similar to: reshape question

R users, I intention is to calculate some summary statistics across factor levels. I know that in Hmisc package there is a summary function which produces neat summary statistics when using "cross" option. I would like to produce similar output with N and Missing columns but produce a data.frame. Is there any built-in function for that? #example data install.packages("Hmisc")

Given a data frame of a categorical variable and two continuous variables, I would like to display one continuous variable against the other for each value of the categorical variable, all superimposed on the same plot. For example: data(Indometh); str(Indometh) Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame': 66 obs. of 3

Full_Name: Kevin Wright Version: 1.4 OS: Windows 95 Submission from: (NULL) (170.54.59.160) Note: This was the 1.4 build for Windows that Brian Ripley made available. The first example in the help for reshape doesn't work for me. When I cut and paste, this is what happens: > data(Indometh,package="nls") > summary(Indometh) Subject time conc

Hi, I was trying to run the example of Indomethacin kinetics from the book: ## From Pinheiro/Bates, Mixed-Effects-Models in S and S-Plus, ## Springer, Second Printing 2001, Section 6.2 library(nlme) plot(Indometh) fm1Indom.nls <- nls(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) summary(fm1Indom.nls) plot(fm1Indom.nls,Subject~resid(.),abline=0) ## .... the last plot command gives me the

Hi, I have added a second y-axis via axis() to a plot. Then I tried to add an y axis title. But the axis() function seems to have no argument like ylab. and if I add a title with title() the text is centered at the first axis, not the second defined one. Is there a way to add an axis-title that orientates it's position at the last defined axis?

*Respected Sir, I have a simple question regarding plots of time series in R. I have to plot "conc" against "time" **for each individual and display in the same panel for the in-built dataset "Indometh" in R. * ***I have six time series, say subject1.ts, subject2.ts, ............., subject6.ts. The observations are taken at an interval of 0.25 hr. All of the series

Hello all, First of all, thanks for your examples to calculate the mode value. But now my problem is the next: I have a?factor and I'm trying to convert it to number with as.numeric(x) but when I see the number it's different, I mean if I have: b<-1,042 d<-as.numeric(b) then when I show d, it appears 432 and I want to work with 1,042 because then I want to calculate the median of

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Hi all, I have a matrix of 474 rows (samples) with 565 columns (variables). each of the 474 samples belong to one of 120 groups, with the groupings as a column in the above matrix. For example, the group column would be: 1 1 1 2 2 2 . . . 120 120 I want to randomly select one from each group. Not all the groups have the same number of samples, some have 4, some 3 etc. Is there a function to

DeaR I am trying to use a dynamically create formula with nlsList and nlme, but I cannot get the environment of the string-generated formal to work similarly to the manually entered one. Any idea? Dieter #----- library(nlme) # Pinheiro/Bates p 280 fm1Indom.lis = nlsList(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) nlme(fm1Indom.lis,random=pdDiag(A1+lrc1+A2~1)) # works... # Simulating

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

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

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]

#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.",

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 =

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

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,

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

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"