Other Possible Calculations
Serial Position
This function prepares the data for a serial position curve analysis or visualization. Please note, it assumes you are using the output from above, but any output with these columns would work fine. The arguments are roughly the same as the overall scoring function. We’ve also included some ggplot2 code as an example to help show how you might use our output for plotting. These graphs aren’t too exciting with a small example!
serial_output <- serial_position(data = free_output$DF_Scored,
key = answer_key_free$Answer_Key,
position = "position",
scored = "Scored",
answer = "Answer",
group.by = "Disease.Condition")
head(serial_output)
#> Disease.Condition Tested.Position Freq Proportion.Correct SE
#> 1 healthy 1 1 0.3333333 0.2721655
#> 2 healthy 2 1 0.3333333 0.2721655
#> 3 healthy 3 1 0.3333333 0.2721655
#> 4 sick 3 0 0.0000000 0.0000000
#> 5 sick 4 0 0.0000000 0.0000000
#> 6 sick 5 0 0.0000000 0.0000000
ggplot(serial_output, aes(Tested.Position, Proportion.Correct, color = Disease.Condition)) +
geom_line() +
geom_point() +
xlab("Tested Position") +
ylab("Probability of First Response") +
theme_bw() 
Conditional Response Probability
Conditional response probability is the likelihood of answers given the current answer set. Therefore, the column participant_lags represents the lag between the written and tested position (e.g., chair was listed second, which represents a lag of -6 from spot number 8 on the answer key list). The column Freq represents the frequency of the lags between listed and shown position, while the Possible.Freq column indicates the number of times that frequency could occur given each answer listed (e.g., given the current answer, a tally of the possible lags that could still occur). The CRP column calculates the conditional response probability, or the frequency column divided by the possible frequencies of lags.
crp_output <- crp(data = free_output$DF_Scored,
key = answer_key_free$Answer_Key,
position = "position",
scored = "Scored",
answer = "Answer",
id = "Sub.ID")
head(crp_output)
#> Sub.ID participant_lags Freq Possible.Freq Disease.Condition CRP
#> 1 1 -27 0 0 healthy 0
#> 2 1 -26 0 0 healthy 0
#> 3 1 -25 0 0 healthy 0
#> 4 1 -24 0 0 healthy 0
#> 5 1 -23 0 0 healthy 0
#> 6 1 -22 0 0 healthy 0
crp_output$participant_lags <- as.numeric(as.character(crp_output$participant_lags))
ggplot(crp_output, aes(participant_lags, CRP, color = Disease.Condition)) +
geom_line() +
geom_point() +
xlab("Lag Distance") +
ylab("Conditional Response Probability") +
theme_bw()
Probability of First Response
Participant answers are first filtered for their first response, and these are matched to the original order on the answer key list (Tested.Position). Then the frequency (Freq) of each of those answers is tallied and divided by the number of participants overall or by group if the group.by argument is included (pfr).
pfr_output <- pfr(data = free_output$DF_Scored,
key = answer_key_free$Answer_Key,
position = "position",
scored = "Scored",
answer = "Answer",
id = "Sub.ID",
group.by = "Disease.Condition")
head(pfr_output)
#> Tested.Position Disease.Condition Freq pfr
#> 1 1 healthy 1 0.3333333
#> 2 2 healthy 1 0.3333333
#> 3 3 healthy 0 0.0000000
#> 4 27 healthy 0 0.0000000
#> 5 28 healthy 1 0.3333333
#> 6 1 sick 0 0.0000000
pfr_output$Tested.Position <- as.numeric(as.character(pfr_output$Tested.Position))
ggplot(pfr_output, aes(Tested.Position, pfr, color = Disease.Condition)) +
geom_line() +
geom_point() +
xlab("Tested Position") +
ylab("Probability of First Response") +
theme_bw()