trimr is an R package that implements most commonly-used response time trimming methods, allowing the user to go from a raw data file to a finalised data file ready for inferential statistical analysis.
The trimming functions fall broadly into three families (together with the function names for each method implemented in trimr):
Of course, you would like to use trimr on your own data. Below are some demonstrations on how to use trimr that utilise some data that comes with the package. It’s important to note that if you are using your own data, your data MUST contain columns with the same names as used below; names in R are case-sensitive, so make sure the case is the same, too. You can have other columns in your data file (which will just be ignored by trimr), but the relevant columns that code for participant, condition, rt, and accuracy must be names accordingly.
trimr ships with some example data—“exampleData”—that the user can explore the trimming functions with. This data is simulated (i.e., not real), and has data from 32 subjects. This data is from a task switching experiment, where RT and accuracy was recorded for two experimental conditions: Switch, when the task switched from the previous trial, and Repeat, when the task repeated from the previous trial. (If you have data from a factorial design (i.e., the condition codes are spread over more than one column), then please see the final section of this vignette for how to deal with this in trimr).
# load the trimr package
library(trimr)
# activate the data
data(exampleData)
# look at the top of the data
head(exampleData)## participant condition rt accuracy
## 1 1 Switch 1660 1
## 2 1 Switch 913 1
## 3 1 Repeat 2312 1
## 4 1 Repeat 754 1
## 5 1 Switch 3394 1
## 6 1 Repeat 930 1The exampleData consists of 4 columns:
At a minimum, users using their own data need columns with these names in their data frame they are using trimr for. The user can use RTs logged in milliseconds (as here) or in seconds (e.g., 0.657). The user can control the number of decimal places to round the trimmed data to.
The absolute value criterion is the simplest of all of the trimming methods available (except of course for having no trimming). An upper- and lower-criterion is set, and any response time that falls outside of these limits are removed. The function that performs this trimming method in trimr is called absoluteRT.
In this function, the user decalares lower- and upper-criterion for RT trimming (minRT and maxRT arguments, respectively); RTs outside of these criteria are removed. Note that these criteria must be in the same unit as the RTs are logged in within the data frame being used. The function also has some other important arguments:
In this first example, let’s trim the data using criteria of RTs less than 150 milliseconds and greater than 2,000 milliseconds, with error trials removed before trimming commences. Let’s also return the mean RTs for each condition, and round the data to zero decimal places.
# perform the trimming
trimmedData <- absoluteRT(data = exampleData, minRT = 150, maxRT = 2000,
digits = 0)
# look at the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 901 742
## 2 2 1064 999
## 3 3 1007 802
## 4 4 1000 818
## 5 5 1131 916
## 6 6 1259 1067Note that trimr returns a data frame with each row representing each participant in the data file (logged in the participant column), and separate columns for each experimental condition in the data.
If the user wishes to recive back trial-level data, change the “returnType” argument to “raw”:
# perform the trimming
trimmedData <- absoluteRT(data = exampleData, minRT = 150, maxRT = 2000,
returnType = "raw", digits = 0)
# look at the top of the data
head(trimmedData)## participant condition rt accuracy
## 1 1 Switch 1660 1
## 2 1 Switch 913 1
## 4 1 Repeat 754 1
## 6 1 Repeat 930 1
## 7 1 Switch 1092 1
## 11 1 Repeat 708 1Now, the data frame returned is in the same shape as the initial data file, but rows containing trimmed RTs are removed.
This trimming method uses a standard deviation multiplier as the upper criterion for RT removal (users still need to enter a lower-bound manually). For example, this method can be used to trim all RTs 2.5 standard deviations above the mean RT. This trimming can be done per condition (e.g., 2.5 SDs above the mean of each condition), per participant (e.g., 2.5 SDs above the mean of each participant), or per condition per participant (e.g., 2.5 SDs above the mean of each participant for each condition).
In this function, the user delcares a lower-bound on RT trimming (e.g., 150 milliseconds) and an upper-bound in standard deviations. The value of standard deviation used is set by the SD argument. How this is used varies depending on the values the user passes to two important function arguments:
Note that if both are set to TRUE, the trimming will occur per participant per condition (e.g., if SD is set to 2.5, the function will trim RTs 2.5 SDs above the mean RT of each participant for each condition).
