similar to: Quantreg - 'could not find function"rq"'

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Hi, I have a numeric pixel image which I would like to divide into factors for analysis in Spatstat. I have found that I can use cut.im() function to divide the range of pixel values into a series of equal length intervals (e.g. if my pixels values range from 0 to 60, cut.im(X.im,breaks=2) will produce two factors one containing pixel values 0-30 and one containing pixel values of 30 - 60, or

Removing rows with NAs, using na.omit(), doesn't seem to be working for me. Dataset: > str ( ex10s ) 'data.frame': 2189576 obs. of 5 variables: $ LOPNR : int 58 58 58 58 64 64 64 64 64 64 ... $ DIAGNOS: Factor w/ 173 levels "F20","F200","F2000",..: 128 128 128 128 105 105 105 160 105 105 ... $ X_DATE : int 20060821 20061207 20080102 20090904

Hi guys, I am updating a package because of data format in data folder. So I just change an extension of a file to .txt ... nothing more. I get this error on the R CMD check ** help *** installing help indices ** building package indices ... Error in read.table(zfile, header = TRUE, as.is = FALSE) : more columns than column names ERREUR : installing package indices failed Note that the R CMD

Hi I am using the quantreg package for median regression for a large series of subsets of data. It works fabulously for all but one subset. When it reaches this subset, R takes the command and never responds. I end up having to kill R and restart it. It appears to be something with the particular data subset, but I can't pinpoint the problem. Here are some details Operating system:

Hello all, I would like to compute a quantile regression using rq (from the quantreg package), while keeping one of the coefficients fixed. Is it possible to set an offset for rq in quantreg? (I wasn't able to make it to work) Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili at gmail.com |? 972-52-7275845 Read me:

Hello All, Is there a way of producing an ANOVA table split into contrasts, thus showing the contrasts sums of squares and associated p-values? Thanks, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle

Hi all, Has anyone tested for FA in R? I need to seperate out the variance due to measurement error from variation between individuals (following Palmer & Strobeck 1986). Andy Higginson Animal Behaviour and Ecology Research Group School of Biology University of Nottingham NG7 2RD U.K. This message has been checked for viruses but the contents of an attachment may still contain

Hi all, I'm using spatstat to investigate the spatial structure of an arid shrub population. The first-order intensity of my data does not appear to be homogenous, so I would like to use inhomogeneous techniques. I realise there is a inhomogeneous K-function available in spatstat, but there doesn't not appear to be one for the pair-correlation function (O-ring statistic). As such I was

Dear all, I am having v.4.36 of Quantreg package and I noticed strange behaviour when weights were added. Could anyone please explain me what if the results are really strange or the behavioiur is normal. As an example I am using dataset Engel from the package and my own weights. x<-engel[1:50,1] y<-engel[1:50,2] w<-c(0.00123, 0.00050, 0.00126, 0.00183, 0.00036, 0.00100, 0.00122,

Dear R Users, I suppose this is a school boy question, but here it is anyway. I'm trying to re-create the residuals for a poisson GLM with simulated data; x<-rpois(1000,5) model<-glm(x~1,poisson) my.resids<-(log(x)- summary(model)$coefficients[1]) plot(my.resids,residuals(model)) This shows that my calculated residuals (my.resids) are not the same as residuals(model). p 65 of

I want to form a 3x3 crosstabulation for the signs of two vectors (i.e. Negative, Zero, Positive). The problem is that I am simulating the data so for some iterations one of the categories is absent. Thus the resulting table shrinks to 3x2. I want it to be 3x3 with zero column corresponding to the missing category. Moreover, I have tried but failed to give the dimension names. -- Sohail Chand

Hello R Users, I am investigating the basic use of the LME function, using the following example; Response is Weight, covariate is Age, random factor is Genotype model.lme <- lme (Weight~Age, random=~ 1|Genotype) After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758. I am comparing the above model with the AOV function; model.aov <- aov (Weight~Age + Genotype)

Hi, I am trying to use quantile regression to perform weighted-comparisons of the median across groups. This works most of the time, however I am seeing some odd output in summary(rq()): Call: rq(formula = sand ~ method, tau = 0.5, data = x, weights = area_fraction) Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 45.44262 3.64706 12.46007

Dear All, I wonder if anyone can advise me as to whether there is a consensus as to how the effect size should be calculated from GLIM models in R for any specified significant main effect or interaction. In investigating the causes of variation in infection in wild animals, we have fitted 4-way GLIM models in R with negative binomial errors. These are then simplified using the STEP procedure,

Dear All, I'm rather a beginner on nested ANOVAs, so here goes with my 2 questions; Qu 1: I'm modelling the number of galls on a leaf (the response variable) as a function of; the tree on which I find the leaf, the branch on which I find the leaf. Then, the tree and the branch are both random factors, and I'm quite happy that I should write; aov(galls~tree/branch +

Hi all, I am using quadratcount in spatstat to divide a window containing a point pattern into a grid of quadrats containing the intensity of points in each quadrat. My data is in UTM co-ordinates. My window is defined as follows: >p15<-ppp(x,y,window=owin(c(341710,342100),c(3126465,3126780)),marks=NUL L, checks=TRUE) Giving me a distance of 390m in the 'x' direction and 315m in

I was trying "quantreg" package, lm1 <- lm(y~x) rq1 <- rq(y~x) plot(summary(rq1)) #then got a warning says singular value, etc. but this line can be omited plot(lm1) #crash here It happened every time on my PC, Windows XP Pro Serv. Pack 1, Pentium(4) 3.00G. [[alternative HTML version deleted]]

Dear R-help, Has anyone been able to use the quantreg package on Windows successfully? I tried to load it and get the following: > library(quantreg) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "C:/PROGRA~1/R/rw1031/library/quantreg/libs/quantreg.dll": LoadLibrary failure: The specified procedure could not be found. Error in

Hi all, I'm trying to load the quantreg package but keep running into problems no matter which method I have tried. Does anybody know what this error (below) means in plain language and what I might do to get this installed. I have not had problems downloading/installing/running packages in the past. thanks in advance, derek Begin R output: trying URL