Description Usage Arguments Details Value Examples
This is the main function of the package and fits the multivariate functional additive regression model with potentially nested or crossed functional random intercepts.
1 2 3 4 5 6 7 8 9  multiFAMM(data, fRI_B = FALSE, fRI_C = FALSE, nested = FALSE,
bs = "ps", bf_mean = 8, bf_covariates = 8, m_mean = c(2, 3),
covariate = FALSE, num_covariates = NULL, covariate_form = NULL,
interaction = FALSE, which_interaction = matrix(NA), bf_covs, m_covs,
var_level = 1, use_famm = FALSE, save_model_famm = FALSE,
one_dim = NULL, mfpc_weight = FALSE, mfpc_cutoff = 0.95,
number_mfpc = NULL, mfpc_cut_method = c("total_var", "unidim"),
final_method = c("w_bam", "bam", "gaulss"), weight_refit = FALSE,
verbose = TRUE, ...)

data 
Data.table that contains the information with some fixed variable names, see Details. 
fRI_B 
Boolean for including functional random intercept for individual
(B in Cederbaum). Defaults to 
fRI_C 
Boolean for including functional random intercept
for word (C in Cederbaum). Defaults to 
nested 

bs 
Spline basis function, only tested for "ps" (as in sparseFLMM). 
bf_mean 
Basis dimension for functional intercept (as in sparseFLMM). 
bf_covariates 
Basis dimension for all covariates (as in sparseFLMM). 
m_mean 
Order of penalty for basis function (as in sparseFLMM). 
covariate 
Covariate effects (as in sparseFLMM). 
num_covariates 
Number of covariates included in the model (as in sparseFLMM). 
covariate_form 
Vector of strings for type of covariate (as in sparseFLMM). 
interaction 
TRUE if there are interactions between covariates (as in
sparseFLMM). Defaults to 
which_interaction 
Symmetric matrix specifying the interaction terms (as in sparseFLMM). 
bf_covs 
Vector of marginal basis dimensions for fRI covariance estimation (as in sparseFLMM). 
m_covs 
List of marginal orders for the penalty in fRI covariance estimation (as in sparseFLMM). 
var_level 
Prespecified level of explained variance on each dimension (as in sparseFLMM). Defaults to including all nonnegative Eigenvalues. 
use_famm 
Reestimate the mean in FAMM context (as in sparseFLMM)  overwritten by one_dim. 
save_model_famm 
Give out the FAMM model object (as in sparseFLMM)  overwritten by one_dim. 
one_dim 
Specify the name of the dimension if sparseFLMM is to be computed only on one dimension. 
mfpc_weight 
TRUE if the estimated univariate error variance is to be used as weights in the scalar product of the MFPCA. 
mfpc_cutoff 
Prespecified level of explained variance of results of MFPCA. Defaults to 0.95. 
number_mfpc 
List containing the number of mfPCs needed for each variance component e.g. list("E" = x, "B" = y). 
mfpc_cut_method 
Method to determine the level of explained variance

final_method 
Function used for estimation of final model to allow for potential heteroscedasticity ("w_bam", "bam", "gaulss"). 
weight_refit 
Get the weights for the weighted bam by first refitting the model under an independence assumption but with mfpc basis functions. Defaults to FALSE. 
verbose 
Print progress of the multifamm. Defaults to TRUE. 
... 
Additional arguments to be passed to (mainly) the underlying sparseFLMM function. 
Expand the method proposed by Fabian Scheipl to incorporate the variance decomposition developed by Cederbaum et al. (2016). To account for the correlation between the dimensions, the MFPCA approach by Happ and Greven (2016) is applied.
The data set has to be of the following format:
y_vec (numeric): vector of response values
t (numeric): observation point locations
n_long (integer): curve identification
subject_long (integer): subject identification (NEEDS TO BE SPECIFIED)
word_long (integer): word identification
combi_long (integer): repetition
dim (factor): level of the dimension
covariate.1 (numeric): potential covariate(s) named with trailing 1,2,3,...
It is possible to introduce weights for the final estimation of the multiFAMM. Currently, it is only implemented to use the inverse of the univariate measurement error estimates as weights. Note that negative values of variance estimates are set to zero in fast symmetric additive covariance smoothing. In order to still include weights, zerovalues are substituted by values of the smallest positive variance estimate.
A list with five elements
the final multivariate FAMM
the sparseFLMM output for each of the dimensions
information on the untruncated MPFCA results
the truncated MFPC output
the data used to fit the model.
1 2 3 4 5 6 7 8 9 10 11  # subset of the phonetic data (very small subset, no meaningful results can
# be expected and no random effects other than the random smooth should be
# included in the model)
data(phonetic_subset)
m < multiFAMM(data = phonetic_subset, covariate = TRUE, num_covariates = 2,
covariate_form = c("by", "by"), interaction = TRUE,
which_interaction = matrix(c(FALSE, TRUE, TRUE, FALSE),
nrow = 2, ncol = 2), bf_covs = c(5), m_covs = list(c(2, 3)),
mfpc_cut_method = "total_var", final_method = "w_bam")

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