Dev

NEWS.md

@@ -1,3 +1,8 @@
+# Changes in version 0.8.0
+
++ Added implicit regularization of selected basis functions to the GLMM mode using a NB LASSO. 
++ Switched candidate knot subsampling to a uniform sequence of candidate knots across pseudotime's support. 
+
 # Changes in version 0.7.9
 
 + Added `geneProgramDrivers()` function to compute & test correlations of expression with gene module scores.

R/fitGLMM.R

@@ -145,6 +145,7 @@ fitGLMM <- function(X_pred = NULL,
                     paste0("h_PT_", round(as.numeric(stats::quantile(X_pred[, 1], 1/3)), 4)),
                     paste0("h_", round(as.numeric(stats::quantile(X_pred[, 1], 2/3)), 4), "_PT"),
                     paste0("h_PT_", round(as.numeric(stats::quantile(X_pred[, 1], 2/3)), 4)))
+    nonzero_coefs <- seq(4)
     if (is.null(Y.offset)) {
       glmm_mod <- glmmTMB::glmmTMB(Y ~ X1 + X2 + X3 + X4 + (1 + X1 + X2 + X3 + X4 | subject),
                                    data = glmm_basis_df,

R/marge2.R

@@ -157,7 +157,9 @@ marge2 <- function(X_pred = NULL,
         q95 <- stats::quantile(X_pred[, v], 0.95)
         X_red <- X_red[X_red > q05 & X_red < q95]
         if (length(X_red) > n.knot.max) {
-          X_red <- sample(X_red, size = n.knot.max)
+          X_red <- seq(min(X_red),
+                       max(X_red),
+                       length.out = n.knot.max)
         }
       } else {
         # original candidate knot selection from 2017 Stoklosa & Warton paper