matrix coercion for 1-dim PC projections

R/clever.R

@@ -389,7 +389,7 @@ clever = function(
     } else {
       projection$svd <- X.svd
     }
-    projection$svd$u <- projection$svd$u[,chosen_PCs_ordered]
+    projection$svd$u <- matrix(projection$svd$u[,chosen_PCs_ordered], nrow=T_)
     if(PCs_detrend & projection_method!="PCATF"){
       projection$svd$u_detrended <- projection$svd$u - apply(projection$svd$u, 2, est_trend)
       attributes(projection$svd$u_detrended)$dimnames <- NULL
@@ -438,13 +438,13 @@ clever = function(
         } else {
           projection$indices <- projection$indices[1:max_keep]
         }
-        projection$svd$u <- projection$svd$u[,1:max_keep]
+        projection$svd$u <- matrix(projection$svd$u[,1:max_keep], ncol=T_)
         if(PCs_detrend & projection_method!="PCATF"){
-          projection$svd$u_detrended <- projection$svd$u_detrended[,1:max_keep]
+          projection$svd$u_detrended <- matrix(projection$svd$u_detrended[,1:max_keep], ncol=T_)
         }
         projection$svd$d <- projection$svd$d[1:max_keep]
         if(solve_directions){
-          projection$svd$v <- projection$svd$v[,1:max_keep]
+          projection$svd$v <- matrix(projection$svd$v[,1:max_keep], ncol=T_)
         }
       }
     }

R/functions_levdist.R

@@ -40,12 +40,12 @@ PC.robdist <- function(U){
 
   best <- c(cov.mcd(U)$best)
   inMCD <- 1:n %in% best
-  U_in <- U[best,] # observations that are used for the MCD estimates calculation
+  U_in <- matrix(U[best,], ncol=Q) # observations that are used for the MCD estimates calculation
   xbar_star <- colMeans(U_in) # MCD estimate of mean
   U_ins <- scale(U_in, center = TRUE, scale = FALSE) # Damon: replace with U_ins - xbar_star?
   nU <- nrow(U_ins)
   S_star <- (t(U_ins) %*% U_ins)/(nU-1) # MCD estimate of covariance
-  mah <- (apply(U,1, function(x) t(x-xbar_star) %*% solve(S_star) %*% (x-xbar_star)))
+  mah <- (apply(U, 1, function(x) t(x-xbar_star) %*% solve(S_star) %*% (x-xbar_star)))
   # Scale left-out observations to follow F-distribution.
   Fparam <- fit.F(Q, n, sum(inMCD))
   outMCD_scale <- Fparam$c * (Fparam$m - Q + 1) / (Q * Fparam$m)