trim: Trim Values Based on Mean and Standard Deviation

In sfraundorf/psycholing: R Functions for Common Psycholingusitic and Cognitive Designs

Trim Values Based on Mean and Standard Deviation

Description

Identifies values of x that are a certain number of standard deviations from the mean and treats them as missing (NA) values (e.g., values more than 3 standard deviations from the mean are considered missing) so that they can be easily deleted.

Usage

trim(x, sds = 3, print = TRUE, na.rm = FALSE)

Arguments

x	vector of values to which the trimming procedure should be applied.
sds	number of standard deviations away from the mean at which values should be considered missing.
print	a logical value indicating whether to display the number and percentage of extreme values.
na.rm	a logical value indicating whether existing NA values should be ignored when computing the mean and standard deviation.

Details

This function is provided because this is a standard data processing procedure in the psycholinguistic literature and not as an endorsement or rejection of this or any other procedure.

Value

vector in which values more than the specified number of standard deviations from the mean have been replaced with NA.

See Also

flag.by.sds to identify such values without deleting them.

fence to replace such observations with the boundary value.

trim.by.group to trim values a certain number of standard deviations away from a group mean (e.g., the mean within a condition or the mean for a research participant) rather than the overall mean.

Examples

my.data <- data.frame(RT=rnorm(300, mean=789, sd=150))
my.data$TrimmedRT <- trim(my.data$RT, sds=2.5)

sfraundorf/psycholing documentation built on April 23, 2022, 2:50 a.m.