spm_con_1d_g: Fitting a 1-D genetic SPM model with constant parameters

In izhbannikov/spm: Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Fitting a 1-D genetic SPM model with constant parameters

Description

This function implements a continuous genetic SPM model by assuming all the parameters are constants.

Usage

spm_con_1d_g(
  spm_data,
  gene_data,
  a = NA,
  b = NA,
  q = NA,
  f = NA,
  f1 = NA,
  mu0 = NA,
  theta = NA,
  effect = c("a"),
  lower = c(),
  upper = c(),
  control = list(xtol_rel = 1e-06),
  global = FALSE,
  verbose = TRUE,
  ahessian = FALSE,
  method = "lbfgs",
  method.hessian = "L-BFGS-B"
)

Arguments

spm_data	A dataset for the SPM model. See the STPM pacakge for more details about the format.
gene_data	A two column dataset containing the genotypes for the individuals in spm_data. The first column id is the ID of the individuals in spm_data, and the second column geno is the genotype.
a	The initial value for the paramter a. The initial value will be predicted if not specified.
b	The initial value for the paramter b. The initial value will be predicted if not specified.
q	The initial value for the paramter q. The initial value will be predicted if not specified.
f	The initial value for the paramter f. The initial value will be predicted if not specified.
f1	The initial value for the paramter f_1. The initial value will be predicted if not specified.
mu0	The initial value for the paramter μ_0 in the baseline hazard. The initial value will be predicted if not specified.
theta	The initial value for the paramter θ in the baseline hazard. The initial value will be predicted if not specified.
effect	A character vector of the parameters that are linked to genotypes. The vector can contain any combination of a, b, q, f, mu0.
lower	A vector of the lower bound of the parameters.
upper	A vector of the upper bound of the parameters.
control	A list of the control parameters for the optimization paramters.
global	A logical variable indicating whether the MLSL (TRUE) or the L-BFGS (FALSE) algorithm is used for the optimization.
verbose	A logical variable indicating whether initial information is printed.
ahessian	A logical variable indicating whether the approximate (FALSE) or analytical (TRUE) Hessian is returned.
method	Optimization method. Can be one of the following: lbfgs, mlsl, mma, slsqp, tnewton, varmetric. Default: lbfgs.
method.hessian	Optimization method for hessian calculation (if ahessian=F). Default: L-BFGS-B.

Value

est The estimates of the parameters.

hessian The Hessian matrix of the estimates.

hessian The Hessian matrix of the estimates.

lik The minus log-likelihood.

con A number indicating the convergence. See the 'nloptr' package for more details.

message Extra message about the convergence. See the 'nloptr' package for more details.

beta The coefficients of the genetic effect on the parameters to be linked to genotypes.

References

He, L., Zhbannikov, I., Arbeev, K. G., Yashin, A. I., and Kulminski, A.M., 2017. Genetic stochastic process model for detecting pleiotropic and interaction effects with longitudinal data.

Examples

## Not run:  
library(stpm) 
data(ex_spmcon1dg)
res <- spm_con_1d_g(ex_data$spm_data, ex_data$gene_data, 
a = -0.02, b=0.2, q=0.01, f=3, f1=3, mu0=0.01, theta=1e-05, 
upper=c(-0.01,3,0.1,10,10,0.1,1e-05), lower=c(-1,0.01,0.00001,1,1,0.001,1e-05), 
effect=c('q'))

## End(Not run)

izhbannikov/spm documentation built on Sept. 10, 2022, 12:15 p.m.