Description Usage Arguments Value See Also Examples
mpgex_regr
is a function that wraps all the necessary subroutines
for performing predictions on gene expressions. Initially, it optimizes the
parameters of the basis functions so as to learn the methylation profiles.
Then uses the learned parameters / coefficients of the basis functions as
input features for performing linear regression in order to predict/regress
the corresponding gene expression data.
1 2 3 4 |
formula |
An object of class |
x |
The binomial distributed observations, which has to be a list where each element is an L x 3 dimensional matrix. |
y |
Corresponding gene expression data for each element of the list x |
model_name |
A charcter denoting the regression model. |
w |
Optional vector of initial parameter / coefficient values. |
basis |
Optional basis function object, default is
|
train_ind |
Optional vector containing the indices for the train set. |
train_perc |
Optional parameter for defining the percentage of the dataset to be used for training set, the remaining will be the test set. |
fit_feature |
Additional feature on how well the profile fits the methylation data. |
cpg_dens_feat |
Additional feature for the CpG density across the promoter region. |
opt_method |
Parameter for defining the method to be used in the
optimization procedure, see |
opt_itnmax |
Optional parameter for defining the max number of
iterations of the optimization procedure, see |
is_parallel |
Logical, indicating if code should be run in parallel. |
no_cores |
Number of cores to be used, default is max_no_cores - 1. |
is_summary |
Logical, print the summary statistics. |
An mpgex object consisting of the following elements:
bpr_optim
, bpr_likelihood
,
polynomial.object
, rbf.object
,
design_matrix
1 2 3 4 5 | obs <- bpr_data
y <- gex_data
basis <- rbf.object(M = 5)
out <- mpgex_regr(x = obs, y = y, basis = basis, is_parallel = FALSE, fit_feature = "RMSE", cpg_dens_feat = TRUE,
opt_itnmax = 10)
|
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