Free Recall Example
Libraries and Data
Please see manuscript for a long description of the following data.
We will load the example data, and you can use the ? with
the dataset name to learn more about the data.
library(lrd)
data("wide_data")
head(wide_data)
#> Sub.ID
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> 6 6
#> Response
#> 1 basket, chair, clothes, flowrs, glasses, fan, windows, carpet, lamp, picture frame, remote
#> 2 windows, bed, books, shelf, pictures
#> 3 bacpack, chair, glasses, mirror, iphone, pillow, stereo, nightstand, computer, door, magazines, quilt
#> 4 vase, blinds, computer, magazine, books, bed, blanket, carpet, dresser
#> 5 bed, blankets, closet, windows, books, fan
#> 6 bed, blankets, dreser, nightstand, end table, stereo, flwrs, vase
#> Disease.Condition
#> 1 healthy
#> 2 healthy
#> 3 healthy
#> 4 sick
#> 5 sick
#> 6 sick
#?wide_data
data("answer_key_free")
head(answer_key_free)
#> Answer_Key
#> 1 backpack
#> 2 basket
#> 3 bed
#> 4 blanket
#> 5 blinds
#> 6 books
#?answer_key_free
library(ggplot2)Data Restructuring
Data Cleanup
Scoring in lrd is case sensitive, so we will use
tolower() to lower case all correct answers and participant
answers.
Score the Data
You should define the following:
- data = dataframe of participant responses
- responses = column name of the participant answers
- key = column name of the answer key
- id = column name of the participant id number
- cutoff = the Levenshtein distance value you want to use for scoring (0 no changes exactly the same, higher numbers allow more variance in the word)
- flag = calculate z scores for outliers (TRUE/FALSE)
- group.by = column name(s) for grouping variables
free_output <- prop_correct_free(data = DF_long,
responses = "response",
key = answer_key_free$Answer_Key,
id = "Sub.ID",
cutoff = 1,
flag = TRUE,
group.by = "Disease.Condition")
str(free_output)
#> List of 3
#> $ DF_Scored :'data.frame': 51 obs. of 6 variables:
#> ..$ Responses : chr [1:51] "bacpack" "basket" "bed" "bed" ...
#> ..$ Sub.ID : num [1:51] 3 1 2 4 5 6 4 6 5 4 ...
#> ..$ position : num [1:51] 1 1 2 6 1 1 7 2 2 2 ...
#> ..$ Disease.Condition: chr [1:51] "healthy" "healthy" "healthy" "sick" ...
#> ..$ Answer : chr [1:51] "backpack" "basket" "bed" "bed" ...
#> ..$ Scored : num [1:51] 1 1 1 1 1 1 1 1 1 1 ...
#> $ DF_Participant:'data.frame': 6 obs. of 5 variables:
#> ..$ Disease.Condition : chr [1:6] "healthy" "healthy" "healthy" "sick" ...
#> ..$ Sub.ID : num [1:6] 1 2 3 4 5 6
#> ..$ Proportion.Correct : num [1:6] 0.393 0.143 0.393 0.321 0.214 ...
#> ..$ Z.Score.Group : num [1:6] 0.577 -1.155 0.577 1.155 -0.577 ...
#> ..$ Z.Score.Participant: num [1:6, 1] 1.082 -1.31 1.082 0.399 -0.626 ...
#> .. ..- attr(*, "scaled:center")= num 0.28
#> .. ..- attr(*, "scaled:scale")= num 0.105
#> $ DF_Group :'data.frame': 2 obs. of 4 variables:
#> ..$ Disease.Condition: chr [1:2] "healthy" "sick"
#> ..$ Mean : num [1:2] 0.31 0.25
#> ..$ SD : num [1:2] 0.1443 0.0619
#> ..$ N : int [1:2] 3 3Output
We can use DF_Scored to see the original dataframe with
our new scored column - also to check if our answer key and participant
answers matched up correctly! The DF_Participant can be
used to view a participant level summary of the data. Last, if a
grouping variable is used, we can use DF_Group to see that
output.
