$activation == T 
                        & (events$field_type == 'field' | events$field_type == 'field_nT') & events$changed_selection == F))


df_for_table <- data.frame(type = c('ball', 'cell'),
                           TP = c(TP_ball, TP_cell),
                           TN = c(TN_ball, TN_cell),
                           FP = c(FP_ball, FP_cell),
                           FN = c(FN_ball, FN_cell),
                           INTENT = c(TP_ball + FN_ball, TP_cell + FN_cell),
                           SPONT = c(TN_ball + FP_ball, TN_cell + FP_cell),
                           SENS = c((TP_ball / (TP_ball + FN_ball)), (TP_cell / (TP_cell + FN_cell))),
                           SPEC = c((1 - (FP_ball / (TN_ball + FP_ball))), (1 - (FP_cell / (TN_cell + FP_cell)))))
}

Random Response table

``` {r, echo=FALSE} if(exists('df_for_table')){ kable(df_for_table, format = 'markdown', align = 'l', digits = 3, padding = 4) } else { cat('No records with random classifier were made') }

\pagebreak

## EEG Epochs

### True positive epochs

```r
draw_eeg_epochs_random(experiment, 'true_positive')

\pagebreak

True negative epochs

draw_eeg_epochs_random(experiment, 'true_negative')

\pagebreak

False negative epochs

draw_eeg_epochs_random(experiment, 'false_negative')

\pagebreak

Eye speed epochs

draw_eye_epochs(experiment)

\pagebreak

remove(experiment)
remove(res)
remove(my_path)
remove(experiment_date)
remove(report_date)

LIKANblk/eyelinesOnline documentation built on May 23, 2019, 10:33 p.m.