toscca (version 0.1.0)

tosccamm: Computes TOSCCA-MM

Description

This function estimates sparse canonical vectors for matrices with multiple measurements and the trajectories of the latent variables.

Usage

tosccamm(
  A,
  B,
  nonzero_a,
  nonzero_b,
  folds = 1,
  parallel_logic = FALSE,
  silent = FALSE,
  ATest_res = NULL,
  BTest_res = NULL,
  model = "lme",
  lmeformula = " ~ -1 + time + (1|id)",
  arformula = c(1, 0, 0)
)

Value

Canonical vectors for k components.

List with estimated tosccamm parameters.

Arguments

A

A data.frame with id and time as first two columns.

B

A data.frame with id and time as first two columns.

nonzero_a

Integer. Threshold parameter for A.

nonzero_b

Integer. Threshold parameter for B.

folds

Integer. Indicates number of folds to perform.

parallel_logic

Logical. TRUE to parallelise folds. Default is FALSE.

silent

Logical. TRUE to keep silent output messages. Default is FALSE.

ATest_res

NULL. Keep NULL.

BTest_res

NULL. Keep NULL.

model

Character. c("lme", "ar"). Model to fit longitudinal latent space.

lmeformula

Character. LME formula. Default is " ~ -1 + time + (1|id)".

arformula

Numeric vector. Choice of ARIMA. Default is c(1,0,0).

Examples

# \donttest{
# example code
#sample size etc
N = 10
p = 25
q = 5
X0 = list()
Y0 = list()

#Some associations with the true signal
cwa = (6:10) / 10
cwb  = -(2:3) / 10

alpha = rep(0, p)
beta = rep(0, q)

loc_alpha = 1:length(alpha)
loc_beta  = 1:length(beta)

alpha[loc_alpha] = cwa
beta[loc_beta] = cwb

sg = matrix(c(1, 0.6, 0.3, rep(0, 2),
              0.6, 1, 0.6, 0.3, rep(0, 1),
              0.3, 0.6, 1, 0.6, 0.3,
              rep(0,1), 0.3, 0.6, 1, 0.6,
              rep(0,2), 0.3, 0.6, 1), ncol = 5)
for(i in 1:N)
{
  times = 1:5
  Zi1 = (sin(100*times))^times +   times * 0.65 +rnorm(1,0,0.95)
  Zi = cbind(Zi1)
  #Simulate data and add some noise
  X0i = sapply(1:p, function(a) MASS::mvrnorm(1, (Zi %*% t(alpha))[,a], Sigma = sg))
  Y0i = sapply(1:q, function(a) MASS::mvrnorm(1, (Zi %*% t(beta))[,a], Sigma = sg))

  colnames(X0i) = paste0("X", 1:ncol(X0i))
  colnames(Y0i) = paste0("Y", 1:ncol(Y0i))
  #Check the simulated cross correlation
  #image(cor(X0i, Y0i))

  #Remove some observations
  # p_observed = 1
  X0i = cbind(id=i, time=times, X0i)#[rbinom(length(times),1,p_observed)==1,]
  Y0i = cbind(id=i, time=times, Y0i)#[rbinom(length(times),1,p_observed)==1,]

  X0[[i]] = X0i
  Y0[[i]] = Y0i
}

X0 = do.call("rbind", X0)
Y0 = do.call("rbind", Y0)

X = data.frame(X0); Y = data.frame(Y0)
nonz_a = c(2, 5, 10, 20)
nonz_b =  c(2, 3, 4)

mod <- tosccamm(X, Y, folds = 2, nonzero_a = nonz_a, nonzero_b = nonz_b, silent = TRUE)
# }