similar to: slightly OT: (un)supervised clustering?

Hi, I'm analyzing experimental results where two different events ("T1" and "T2") can occur or not during an experiment. I made my experiments with one factor ("Substrate") with two levels ("Sand" and "Clay"). I would like to know wether or not "Substrate" affects the occurrence probability of the two events. Moreover, for each

Hi, there I'm trying to get the value of the Mean Square from the ANOVA model summary that comes from specifying the error term, and am wondering if one can actually do this ( I know it's possible when using anova(lm) objects and the like, but I'm having a tough time with it under this framework). There does appear to be some indexing in the output of this type, but perhaps not

I'm just getting to grips with using ode function and have used the examples and vignettes to produce a small model of a one-pool, michaelis-menten, enzyme kinetic reaction. The rate of flux of substrate into pool A is constant (fluxoa) however the rate of flux out of pool A is controlled by the HMM equation (v = Vmax/ ( 1 + (Km / Concentration A )) ). This function works fine and

Hi all, I have a very easy questions (I hope). I had measure a property of plants, growing in three different substrates (A, B and C). The rest of the conditions remained constant. There was very high variation on the results. I want to do address, whether there is any difference in the response (my measurement) from substrate to substrate?

Hi there, I am fairly new to R and I have been trying to figure out how to separate a column of substrate data that I have into separate parts using R. An example of my data looks like this is: gmtTime Classification ClassValue Species Count 1449 Sand 30 1449 Mud 70 Brittle Star 1 1449 Mud 70

UseRs, I am working on a dataset (see small example below) where individuals were followed on a specific date-time combo and multiple repeated measurements were taken (e.g., height in meters, behavior class in 2 letter code). Observation numbers varied between individual (ranging from 1 observation for each date-time combo to >50) I am trying to summarize the data into 1 row per

Hi all; I'm trying to display a 2 panel plot for the Puromycin data from R with 2 different non-linear models fitted to each group. The problem is that as far as I know panel.number doesn't work in the latest version of R. Can anyone give a hint how to solve this? Here is the code that I used before and now doesn't work xyplot(rate ~conc| state,Puromycin,

1) deleting path/to/(Something) Word Word Anotherword--Word Word.pdf IO error encountered -- skipping file deletion I think either the -- is causing it to fail, or more likely, the next filename, which it didn't bother to print out, which has an ampersand in the name, causes it to fail. I am using SSH as a substrate, of course, and I think you guys failed to escape things, so the ampersand

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

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

I have a function for plotting soil texture that I am having a problem with. The function is: plot.psd <- function(sand, clay, ...) { conv.ter <- function(x, y) { x.con <- 100 - x - y*cos(1/3*pi) y.con <- y*sin(1/3*pi) data.frame(x=x.con, y=y.con) } plot(conv.ter(sand, clay), xlim = c(0,100), ylim = c(0,100*sin(1/3*pi)), axes = FALSE, xlab="",

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

hello, I am very new to R. My current data set is a mix of values and categories. It is a geoscience data set, with values per rock sample. Case in point, each sample belongs to a lithology class, and each sample has several physical property measurements (density, porosity...). I want to be able to plot these physical properties for all samples in each lithology class. this is how i'm doing

Dear R_Users, I am confronted to a technical issue that I wouldn't even know how to start to handle and where to look at. It is about data manipulation in a dataframe and value substitution Imagine I have a matrix describing a set of chemical equations. Colnames are Components names and Rownames are Reaction names Each cell in that matrix corresponds to the stoechiometric coefficient to the

I am a Professor of Statistics at Indira Gandhi Krishi Vishwavidyalaya, Raipur, India. While teaching in class about analysis of variance using R, I was doing a one-way analysis for the two data-sets given below in the R-class. I got a typical error in "emmeans" package, please help: Data-set-1: -------------- Medley and Clements (1998) investigated the impact of zinc contamination (and

Dear all, I've taken a subset of data from a data frame using crb<-subset(all.raw, creek %in% c("CR") & year %in% c(2000,2001) & substrate %in% ("b")) this works fine, except that all of the original factor levels are maintained. This results in NA's for these empty levels when I try to do summaries based on factors using by(). Is there a simple way to drop

Hey All, I’m attempting to gather stats on usage of the “metal”, by which I mean the physical host’s hardware. I would like to know the CPU, IO, and network stats for the hardware. Normally I would get these from /proc/stat, /proc/diskstat, and ifconfig respectively. However, because of the segregation of VMs and Dom0’s special relationship as a paravirtualized I don’t know if I can trust this

Sorry this may well be defined as Off Topic. I apologize in advance. I am interested in performing what I think would be a probabilistic sensitivity simulation. I've done some crude ones before in excel but I'm wondering if R can help me do it more effectively? I have a set of theoretical variables for simplicity lets use (what I think) is an easier example: I have a peg and a hole

On 07/01/18 02:19, Akhilesh Singh wrote: > I am a Professor of Statistics at Indira Gandhi Krishi Vishwavidyalaya, > Raipur, India. While teaching in class about analysis of variance using R, > I was doing a one-way analysis for the two data-sets given below in the > R-class. I got a typical error in "emmeans" package, please help: > > Data-set-1: > --------------