Estimate the RiskShrink Hard Thresholding Level
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3.2.3. Soft thresholding: LASSO-Q and EN-Quantile (EN-Q) One caveat of the restricted MIDAS approach presented above is that the predetermined choice of the weighting function might result in a lag structure for the high-frequency predictor that fails to maximize forecast accuracy. Thus, as ...
The residual AUC for the five binary traits were calculated using the “roc.binary” function in the R package RISCA version 1.0171 adjusting for the same set of covariates adjusted for the continuous traits. Calculation of the 95% bootstrap confidence intervals and p values for comparing R2 ...
The predicted cumulative risk curves (as a function of timet) were calculated using ‘survfit.coxph’ within each stratum of sex as $$\begin{array}{*{20}{c}} {1 - \widehat S\left( t \right) = 1 - {\mathrm{exp}}\left( { - \widehat {H}_0\left( t \right){\mathrm{exp}...
Based on this definition, AUC can be approximated by a function of the GWAS summary statistics. This method can tune the parameters of a PRS model with summary statistics from another GWAS. To maximize the power of identifying loci associated with common diseases, some large consortia have ...
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11Citations 15Altmetric Metrics Abstract Remarkably little is known about the structure, formation, and dynamics of supply- and production networks that form one foundation of society. Neither the resilience of these networks is known, nor do we have ways to systematically monitor their ongoing chang...
We estimated the 95% credible intervals by using the ci.auc function from the R package “pROC”. DeLong’s test was conducted to assess potential significant differences between curves using the roc.test function from the R package “pROC”. CSF biomarkers In the J-ADNI cohort, cerebrospinal...