similar to: Error in model.frame: invalid variable type

Hi All: I came across the following problem while working with a dataset, and wondered if there could be a solution I sought here. My dataset consists of information on 402 individuals with the followng five variables (age,sex, status = a binary variable with levels "case" or "control", mma, dma). During data check, I found that in the raw data, the data entry

Hello, I have a problem concerning logistic regressions. When I add a quadratic term to my linear model, I cannot draw the line through my scatterplot anymore, which is no problem without the quadratic term. In this example my binary response variable is "incidence", the explanatory variable is "sun": > model0<-glm(incidence~1,binomial) >

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) *

Full_Name: Ben Cooper Version: 1.5.0 OS: linux Submission from: (NULL) (134.174.187.90) The help page for glm.nb (in MASS package) says that it takes "Any other arguments for the glm() function except family" One such argument is start "starting values for the parameters in the linear predictor." However, when called with starting values glm.nb returns: Error in

I have two data sets: File1.txt: Name id1 id2 id3 ... N1 0 1 0 ... N2 0 1 1 ... N3 1 1 -1 ... ... File2.txt: Group id1 id2 id3 ... G1 1.22 1.34 2.44 ... G2 2.33 2.56 2.56 ... G3 1.56 1.99 1.46 ... ... I like to do: x1<-c(0,1,0,...) y1<-c(1.22,1.34, 2.44, ...)

Hello, I am try to perform a modeling which is relevant in a strongly heteroscedastic context. So I perform a dual modeling (modeling of both mean and variance of a response) in using the following loop: jointmod <- function(formula, data, itercrit=10,devcrit=0.0001) { # # Init step # init <- glm(formula=formula,family=gaussian, data=data) response <-

Hi all, I'd like to fit model where the terms both are in the data.frame, mydata say, and are vectors *not in the data.frame*. >obj<-glm(y~x, data=mydata) #works >Z<-pmax(mydata$x-20,0) >(length(Z)==length(obj$y)) >[1] TRUE >update(obj,.~.+Z) #works However for some subset it doesn't works: >obj<-glm(y~x, data=mydata, subset=f==1) #works

Hi, I'm using a loop to evaluate several models by taking adjacent variables from my dataframe. When i try to get predictions for new values, i get an error message about a missing variable in my new dataframe. Below is an example adapted from ?gam in mgcv package library(mgcv) set.seed(0) n<-400 sig<-2 x0 <- runif(n, 0, 1) x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <-

I am fittling a spline to a variable in a regression model, I am then using the predict.glm funtion to make some predictions. When I use bs to fit the spline I don't have any problems using the predict.glm function however when I use ns I get the following error: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is

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

Dear list, I am modelling fish distributions using the glm-function followed by the step-function, and then want to cross-validate the model via the cv.glm-function from the {boot} package. I am working on fish distributions on coral reefs. The code I have works for one fish species. Since I have 227 fishes, I wrote a loop. Now the cv.glm-function comes up with an error message: "Error in

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)

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) >

Full_Name: James Signorovitch Version: 2.2.1 OS: WinXP Submission from: (NULL) (134.174.182.203) In the code below, fn1() and fn2() fail with the messages given in the comments. Strangely, fn2() fails for all data sets I've tried except for those with 100 rows. The same errors occur if glm() is used in place of lm(), or if R 2.1.1 is used on a unix system. Thanks for looking into this.

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 trying to figure out how one might go about simulating a landscape (matrix) in R. For example if one wanted to generate a simulated landscape of precipitation values for some area (say a 100 X 100 matrix) they could generate 10,000 numbers using a random normal distribution with a mean and std. dev. and randomly allocate these generated numbers to the grid cells. However, this is too

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,

I'm having sporadic problems with glm on my alpha workstation running Compaq Tru64 UNIX V5.0 (I can't replicate the problem on my Linux PC). I have the same problem whether I use a version of R built with the native C-compiler or a version compiled with gcc. In both cases I've built with g77. I want to do some simulations with a call to glm in each rep. A simplified version would be

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,