In this example, let’s trim RTs faster than 150 milliseconds, and greater than 3 SDs above the mean of each participant, and return the mean RTs:
# trim the data
trimmedData <- sdTrim(data = exampleData, minRT = 150, sd = 3,
perCondition = FALSE, perParticipant = TRUE,
returnType = "mean", digits = 0)
# look at the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 1042 775
## 2 2 1136 1052
## 3 3 1020 802
## 4 4 1094 834
## 5 5 1169 919
## 6 6 1435 1156Now, let’s trim per condition per participant:
# trim the data
trimmedData <- sdTrim(data = exampleData, minRT = 150, sd = 3,
perCondition = TRUE, perParticipant = TRUE,
returnType = "mean", digits = 0)
# look at the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 1099 742
## 2 2 1136 1038
## 3 3 1028 802
## 4 4 1103 834
## 5 5 1184 916
## 6 6 1461 1136Three functions in this family implement the trimming methods proposed & discussed by van Selst & Jolicoeur (1994): nonRecursive, modifiedRecursive, and hybridRecursive. van Selst & Jolicoeur noted that the outcome of many trimming methods is influenced by the sample size (i.e., the number of trials) being considered, thus potentially producing bias. For example, even if RTs are drawn from identical positively-skewed distributions, a “per condition per participant” SD procedure (see sdTrim above) would result in a higher mean estimate for small sample sizes than larger sample sizes. This bias was shown to be removed when a “moving criterion” (MC) was used; this is where the SD used for trimming is adapted to the sample size being considered.
The non-recursive method proposed by van Selst & Jolicoeur (1994) is very similar to the standard deviation method outlined above with the exception that the user does not specify the SD to use as the upper bound. The SD used for the upper bound is rather decided by the sample size of the RTs being passed to the trimming function, with larger SDs being used for larger sample sizes. Also, the function only trims per participant per condition.
The nonRecursive function checks the sample size of the data being passed to it, and looks up the SD criterion required for the data’s sample size. The function looks in a data file contained in trimr called linearInterpolation. Should the user wish to see this data file (although the user will never need to access it if they are not interested), type:
# load the data
data(linearInterpolation)
# show the first 20 rows (there are 100 in total)
linearInterpolation[1:20, ]## sampleSize modifiedRecursive nonRecursive
## 1 4 8.000 1.4580
## 2 5 6.200 1.6800
## 3 6 5.300 1.8410
## 4 7 4.800 1.9610
## 5 8 4.475 2.0500
## 6 9 4.250 2.1200
## 7 10 4.110 2.1700
## 8 11 4.000 2.2200
## 9 12 3.920 2.2460
## 10 13 3.850 2.2740
## 11 14 3.800 2.3100
## 12 15 3.750 2.3260
## 13 16 3.728 2.3390
## 14 17 3.706 2.3520
## 15 18 3.684 2.3650
## 16 19 3.662 2.3780
## 17 20 3.640 2.3910
## 18 21 3.631 2.3948
## 19 22 3.622 2.3986
## 20 23 3.613 2.4024Notice there are two columns. This current function will only look in the nonRecursive column; the other column is used by the modifiedRecursive function, discussed below. If the sample size of the current set of data is 16 RTs (for example), the function will use an upper SD criterion of 2.359, and will proceed much like the sdTrim function’s operations.
Note the user can only be returned the mean trimmed RTs (i.e., there is no “returnType” argument for this function).
# trim the data
trimmedData <- nonRecursive(data = exampleData, minRT = 150, digits = 0)
# see the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 1053 732
## 2 2 1131 1026
## 3 3 1017 799
## 4 4 1089 818
## 5 5 1169 908
## 6 6 1435 1123The modifiedRecursive function is more involved than the nonRecursive function. This function performs trimming in cycles. It first temporarily removes the slowest RT from the distribution; then, the mean of the sample is calculated, and the cut-off value is calculated using a certain number of SDs around the mean, with the value for SD being determined by the current sample size. In this procedure, required SD decreases with increased sample size (cf., the nonRecursive method, with increasing SDs with increasing sample size; see the linearInterpolation data file above); see Van Selst and Jolicoeur (1994) for justification.
The temporarily removed RT is then returned to the sample, and the fastest and slowest RTs are then compared to the cut-off, and removed if they fall outside. This process is then repeated until no outliers remain, or until the sample size drops below four. The SD used for the cut-off is thus dynamically altered based on the sample size of each cycle of the procedure, rather than static like the nonRecursive method.
