predict.fmca {cfda}R Documentation

Predict using RMixtComp

Description

Predict the cluster of new samples.

Usage

## S3 method for class 'fmca'
predict(
  object,
  newdata = NULL,
  nCores = max(1, ceiling(detectCores()/2)),
  verbose = TRUE,
  ...
)

Arguments

object

output of compute_optimal_encoding function.

newdata

data.frame containing id, id of the trajectory, time, time at which a change occurs and state, associated state. All individuals must begin at the same time T0 and end at the same time Tmax (use cut_data)..

nCores

number of cores used for parallelization. Default is the half of cores.

verbose

if TRUE print some information

...

parameters for integrate function (see details).

Value

principal components for the individuals

Author(s)

Quentin Grimonprez

See Also

compute_optimal_encoding

Examples

# 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,
                            labels = c("A", "C", "G", "T"))
d_JK2 <- cut_data(d_JK, Tmax)

# create basis object
m <- 6
b <- create.bspline.basis(c(0, Tmax), nbasis = m, norder = 4)


# compute encoding
encoding <- compute_optimal_encoding(d_JK2, b, computeCI = FALSE, nCores = 1)

# predict principal components
d_JK_predict <- generate_Markov(n = 5, K = K, P = PJK, lambda = lambda_PJK, Tmax = Tmax,
                            labels = c("A", "C", "G", "T"))
d_JK_predict2 <- cut_data(d_JK, Tmax)

pc <- predict(encoding, d_JK_predict2, nCores = 1)



[Package cfda version 0.9.9 Index]