similar to: Error in model.frame

Dear expeRts, I came across the following error in using model.frame: # make a data.frame jet=data.frame(y=rnorm(10),x1=rnorm(10),x2=rnorm(10),rvar=rnorm(10)) # spec of formula mf1=y~x1+x2 # make the model.frame mf=model.frame(formula=mf1,data=jet,weights=rvar) Which gives the desired output: > mf y x1 x2 (weights) 1 0.8041254 0.1815366 0.4999551 1.4957814 2

I'm a bit confused about the nls package, I'm trying to use it for curve fitting. First off, the documentation for nls says ``see `nlsControl' for the names of the settable control values'' -- this is wrong, it should be nls.control (minor point but had me confused for a moment). Now I'll try something very simple (maybe too simple):

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

Dear list, I may have found a bug in model.frame.default (called by the lm function). The problem arises in my R dev version but not in my R 2.4.0. Here is my config : > version _ platform x86_64-unknown-linux-gnu arch x86_64

I hope you can help me. I am new to "R" and am working through the example of quantile regression analysis in the paper by Roger Koenker (quantile regression in R: a Vignette. I got to page 6 and in trying to execute the following code I received the error below. I have searched on the web and while I have found similar error messages, I have not been able to ascertain the root cause.

I initially thought, this should better be posted to r-devel but alas! no response. so I try it here. sory for the lengthy explanation but it seems unavoidable. to quickly see the problem simply copy the litte example below and execute f(n=5) which crashes. called with n != 5 (and of course n>3 since there are 3 parameters in the model...) everything is as it should be. in detail:

hi all i have a function that uses two inputs, say xdata and ydata. An example is the following, simple1<-function(xdata,ydata) { ofit<-lm(ydata~xdata) list(ofit) } say i use arbitray number for xdata and ydata such that D = x1 x2 y 1 1 10 2 6 6 3 10 7 x<-D[,1:2] and y<-D[,3] if one uses these inputs and rund the program we get the following: >simple(xdata=x,ydata=y)

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Error in model.frame When I run the following nls model an error message appears and I dont know how to solve that. Could you help me?? mat = c(1,2,3,4,5,6,7,8,9,12,16,24,36,48,60) for (i in 1:length(j30)) { bliss = nls(c(j[i,1:length(mat)]) ~ b0 + b1*((1-exp(-k1*mat))/(k1*mat)) + b2*(((1-exp(-k2*mat))/(k2*mat))-exp(-k2*mat)), start = list(k1=0.1993, k2=0.1993, b0= 22.0046,

Greetings List, I'm having some trouble with the use of the names function in a formula. Below is an example of something that works (i.e first line), and the second line is the same formula which doesn't. I want to be able to reference the column of the dataC table so I can run the variogram iteratively with a loop. > v<-variogram(A1~1,loc=~x+y, dataC) >

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

Dear all, I have tried to merge two cross sections (crossA and crossB) as follows, both crossA and crossB include the same variables, e.g., ID,y,x1,x2: Panel <- merge(crossA,crossB,by="ID") I have, thereafter, tried to run the following pooled OLS: OLS <- lm(Panel$y~Panel$x1+Panel$x2) and have received the following error message "Error in model.frame(formula, rownames,

I'm trying to learn to use manova(), and don't understand why none of the following work: > data(iris) > fit <- manova(~ Species, data=iris) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : incompatible dimensions > fit <- manova(iris[,1:4] ~ Species, data=iris) Error in model.frame(formula, rownames, variables, varnames, extras,

I would like to know if clogit function can be used as below clogit(group~., data=dataframe) When I try to use in above format it takes a long time, I would appreciate some pointers to get multiple combinations tested. set.seed(100) d=data.frame(x=rnorm(20)+5, x1=rnorm(20)+5, x2=rnorm(20)+5, x3=rnorm(20)+5, x4=rnorm(20)+5, x5=rnorm(20)+5, x6=rnorm(20)+5, x7=rnorm(20)+5, x8=rnorm(20)+5,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

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I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many

--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

I tried this : > mosaicplot(stoc ~ q9r + segca,data=tmp2,color=T) : works fine. And now, this : > mosaicplot(stoc ~ q9r + segca, data=tmp2, color=T, main="Big title") Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid variable type I'm probably stupid and missed something simple in the manual (and wouldn't like to be