# Conditional Response Probability for Multiple Lists This function calculates the conditional response probability of each lag position. Participants' lag between subsequent named items is tallied and then divided by the possible combination of subsequent lags given their response pattern. This function was designed to handle multiple or randomized lists across participants. ```r crp_multiple(data, position, answer, id, key, key.trial, id.trial, scored) ``` ## Arguments - `data`: a dataframe of the scored free recall that you would like to calculate - use prop_correct_free() for best formatting. - `position`: a column name in the dataframe that contains answered position of each response in quotes (i.e., "column") - `answer`: a column name of the answer given for that position in the original dataframe. - `id`: a column name of the participant id in the original dataframe. - `key`: 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. - `key.trial`: 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. - `id.trial`: 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. - `scored`: a column in the original dataframe indicating if the participant got the answer correct (1) or incorrect (0). ## Returns - **DF_CRP**: A dataframe of the proportion correct for each conditional lag position including any other between subjects variables present in the data. ## Details This output can then be used to create a CRP visualizations, and an example can be found in our manuscript/vignettes. ## Examples ```r 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) head(scored_output$DF_Participant) crp_output <- crp_multiple(data = scored_output$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(crp_output) ```

crp_multiple function