Update uni.smooth.const-with-po.r

R/uni.smooth.const-with-po.r

@@ -167,7 +167,7 @@ smooth.construct.mpi.smooth.spec<- function(object, data, knots)
 Predict.matrix.mpi.smooth<-function(object,data)
 ## prediction method function for the `mpi' smooth class
 { m <- object$m+1; # spline order, m+1=3 default for cubic spline
-  q <- object$df +1
+  q <- object$df ## +1
   Sig <- matrix(0,q,q)   # Define Matrix Sigma
   # elements of matrix Sigma for increasing smooth
   for (i in 1:q)  Sig[i,1:i] <- 1
@@ -178,24 +178,25 @@ Predict.matrix.mpi.smooth<-function(object,data)
   n <- length(x)
   ind <- x<=ul & x>=ll ## data in range
   if (sum(ind)==n) { ## all in range
-     X <- spline.des(object$knots,x,m)$design
-     X <- X%*%Sig 
+     X <- splines::spline.des(object$knots,x,m)$design
+     X <- X[,2:(q+1)]%*%Sig ## X <- X%*%Sig 
   } else { ## some extrapolation needed 
      ## matrix mapping coefs to value and slope at end points...
-     D <- spline.des(object$knots,c(ll,ll,ul,ul),m,c(0,1,0,1))$design
+     D <- splines::spline.des(object$knots,c(ll,ll,ul,ul),m,c(0,1,0,1))$design
      X <- matrix(0,n,ncol(D)) ## full predict matrix
-     if (sum(ind)> 0)  X[ind,] <- spline.des(object$knots,x[ind],m)$design ## interior rows
+     if (sum(ind)> 0)  X[ind,] <- splines::spline.des(object$knots,x[ind],m)$design ## interior rows
      ## Now add rows for linear extrapolation...
      ind <- x < ll 
      if (sum(ind)>0) X[ind,] <- cbind(1,x[ind]-ll)%*%D[1:2,]
      ind <- x > ul
      if (sum(ind)>0) X[ind,] <- cbind(1,x[ind]-ul)%*%D[3:4,]
-     X <- X%*%Sig 
+     X <- X[,2:(q+1)]%*%Sig 
   }
   X
 }
 
 
+
 ########################################################
 ### Adding Monotone decreasing SCOP-spline construction 
 ########################################################