pfr_multiple.RdThis function calculates the probability of first recall for each serial position. The total number of times an item was recalled first is divided by the total number of first recalls (i.e., the number of participants who wrote anything down!).
pfr_multiple(
data,
position,
answer,
id,
key,
key.trial,
id.trial,
scored,
group.by = NULL
)a dataframe of the scored free recall that you would like to calculate - use prop_correct_free() for best formatting.
a column name in the dataframe that contains answered position of each response in quotes (i.e., "column")
a column name of the answer given for that position in the original dataframe.
a column name of the participant id in the original dataframe.
a vector containing the scoring key or data column name. This column does not have to be included in the original dataframe. We assume your answer key is in the tested position order. You should not include duplicates in your answer key.
a vector containing the trial numbers for each answer. Note: If you input long data (i.e., repeating trial-answer responses), we will take the unique combination of the responses. If a trial number is repeated, you will receive an error. Key and key.trial can also be a separate dataframe, depending on how your output data is formatted.
a column name containing the trial numbers for the participant data from the original dataframe. Note that the free response "key" trial and this trial number should match. The trial key will be repeated for each answer a participant gave.
a column in the original dataframe indicating if the participant got the answer correct (1) or incorrect (0).
an optional argument that can be used to group the output by condition columns. These columns should be in the original dataframe and concatenated c() if there are multiple columns
A dataframe of the probability of first response for each position including group by variables if indicated.
This output can then be used to create a PFR visualizations, and an example can be found in our manuscript/vignettes.
data("multi_data")
data("multi_answers")
DF_long <- arrange_data(data = multi_data,
responses = "Response",
sep = " ",
id = "Sub.ID",
repeated = "List.Number")
library(reshape)
multi_answers$position <- 1:nrow(multi_answers)
answer_long <- melt(multi_answers,
measured = colnames(multi_answers),
id = "position")
colnames(answer_long) <- c("position", "List.ID", "Answer")
answer_long$List.ID <- gsub(pattern = "List",
replacement = "",
x = answer_long$List.ID)
DF_long$response <- tolower(DF_long$response)
answer_long$Answer <- tolower(answer_long$Answer)
answer_long$Answer <- gsub(" ", "", answer_long$Answer)
scored_output <- prop_correct_multiple(data = DF_long,
responses = "response",
key = answer_long$Answer,
key.trial = answer_long$List.ID,
id = "Sub.ID",
id.trial = "List.Number",
cutoff = 1,
flag = TRUE)
head(scored_output$DF_Scored)
#> Responses position Sub.ID List.Number Answer Scored
#> 1 basil 8 5 1 basil 1
#> 2 basil 7 3 1 basil 1
#> 3 bayleaves 7 1 1 bayleaves 1
#> 4 bayleaves 8 3 1 bayleaves 1
#> 5 bayleaves 3 5 1 bayleaves 1
#> 6 celeryseed 1 5 1 celeryseed 1
head(scored_output$DF_Participant)
#> Sub.ID Proportion.Correct List.Number Z.Score.Participant
#> 1 1 0.30 1 -1.1547005
#> 2 3 0.45 1 0.5773503
#> 3 5 0.45 1 0.5773503
#> 4 1 0.45 2 -0.5773503
#> 5 3 0.45 2 -0.5773503
#> 6 5 0.55 2 1.1547005
head(scored_output$DF_Group)
#> NULL
pfr_output <- pfr_multiple(data = scored_output$DF_Scored,
key = answer_long$Answer,
position = "position",
scored = "Scored",
answer = "Answer",
id = "Sub.ID",
key.trial = answer_long$List.ID,
id.trial = "List.Number")
head(pfr_output)
#> Tested.Position Freq pfr List.ID
#> 1 12 1 0.3333333 1
#> 2 14 1 0.3333333 1
#> 3 20 1 0.3333333 1
#> 4 6 1 0.3333333 2
#> 5 13 1 0.3333333 2
#> 6 14 1 0.3333333 2