#Overall
free_output$DF_Scored
#> Responses Sub.ID position Disease.Condition Answer Scored
#> 1 bacpack 3 1 healthy backpack 1
#> 2 basket 1 1 healthy basket 1
#> 3 bed 2 2 healthy bed 1
#> 4 bed 4 6 sick bed 1
#> 5 bed 5 1 sick bed 1
#> 6 bed 6 1 sick bed 1
#> 7 blanket 4 7 sick blanket 1
#> 8 blankets 6 2 sick blanket 1
#> 9 blankets 5 2 sick blanket 1
#> 10 blinds 4 2 sick blinds 1
#> 11 books 5 5 sick books 1
#> 12 books 2 3 healthy books 1
#> 13 books 4 5 sick books 1
#> 14 carpet 1 8 healthy carpet 1
#> 15 carpet 4 8 sick carpet 1
#> 16 chair 1 2 healthy chair 1
#> 17 chair 3 2 healthy chair 1
#> 18 closet 5 3 sick closet 1
#> 19 clothes 1 3 healthy clothes 1
#> 20 computer 3 9 healthy computer 1
#> 21 computer 4 3 sick computer 1
#> 22 door 3 10 healthy door 1
#> 23 dreser 6 3 sick dresser 1
#> 24 dresser 4 9 sick dresser 1
#> 25 end table 6 5 sick <NA> 0
#> 26 fan 1 6 healthy fan 1
#> 27 fan 5 6 sick fan 1
#> 28 flowrs 1 4 healthy flowers 1
#> 29 flwrs 6 7 sick <NA> 0
#> 30 glasses 3 3 healthy glasses 1
#> 31 glasses 1 5 healthy glasses 1
#> 32 iphone 3 5 healthy <NA> 0
#> 33 lamp 1 9 healthy lamp 1
#> 34 magazine 4 4 sick magazines 1
#> 35 magazines 3 11 healthy magazines 1
#> 36 mirror 3 4 healthy mirror 1
#> 37 nightstand 6 4 sick nightstand 1
#> 38 nightstand 3 8 healthy nightstand 1
#> 39 picture frame 1 10 healthy picture frame 1
#> 40 pictures 2 5 healthy <NA> 0
#> 41 pillow 3 6 healthy pillow 1
#> 42 quilt 3 12 healthy quilt 1
#> 43 remote 1 11 healthy remote 1
#> 44 shelf 2 4 healthy shelf 1
#> 45 stereo 3 7 healthy stereo 1
#> 46 stereo 6 6 sick stereo 1
#> 47 vase 6 8 sick vase 1
#> 48 vase 4 1 sick vase 1
#> 49 windows 2 1 healthy windows 1
#> 50 windows 1 7 healthy windows 1
#> 51 windows 5 4 sick windows 1
#Participant
free_output$DF_Participant
#> Disease.Condition Sub.ID Proportion.Correct Z.Score.Group Z.Score.Participant
#> 1 healthy 1 0.3928571 0.5773503 1.0819232
#> 2 healthy 2 0.1428571 -1.1547005 -1.3096965
#> 3 healthy 3 0.3928571 0.5773503 1.0819232
#> 4 sick 4 0.3214286 1.1547005 0.3986033
#> 5 sick 5 0.2142857 -0.5773503 -0.6263766
#> 6 sick 6 0.2142857 -0.5773503 -0.6263766
#Group
free_output$DF_Group
#> Disease.Condition Mean SD N
#> 1 healthy 0.3095238 0.14433757 3
#> 2 sick 0.2500000 0.06185896 3Other Possible Calculations
Serial Position
This function prepares the data for a serial position curve analysis
or visualization. Please note, it assumes you are using the output from
above, but any output with these columns would work fine. The arguments
are roughly the same as the overall scoring function. We’ve also
included some ggplot2 code as an example to help show how
you might use our output for plotting. These graphs aren’t too exciting
with a small example!
serial_output <- serial_position(data = free_output$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()