simfd.ed | R Documentation |
This function applies the simm.fda methodology in a setting of certain exponential models (e.g. poisson) followiong the approach outlined in article[..]
simfd.ed(y, t, basis_fct, warp_fct, ed_fct, amp_cov = NULL, warp_cov = NULL, iter = c(5, 5), w0 = NULL, u0 = NULL, use.nlm = c(FALSE, FALSE), suppressLik = FALSE, amp_cov_par = NULL, paramMax = rep(TRUE, length(amp_cov_par)), warp_opt = TRUE, like_optim_control = list(), design = NULL, inner_parallel = FALSE, save_temp = NULL)
y |
List of observations. NAs allowed |
t |
List of corresponding time points. NAs not allowed |
basis_fct |
Basis function for spline |
warp_fct |
Warp function |
ed_fct |
Function defining the exponential family. Must have attribute diff2. See Afkt for examples. |
amp_cov |
Amplitude covariance function. Must be on the form |
warp_cov |
Warp covariance function. Must be on the form |
iter |
two-dimensional integer of maximal number of outer iterations & maximal number of inner iterations per outer iteration. |
w0 |
Starting values for predicted warps. Should only be used if you have results from a previous run. |
u0 |
Starting values for predicted trajectories. Should only be used if you have results from a previous run. |
use.nlm |
Use |
suppressLik |
Suppress if likelihood has increased |
amp_cov_par |
Starting values for amplitude covariance parameters. There are no defaults. |
paramMax |
Logical vector. Which amplitude parameters to optimise over? Defaults to all parameters. May be overwritten by supplying control parameters. |
warp_opt |
If FALSE, warp covariance parameters are kept fixed. |
like_optim_control |
List of control options for optimization in outer loop. See ppMulti for details. |
design |
Design for the experiments. Should be given as a list of one-dimensional vectors or as a design matrix. |
inner_parallel |
Should the inner optimization be done in parallel? |
save_temp |
Save estimates after each outer iteration? NULL or the file path. |
pr |
Printing option. |
sim.fd can only handle one-dimensional functional data. It should NOT be applied to Gaussian data, unless sigma^2 is known and fixed beforehand. Not all relevant control parameters have been checked for compliance; however parallelization does work (on Linux)
A list of estimates and predictions of w and u
ppMulti
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