Does this look similar to the error you are getting:
> while(NA == TRUE) 1
Error in while (NA == TRUE) 1 : missing value where TRUE/FALSE needed
SO 'notconverged' is probably equal to NA. BTW, what is the value of
'tol'; I do not see it defined. So when computing 'notconverged' you
have generated an NA. You can test it to see if this is true.
You can use the following command:
options(error=utils::recover)
and then learn how to use the 'browser' to examine variables when the
error occurs.
On Fri, Dec 23, 2011 at 5:44 AM, Michael Pearmain
<michael.pearm...@gmail.com> wrote:
> Merry Xmas to all,
>
> I am writing a function and curiously this runs sometimes on one data set
> and fails on another and i cannot figure out why.
> Any help much appreciated.
>
> If i run the code below with
> data <- iris[ ,1:4]
> The code runs fine, but if i run on a large dataset i get the following
> error (showing data structures as matrix is large)
>
>> str(cluster.data)
> num [1:9985, 1:811] 0 0 0 0 0 0 0 0 0 0 ...
> - attr(*, "dimnames")=List of 2
> ..$ : NULL
> ..$ : chr [1:811] "1073949105" "1073930585" "1073843224" "1073792624" ...
> #(This is intended to be chr)
>> str(iris)
> 'data.frame': 150 obs. of 5 variables:
> $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
> $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
> $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1
> 1 1 1 ...
>> str(as.matrix(iris[,1:4]))
> num [1:150, 1:4] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
> - attr(*, "dimnames")=List of 2
> ..$ : NULL
> ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
>
> n.cols <- ncol(data)
> n.rows <- nrow(data)
> X <- as.matrix(data)
> stepsize <- 0.05
> c1 <- (2 * pi) ** (n.cols / 2)
> c2 <- n.rows * (smoothing ** (n.cols + 2))
> c3 <- n.rows * (smoothing ** n.cols)
>
> Kexp <- function(sqs){
> return (exp((-1 * sqs) / (2 * smoothing ** 2)))
> }
>
> FindGradient <- function(x){
> XmY <- t(x - t(X))
> sqsum <- rowSums(XmY * XmY)
> K <- sapply(sqsum, Kexp)
> dens <- ((c1 * c3) ** -1) * sum(K)
> grad <- -1 * ((c1 * c2) ** -1) * colSums(K * XmY)
> return (list(gradient = grad,
> density = dens))
> }
>
> attractors <- matrix(0, n.rows, n.cols)
> densities <- matrix(0, n.rows)
>
>
>> density.attractors <-
> sapply(rep(1:n.rows), function(i) {
> notconverged <- TRUE
> # For each row loop through and find the attractor and density value.
> x <- (X[i, ])
> iters <- as.integer(1)
> # Run gradient ascent for each point to obtain x*
> while(notconverged == TRUE) {
> find.gradient <- FindGradient(x)
> next.x <- x + stepsize * find.gradient$gradient
> change <- sqrt(sum((next.x - x) * (next.x - x)))
> notconverged <- ifelse(change > tol, TRUE, FALSE)
> x <- next.x
> iters <- iters + 1
> }
>
> # store the attractor and density value
> return(c(densities[i, ] <- find.gradient$density,
> attractors[i, ] <- x))
> })
>
> Error in while (notconverged == TRUE) { :
> missing value where TRUE/FALSE needed
>>
>
> Any help would be great
>
> Mike
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
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