kriging_rss | R Documentation |

Under the null, the rss model with regularized LD
matrix is *z|R,s ~ N(0, (1-s)R + s I))*. We use a mixture of
normals to model the conditional distribution of z_j given other z
scores, *z_j | z_{-j}, R, s ~ ∑_{k=1}^{K} π_k
N(-Ω_{j,-j} z_{-j}/Ω_{jj}, σ_{k}^2/Ω_{jj})*,
*Ω = ((1-s)R + sI)^{-1}*, *σ_1, ..., σ_k*
is a grid of fixed positive numbers. We estimate the mixture
weights *π* We detect the possible allele switch issue
using likelihood ratio for each variant.

kriging_rss( z, R, n, r_tol = 1e-08, s = estimate_s_rss(z, R, n, r_tol, method = "null-mle") )

`z` |
A p-vector of z scores. |

`R` |
A p by p symmetric, positive semidefinite correlation matrix. |

`n` |
The sample size. (Optional, but highly recommended.) |

`r_tol` |
Tolerance level for eigenvalue check of positive semidefinite matrix of R. |

`s` |
an estimated s from |

a list containing a ggplot2 plot object and a table. The plot compares observed z score vs the expected value. The possible allele switched variants are labeled as red points (log LR > 2 and abs(z) > 2). The table summarizes the conditional distribution for each variant and the likelihood ratio test. The table has the following columns: the observed z scores, the conditional expectation, the conditional variance, the standardized differences between the observed z score and expected value, the log likelihood ratio statistics.

# See also the vignette, "Diagnostic for fine-mapping with summary # statistics." set.seed(1) n = 500 p = 1000 beta = rep(0,p) beta[1:4] = 0.01 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X %*% beta + rnorm(n)) ss = univariate_regression(X,y) R = cor(X) attr(R,"eigen") = eigen(R,symmetric = TRUE) zhat = with(ss,betahat/sebetahat) cond_dist = kriging_rss(zhat,R,n = n) cond_dist$plot

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