plot.fmca {cfda}

Plot the optimal encoding

Description

Plot the optimal encoding

Usage

## S3 method for class 'fmca'
plot(
  x,
  harm = 1,
  states = NULL,
  addCI = FALSE,
  coeff = 1.96,
  col = NULL,
  nx = 128,
  ...
)

Arguments

x

output of compute_optimal_encoding function

harm

harmonic to use for the encoding

states

states to plot (default = NULL, it plots all states)

addCI

if TRUE, plot confidence interval (only when computeCI = TRUE in compute_optimal_encoding)

coeff

the confidence interval is computed with +- coeff * the standard deviation

col

a vector containing color for each state

nx

number of time points used to plot

...

not used

Details

The encoding for the harmonic h is a_{x}^{(h)} \approx \sum_{i=1}^m \alpha_{x,i}^{(h)}\phi_i.

Value

a ggplot object that can be modified using ggplot2 package.

Author(s)

Quentin Grimonprez

See Also

Other encoding functions: compute_optimal_encoding(), get_encoding(), plotComponent(), plotEigenvalues(), predict.fmca(), print.fmca(), summary.fmca()

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)
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)

# plot the encoding produced by the first harmonic
plot(encoding)


# modify the plot using ggplot2
library(ggplot2)
plot(encoding, harm = 2, col = c("red", "blue", "darkgreen", "yellow")) +
  labs(title = "Optimal encoding")