similar to: transforming data based on factors in a dataframe

Dear R help contributers, I have several x,y scatter plots and I would like to plot them slightly offset, similar to: http://www.mathworks.com/matlabcentral/fx_files/24368/2/content/style4.jpg I've looked all over for this answer with no luck. Just a function name or previous example would probably be enough for me to figure it out. Thank-you in advance, Carly

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]

Hello everyone, I am trying to solve 2D differential equations using finite difference scheme in R. I have been able to work with the equations with only one spatial dimensions but I want to extend it to the two dimensional problem. For example i can simulate one dimensional diffusion using a code like the following. But I want to write a similar code for,say, a two dimensional diffusion

The formula attribute of the builtin CO2 dataset seems a bit strange: > formula(CO2) Plant ~ Type + Treatment + conc + uptake What is one supposed to do with that? Certainly its not suitable for input to lm and none of the examples in ?CO2 use the above.

Hi all R users, I have problems with my second loop for drawing the three curves in the same graphic. I need help please Thank you in advance ######################################################################### simulation <- function(k, n){ conc <- seq(0,100,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i] <- (k

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"

Hi, I think you're misunderstanding which set of variables go on either side of the formula. Is this what you're looking for? > aggregate(OD ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD 1 1 BACT 1 765.3333 2 1 BACT 2 745.3333 3 1 BACT 3 675.0000 > aggregate(ODnorm ~ Time + Target + Conc, data = df, FUN =

I am following a document teaching how to use regression and right at the onset I get an R error. I understand that variables "conc" and "signal" should have the same length but I am using the what R manual suggests to drop the error and it is not cooperating. What gives? The manual says that the default is na.ombit by the way, not that it's addition does anything How

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

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Hello, I have a data frame with different groups (Time, Target, Conc) and each entry has a triplicate value of the measurements OD and ODnorm. How can I merge the triplicates into a single mean value? I tried the following: ``` df = data.frame(Time=rep(1, 9), Well=paste("A", 1:9, sep=""), OD=c(666, 815, 815, 702, 739, 795, 657, 705, 663),

Hallo Everybody How do you specify arguments for a function used within another function? Here is my problem: I am reconstructing a calculator for the burden of disease due to air pollution from publications and tools published by the WHO. The calculations make use of published dose-response relationships for particular health end-points. This is then applied to populations with known or

#how do I break these up into first two letters (RM), number, and then the last part #is there an easily accessible regex tutorial on the internet? v = (structure(1:122, .Label = c("RM215Temp", "RM215SpCond", "RM215DO.Conc", "RM215Depth", "RM215pH", "RM215ORP", "RM215Turbidity.", "RM215Battery", "RM215DO.",

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >

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

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

Dear R experts! I'd to fit data by 'nls' with me-supplied function 'fcn'. 1) I'd like 'fcn' to accept arbitrary arguments, i.e. I defined it as f(...) {<body>}. (Ok, that's not actually impotant). 2) Second, I would NOT like to supply every parameter in the formula. To illustrate this, let's look at the last example of 'nls' help

Also, > aggregate(cbind(OD, ODnorm) ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD ODnorm 1 1 BACT 1 765.3333 108.33333 2 1 BACT 2 745.3333 88.33333 3 1 BACT 3 675.0000 18.00000 (You might wish for "cbind(OD,ODnorm) ~ . - Well", but aggregate.formula is not smart enough for that.) -pd > On 24 Oct 2023, at

I have used Sweave a lot the latest year, but never really used any long function calls. If I have code which look like this ------------------------------------------------------------- gof <- benthic.flux(ID="Gulf of Finland", meas.conc=conc, bw.conc=bw.conc, time=times,

In the example R script below, horizontal gray gridlines are drawn at y coordinates where the points are drawn with the code: panel.abline(h=y, v=xScale, col.line="gray") How do I change this so that the horizontal gray gridlines are drawn at y coordinates where the y labels are drawn? The challenge is that each panel has different y-ranges (in my real example the y-ranges and