This function evaluates the Lambda matrix such as per column : Lambda_i = int_0^T X(t) phi_i(t) dt. The curve_type input is important function of the type of data you work with 'cat' for Categorical Functional Data 'num' for Scalar Functional Data
Usage
evaluate_lambda(
df,
basis,
curve_type = NULL,
int_mode = 1,
id_col = "id",
time_col = "time",
nb_pt = 10,
subdivisions = 100,
regul_time = seq(basis$rangeval[1], basis$rangeval[2], 1),
parallel = TRUE
)Arguments
- df
dataframe X(t)
- basis
basis fd object
- curve_type
a character, 'cat' for Categorical FD, 'num' for Scalar FD
- int_mode
integration mode, 1 for integrate, 2 for pracma::trapz
- id_col
a character for the id column, default 'id'
- time_col
a character for the time column, default 'time'
- nb_pt
number of points for the integration, default value : 10
- subdivisions
default parameter of R function integrate; default value : 100
- regul_time
regul_time a vector of time regularization values default basis rangeval per 1
- parallel
a boolean to use parallelization, default TRUE
Examples
df = generate_X_df(nind=100, start=0, end=100, curve_type = 'cat',
lambda_0=0.2, lambda_1=0.1, prob_start=0.5)
basis = create_bspline_basis(0, 100, 10, 4)
Lambda = evaluate_lambda(df, basis, curve_type = 'cat')
#> Warning: package 'future' was built under R version 4.4.3
df = generate_X_df(nind=100, start=0, end=100, curve_type = 'num')
basis = create_bspline_basis(0, 100, 10, 4)
Lambda = evaluate_lambda(df, basis, curve_type = 'num', int_mode = 2)