GE_scoreeq_sim: GE_scoreeq_sim.R

Description Usage Arguments Value Examples

View source: R/GE_scoreeq_sim.R


Here we perform simulation to verify that we have solved for the correct alpha values in GE_bias_norm_squaredmis(). Make the same assumptions as in GE_bias_norm_squaredmis().


GE_scoreeq_sim(num_sims = 5000, num_sub = 2000, beta_list, rho_list, prob_G,
  cov_Z = NULL, cov_W = NULL, corr_G = NULL)



The number of simulations to run, we suggest 5000.


The number of subjects to generate in every simulation, we suggest 2000.


A list of the effect sizes in the true model. Use the order beta_0, beta_G, beta_E, beta_I, beta_Z, beta_M. If G or Z or M is a vector, then beta_G/beta_Z/beta_M should be vectors. If Z and/or M/W do not exist in your model, then set beta_Z and/or beta_M = 0.


A list of expectations (which happen to be covariances if all covariates are centered at 0) in the order specified by GE_enumerate_inputs(). If Z and/or M/W do not exist in your model, then treat them as constants 0. For example, if Z doesn't exist and W includes 2 covariates, then set cov(EZ) = 0 and cov(ZW) = (0,0). If describing expectations relating two vectors, i.e. Z includes two covariates and W includes three covariates, sort by the first term and then the second. Thus in the example, the first three terms of cov(ZW) are cov(Z_1,W_1),cov(Z_1,W_2), cov(Z_1,W_3), and the last three terms are cov(Z_3,W_1), cov(Z_3,W_2), cov(Z_3,W_3).


Probability that each allele is equal to 1. Since each SNP has two alleles, the expectation of G is 2*prob_G. Should be a d*1 vector.


Should be a matrix equal to cov(Z) or NULL if no Z.


Should be a matrix equal to cov(W) or NULL if no W.


Should be a matrix giving the *pairwise correlations* between each SNP in the set, or NULL. Must be specified if G is a vector. For example, the [2,3] element of the matrix would be the pairwise correlation between SNP2 and SNP3.


A list of the fitted values alpha


GE_scoreeq_sim( num_sims=10, num_sub=1000, beta_list=as.list(runif(n=6, min=0, max=1)), 
rho_list=as.list(rep(0.3,6)), prob_G=0.3, cov_Z=1, cov_W=1)

GEint documentation built on Aug. 10, 2017, 1:03 a.m.