registr (version 2.2.1)

register_fpca: Register curves using constrained optimization and GFPCA

Description

Function combines constrained optimization and GFPCA to estimate warping functions for exponential family curves. See argument family for which families are supported. Warping functions are calculated by the function registr. The GFPCA step can be performed either using the variational EM-based GFPCA approaches of Wrobel et al. (2019) (fpca_type = "variationalEM", default) or the mixed model-based two-step approach of Gertheiss et al. (2017) (fpca_type = "two-step").

Warping functions by default are forced to start and end on the diagonal to be domain-preserving. This behavior can be changed by setting incompleteness to some other value than NULL and a reasonable lambda_inc value. For further details see the accompanying vignette.

The number of functional principal components (FPCs) can either be specified directly (argument npc) or chosen based on the explained share of variance in each iteration (argument npc_criterion).

By specifying cores > 1 the registration call can be parallelized.

Usage

register_fpca(
  Y,
  Kt = 8,
  Kh = 4,
  family = "gaussian",
  incompleteness = NULL,
  lambda_inc = NULL,
  Y_template = NULL,
  max_iterations = 10,
  npc = NULL,
  npc_criterion = NULL,
  fpca_type = "variationalEM",
  fpca_maxiter = 50,
  fpca_seed = 1988,
  fpca_error_thresh = 1e-04,
  fpca_index_significantDigits = 4L,
  cores = 1L,
  verbose = 1,
  ...
)

Value

An object of class registration containing:

Y

The observed data plus variables t_star and t_hat which are the unregistered grid and registered grid, respectively.

fpca_obj

List of items from FPCA step.

family

Used exponential family.

index_warped

List of the (warped) index values for each iteration. Has 'convergence$iterations + 2' elements since the first two elements contain the original (observed) index and the warped index values from the preprocessing registration step (see Details), respectively.

hinv_innerKnots

List of inner knots for setting up the spline bases for the inverse warping functions. Only contains NULL values for Kh <= 4.

hinv_beta

Matrix of B-spline basis coefficients used to construct the subject-specific inverse warping functions. From the last performed registration step. For details see ?registr.

convergence

List with information on the convergence of the joint approach. Containing the following elements:

converged
Indicator if the joint algorithm converged or if not (i.e., max_iterations was reached)

iterations
Number of joint iterations that were performed.

delta_index
Vector of mean squared differences between the (warped) index values (scaled to [0,1] based on the size of the observed domain) in the current and the previous iteration. Convergence is reached if this measure drops below 0.0001.

registration_loss
Vector of the loss in each iteration of the algorithm. Calculated in the registration step using the exponential family likelihood with natural parameter from the FPCA step. Has 'iterations + 1' elements since the first element contains the loss of the preprocessing registration step (see Details).

Arguments

Y

Dataframe. Should have values id, value, index.

Kt

Number of B-spline basis functions used to estimate mean functions and functional principal components. Default is 8. If fpca_type = "variationalEM" and npc_criterion is used, Kt is set to 20.

Kh

Number of B-spline basis functions used to estimate warping functions h. Default is 4.

family

One of c("gaussian","binomial","gamma","poisson"). Families "gamma" and "poisson" are only supported by fpca_type = "two-step". Defaults to "gaussian".

incompleteness

Optional specification of incompleteness structure. One of c("leading","trailing","full"), specifying that incompleteness is present only in the initial measurements, only in the trailing measurements, or in both, respectively. For details see the accompanying vignette. Defaults to NULL, i.e. no incompleteness structure. Can only be set when warping = "nonparametric".

lambda_inc

Penalization parameter to control the amount of overall dilation of the domain. The higher this lambda, the more the registered domains are forced to have the same length as the observed domains. Only used if incompleteness is not NULL.

Y_template

Optional dataframe with the same structure as Y. Only used for the initial registration step. If NULL, curves are registered to the overall mean of all curves in Y as template function. If specified, the template function is taken as the mean of all curves in Y_template. Defaults to NULL.

max_iterations

Number of iterations for overall algorithm. Defaults to 10.

npc, npc_criterion

The number of functional principal components (FPCs) has to be specified either directly as npc or based on their explained share of variance. In the latter case, npc_criterion has to be set to a number between 0 and 1. For fpca_type = "two-step", it is also possible to cut off potential tails of subordinate FPCs (see gfpca_twoStep for details).

fpca_type

One of c("variationalEM","two-step"). Defaults to "variationalEM".

