diff --git a/R/rasterCVA.R b/R/rasterCVA.R
index 9b38787..c868812 100644
--- a/R/rasterCVA.R
+++ b/R/rasterCVA.R
@@ -21,15 +21,13 @@
 #' Returns a SpatRaster with two layers: change vector angle and change vector magnitude
 #' @export 
 #' @examples
-#' \dontrun{
 #' library(terra)
 #' pca <- rasterPCA(lsat)$map
-#' 
-#' ## Do change vector analysis 
+#'
+#' ## Do change vector analysis
 #' cva <- rasterCVA(pca[[1:2]], pca[[3:4]])
 #' cva
 #' plot(cva)
-#' }
 rasterCVA <- function(x, y, tmf = NULL, nct = NULL,  ...) {
 	x <- .toTerra(x)
 	y <- .toTerra(y)
@@ -42,7 +40,7 @@ rasterCVA <- function(x, y, tmf = NULL, nct = NULL,  ...) {
 	if(!is.null(tmf) && !is.null(nct)) {
 		stop("'tmf' and 'nct' are exclusive options, cannot use both.", call. = FALSE)
 	}
-	
+
 	if(!is.null(tmf)) {
 		maxMag <- sqrt(sum((as.numeric(t(terra::global(x, "max", na.rm=T))) - as.numeric(t(terra::global(y, "max", na.rm=T))) )^2))*2
 		medianBuckets <- seq(1e-10, maxMag, length.out = 2e5)
@@ -98,10 +96,28 @@ rasterCVA <- function(x, y, tmf = NULL, nct = NULL,  ...) {
 			nct <- tmf * medianEstimate
 			rm(RStoolbox_rasterCVAEnv)
 		}
-		out <- do.call(terra::clamp, c(list(x = out, lower=nct), ellips))
+		out <- terra::clamp(out, lower = nct, ... = ellips)
 		
 		names(out) <- c("angle", "magnitude")
 	}
 	
 	return(out)
 }
+
+test <- function(){
+	devtools::load_all()
+
+	r <- rast(val = 0, ncol = 2, nrow = 10)
+	r1 <- r2 <- c(r, r)
+	s <- 4
+	x <- c(0,s,s,s,0,-s,-s,-s, NA, 0)
+	y <- c(s,s,0,-s,-s,-s,0,s, 0, NA)
+	r2[[1]][] <- c(x, x + sign(x)*2)
+	r2[[2]][] <- c(y, y + sign(y)*2)
+
+	expectedDf <- as.matrix(data.frame(angle = c(0,45,90,135,180,225,270,315,NA,NA),
+					magnitude = c(rep(c(s, sqrt(2*s^2)), 4), NA,NA, rep(c((2+s), sqrt(2*(2+s)^2)), 4), NA,NA)))
+
+	rasterCVA(r1,r2, tmf = 0)
+
+}
diff --git a/R/rasterPCA.R b/R/rasterPCA.R
index fc03ee1..0ddbe1d 100644
--- a/R/rasterPCA.R
+++ b/R/rasterPCA.R
@@ -24,7 +24,6 @@
 #' @return Returns a named list containing the PCA model object ($model) and a SpatRaster with the principal component layers ($object).
 #' @export 
 #' @examples
-#' \dontrun{
 #' library(ggplot2)
 #' library(reshape2)
 #' ggRGB(rlogo, 1,2,3)
@@ -43,7 +42,6 @@
 #'   plots <- lapply(1:3, function(x) ggR(rpc$map, x, geom_raster = TRUE))
 #'   grid.arrange(plots[[1]],plots[[2]], plots[[3]], ncol=2)
 #' }
-#' }
 rasterPCA <- function(img, nSamples = NULL, nComp = nlyr(img), spca = FALSE,  maskCheck = TRUE, ...){
     img <- .toTerra(img)
 
diff --git a/man/rasterCVA.Rd b/man/rasterCVA.Rd
index e3dbdb3..7ef16da 100644
--- a/man/rasterCVA.Rd
+++ b/man/rasterCVA.Rd
@@ -33,13 +33,11 @@ For example for a given pixel in image A and B for the red and nir band the chan
  Angles are returned *in degree* beginning with 0 degrees pointing 'north', i.e. the y-axis, i.e. the second band.
 }
 \examples{
-\dontrun{
 library(terra)
 pca <- rasterPCA(lsat)$map
 
-## Do change vector analysis 
+## Do change vector analysis
 cva <- rasterCVA(pca[[1:2]], pca[[3:4]])
 cva
 plot(cva)
 }
-}
diff --git a/man/rasterPCA.Rd b/man/rasterPCA.Rd
index 051c0e4..6e94307 100644
--- a/man/rasterPCA.Rd
+++ b/man/rasterPCA.Rd
@@ -47,7 +47,6 @@ Standardised PCA (SPCA) can be useful if imagery or bands of different dynamic r
 has the same effect as using normalised bands of unit variance.
 }
 \examples{
-\dontrun{
 library(ggplot2)
 library(reshape2)
 ggRGB(rlogo, 1,2,3)
@@ -67,4 +66,3 @@ if(require(gridExtra)){
   grid.arrange(plots[[1]],plots[[2]], plots[[3]], ncol=2)
 }
 }
-}