# trim the data
trimmedData <- modifiedRecursive(data = exampleData, minRT = 150, digits = 0)
# see the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 792 691
## 2 2 1036 927
## 3 3 958 716
## 4 4 1000 712
## 5 5 1107 827
## 6 6 1309 1049van Selst and Jolicoeur (1994) reported slight opposing trends of the non-recursive and modified-recursive trimming methods (see page 648, footnote 2). They therefore, in passing, suggested a “hybrid-recursive” method might balance the opposing trends. The hybrid-recursive method simply takes the average of the non-recursive and the modified-recursive methods.
# trim the data
trimmedData <- hybridRecursive(data = exampleData, minRT = 150, digits = 0)
# see the top of the data
head(trimmedData)## participant Switch Repeat
## 1 1 923 711
## 2 2 1083 976
## 3 3 987 757
## 4 4 1044 765
## 5 5 1138 867
## 6 6 1372 1086In the example data that ships with trimr, the RT data comes from just two conditions (Switch vs. Repeat), which are coded in the column “condition”. However, in experimental psychology, factorial designs are prevalent, where RT data comes from more than one independent variable, with each IV having multiple levels. How can trimr deal with this format?
First, let’s re-shape the exampleData set to how data might be stored from a factorial design. Let there be two IVs, each with two levels:
The taskSequence factor is coding whether the task has Switched or Repeated from the task on the previous trial (as before). The reward factor is coding whether the participant was presented with a reward or not on the current trial (presented randomly). Let’s reshape our data frame to match this fictitious experimental scenario:
# get the example data that ships with trimr
data(exampleData)
# pass it to a new variable
newData <- exampleData
# add a column called "taskSequence" that logs whether the current task was a
# repetition or a switch trial (which is currently coded in the "condition")
# column
newData$taskSequence <- newData$condition
# add a column called "reward" that logs whether the participant received a
# reward or not. Fill it with random entries, just for example. This uses R's
# "sample" function
newData$reward <- sample(c("Reward", "NoReward"), nrow(newData),
replace = TRUE)
# delete the "condition" column
newData <- subset(newData, select = -condition)
# now let's look at our new data
head(newData)## participant rt accuracy taskSequence reward
## 1 1 1660 1 Switch Reward
## 2 1 913 1 Switch NoReward
## 3 1 2312 1 Repeat NoReward
## 4 1 754 1 Repeat NoReward
## 5 1 3394 1 Switch NoReward
## 6 1 930 1 Repeat RewardThis now looks how data typically comes in from a factorial design. Now, to get trimr to work on this, we need to create a new column called “condition”, and to place in this column the levels of all factors in the design. For example, if the first trial in our newData has taskSequence = Switch and reward = NoReward, we would like our condition entry for this trial to read “Switch_NoReward”. This is simple to do using R’s “paste” function. (Note that this code can be adapted to deal with any number of factors.)
# add a new column called "condition", and fill it with information from both
# columns that code for our factors
newData$condition <- paste(newData$taskSequence, "_", newData$reward, sep = "")
# let's again look at the data
head(newData)## participant rt accuracy taskSequence reward condition
## 1 1 1660 1 Switch Reward Switch_Reward
## 2 1 913 1 Switch NoReward Switch_NoReward
## 3 1 2312 1 Repeat NoReward Repeat_NoReward
## 4 1 754 1 Repeat NoReward Repeat_NoReward
## 5 1 3394 1 Switch NoReward Switch_NoReward
## 6 1 930 1 Repeat Reward Repeat_RewardNow we can pass this data frame to trimr, and it will work perfectly.
# trim the data
trimmedData <- sdTrim(newData, minRT = 150, sd = 2.5)
# check it worked
head(trimmedData)## participant Switch_Reward Switch_NoReward Repeat_NoReward Repeat_Reward
## 1 1 1084.229 1019.515 742.565 734.830
## 2 2 1115.950 1119.172 1028.729 1016.534
## 3 3 1047.910 985.953 822.183 770.829
## 4 4 1068.477 1101.639 762.881 893.101
## 5 5 1164.941 1165.910 916.508 879.602
## 6 6 1375.351 1490.292 1142.202 1111.758Van Selst, M. & Jolicoeur, P. (1994). A solution to the effect of sample size on outlier elimination. Quarterly Journal of Experimental Psychology, 47 (A), 631–650.