Description Usage Arguments Value References Examples
Permutes the group label of the samples in order to construct the AR empirical distibution
1 2 3 4 5 6 7 8 | permutation(
perm.dat,
n.perm = 500,
method = "nbinomial",
points,
lev,
parall = FALSE
)
|
perm.dat |
dataframe has the Count, Group, ID, Time |
n.perm |
number of permutations |
method |
The fitting method (negative binomial, LOWESS) |
points |
The points at which the prediction should happen |
lev |
the two level's name |
parall |
boolean to indicate whether to use multicore. |
returns the fitted model for all the permutations
Ahmed Metwally (ametwall@stanford.edu)
1 2 3 4 5 6 7 8 9 10 11 | data(metalonda_test_data)
n.sample = 5
n.timepoints = 10
n.perm = 3
n.group = 2
Group = factor(c(rep(0, n.sample*n.timepoints), rep(1, n.sample*n.timepoints)))
Time = rep(rep(1:n.timepoints, times = n.sample), 2)
ID = factor(rep(1:(2*n.sample), each = n.timepoints))
points = seq(1, 10, length.out = 10)
aggregate.df = data.frame(Count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID)
prm = permutation(aggregate.df, n.perm = 3, method = "nbinomial", points)
|
# of subjects = 10
Permutation = 1
Permutation = 2
Permutation = 3
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