Per Issue #64, cleaning up the package. Version bump to trigger bioco…

…nductor build

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: pathwayPCA
 Type: Package
 Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection
-Version: 0.99.2
+Version: 0.99.3
 Authors@R: c(person("Gabriel", "Odom", email = "gabriel.odom@med.miami.edu", role = c("aut","cre")),
   person("James", "Ban", email = "yuguang.ban@med.miami.edu", role = c("aut")),
   person("Lizhong", "Liu", email = "lxl816@miami.edu", role = c("aut")),

R/aesPC_calculate_LARS.R

@@ -39,6 +39,20 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use AESPCA_pVals() instead
+#'   
+#'   X_mat <- as.matrix(colonSurv_df[, 5:50])
+#'   X_mat <- scale(X_mat)
+#'   
+#'   XtX <- t(X_mat) %*% X_mat
+#'   A_mat <- svd(XtX)$v
+#'   
+#'   lars.lsa(
+#'     Sigma0 = XtX,
+#'     b0 = A_mat[1, ] * sign(A_mat[1, 1]),
+#'     n = ncol(X_mat)
+#'   )
+#'   
+#'   
 lars.lsa <- function(Sigma0, b0, n,
                      type = c("lar","lasso"),
                      max.steps = NULL,

R/superPC_model_CoxPH.R

@@ -41,7 +41,28 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
-
+#'   
+#'   
+#'   p <- 500
+#'   n <- 50
+#' 
+#'   x_mat <- matrix(rnorm(n * p), nrow = p, ncol = n)
+#'   x_df <- data.frame(x_mat)
+#'   time_int <- rpois(n, lambda = 365 * 2)
+#'   obs_logi <- sample(
+#'     c(FALSE, TRUE),
+#'     size = n,
+#'     replace = TRUE,
+#'     prob = c(0.2, 0.8)
+#'   )
+#' 
+#'   coxTrain_fun(
+#'     x = x_df,
+#'     y = time_int,
+#'     censoring.status = !obs_logi
+#'   )
+#'   
+#'   
 coxTrain_fun <- function(x, y, censoring.status, s0.perc = NULL){
   # browser()
 

R/superPC_model_GLM.R

@@ -29,6 +29,25 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
+#'   
+#'   
+#'   p <- 500
+#'   n <- 50
+#' 
+#'   x_mat <- matrix(rnorm(n * p), nrow = p, ncol = n)
+#'   obs_logi <- sample(
+#'     c(FALSE, TRUE),
+#'     size = n,
+#'     replace = TRUE,
+#'     prob = c(0.2, 0.8)
+#'   )
+#' 
+#'   glmTrain_fun(
+#'     x = x_mat,
+#'     y = obs_logi
+#'   )
+#'   
+#'   
 glmTrain_fun <- function(x, y, family = binomial){
 
   glmCoeffs <- function(x_mat){

R/superPC_model_LS.R

@@ -36,6 +36,20 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
+#'   
+#'   
+#'   p <- 500
+#'   n <- 50
+#' 
+#'   x_mat <- matrix(rnorm(n * p), nrow = p, ncol = n)
+#'   time_int <- rpois(n, lambda = 365 * 2)
+#'   
+#'   olsTrain_fun(
+#'     x = x_mat,
+#'     y = time_int
+#'   )
+#'   
+#'   
 olsTrain_fun <- function(x, y, s0.perc = NULL){
   # browser()
 

R/superPC_model_tStats.R

@@ -72,7 +72,39 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
-
+#'   
+#'   
+#'   data("colon_pathwayCollection")
+#'   data("colonSurv_df")
+#'   
+#'   colon_OmicsSurv <- CreateOmics(
+#'     assayData_df = colonSurv_df[,-(2:3)],
+#'     pathwayCollection_ls = colon_pathwayCollection,
+#'     response = colonSurv_df[, 1:3],
+#'     respType = "surv"
+#'   )
+#'   
+#'   asthmaGenes_char <-
+#'     getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs
+#'     
+#'   data_ls <- list(
+#'     x = t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ],
+#'     y = getEventTime(colon_OmicsSurv),
+#'     censoring.status = getEvent(colon_OmicsSurv),
+#'     featurenames = asthmaGenes_char
+#'   )
+#'   
+#'   superpcFit <- superpc.train(
+#'     data = data_ls,
+#'     type = "surv"
+#'   )
+#'   
+#'   superpc.st(
+#'     fit = superpcFit,
+#'     data = data_ls
+#'   )
+#'   
+#'   
 superpc.st <- function(fit,
                        data,
                        n.threshold = 20,

R/superPC_model_train.R

@@ -46,7 +46,34 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
-
+#'   
+#'   
+#'   data("colon_pathwayCollection")
+#'   data("colonSurv_df")
+#'   
+#'   colon_OmicsSurv <- CreateOmics(
+#'     assayData_df = colonSurv_df[,-(2:3)],
+#'     pathwayCollection_ls = colon_pathwayCollection,
+#'     response = colonSurv_df[, 1:3],
+#'     respType = "surv"
+#'   )
+#'   
+#'   asthmaGenes_char <-
+#'     getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs
+#'     
+#'   data_ls <- list(
+#'     x = t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ],
+#'     y = getEventTime(colon_OmicsSurv),
+#'     censoring.status = getEvent(colon_OmicsSurv),
+#'     featurenames = asthmaGenes_char
+#'   )
+#'   
+#'   superpc.train(
+#'     data = data_ls,
+#'     type = "surv"
+#'   )
+#'   
+#'   
 superpc.train <- function(data,
                           type = c("survival", "regression", "categorical"),
                           s0.perc = NULL){

R/superPC_modifiedSVD.R

@@ -40,6 +40,24 @@
 #' @examples
 #'   # DO NOT CALL THIS FUNCTION DIRECTLY.
 #'   # Use SuperPCA_pVals() instead
+#'   
+#'   
+#'   data("colon_pathwayCollection")
+#'   data("colonSurv_df")
+#'   
+#'   colon_OmicsSurv <- CreateOmics(
+#'     assayData_df = colonSurv_df[,-(2:3)],
+#'     pathwayCollection_ls = colon_pathwayCollection,
+#'     response = colonSurv_df[, 1:3],
+#'     respType = "surv"
+#'   )
+#'   
+#'   asthmaGenes_char <-
+#'     getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs
+#'   
+#'   mysvd(t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ])
+#'   
+#'   
 
 mysvd <- function(mat, method = svd, n.components = NULL){