vb_fit_rarecommon: Fit Variational Bayes model
In yhai943/BLMM: Bayesian linear mixed model with multiple random effects

Description Usage Arguments Details Examples

View source: R/vb_fit_rarecommon.R

Take in genotype, phenotype and analyses the target sequence by using BLMM.

vb_fit_rarecommon(
  y,
  genotype0,
  max_iter = 25000,
  weight.type = NULL,
  maf.filter.snp = 0.01,
  epsilon_conv = 1e-04,
  Bsigma2beta = 1,
  theta_beta = 0.1,
  theta_u = 0.1,
  verbose = TRUE,
  kernel = "Lin"
)

`y`	a phenotype vector of length n
`genotype0`	a list of genotype matrices of the target sequence
`max_iter`	maximum number of iteration
`weight.type`	type of weight function
`maf.filter.snp`	a filtering threshold of minor allele frequency for the isolated predictors
`epsilon_conv`	a numeric value giving the interval endpoint
`Bsigma2beta`	a numeric value for sigma beta
`theta_beta`	probability of causal variants
`theta_u`	probability of causal regions
`verbose`	informative iteration messages
`kernel`	kernel type for covariance matrix

A hybrid model that includes a sparsity regression model and a LMM with multiple random effects. The sparsity regression model part is designed to capture the strong predictive effects from isolated predictors, whereas the LMM part is to capture the effects from a group of predictors located in nearby genetic regions.

1
2
3

data("data", package = "BLMM")
# choose model type: 1. "uw" for BLMM-UW; 2. "beta" for BLMM-BETA; 3. "wss" for BLMM-WSS;
fit <- vb_fit_rarecommon(y = y_train, genotype = data, weight.type = "wss")