similar to: Subtraction of group means using AGGREGATE and MERGE

Dear R experts, I would really appreciate if you had an idea on how to use more efficiently the aggregate method: More specifically, I would like to calculate the mean of certain values on a data frame,? grouped by various attributes, and then create a new column in the data frame that will have the corresponding mean for every row. I attach part of my code: matchMean <-

Hi, Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not accurately reflect how closely the model (GAM) fits the data. I was told that the accuracy of the correlation can be improved using a root mean square deviation (RMSD) calculation on binned data. For example, let 'o' be the real, observed data and 'm' be the model data. I believe I can calculate

Dear Developers, Having spent time developing and thinking about how data aggregation and summary statistics can be enhanced in R, I would like to present my ideas/efforts in the form of two commands: The first, which for now I called 'collap', is an upgrade of aggregate that accommodates and extends the functionality of aggregate in various respects, most importantly to work with

Hi, I have a 1.000 observations with 10 attributes (of different types: numeric, dicotomic, categorical ecc..) and a measure M. I need to cluster these observations in order to assign a new observation (with the same 10 attributes but not the measure) to a cluster. I want to calculate for the new observation a measure as the average of the meausures M of the observations in the cluster

I've noticed that I get a warning message every time a package masks some functions from Hmisc. The warning message says : Warning message: In identical(get(., i), get(., lib.pos)) : ignoring non-pairlist attributes This happens with eg: library(plyr) library(xtable) I think I've seen this passing by before, but I'm not sure any more. Just thought I'd mention it. Cheers Joris

Joris, the point is that 'z' is NOT used as a predictor in the model. Therefore it should not affect predictions. Also, I find it suspicious that the error only occurs when the response variable conitains missings and 'z' is unique (I have tested several other cases to confirm this). -Mark Op vr 16 mrt. 2018 om 13:03 schreef Joris Meys <jorismeys at gmail.com>: >

Hi, I would like to have an index for a column in a matrix encoded in a cell of the same matrix. For example: x = matrix(c(11,12,13,1, 21,22,23,3, 31,32,33,2),byrow=T,ncol=4) In this case, column 4 is the index. I then access the column specified in the index by: > for (i in 1:3) print(x[i,x[i,4]]) [1] 11 [1] 23 [1] 32 > > for (i in 1:3) {x[i,x[i,4]] <- x[i,x[i,4]] + 5} > x

?typeof? is your friend here: > typeof(`[`) [1] "special" > typeof(mc[[1]]) [1] "symbol" > typeof(mc2[[1]]) [1] "special" so mc[[1]] is a symbol, and thus not a primitive. - Lukas > On 28 Mar 2017, at 14:46, Michael Lawrence <lawrence.michael at gene.com> wrote: > > There is a difference between the symbol and the function (primitive >

On 31/01/2018 6:33 AM, Joris Meys wrote: > 3. given your criticism, I'd like your opinion on where I can improve > the documentation of https://github.com/CenterForStatistics-UGent/pim. > I'm currently busy updating the help files for a next release on CRAN, > so your input is more than welcome. After this invitation I sent some private comments to Joris. I would say his

Dear gurus, I was utterly surprised to learn that one of my examples illustrating the need of match.fun() doesn't give me the expected result. center <- function(x,FUN) FUN(x) center(1:10, mean) mean <- 4 center(1:10, mean) Used to give me the error message "could not find function FUN". Now it just works, even though I didn't expect it to. I believe this is at least

I would like to prepare the data for barplot. But I only have the data frame now. x1=rnorm(10,mean=2) x2=rnorm(20,mean=-1) x3=rnorm(15,mean=3) data=data.frame(x1,x2,x3) If there a way to put data within a specific range? The expected result is as follows: range x1 x2 x3 -10-0 2 5 1 (# points in this

Dear Mr. or Ms.,   I used the R-software to run the zero-inflatoin negative binomial model (zeroinfl()) .   Firstly, I introduced one dummy variable to the model as an independent variable, and I got the estimators of parameters. But the results are not satisfied to me. So I introduced three dummy variables to the model. but I could not get the results. And the error message is

On 01/04/2015 1:35 PM, Gabriel Becker wrote: > Joris, > > > The second argument to evalq is envir, so that line says, roughly, "call > environment() to generate me a new environment within the environment > defined by data". I think that's not quite right. environment() returns the current environment, it doesn't create a new one. It is evalq() that created

Seriously, if a method gives a wrong result, it's wrong. line() does NOT implement the algorithm of Tukey, even not after the patch. We're not discussing Excel here, are we? The method of Tukey is rather clear, and it is NOT using the default quantile definition from the quantile function. Actually, it doesn't even use quantiles to define the groups. It just says that the groups

Dear all, I'm updating a package regarding a new type of models, and I'm looking to extend the formula interface with two functions (L() and R() ) for construction of these models. I want to use as much of the formula interface as possible, and hoped to do something similarly to I(). I know the I() function does nothing more than add the class "AsIs". I've been browsing the

Hi, I want to give a summary or anova for "arima" model in R, as "summary", and "anova" for "lm". As including various intervention factors in arima(xreg = ) part, I want to assess the significancy of thse factors. I can do it using interrupted analysis of time series by linear regression, but want to see whether arima model works for the data first.

can anyone know where i can find information on compile hmisc on windows, especially 64 windows? thanks, _________________________________________________________________ The New Busy is not the too busy. Combine all your e-mail accounts with Hotmail. ID28326::T:WLMTAGL:ON:WL:en-US:WM_HMP:042010_4 [[alternative HTML version deleted]]

Dear all, I want to apply a weighted median on a huge dataset, and I remember a function from the package R.basic that could do this using an internal sorting algorithm qsort. This speeded things up quite a bit. Alas, I can't find that package anywhere anymore. There is a weighted.median function in the package limma too, but I didn't use that before. Anybody who knows what happened to

Apparently, as.POSIXlt takes one o'clock as the start of the day : > as.POSIXlt(0,origin="1970-01-01") [1] "1970-01-01 01:00:00 CET" > as.POSIXlt(0,origin="1970-01-01 00:00:00") [1] "1970-01-01 01:00:00 CET" > as.POSIXlt(0,origin="1970-01-01 23:59:59") [1] "1970-01-02 00:59:59 CET" Cheers -- Joris Meys Statistical

All, How can we find a distance matrix for categorical data ie. given a csv below var1 var2 var3 var4 element1-1 yes x a k element1-2 no y b l element1-3 maybe y c m how can i compute the distance matrix between all the elements Actually i need it to create clusters on top