similar to: NPMC

Hi folks, sorry if this has been answered before, I searched long and hard before deciding to make a thread. I'm trying to include multiple variables in a non-parametric analysis (hah!). So far what I've managed to figure out is that the NPMC package from CRAN MIGHT be able to do what I need, but I can't get it to. First I created a dataset as NPMC calls for. > Ind=Individual

I followed the threads that enquired about doing post-hoc tests after doing Kruskal testing. It took me to npmc. But npmc is giving an output I do not understand. I noticed a thread entitled "npmc function: 'x' must be atomic" but there never appeared to be a resolution. > npmc(npmcinput) Error in sort(unique.default(x), na.last = TRUE) : 'x' must be atomic

Hi there, I am having a problem with package npmc. If I use the included datasets (brain, kronen) everything is fine, but if I try and use my dataset I receive an error message: Error in probval.GenzBretz(algorithm, n, df, lower, upper, infin, corr, : NAs in foreign function call (arg 2) In addition: Warning message: In probval.GenzBretz(algorithm, n, df, lower, upper, infin, corr, : NAs

Hi, I'm having trouble getting the function `npmc' working. I have a 2 column 30 row table called `mydata': > npmc(mydata, df=2, alpha=0.05) Error in sort(unique.default(x), na.last = TRUE) : `x' must be atomic > is.atomic(mydata) [1] TRUE Is there anything I have overlooked? Am I invoking the function correctly? I wasn't able to find any hints in the archives

Hi Does anyone know where is the package: npmc (Nonparametric Multiple Comparisons). I found the reference on R Site Search, but not the package itself on CRAN as suggested. Thanks Mauricio

Hello, I am trying to run a nonparametric comparison of multiple datasets (A-E). After importing my csv file I sorted the variable value by the letter class (representing height along a streambank). a=Z[Letter=="A"] b=Z[Letter=="B"] c=Z[Letter=="C"] d=Z[Letter=="D"] e=Z[Letter=="E"] Next I tested normality and variance assumptions and the

Hi, I recently tried to conduct non-parametric multiple comparison with npmc package, and encountered some problem; sometimes it stopped with following error. Error in uniroot(f = function(arg) p - z.dist(arg, corr = corr, df = df, : f() values at end points not of opposite sign Although I'm not quite sure about calculation of z.quaintile in npmc.R, it seems increment of

Hi, I sent an earlier message concerning this, but perhaps I wasn't clear enough. Up until about a month ago, I downloaded packages by clicking on the filename of the package. Now this action leads me to a directory listing, e.g.: drwxr-xr-x hornik/users 0 2002-03-20 18:00:54 npmc/ -rw-r--r-- hornik/users 314 2002-03-20 11:33:42 npmc/DESCRIPTION -rw-r--r-- hornik/users 202

Hi, I am using R 2.10.1 and I have a doubt. Do you know how many cases can R handle? I want to use the library npmc but if I have more than 4,500 cases I get an error message. If I use less than 4500 cases I don´t have problems with this library. Is there any way to increase the number of cases in order to use this library. Thanks in advance Julio Flores [[alternative HTML version

Hi all, I am trying to interparete the result of the following output from lm; fit1 =lm(Feed _Intake ~ weight + season + weight*season) Season has three classes(x,y,z) Reults are Estimate (Intercept) 21.51559 weight 2.13051 factor(season)y 10.59739 factor(season)z 1.30421 weight:factor(season)y

On Thu, May 03, 2018 at 01:09:30AM -0400, Kevin Korb via rsync wrote: > --delete only works when you sync a dir not individual files. So... > rsync -vai --delete --include='g*' --include='G*' --exclude='*' > /share/Public/videos/ /share/Backup-HDD-03 That kinda didn't work: rsync -vai --delete --include='S[A-Z]*' --include='S[a-h]*'

Hi all I can't find what I am looking for so I am asking here. I have a dataset that looks something like this. Year season percent_below 2000 Winter 6.9179870 2000 Spring 1.6829436 2000 Summer 1.8463501 2000 Autumn 3.8184993 2001 Winter 2.8832806 2001 Spring 2.5870511 2001 Summer 0.0000000 2001 Autumn 4.7248240 2002 Winter

Does anyone know of a way of dealing with unbalanced mixed effects (fixed and random factors) for fully factorial designs. An example of such data is given below; The response variable is SQRTRECRUITS SEASON is a random factor DENSITY is a fixed factor Thus DENSITY:SEASON is a fixed factor. Therefore, whereas the effects of SEASON and DENSITY:SEASON should be tested against the overall

Hi there all, This is my first post to the list and I'll first say a few things: - R is great! - The archives of this list have helped me solve all of my problems/questions so far - I only know enough statistics "to be dangerous" I'm looking for a way to do post-hoc tests for the Friedman test. I have a dataset from a within-subjects design with 5 conditions where some of

Hi, I was hoping to get some advice regarding the testing of interactions, when one factor is modelled as a random effect... I have a model with binomial error structure where the response variable is the proportion of time spent at the main sett (animals were tracked for 28 consecutive days in each season, and were recorded either at the main sett or an outlier sett, so the response variable is

Hi, I was wondering if someone could please help me. I am doing a logistic regression to compare size at maturity between 3 seasons. My model is: fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat) where Mature is a binary response, 0 for immature, 1 for mature. There are 3 Seasons. The Season * Size interaction is significant. I would like to compare the size at 50%

I've got a problem with the sparseby command (reshape library), and I have reached the peak of my R knowledge (it isn't really that high). I have a small data frame of 23 rows and 15 columns, here is a subset, the first four columns are factors and the rest are numeric (only one, line54 is provided). bearID YEAR Season SEX line54 5 1900 8 3 0 16.3923519 11 2270

Dear R Developers, the following CRAN packages do not cleanly pass R CMD check for quite some time now and did not have any updates since the time given. Several attempts by the CRAN admins to contact the package maintainers had no success. norm, 1.0-9, 2002-05-07, WARN sound, 0.6, 2002-08-31, WARN Bhat, 0.9-07, 2003-11-04, WARN HI, 0.1, 2003-11-04, WARN mmlcr, 1.3.2, 2003-11-15, WARN mvnmle,

Dear all, Apologies for this beginner's question. I have a variable Price, which is associated with factors Season and Crop, each of which have several levels. The Price variable contains missing values (NA), which I want to substitute by the mean of the remaining (non-NA) Price values of the same Season-Crop combination of levels. Price Crop Season 10 Rice Summer 12

Hi all, I have a couple of questions. 1) Is there a more effective way to remove the following pattern (patternrm) from the station_id_Timeseries (see below) patternrm <- c(_2000_4_OND, _2001_1_JFM, _2001_2_AMJ, _2001_3_JAS, _2001_4_OND, _2002_1_JFM, _2002_2_AMJ, _2002_3_JAS, _2002_4_OND, _2003_1_JFM, _2003_2_AMJ, _2003_3_JAS, _2003_4_OND, _2004_1_JFM, _2004_2_AMJ, _2004_3_JAS, _2000_4_OND) #