Description Usage Arguments Details Value Author(s) See Also Examples
Give the complete formula and generate the expected kernel library.
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formula |
(formula) A symbolic description of the model to be fitted. |
data |
(dataframe, n*(p+q1+q2)) A dataframe to be fitted. See Details. |
kern_par |
(dataframe, K*3) A dataframe indicating the parameters of base kernels to fit random effects. See Details. |
fixed_num |
(integer) A numeric number specifying the dimension of fixed effects. |
label_names |
(list) A character string indicating all the interior variables included in each group of random effect. See Details. |
It processes data based on formula and label_names and creates a kernel library according to the parameters given in kern_par.
* label_names: for two groups of random effects with sizes q1 and q2 respectively, label_names contains two elements. The length of the first element is q1, indicating the names of q1 interiors variables, and the length of second one is q2, indicating the names of q2 interiors variables.
* data: for a data with n observations and p+q variables (with sub-groups of sizes (q1, q2), q=q1+q2), the dimension of dataframe is n*(p+q). All entries should be numeric and the column name of response is "Y", while the column names of q variables are the ones from label_names.
* kern_par: for a library of K kernels, the dimension of this dataframe is K*3. Each row represents a kernel. The first column is method, with entries of character class. The second and the third are l and p respectively, both with entries of numeric class.
Y |
(vector of length n) Reponses of the dataframe. |
X |
(dataframe, n*p) Fixed effects variables in the dataframe (could contains several subfactors). |
Z1 |
(dataframe, n*q1) The first group of random effects variables in the dataframe (could contains several subfactors). |
Z2 |
(dataframe, n*q2) The second group of random effects variables in the dataframe (could contains several subfactors). |
kern_list |
(list of length K) A list of kernel functions given by user. |
Wenying Deng
method: generate_kernel
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