[R] missing value where TRUE/FALSE needed

[jim holtman]

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.pearmain at 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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-- 
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.