similar to: curve fitting problem

I am trying to fit a nonlinear regression model to data. There are several predictor variables and 8 parameters. I will write the model as Y ~ Yhat(theta1,...,theta8) OK, I can do this using nls() - but "only just" as there are not as many observations as might be desired. Now the problem is that we have a factor "Site" and I want to include a corresponding error

For the vcdExtra package, I'm exploring methods to generate and fit collections of glm models, and handling lists of such model objects, of class "glmlist". The simplest example is fitting all k-way models, from k=0 (null model) to the model with the highest-order interaction. I'm having trouble writing a function, Kway (below) to do what is done in the example below >

Hello, I'm having problems reading a shapefile with read.shape (maptools). I'm absolutely sure my file is there, but I get "no such file". The wd is ok, since read.table for example does find the file. > getwd() [1] "D:/somedirectory/R scripts" > read.table("cities.shp") Error in read.table("cities.shp") : empty beginning of file In addition:

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

I am curious as to the "lm" calculation of R2 (multiple coefficient of determination, I assume) when intercept is zero. I have 18 data points, two independent variables: First, a model with an intercept: > mod0=lm(Div~Rain+Evap,data=test) > summary(mod0)$r.squared [1] 0.6257541 > cor(predict(mod0),test$Div)^2 [1] 0.6257541 The $r.squared and the result from "cor"

Hi, I'm trying to find a way to determine how many digits a number has. I tried using nchar(paste(number)), but unfortunately paste will reduce 8.00 to "8". Any thoughts? Pieter -- This message was sent on behalf of pieterprovoost at gmail.com at openSubscriber.com http://www.opensubscriber.com/messages/r-help at stat.math.ethz.ch/topic.html

Hello, I'm trying to find out how I can move the y axis label of a horizontal barplot further away from the axis, in order to avoid overlap between the names (which I have rotated using las=1) and the label. I have been fiddling with the graphical parameters but nothing seems to work. Maybe a graphical representation of the graphical parameters would be helpful (cf the CSS box model).

In my own package, I want to use the default S3 method of the generic function lrtest() from the lmtest package. Since I need only one function from lmtest, I tried to use importFrom in my NAMESPACE: importFrom(lmtest, lrtest) However, this fails R CMD check in the examples: Error in UseMethod("lrtest") : no applicable method for 'lrtest' applied to an object of class

Hi, I'm having some problems with a bubble plot (ps package). I don't want tick marks on all four sides (just two), I want to have a smaller font size, and I would like to be able to define bubble sizes shown in the legend (now it shows 0, 0, 0, 9.747 and 4265.757 which is not really convenient. Passing some of the standard plot arguments didn't help (in fact, nothing changed).

Hi, which test do I have to use if I want to test if the following data follow a monotone trend; 0min 5min 10min 20min 30min 5 20 55 70 90 ... where the dependent variable contains frequencies. And how is that implemented in R? thanks for any help (on this more statistical-question ...).

Hello, after calculating a multinomial logit regression on my data, I compared the output to an output retrieved with SPSS 18 (Mac). The coefficients appear to be the same, but the logLik (and therefore fit) values differ widely. Why? The regression in R: set.seed(1234) df <- data.frame( "y"=factor(sample(LETTERS[1:3], 143, repl=T, prob=c(4, 1, 10))), "a"=sample(1:5,

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

I received the following email note re: the vcdExtra package > A vcd update has shown that packages TIMP and vcdExtra are not > declaring their dependence on colorspace/MASS: see > > http://cran.r-project.org/web/checks/check_results_vcdExtra.html But, I can't see what to do to avoid this, nor understand what has changed in R devel. Sure enough, CRAN now reports errors in

Hello, I'm trying to produce graphs automatically from data stored in database. Before saving the graphs, I would like to maximize the size of the graphs. The best would be to directly open maximized windows with x11() but up to now I failed doing it. I tried different widths and heighs but I never managed to obtain a full screen window. Is there a command to do it ? Thanks in advance, Ptit

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm and lmer

> From: Pieter Provoost > > I'm sure it's possible to display the variable names by making a small > change to the code. Someone did this last week but I can't > contact this > person now to ask how. I tried this > > vars <- names(Rdata) > pairs(Rdata, labels=vars,...) > > but that doesn't work (formal argument "labels" matched by