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Hi folks, Win7 64bit I tried to save DNase, a data set on database, as .txt file for future use with load. I can't do it on fly; > save(DNase, file="C:/Users/satimis/Documents/aaa.txt") > load(file="C:/Users/satimis/Documents/aaa.txt") > aaa Error: object 'aaa' not found > aaa.txt Error: object 'aaa.txt' not found I must perform following

Hello, How do I specify the formula and random effects without a startup object ? I thought it would be a mixture of nls and lme. after trying very hard, I ask for help on using nlme. Can someone hint me to some examples? I constructed a try using the example from nls: #variables are density, conc and Run #all works fine with nls DNase1 <- subset(DNase, Run == 1 ) fm2DNase1 <- nls(

Hi! When checking validity of a model for a large number of experimental data I thought it to be interesting to check the information provided by the summary method programmatically. Still I could not find out which method to use to get to those data. Example (not my real world data, but to show the point): [BEGIN] > DNase1 <- subset(DNase, Run == 1) > fm1DNase1 <- nls(density ~

Hello, SAS' point and click interface has the option of produce a scatterplot with a superimposed confidence ellipse for the correlation coefficient. Since I generally like R so much better, I would like to reproduce this in R. I've been playing with the ellipse package. In order to have the points and the ellipse on the same graph I've done the following. (Load ellipse

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

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 would like to be able to access the calculated results from the nls package. Using the example in R, fm3DNase1 we can reurn certain parts of the calculations: > coef(fm3DNase1) Asym xmid scal 2.345179 1.483089 1.041454 > resid(fm3DNase1) [1] -0.0136806237 -0.0126806237 0.0089488569 0.0119488569 -0.0025803222 [6] 0.0064196778 0.0026723396 -0.0003276604

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Hola amigos, Supongamos que se quiere ejecutar un modelo no lineal con nls. Pensemos en el ejemplo de la ayuda: DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) summary(fm1DNase1) Aquí se está modelando la densidad óptica de un ensayo relacionada de forma no lineal (logística) con (el logaritmo) de la concentración de una proteína.

Hello, I am drawing some data with ggplot2 and would like to reverse the order of the y axis and set a custom range for it. I can do the former but when I add the key `limits` to `scale_y_reverse` I get an error as shows below and, worse, no data shown in the plot. How can I properly set a reverse y scale with a defined range in ggplot2? Thank you ``` A <- rep(c(0, 3, 6, 12, 24), 3) B <-

On 05/03/2024 7:16 a.m., Luigi Marongiu wrote: > A <- rep(c(0, 3, 6, 12, 24), 3) > B <- c(rep(0,5), rep(1,5), rep(2,5)) > V <- c(27.5, 27.01, 27.75, 27.98, 27.4, 25.69, > 26.17, 27.78, 26.08, > 24.97, 23.18, 21.78, 22.49, 21.85, 22.2) > df <- data.frame(Conc = A, Time = B, Value = V) > df$Conc = as.factor(df$Conc) >

After some processing... ct.df <- data.frame(time,conc) ct.df gives time conc 1 0 164.495456 2 1 133.671185 3 2 108.622975 4 3 88.268468 5 4 71.728126 6 5 58.287225 7 6 47.364971 8 7 38.489403 9 8 31.276998 10 9 25.416103 11 10 20.653462 12 11 16.783276 13 12 13.638313 14

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

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")

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

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

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.",

Can someone help with a fairly simple task? I have a data set where I would like to insert a 0 time event between individuals: what I have: VAR DATE TIME CONC COVAR 1 NOV2 0.25 10 group1 1 NOV2 0.5 20 group1 1 NOV2 1 5 group1 1 NOV2 2 1 group1 1 NOV2 3 0.1 group1 2 NOV2 0.25 10