similar to: Irregular 3d objects with rgl

Dear R users, today I've got the following problem. Here you are a dataframe as example. There are some SAMPLES for which a CONCentration was recorded through TIME. The time during which the concentration was recorded is not always the same, 10 points for Sample A, 7 points for Sample B and 11 for sample C Also the initial concentration was not the same for the three samples. I would like

Hi I have measured the UV absorbance (abs) of 10 solutions of a substance at known concentrations (conc) and have used a linear model to plot a calibration graph with confidence limits. I now want to predict the concentration of solutions with UV absorbance results given in the new.abs data.frame, however predict.lm only appears to work for new "conc" variables not new "abs"

Hello, I have a question about how to plot a series of data. The folloqing is my data matrix of n > n 25p 5p 2.5p 0.5p 16B-E06.g 45379 4383 5123 45 16B-E06.g 45138 4028 6249 52 16B-E06.g 48457 4267 5470 54 16B-E06.g 47740 4676 6769 48 37B-B02.g 42860 6152 19276 72 35B-A02.g 48325 12863 38274 143 35B-A02.g 48410 12806 39013 175 35B-A02.g 48417 9057 40923

Hello, I have two data frames, NameAndConc.df and WhichOnes.df. NameAndConc.df has two columns, the first column is the names of some material, and the second column is the concentration of active ingredient. > NameAndConc.df name conc 1 material1 0.8 2 material2 0.5 3 material3 0.4 WhichOnes.df has two columns, each of which specifies which material. > WhichOnes.df

Hello, I'm new here, but will try to be as specific and complete as possible. I'm trying to use “lm“ to first estimate parameter values from a set of calibration measurements, and then later to use those estimates to calculate another set of values with “predict.lm”. First I have a calibration dataset of absorbance values measured from standard solutions with known concentration of

I am attempting to estimate LC50 (analogous to LD50, but uses exposure concentration rather than dose) by fitting a Weibull model; but I can't seem to get it to work. From what I can gather, I should be using survreg() from the survival package. The survreg() function relies on time-to-event data; my data result from 96 h exposures (i.e., dead or alive after a fixed period; 96 h). I've

Dear list: I'm using bwplot to compare concentrations by location and treatment as in: # using built in data bwplot( conc ~ Type : Treatment, data = CO2 ) I would like the order of the plots to be: 3,4,1,2. I can't seem to figure this out with index.cond or permc.cond. Any help is appreciated! Brad Eck [[alternative HTML version deleted]]

Kevin, Maybe also look at what air quality monitoring is being done in area. https://cran.r-project.org/web/packages/RAQSAPI/vignettes/RAQSAPIvignette.html Depends what and how near, but might be something relevant there? Karl Dr Karl Ropkins Transport Studies | Environment | University of Leeds ------------------------------ Message: 2 Date: Tue, 12 Dec 2023 07:52:59 -0800 From: Bert Gunter

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

summary: how to convert a 3D array (as obtained from ncdf4::ncvar_get) to a dataframe suitable for use by lattice::levelplot(), e.g., levelplot(conc ~ lon * lat | lev, data = data.frame) details: I have atmospheric data in netCDF files specifying gas concentrations over a 3D space with dimensions longitude, latitude, and (vertical) level. I need to plot the data by level. Since there are

I have assayed the concentrations of various metal elements in different anatomic regions of two strains of mice. Now, for each element, in each region, I want to do a t test to find whether there is any difference between the two strains. Here is what I did (using simulated data as an example): # create the data frame > elemconc = data.frame(expand.grid(id=1:3, geno=c('exp',

Shalini Raghavan 3M Pharmaceuticals Research Building 270-03-A-10, 3M Center St. Paul, MN 55144 E-mail: sraghavan at mmm.com Tel: 651-736-2575 Fax: 651-733-5096 ----- Forwarded by Shalini Raghavan/US-Corporate/3M/US on 08/16/2004 11:25 AM ----- Shalini

> From: "Wilfred L. Guerin" <wilfredguerin at gmail.com> > Subject: [LLVMdev] LLVM Expansions > > It is very relevant that LLVM look into handeling HDL and other binary > and analogue operation modeling capbilities, as well as expand this what is binary? you mean digital right? > Without confirming the true characteristics of the lower structure > types and

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

Hello All, I have a more statistical question, and how this is implemented in R. The problem is the following: We have 2 different solutions (samples), which are filtered and then the concentration of the filtrate is measured. We want to evaluate how the filter proces and the concentration measurement influences the detection of the difference of the two solutions and which step has which

Thanks to the lattice gurus on this list, and having reference to the excellent open-access Sarkar 2008 ISBN 978-0-387-75968-5 e-ISBN 978-0-387-75969-2 http://dx.doi.org/10.1007/978-0-387-75969-2 I now know how to label lattice panels by variable value: see thread starting @ https://stat.ethz.ch/pipermail/r-help/2012-November/329450.html (and demonstrated below). This allows me to use

Dear All, I have a data sets as shown below A (Patient ID ), B and C are the Concentration of drug in blood on day 1 and day 4, D is the difference in conc. To do this in R I have written a code as follows, identified the number of patients who have more concentration on day 4 . Here I want to identify specifically the patient ID (is he patient 1 or 2 or 5 and 7), whose concentration is more. How

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

Dear all, Is there a way to retrieve standard errors from nls models? The help page tells me that arguments such as se.fit are ignored... Many thanks and best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49 (0)551 39 8806 Homepage http://www.gwdg.de/~cscherb1

Hello all R users, My simulation function works correctly, but I have problems with plot function. You will find the following code using it. Thank you for your help ##################################################" simulation <- function(k, n){ conc <- seq(0,10,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i]