Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics. 2009; 25 (6):714–721. doi: 10.1093/bioinformatics/btp041. [ Cross Ref ]Wu TT, Chen YF, Hastie T, Sobel E, Lange K. Genome-wide association analysis by lasso penalized ...
elastic net regression: the combination of ridge and lasso regression. It shrinks some coefficients toward zero (like ridge regression) and set some coefficients to exactly zero (like lasso regression) This chapter describes how to compute penalized logistic regre...
9.1 Penalized Logistic Regression: The glmnet Package We want to predict, using a GLM, the binary outcome, but want to penalize coefficients that large in magnitude. This penalization is desirable because it helps reduce overfitting to the sample and increase generalizability of the model to new ...
惩罚回归(Penalized regression)包含一个约束，即选择回归系数使残差平方和和最小，加上惩罚项，惩罚项的...
J. (2010). SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genetic Epidemiology, 34(8), 879–891. Babyak, M. A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. ...
Linear regression一般只对low dimension适用，比如n=50, p=5，而且这五个变量还不存在multicolinearity....
This type thinking has been extended to some penalized shrinkage regression methods, but not yet for logistic regression to our knowledge. We conclude that there is much work to be done and fully expect to see other papers like this one in the future and hopefully statistical practice can ...
There is great difficulty in maximizing the penalized likelihood function using heavy-tailed and small-scaled priors. For example, using a small scale for \(\sqrt{{\omega }_{1}}\) such as e−5, the R function bayesglm in R package ARM (which implements penalized logistic regression with...
COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION 热度: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors 热度: Block Coordinate Descent for Regularized Multi-convex… 热度: ...
这里代码的意思是,先调用penalized包,之后,利用包中的函数penalized包实现LASSO变量筛选,模型中的Surv(OS, Death)这里是因变量生存时间、生存状态;penalized=hepatoCellularNoMissing[,23:48] 指模型LASSO筛选的变量是数据中的第23至48列的26个基因位点;standardize=T 是指对数据做标化处理(LASSO的要求,矩阵非奇异);...