Description Usage Arguments Examples
Fits a basic Markov-model on dyadic sequences. The transition matrix is converted into equivalent APIM-beta-coefficients. Bootstrapping is used for approximating p-values. (H1: Effect is different from zero)
1 2 | Basic_Markov_as_APIM(x, first, second, boot = 1000, SimOut = FALSE,
CPU = 1, sim = "ordinary", parallel = "no")
|
x |
Dataframe or matix containing the sequences (not combined!) |
first |
a vector that indicates all columns of the first sequence |
second |
a vector that indicates all columns of the second sequence |
boot |
number of bootstrap samples |
SimOut |
For simulation purposes: If TRUE output and tansition matrix will be omitted. |
CPU |
passes argument to boot() |
sim |
passes argument to boot() |
parallel |
passes argument to boot() |
1 2 3 4 5 6 7 8 9 10 11 12 | ## Not run:
# Simulating example-data:
trans1<-APIMtoTrans(B0_1=0, AE_1=1, PE_1=0, Int_1=0,
B0_2=0, AE_2=0, PE_2=0, Int_2=0)
x<-simSeqSample(trans=trans1, initial=rep(.25,4), length=100, N=100)
# Running the function,
# small boot-size sample only for demonstration purposes!
Basic_Markov_as_APIM(x, 1:100, 101:200, boot=10)
## End(Not run)
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