model_selection_component {IMIX}

Component Selection

Description

Model selection for components based on AIC and BIC values for models in IMIX

Usage

model_selection_component(
  data_input,
  data_type = c("p", "z"),
  tol = 1e-06,
  maxiter = 1000,
  seed = 10,
  verbose = FALSE
)

Arguments

data_input

An n x d data frame or matrix of the summary statistics z score or p value, n is the nubmer of genes, d is the number of data types. Each row is a gene, each column is a data type.

data_type

Whether the input data is the p values or z scores, default is p value

tol

The convergence criterion. Convergence is declared when the change in the observed data log-likelihood increases by less than epsilon.

maxiter

The maximum number of iteration, default is 1000

seed

set.seed, default is 10

verbose

Whether to print the full log-likelihood for each iteration, default is FALSE

Value

Selected number of components based on AIC and BIC

Component_Selected_AIC

Selected number of components by AIC with the smallest AIC value among all components and models

Component_Selected_BIC

Selected number of components by BIC with the smallest BIC value among all components and models

AIC/BIC

The AIC and BIC values for all components for IMIX_ind_unrestrict, IMIX_cor_twostep, and IMIX_cor

IMIX_ind_unrestrict

A list of the IMIX_ind_unrestrict for all components 1,2,...2^d, this step was fitted using R package "Mclust", more details of the output can be found there

IMIX_cor_twostep

A list of the IMIX_cor_twostep for all components 1,2,...2^d, here, the mean is the estimated value of IMIX_ind_unrestrict

IMIX_cor

A list of the IMIX_cor_twostep for all components 1,2,...2^d

References

Ziqiao Wang and Peng Wei. 2020. “IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration.” Bioinformatics. <doi:10.1093/bioinformatics/btaa1001>. Luca Scrucca, Michael Fop, T. Brendan Murphy, and Adrian E. Raftery. 2016. “mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.” The R Journal 8 (1): 289–317. <doi:10.32614/RJ-2016-021>.

Examples

# A toy example
data("data_p")
set.seed(10)
data <- data_p[sample(1:1000,20,replace = FALSE),]
select_comp0 <- model_selection_component(data, data_type = "p", seed = 20)


# First load the data
data("data_p")

# Perform model selections on the data
select_comp1 = model_selection_component(data_p, data_type = "p", seed = 20)