similar to: how to count unique observations by variables

I am doing a cluster analysis with hclust. I want to get hclust to output the Hotelling's T squared statistic for each cluster so I can evaluate is data points should be in a cluster or not. My research to answer this question has been unsuccessful. Does anyone know how to get hclust to output the Hotelling's T squared statistic for each cluster? Mike [[alternative HTML version

Dear List, i know there are some solutions for this in the archive, but they're not very good for numeric matrices, since they usually convert rows/columns to character strings. Is there an easy way to do $subject for numeric matrices properly, or i need to do it by hand? Thanks, Gabor

R community, I would like to know how to extract rows from a data frame (DF) such that each row in the new data frame (D.F) represents the first instance of a unique variable pairing in the original dataframe (ordered first by variable V1 then by variable V2). The unique function does not seem to be able to accomplish this. I imagine there must be a simple solution for this, but I can't

I believe you need to spend time with an R tutorial or two: a data frame (presumably the "table" data structure you describe) can *not* contain "blanks" -- all columns must be the same length, which means NA's are filled in as needed. Also, 8e^5 * 7e^4 = 5.6e^10, which almost certainly will not fit into any local version of R (maybe it would in some server version --

Hi Allaisone, I took a slightly different approach but you might find this either as or more useful than your approach, or at least a start on the path to a solution you need. df1 <- data.frame(CustId=c(1,1,1,2,3,3,4,4,4),DietType=c("a","c","b","f","a","j","c","c","f"),

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

Greetings, I have a vector of the form: [10,8,1,3,0,8,NA,NA,NA,NA,2,1,6,NA,NA,NA,0,5,1,9...] That is, a combination of sequences of non-missing values and missing values, with each sequence possibly of a different length. I'd like to create another vector which will help me pick out the sequences of non-missing values. For the example above, this would be:

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Hello everyone, I'm stumped. I'd like to create a scatterplot matrix with circular reference lines. Here is an example in 2d: library(ellipse) set.seed(1) dat <- matrix(rnorm(300), ncol = 3) colnames(dat) <- c("X1", "X2", "X3") dat <- as.data.frame(dat) grps <- factor(rep(letters[1:4], 25)) panel.circ <- function(x, y, ...) { circ1

Folks, I'm checking the structure of a dataframe for duplicate parameters at a site station (i.e depth should be measured once, not twice), using aggregate to count each parameter within a site station. The fake data below has only 26000 rows, and takes roughly 14 seconds. My real data has 750000 rows and I had to stop execution after about an hour. The by() function is faster, but I

Hi All I have a datafram which looks like this : CustomerID DietType 1 a 1 c 1 b 2 f 2 a 3 j 4 c 4 c 4 f And I would like to reshape this so I can

Dear R Users and Developers, I am trying to do the equivalent of v <- c(1,2,3,3,2,1,) vu <- unique(v) for a vector such as v2 <- c(1.02, 2.03, 1.00, 3.04, 3.06) vut <- ... As indicated in the subject, we need approximately unique values with a defined tolerance, i.e. for the v2 vector the resulting vut vector using a tolerance of .1 should return e.g. [1] 1.02 2.03 3.06 Also,

Hi, I apologize for this often/old question. I found some hints but couldn't solve the problem so far. I have C functions (incl. the header files) as well as the R wrapper functions which I want to use for faster calculations. These functions are included in a R package. The installation process seems to be ok (no errors). I also can load the package without errors. But when I call the

lme() and gls() models from the nlme package are all crashing with R.3.4.0. Identical code ran correctly, without error in R 3.3.3 and earlier versions. The behavior is easily demonstrated using one of the examples form the lme() help file, along with two simple variants. I have commented the errors generated by these calls, as well as the lines of code generating them, in the code example below.

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

Df contains Session_Setup DCT RevDataVols_bin counts comp 1 Session_Setup RLL 1 NA Session_Setup+RLL+1 2 Session_Setup RLL 2 NA Session_Setup+RLL+2 3 Session_Setup RLL 3 NA Session_Setup+RLL+3 4 Session_Setup RLL 4 NA Session_Setup+RLL+4 5 Session_Setup RLL 5

Hi, we're using a GSM-Gateway on asterisk to forward incoming calls to the cellphones, but, of course, the cellphones always display the callerid from the gateway. Does anyone know a symbian app that could (on an incoming call) connect via grps/3G to a database behind the asterisk and fetch the real callerid and do a calleridname-lookup on a number? -------------- next part -------------- An

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1