sPLS-DRCOX
This function performs a sparse partial least squares deviance residual Cox (sPLS-DRCOX) (based on plsRcox R package). The function returns a Coxmos model with the attribute model as "sPLS-DRCOX".
This function performs a sparse partial least squares deviance residual Cox (sPLS-DRCOX) (based on plsRcox R package). The function returns a Coxmos model with the attribute model as "sPLS-DRCOX".
splsdrcox_penalty( X, Y, n.comp = 4, penalty = 0.5, x.center = TRUE, x.scale = FALSE, remove_near_zero_variance = TRUE, remove_zero_variance = FALSE, toKeep.zv = NULL, remove_non_significant = FALSE, alpha = 0.05, MIN_EPV = 5, returnData = TRUE, verbose = FALSE ) splsdrcox_penalty( X, Y, n.comp = 4, penalty = 0.5, x.center = TRUE, x.scale = FALSE, remove_near_zero_variance = TRUE, remove_zero_variance = FALSE, toKeep.zv = NULL, remove_non_significant = FALSE, alpha = 0.05, MIN_EPV = 5, returnData = TRUE, verbose = FALSE )
X: Numeric matrix or data.frame. Explanatory variables. Qualitative variables must be transform into binary variables.Y: Numeric matrix or data.frame. Response variables. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.n.comp: Numeric. Number of latent components to compute for the (s)PLS model (default: 10).penalty: Numeric. Penalty for sPLS-DRCOX. If penalty = 0 no penalty is applied, when penalty = 1 maximum penalty (no variables are selected) based on 'plsRcox' penalty. Equal or greater than 1 cannot be selected (default: 0.5).x.center: Logical. If x.center = TRUE, X matrix is centered to zero means (default: TRUE).x.scale: Logical. If x.scale = TRUE, X matrix is scaled to unit variances (default: FALSE).remove_near_zero_variance: Logical. If remove_near_zero_variance = TRUE, near zero variance variables will be removed (default: TRUE).remove_zero_variance: Logical. If remove_zero_variance = TRUE, zero variance variables will be removed (default: TRUE).toKeep.zv: Character vector. Name of variables in X to not be deleted by (near) zero variance filtering (default: NULL).remove_non_significant: Logical. If remove_non_significant = TRUE, non-significant variables/components in final cox model will be removed until all variables are significant by forward selection (default: FALSE).alpha: Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).MIN_EPV: Numeric. Minimum number of Events Per Variable (EPV) you want reach for the final cox model. Used to restrict the number of variables/components can be computed in final cox models. If the minimum is not meet, the model cannot be computed (default: 5).returnData: Logical. Return original and normalized X and Y matrices (default: TRUE).verbose: Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).Instance of class "Coxmos" and model "sPLS-DRCOX". The class contains the following elements: X: List of normalized X data information.
(data): normalized X matrix(weightings): sPLS weights(weightings_norm): sPLS normalize weights(W.star): sPLS W* vector(loadings): sPLS loadings(scores): sPLS scores/variates(E): error matrices(x.mean): mean values for X matrix(x.sd): standard deviation for X matrixY: List of normalized Y data information.
(deviance_residuals): deviance residual vector used as Y matrix in the sPLS.(dr.mean): mean values for deviance residuals Y matrix(dr.sd): standard deviation for deviance residuals Y matrix'(data): normalized X matrix(weightings): sPLS weights(loadings): sPLS loadings(scores): sPLS scores/variates(ratio): r value for the sPLS model (used to perform predictions)(y.mean): mean values for Y matrix(y.sd): standard deviation for Y matrix'survival_model: List of survival model information.
fit: coxph object.AIC: AIC of cox model.BIC: BIC of cox model.lp: linear predictors for train data.coef: Coefficients for cox model.YChapeau: Y Chapeau residuals.Yresidus: Y residuals.penalty: Penalty value selected.
n.comp: Number of components selected.
var_by_component: Variables selected in each PLS component.
call: call function
X_input: X input matrix
Y_input: Y input matrix
B.hat: sPLS beta matrix
R2: sPLS acumulate R2
SCR: sPLS SCR
SCT: sPLS SCT
alpha: alpha value selected
nsv: Variables removed by cox alpha cutoff.
nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.
nz_coeffvar: Variables removed by coefficient variation near zero.
class: Model class.
time: time consumed for running the cox analysis.
Instance of class "Coxmos" and model "sPLS-DRCOX". The class contains the following elements: X: List of normalized X data information.