fpca_maxiter

Only used if fpca_type = "variationalEM". Number to pass to the maxiter argument of `bfpca()` or `fpca_gauss()`. Defaults to 50.

fpca_seed

Only used if fpca_type = "variationalEM". Number to pass to the seed argument of `bfpca()` or `fpca_gauss()`. Defaults to 1988.

fpca_error_thresh

Only used if fpca_type = "variationalEM". Number to pass to the error_thresh argument of `bfpca()` or `fpca_gauss()`. Defaults to 0.0001.

fpca_index_significantDigits

Only used if fpca_type = "two-step". Positive integer >= 2, stating the number of significant digits to which the index grid should be rounded in the GFPCA step. Coarsening the index grid is necessary since otherwise the covariance surface matrix explodes in size in the presence of too many unique index values (which is the case after some registration step). Defaults to 4. Set to NULL to prevent rounding.

cores

Number of cores to be used. If cores > 1, the registration call is parallelized by using parallel::mclapply (for Unix-based systems) or parallel::parLapply (for Windows). Defaults to 1, no parallelized call.

verbose

Can be set to integers between 0 and 4 to control the level of detail of the printed diagnostic messages. Higher numbers lead to more detailed messages. Defaults to 1.

...

Additional arguments passed to registr and to the gfpca functions (if fpca_type = "variationalEM").

Author

Julia Wrobel Jeff Goldsmith , Alexander Bauer

Details

Requires input data Y to be a dataframe in long format with variables id, index, and value to indicate subject IDs, observation times on the domain, and observations, respectively.

One joint iteration consists of a GFPCA step and a registration step. As preprocessing, one initial registration step is performed. The template function for this registration step is defined by argument Y_template. After convergence or max_iterations is reached, one final GFPCA step is performed.

Examples

### complete binomial curves
Y = simulate_unregistered_curves(I = 20, D = 200)

# estimation based on Wrobel et al. (2019)
reg = register_fpca(Y, npc = 2, family = "binomial",
                    fpca_type = "variationalEM", max_iterations = 5)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  
  ggplot(reg$Y, aes(x = tstar, y = t_hat, group = id)) +
    geom_line(alpha = 0.2) + ggtitle("Estimated warping functions")
  
  plot(reg$fpca_obj, response_function = function(x) { 1 / (1 + exp(-x)) })
}


# \donttest{

# estimation based on Gertheiss et al. (2017)
reg2 = register_fpca(Y, npc = 2, family = "binomial",
                     fpca_type = "two-step", max_iterations = 5,
                     fpca_index_significantDigits = 4)
                     
# example using accelerometer data from nhanes 2003-2004 study
data(nhanes)
nhanes_short = nhanes[nhanes$id %in% unique(nhanes$id)[1:5],]
reg_nhanes   = register_fpca(nhanes_short, npc = 2, family = "binomial", max_iterations = 5)


### incomplete Gaussian curves
data(growth_incomplete)

# Force the warping functions to start and end on the diagonal
reg2a = register_fpca(growth_incomplete, npc = 2, family = "gaussian",
                      incompleteness = NULL, max_iterations = 5)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  
  ggplot(reg2a$Y, aes(x = tstar, y = t_hat, group = id)) +
    geom_line(alpha = 0.2) +
    ggtitle("Estimated warping functions")
  ggplot(reg2a$Y, aes(x = t_hat, y = value, group = id)) +
    geom_line(alpha = 0.2) +
    ggtitle("Registered curves")
}
# Allow the warping functions to not start / end on the diagonal.
# The higher lambda_inc, the more the starting points and endpoints are forced
# towards the diagonal.
reg2b = register_fpca(growth_incomplete, npc = 2, family = "gaussian",
                      incompleteness = "full", lambda_inc = 0.1,
                      max_iterations = 5)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot(reg2b$Y, aes(x = tstar, y = t_hat, group = id)) +
    geom_line(alpha = 0.2) +
    ggtitle("Estimated warping functions")
  ggplot(reg2b$Y, aes(x = t_hat, y = value, group = id)) +
    geom_line(alpha = 0.2) +
    ggtitle("Registered curves")
}

### complete Gamma curves
Y             = simulate_unregistered_curves(I = 20, D = 100)
Y$value       = exp(Y$latent_mean)
registr_gamma = register_fpca(Y, npc = 2, family = "gamma", fpca_type = "two-step",
                              gradient = FALSE, max_iterations = 3)
# }