Description Usage Arguments Value Author(s) References Examples
sem_lucid
provides standard errors (SE) of parameter estimates when performing latent cluster analysis with multi-omics data. SEs are obtained through supplemented EM-algorithm (SEM).
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G |
Genetic features, a matrix |
Z |
Biomarker data, a matrix |
Y |
Disease outcome, a vector |
family |
"binary" or "normal" for Y |
useY |
Using Y or not, default is TRUE |
K |
Pre-specified # of latent clusters, default is 2 |
initial |
A list of initial model parameters will be returned for integrative clustering |
itr_tol |
A list of tolerance settings will be returned for integrative clustering |
Pred |
Flag to compute predicted disease probability with fitted model, boolean, default is TRUE |
Get_SE |
Flag to perform SEM to get SEs of parameter estimates, default is TRUE |
Ad_Hoc_SE |
Flag to fit ad hoc regression models to get SEs of parameter estimates, default is FALSE |
sem_lucid
returns an object of list containing parameters estimates, their corresponding standard errors, and other features:
beta |
Estimates of genetic effects, matrix |
se_beta |
SEM standard errors of Beta |
se_ah_beta |
Ad hoc standard errors of Beta |
mu |
Estimates of cluster-specific biomarker means, matrix |
se_mu |
SEM standard errors of Mu |
se_ah_mu |
Ad hoc standard errors of Mu |
sigma |
Estimates of cluster-specific biomarker covariance matrix, list |
gamma |
Estimates of cluster-specific disease risk, vector |
se_gamma |
SEM standard errors of Gamma |
se_ah_gamma |
Ad hoc standard errors of Gamma |
pcluster |
Probability of cluster, when G is null |
pred |
Predicted probability of belonging to each latent cluster |
Cheng Peng, Zhao Yang, David V. Conti
Cheng Peng, Jun Wang, Isaac Asante, Stan Louie, Ran Jin, Lida Chatzi, Graham Casey, Duncan C Thomas, David V Conti, A Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) with Phenotypic Traits, Bioinformatics, , btz667, https://doi.org/10.1093/bioinformatics/btz667.
Meng, X., & Rubin, D. B. (1991). Using EM to Obtain Asymptotic Matrices : The SEM Algorithm. Journal of the American Statistical Association, 86(416), 899-909. http://doi.org/10.2307/2290503
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