View source: R/GE_translate_inputs.R
GE_translate_inputs | R Documentation |
Mostly for internal use, function called by GE_bias_normal() and GE_scoreeq_sim() to translate the rho_list inputs and return a total covariance matrix for simulation/ checking validity of covariance structure. If invalid covariance structure, will stop and return an error message.
GE_translate_inputs(beta_list, rho_list, prob_G, cov_Z = NULL, cov_W = NULL, corr_G = NULL)
beta_list |
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. |
prob_G |
Probability that each allele is equal to 1. Since each SNP has two alleles, the expectation of G is 2*prob_G. |
cov_Z |
Should be a matrix equal to cov(Z) or NULL if no Z. |
cov_W |
Should be a matrix equal to cov(W) or NULL if no W. |
corr_G |
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. Diagonal should be 1. |
cov_list |
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). |
A list with the elements:
sig_mat_total |
The sigma parameter for rmvnorm call to generate our data. |
GE_translate_inputs( 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)
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