| PMR_summary | R Documentation |
PPMR Summary-level Analysis
PMR_summary(
betaxin,
betayin,
Sigma1sin,
Sigma2sin,
samplen1,
samplen2,
gammain,
alphain,
max_iterin,
epsin
)
betaxin |
Numeric vector of estimated SNP–exposure effects (length 'p'). |
betayin |
Numeric vector of estimated SNP–outcome effects (length 'p'). |
Sigma1sin |
Numeric 'p x p' covariance matrix for the exposure SNP associations (typically an LD matrix). |
Sigma2sin |
Numeric 'p x p' covariance matrix for the outcome SNP associations. |
samplen1 |
Integer. Sample size used to estimate |
samplen2 |
Integer. Sample size used to estimate |
gammain |
Integer flag (0/1). If 1, constrains the gamma parameter to 0. |
alphain |
Integer flag (0/1). If 1, constrains the alpha parameter to 0. |
max_iterin |
Integer. Maximum number of EM iterations (default: 50 or more). |
epsin |
Numeric. Convergence tolerance for the log-likelihood. |
A named list with elements:
alpha |
Estimated causal effect of the mediator on the outcome. |
gamma |
Estimated direct effect of the SNPs on the outcome. |
sigmaX |
Residual variance for the exposure model. |
sigmaY |
Residual variance for the outcome model. |
sigmabeta |
Variance of the genetic effects. |
loglik_seq |
Vector of log-likelihood values across iterations. |
loglik |
Final log-likelihood value. |
iteration |
Number of iterations used before convergence. |
# ---- Simulate simple example data ----
set.seed(123)
p <- 3
n1 <- 10
n2 <- 12
betax <- c(0.2, -0.1, 0.3)
betay <- c(0.1, 0.0, 0.2)
Sigma1 <- matrix(c(0.6, 0.2, 0.1,
0.2, 0.5, 0.1,
0.1, 0.1, 0.4), 3, 3)
Sigma2 <- matrix(c(0.5, 0.1, 0.0,
0.1, 0.6, 0.1,
0.0, 0.1, 0.5), 3, 3)
PMR_summary(
betaxin = betax,
betayin = betay,
Sigma1sin = Sigma1,
Sigma2sin = Sigma2,
samplen1 = n1,
samplen2 = n2,
gammain = 0,
alphain = 0,
max_iterin = 50,
epsin = 1e-6
)
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