Extract predicted survival probabilities from a glmnet fitThomas Hielscher
All flanking genetic variants (±500 Kb region of the respective transcriptomic event) available in the GWAS data of breast cancer were used to build the elastic net model implemented in the glmnet R package, with α = 0.5, as recommended by Gamazon et al.42. Five-fold cross-...
To the best of our knowledge, no available comprehensive ML library in python or R currently provides an elastic net LGR algorithm with the functionality to specify features that can bypass embedded feature selection and be modeled with an L2 penalty (setting the R glmnet penalty factor, for ...
While VAR can provide more interpretabilityFootnote14, we found that the GLMNet and XGBTree models tended to achieve betterR2values and usually lower error metrics. Moreover, we found that the performances vary significantly depending on the target variable and the dataset. The models performed sligh...
The R packages used for these analyses included readxl (v1.4.2), Biobase (v2.58.0), MissForest (v1.5), limma (v3.54.2), class (v7.3–21), pROC (v1.18.0), caret (v6.0–94), glmnet (v4.1–7), randomForest (v4.7–1.1), dynamicTreeCut (v1.63–1), aricode (v1.0.2), ggplot...
Elastic-net regression, from the glmnet package (Zou and Hastie, 2005), was used to predict the nIDPs using FLICA’s subject modes as model regressors (features). This approach is widely-used and has been shown to achieve a robust and state-of-the-art performance in many neuroimaging ...
Using an elastic net regression model (glmnet in R caret package with repeated cross-validation), all five word frequency measures (Zipf values from cpb-lex, cbeebies, cbbc, subtlex-uk, dpb), along with six psycholinguistic features commonly used as control variables (word length, number of...
We used GLMnet penalized regression (elastic-net) applied to survival time (R package glmnet [the R Foundation]) with α = .05 to account for complex interassociations and with the regularization parameter λ14 to achieve the minimum mean squared error, selected using 10-fold cro...
install.packages("glmnet", dependencies=TRUE) install.packages("Rcpp", dependencies=TRUE) To install "qvalue", start R and enter, ## try http:// if https:// URLs are not supported; it also checks for out-of-date packages source("https://bioconductor.org/biocLite.R") biocLite("qvalue...
The LASSO Cox regression model was trained using the “glmnet” R package91, with 325 NMR biomarkers as independent variables and all-cause mortality as the dependent variable. We performed tenfold cross-validation to identify the optimal hyperparameter λ, which controls the magnitude of the penal...