Extract predicted survival probabilities from a glmnet fitThomas Hielscher
We applied Coxnet11using the same methodology to build prognosis models using the microbial abundance estimates provided by Poore et al.10We found six microbial abundance models that had a mean C-index score ≥0.6 and significantly outperformed their corresponding clinical covariate-only models (Fig....
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-...
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 cross-...
“NaN”. The GLMNet and XGBTree algorithms compute theR2using the square of Pearson correlation between the observed and predicted target variables. When the predicted and/or the observed target has no variation, i.e., is a constant, the data will have no deviation from the mean, thus ...
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...
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To install "qvalue", start R and enter, ## try http:// if https:// URLs are not supported; it also checks for out-of-date packages source("https://bioconductor.org/biocLite.R") biocLite("qvalue...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 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...
was controlled by a singleλparameter, which was optimized using tenfold crossvalidation for each training set using the cv.glmnet function of the glmnet library in R. For our training-free comparisons, we selected the subset of features corresponding to cell-type-matched and cell-type-agnostic ...