similar to: How to iterate through different arguments?

temp = "~aparch(" temp1 = paste(temp,1, sep = "") temp2 = paste(temp1,1, sep = ",") temp3 = paste(temp2, ")",sep = "") temp 3 is a character but I want to convert to formula object. How do I do this? -- View this message in context: http://r.789695.n4.nabble.com/how-to-convert-string-to-object-tp2999281p2999281.html Sent from the R help mailing

s= "Hey" a = "Hello" table = rbind(s,a) write.table(table,paste("blah",".PROPERTIES",sep = ""),row.names = FALSE,col.names = FALSE) In my table, how do I output only the words and not the words with the quotations? -- View this message in context: http://r.789695.n4.nabble.com/How-to-unquote-string-in-R-tp3025654p3025654.html Sent from the R

Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

If I have a list: list = c(~garch(1,1), ~arma(1,1)) and I run typeof(list[1]), the output is a list object. But I want each element in the list to be a language object. How do I transform these list objects to language objects? -- View this message in context: http://r.789695.n4.nabble.com/how-to-convert-list-to-language-object-tp2966813p2966813.html Sent from the R help mailing list archive at

hello everybody. i need to log "who did what" on some models. i was reading recipe 59 of rails recipes and i created something like that: class LogSweeper < ActionController::Caching::Sweeper observe :model1, :model2, :model3, :model4, :model5, ... after_save(model) save_log(model, "save") end after_destroy(model) save_log(model, "destroy) end

I have 5 GAMs ( model1, model2, model3, model4 and model5) Before I use some data X(predictor -January to June data) to form a equation and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y'

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR

Dear R user I have 2 problems with lmer. The statistical consultance service of my university has recomended to me to expose those problems here. Sorry for this quite long message. Your help will be greatly appreciated... Gilles San Martin 1) anova() I fit a first model : model1 <- lmer(eclw~1 + density + landsc + temp + landsc:temp + (1|region) + (1|region:pop) + (1|region:pop:family),

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

>May 26, 2017; 11:41am Nelly Reduan Latin Hypercube Sampling when parameters are >defined according to specific probability distributions >Hello, > I would like to perform a sensitivity analysis using a Latin Hypercube Sampling (LHS). >Among the input parameters in the model, I have a parameter dispersal distance which is defined according to an exponential probability

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

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,

Hi, I am trying to fit a curve to data. My command line is: model10=nls(offspring~((A*c^k)/gamma(k))*((degdays-alpha)^(k-1))*exp(-c*(degdays-alpha)), start=list(A=30,k=2,c=.018,alpha=131)) I get the error message: Error in numericDeriv(form[[3]], names(ind), env) : Missing value or an infinity produced when evaluating the model In addition: Warning message: full precision was not

I noticed this in the NEWS: o Datasets 'sunspot.month' and 'sunspot.year' are available separately but not via data(sunspot) (which is used by package lattice to retrieve a dataset 'sunspot'). Now, the 'sunspot' dataset included in lattice was actually a mistake, I realized later that I instead wanted the 'sunspots' data from

Hi all, I need to (quickly!grrr) update IP addresses for which a given machine will answer. I did this: /sbin/ifconfig eth0:0 xx.xx.xx.10 netmask 255.255.255.0 /sbin/ifconfig eth0:1 xx.xx.xx.11 netmask 255.255.255.0 /sbin/ifconfig eth0:2 xx.xx.xx.30 netmask 255.255.255.0 /sbin/ifconfig eth0:3 xx.xx.xx.31 netmask 255.255.255.0 /sbin/ifconfig eth0:4 xx.xx.xx.13 netmask 255.255.255.0