Extract the encoding as an fd object or as a matrix
get_encoding(x, harm = 1, fdObject = FALSE, nx = NULL)Output of compute_optimal_encoding
harmonic to use for the encoding
If TRUE returns a fd object else a matrix
(Only if fdObject = TRUE) Number of points to evaluate the encoding
a fd object or a list of two elements y, a matrix with nx rows containing
the encoding of the state and x, the vector with time values.
The encoding is \(a_{x} \approx \sum_{i=1}^m \alpha_{x,i}\phi_i\).
Other encoding functions:
compute_optimal_encoding(),
plot.fmca(),
plotComponent(),
plotEigenvalues(),
predict.fmca(),
print.fmca(),
summary.fmca()
# Simulate the Jukes-Cantor model of nucleotide replacement
K <- 4
Tmax <- 6
PJK <- matrix(1 / 3, nrow = K, ncol = K) - diag(rep(1 / 3, K))
lambda_PJK <- c(1, 1, 1, 1)
d_JK <- generate_Markov(n = 10, K = K, P = PJK, lambda = lambda_PJK, Tmax = Tmax)
d_JK2 <- cut_data(d_JK, Tmax)
# create basis object
m <- 6
b <- create.bspline.basis(c(0, Tmax), nbasis = m, norder = 4)
# \donttest{
# compute encoding
encoding <- compute_optimal_encoding(d_JK2, b, computeCI = FALSE, nCores = 1)
#> ######### Compute encoding #########
#> Number of individuals: 10
#> Number of states: 4
#> Basis type: bspline
#> Number of basis functions: 6
#> Number of cores: 1
#>
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#>
#> DONE in 0.13s
#> ---- Compute U matrix:
#>
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#>
#> DONE in 1.05s
#> ---- Compute encoding:
#> DONE in 0s
#> Run Time: 1.2s
# extract the encoding using 1 harmonic
encodFd <- get_encoding(encoding, fdObject = TRUE)
encodMat <- get_encoding(encoding, nx = 200)
# }