(data): normalized X matrix(weightings): sPLS weights(weightings_norm): sPLS normalize weights(W.star): sPLS W* vector(loadings): sPLS loadings(scores): sPLS scores/variates(E): error matrices(x.mean): mean values for X matrix(x.sd): standard deviation for X matrixY: List of normalized Y data information.
(deviance_residuals): deviance residual vector used as Y matrix in the sPLS.(dr.mean): mean values for deviance residuals Y matrix(dr.sd): standard deviation for deviance residuals Y matrix'(data): normalized X matrix(weightings): sPLS weights(loadings): sPLS loadings(scores): sPLS scores/variates(ratio): r value for the sPLS model (used to perform predictions)(y.mean): mean values for Y matrix(y.sd): standard deviation for Y matrix'survival_model: List of survival model information.
fit: coxph object.AIC: AIC of cox model.BIC: BIC of cox model.lp: linear predictors for train data.coef: Coefficients for cox model.YChapeau: Y Chapeau residuals.Yresidus: Y residuals.penalty: Penalty value selected.
n.comp: Number of components selected.
var_by_component: Variables selected in each PLS component.
call: call function
X_input: X input matrix
Y_input: Y input matrix
B.hat: sPLS beta matrix
R2: sPLS acumulate R2
SCR: sPLS SCR
SCT: sPLS SCT
alpha: alpha value selected
nsv: Variables removed by cox alpha cutoff.
nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.
nz_coeffvar: Variables removed by coefficient variation near zero.
class: Model class.
time: time consumed for running the cox analysis.
The sPLS-DRCOX function implements the sparse partial least squares deviance residual Cox (sPLS-DRCOX) model, a specialized approach tailored for survival analysis. This method integrates the strengths of the sparse partial least squares (sPLS) technique with the Cox proportional hazards model, leveraging deviance residuals as a bridge.
The function's core lies in its ability to handle high-dimensional data, often encountered in genomics or other omics studies. By incorporating the penalty parameter, which governs the sparsity level, the function offers a fine-grained control over variable selection. This ensures that only the most informative predictors contribute to the model, enhancing interpretability and reducing overfitting.
Data preprocessing is seamlessly integrated, with options to center and scale the predictors, and to remove variables exhibiting near-zero or zero variance. The function also provides a mechanism to retain specific variables, regardless of their variance, ensuring that domain-specific knowledge can be incorporated.
The output is comprehensive, detailing both the sPLS and Cox model components. It provides insights into the selected variables, their contributions across latent components, and the overall fit of the survival model. This rich output aids in understanding the underlying relationships between predictors and survival outcomes.
The sPLS-DRCOX function is grounded in established methodologies and is a valuable tool for researchers aiming to unravel complex survival associations in high-dimensional datasets.
The sPLS-DRCOX function implements the sparse partial least squares deviance residual Cox (sPLS-DRCOX) model, a specialized approach tailored for survival analysis. This method integrates the strengths of the sparse partial least squares (sPLS) technique with the Cox proportional hazards model, leveraging deviance residuals as a bridge.
The function's core lies in its ability to handle high-dimensional data, often encountered in genomics or other omics studies. By incorporating the penalty parameter, which governs the sparsity level, the function offers a fine-grained control over variable selection. This ensures that only the most informative predictors contribute to the model, enhancing interpretability and reducing overfitting.
Data preprocessing is seamlessly integrated, with options to center and scale the predictors, and to remove variables exhibiting near-zero or zero variance. The function also provides a mechanism to retain specific variables, regardless of their variance, ensuring that domain-specific knowledge can be incorporated.
The output is comprehensive, detailing both the sPLS and Cox model components. It provides insights into the selected variables, their contributions across latent components, and the overall fit of the survival model. This rich output aids in understanding the underlying relationships between predictors and survival outcomes.
The sPLS-DRCOX function is grounded in established methodologies and is a valuable tool for researchers aiming to unravel complex survival associations in high-dimensional datasets.
data("X_proteomic") data("Y_proteomic") X <- X_proteomic[,1:50] Y <- Y_proteomic splsdrcox_penalty(X, Y, n.comp = 3, penalty = 0.25, x.center = TRUE, x.scale = TRUE) data("X_proteomic") data("Y_proteomic") X <- X_proteomic[,1:50] Y <- Y_proteomic splsdrcox_penalty(X, Y, n.comp = 3, penalty = 0.25, x.center = TRUE, x.scale = TRUE)
\insertRef Bastien_2008Coxmos
\insertRef Bastien_2015Coxmos
\insertRef Bastien_2008Coxmos
\insertRef Bastien_2015Coxmos
Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es