similar to: gplots

Hi, I want to plot means with standard deviations of Total Nitrogen (TN) across 4 stations (S1-S4) and over 3 years (2007-2009). I want this plot in one panel. I do not want medians (bwplot, boxplot). I have tried a few different packages and it seems that ggplots with plotmeans was the fastest (I am not extremely skilled in writing my own scripts). Unfortunately, there is no grouping

Hi, anybody know why this not works for several plots ? When i set onefile=T the plots are stacked one about another , onefile=F only the first plot is shown in AllbusMeansPlots.svg. [h2 is a data.frame] ......hist and sapply works for several plots nice with RSvgDevice ! Maybe setting the title after apply is a problem, but until yet i didn't found a better solution ? library(RSvgDevice)

"nana" is meant to express "NA, really NA". Your suggestion sounds good. On Thu 2 Jan, 2020, 3:38 AM Pages, Herve, <hpages at fredhutch.org> wrote: > Happy New Year everybody! > > The name (is.nana) doesn't make much sense to me. Can you explain it? > > One alternative would be to add an extra argument (e.g. 'strict') to > is.na(). FALSE by

Hello R-devel, Best wishes in the new year. I am writing to kindly request new R function so NA_real_ can be more easily detected. Currently if one wants to test for NA_real_ (but not NaN) then extra work has to be done: `is.na(x) & !is.nan(x)` Required functionality is already at C level so to address my request there is not that much to do. Kevin Ushey made a nice summary of current R C api

Hi., I would like to conduct a CHAID tree analysis - Chi-square Automatic Interaction Detector. >From what I can make out from MASS, tree, rpart, and a quick search it isn't available as a package. Is that correct or have I missed it? Has anyone an available implementation of it? Thanks Stephen Nana Mail <http://mail.nana.co.il> - Get Your Free Personal Outlook 2003 Now

Hi All, I have conducted the following survival analysis which appears to be OK (thanks BRipley for solving my earlier problem). > surv.mod1 <- survreg( Surv(timep1, relall6)~randgrpc, data=Dataset, dist="weibull", scale = 1) > summary(surv.mod1) Call: survreg(formula = Surv(timep1, relall6) ~ randgrpc, data = Dataset, dist = "weibull", scale = 1)

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Hi All., Following on from all the help I received earlier .. Just so as you know a well known text book (T&F) uses "repeated observations" - I think perhaps they meant repeated measures in retrospect I am a total beginer to ARIMA, but have read a a basic text. I read that ARIMA requires a minimum of 50 repeated measures in time (repeated measures)- Unfortunately, I have 8

Hi All, I am currently conducting a mixed model. I have 7 repeated measures on a simulated clinical trial. If I understand the model correctly, the outcome is the measure (as a factor) the predictors are clinical group and trial (1-7). The fixed factors are the measure and group. The random factors are the intercept and id and group. I tried using 2 functions to calculate mixed effects.

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.

I've thrown a couple of useful functions into a new package "gregmisc" and uploaded them to ftp://ftp.ci.tuwien.ac.at/incoming/gregmisc_0.1.1.tgz Here's DESCRIPTION: Package: gregmisc Description: Misc Functions written/maintained by Gregory R. Warnes Title: Greg's Miscellaneous Functions Version: 0.1 Date: 2001/05/24 Depends: R Maintainer: Gregory R. Warnes

Happy New Year everybody! The name (is.nana) doesn't make much sense to me. Can you explain it? One alternative would be to add an extra argument (e.g. 'strict') to is.na(). FALSE by default, and ignored (with or w/o a warning) when the type of 'x' is not "numeric". H. On 12/31/19 22:16, Jan Gorecki wrote: > Hello R-devel, > > Best wishes in the new

Hi All, I am currently conducting some survival analyses. I would like to extract coefficients at each level of the IVs. I read on a previous posting that dummy regression using coxph was not possible. Therefore I though, hey why not categorize the variables (I realize some folks object to categorization but the paper I am replicating appears to have done so ...) and turn the variables

The following dataframe will illustrate the problem DF<-data.frame(name=rep(1:5,each=2),x1=rep("A",10),x2=seq(10,19,by=1),x3=rep(NA,10),x4=seq(20,29,by=1)) DF$x3[5]<-50 # we have a data frame. we are interested in the columns x2,x3,x4 which contain sparse # values and many NA. DF name x1 x2 x3 x4 1 1 A 10 NA 20 2 1 A 11 NA 21 3 2 A 12 NA 22 4 2 A 13 NA

Hello everyone, I have tried to look for this everywhere and so far have no luck. I have a plotMeans(DV, IV1, IV2, error.bars="se") graph that plots my data (DV-continuous, IVs are factors, IV1 - two levels, IV2-four levels). I am trying to increase a scale of my y-axis (to be consistent with my other graphs), but unfortunately nothing works with "plotMeans" function, which

Hi, I often use plotmeans() from the gplots package to quickly visualize a pattern of change. I would like to be able to plot separate lines for different groups, but the function gives an error when a grouping variable is included in the formula argument. For instance, > require(gplots) > x <- data.frame(Score=rnorm(100), Time=rep(1:10, 10),

Hello to all users and wizards. I am regulary using 'boxplot' function or its analogue - 'bwplot' from the 'lattice' library. But they are, as far as I understand, totally flawed in functionality: they miss ability to select what they would draw 'in the middle' - median, mean. What the box means - standard error, 90% or something else. What the whiskers mean -

Hello, I have a samba PDC with an openLDAP backend. on my windows 7 machines which have joined the Samba PDC domain I cannot see the groups on windows. Does anyone know the reason why ? I can see the mapped groups in groupmap list though root at smb-pdc[~]# net groupmap list Domain Admins (S-1-5-21-831610874-1793854533-4121639242-512) -> Domain Admins Domain Users

Hello, I am using library(gplots) to do something like data(state) x1 <- state.area/10000 x2 <- x1+round((rnorm(length(state.area),3,3))) plotmeans(x1 ~ state.region) Is it possible to plot x2 to x1 in the same graph, something like: linesmeans(x2 ~ state.region) Best wishes, Cheba [[alternative HTML version deleted]]