search for: coarsening

Dear all, I have a grid (data frame) dataset at 0.5 x 0.5 degrees spatial resolution (720 columns x 360 rows; regular spacing) and wish to coarsen this to a resolution of 2.5 x 2.5 degrees. A simple calculation which takes the mean of a block of points to form the regridded values would do the trick. Values which should be excluded from the calculation are -9999 (unless all points within a block

...all, I have gridded data at 5' (minutes) resolution, which I intend to coarsen to 0.5 degrees. How would I go about doing this in R? I've had a search online and haven't found anything obvious, so any help would be gratefully received. I'm assuming that there will be several 'coarsening' techniques available - I'm after something fairly simple, which for example, just takes an average of each 0.5 degree portion of the current dataset. If someone is able to point me in the right direction, then I'd be very grateful. Many thanks, Steve _______________________________...

It is not uncommon to want to coarsen a factor by grouping levels together. I have found one way to do this in R: > sites [1] F A A D A A B F C F A D E E D C F A E D F C E D E F F D B C Levels: A B C D E F > regions <- list(I = c("A","B","C"), II = "D", III = c("E","F")) > library(Epi) > region <-

Hi, I am running a MacBook Pro with OS X 10.6.8. When I try to load library(cem) and run any function, R crashes. Any suggestions as to why this is? Thanks in advance. I think the problem is that I don't have all the "infrastructure" I need to run CEM, but I have no clue what to do either... #Do I have tcltk (I have no clue what that is) capabilities("tcltk") tcltk

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Hi to all, I am trying to run breakpoints() on a fairly large sample (>10.000 observations). The process is very slow, any idea on how to speed this up? I have tried the hpc="foreach" parameter, but this didn't work at all when I tried to run it on a smaller sample. breakpoints(x ~ x.l1 + x.l2 + X.l3 + x.l4 + x.l5 + x.l6 + x.l7 + x.l8 + y.l1 + y.l2 + y.l3 + y.l4 + y.l5 + y.l6

Hi, I want to fit 3 beta distributions to my data which ranges between 0 and 1. What are the functions that I can easily call and specify that 3 beta distributions should be fitted? I have already looked at normalmixEM and fitdistr but they dont seem to be applicable (normalmixEM is only for fitting normal dist and fitdistr will only fit 1 distribution, not 3). Is that right? Also, my data has 26

Hi, sorry by this off. I'm still try to understand nested design. I have the follow example (fiction): I have 12 plots in 4 sizes in 3 replicates (4*3 = 12) In each plot I put 2 species (A and B) to reproduce. After a period I make samples in each board and count the number of individuals total (tot) and individuals A and B (nsp). Others individuals excepts A and B are in total of

Dear R users, I am having trouble devising an efficient way to run a loess() function on all columns of a data.frame (with the x factor remaining the same for all columns) and I was hoping that someone here could help me fix my code so that I won't have to resort to using a for loop. (You'll find the upcoming lines of code in a single block near the end of the message.) Here's a

Dear R-Help listers, I am trying to estimate an proportional odds logistic regression model (or ordered logistic regression) and then make predictions by supplying a hypothetical x vector. However, somehow this does not work. I guess I must have missed something here. I first used the polr function in the MASS package, and I create a data frame and supply it to the predict function (see below):

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

On 7/12/07, Torvald Riegel <torvald at se.inf.tu-dresden.de> wrote: > Here are some comments, quotes are from the draft. > > > an operation based constraint cannot guard other operations > > I think constraints associated with a particular instruction usually apply > to this instruction and previous/subsequent instructions, so this wouldn't > be true. This is the

Hi Stepan, Sorry for the delay. It's great that you are working on MergeFunctions as well and I agree, we should definitely try to combine our efforts to improve MergeFunctions. Just to give you some context, the pass (with the similar function merging patch) is already being used in a production setting. From my point of view, it would be better if we focus on improving its capability

Hello Sean and Tobias, Sean, Thank you. Could you describe Nick's ideas in few words or give me links to your discussion, so I could adapt my ideas to it. Tobias, Your patch fails on several modules in my benchmark (73 of ~1800 tests). I have sent one as attachment. See statistics files for more details, all the .ll files you could simply find in test-suite object directory (after