Function that calculates the mean-centered values of a particular variable for each subject.

calc.mcent(x, id, data, na.rm=TRUE)

Arguments

x

argument to specify the variable.

id

argument to specify a subject id variable.

data

optional data frame that contains the variables specified above.

na.rm

logical indicating whether missing values should be removed before computing the means (default is TRUE).

Details

The function computes the mean-centered values of a particular variable for each subject.

Value

A vector.

Author

Wolfgang Viechtbauer wvb@wvbauer.com

See also

Examples

# illustrative dataset dat <- data.frame(subj=rep(1:4, each=5), obs = 1:5, age = rep(c(20,31,27,22), each=5), stress = c(2,3,NA,4,2, 3,3,NA,3,NA, 1,1,2,6,4, 1,2,1,3,1)) dat
#> subj obs age stress #> 1 1 1 20 2 #> 2 1 2 20 3 #> 3 1 3 20 NA #> 4 1 4 20 4 #> 5 1 5 20 2 #> 6 2 1 31 3 #> 7 2 2 31 3 #> 8 2 3 31 NA #> 9 2 4 31 3 #> 10 2 5 31 NA #> 11 3 1 27 1 #> 12 3 2 27 1 #> 13 3 3 27 2 #> 14 3 4 27 6 #> 15 3 5 27 4 #> 16 4 1 22 1 #> 17 4 2 22 2 #> 18 4 3 22 1 #> 19 4 4 22 3 #> 20 4 5 22 1
# calculate the mean-centered values of the stress variable dat$cstress <- calc.mcent(stress, subj, data=dat) dat
#> subj obs age stress cstress #> 1 1 1 20 2 -0.75 #> 2 1 2 20 3 0.25 #> 3 1 3 20 NA NA #> 4 1 4 20 4 1.25 #> 5 1 5 20 2 -0.75 #> 6 2 1 31 3 0.00 #> 7 2 2 31 3 0.00 #> 8 2 3 31 NA NA #> 9 2 4 31 3 0.00 #> 10 2 5 31 NA NA #> 11 3 1 27 1 -1.80 #> 12 3 2 27 1 -1.80 #> 13 3 3 27 2 -0.80 #> 14 3 4 27 6 3.20 #> 15 3 5 27 4 1.20 #> 16 4 1 22 1 -0.60 #> 17 4 2 22 2 0.40 #> 18 4 3 22 1 -0.60 #> 19 4 4 22 3 1.40 #> 20 4 5 22 1 -0.60
# calculate the subject-level means of the stress variable dat$mstress <- calc.mean(stress, subj, data=dat, expand=TRUE) dat
#> subj obs age stress cstress mstress #> 1 1 1 20 2 -0.75 2.75 #> 2 1 2 20 3 0.25 2.75 #> 3 1 3 20 NA NA 2.75 #> 4 1 4 20 4 1.25 2.75 #> 5 1 5 20 2 -0.75 2.75 #> 6 2 1 31 3 0.00 3.00 #> 7 2 2 31 3 0.00 3.00 #> 8 2 3 31 NA NA 3.00 #> 9 2 4 31 3 0.00 3.00 #> 10 2 5 31 NA NA 3.00 #> 11 3 1 27 1 -1.80 2.80 #> 12 3 2 27 1 -1.80 2.80 #> 13 3 3 27 2 -0.80 2.80 #> 14 3 4 27 6 3.20 2.80 #> 15 3 5 27 4 1.20 2.80 #> 16 4 1 22 1 -0.60 1.60 #> 17 4 2 22 2 0.40 1.60 #> 18 4 3 22 1 -0.60 1.60 #> 19 4 4 22 3 1.40 1.60 #> 20 4 5 22 1 -0.60 1.60