similar to: Summary coefficients give NA values because of singularities

Hi list, i'm using Samba 3.0.14a as standalone-pdc with posix-acl-features. On the fileserver i've mounted an ext3 partition to /home /dev/hdc2 /home type ext3 (rw,usrquota,acl) The following directory structur exists on /home /home +-/public_folder (exported per samba as \\fileserver\public ) | +-/a | +-/a_ann | | +-/anyfolder | | +- anyfiles | +-/a_bert | +-/a_sven | +-/a_eve |

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

I run lm to fit an OLS model where one of the covariates is a factor with 30 levels. I use contr.treatment() to set the base level of the factor, so when I run lm() no coefficients are estimated for that level. But in addition (and regardless of which level I choose to be the base), lm also gives a vector of NA coefficients for another level of my factor. The output says that these coefficients

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I got the report Coefficients: (1 not defined because of singularities) while trying to get my model's coefficients from lm() What shall I do to avoid it and get the one missing coefficient? Thank you. lukas - -- Lukas Kubin lukas.kubin at permonik.com phone: 00420603836180 -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.0.5 (GNU/Linux)

Hello, I want to specify a linear regression model in which the metric outcome is predicted by two factors and their interaction. glm() computes effects for each factor level and the levels of the interaction. In the case of singularities glm() displays "NA" for the corresponding coefficients. However, svyglm() aborts with an error message. Is there a possibility that svyglm()

Hello, i have the following model, poi1<-glm(F~S+T+L+C,family=poisson,x=T) where F,S,T,L are metric and C is a factor variable with the levels "0", "1", "2", "3", "4", "5" and "6" if i do summary(poi1), i get the following Call: glm(formula = F ~ S + T + L + C, family = poisson, x = T) Deviance Residuals: Min

Hi- I'm wrestling with some of my data apparently not being called into a GLM or an LM. I'm looking at factors affecting fish annual catch rates (ie. CPUE) over 30 years. Two of the factors I'm using are sea surface temperature and sea surface temperature anomaly. A small sample of my data is below: CPUE Year Vessel_ID Base_Port Boat_Lgth Planing SST Anomaly 0.127

Hello, I'm doing an lm(y1~x1), no NAs in them, both of length 283. I get out however and 'NA' for the estimate of x1 and summary gives: Residuals: Min 1Q Median 3Q Max -0.1998309 -0.0447269 -0.0006252 0.0390933 0.3141687 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -0.021291 0.003994 -5.331 2.01e-07 *** x1

I am using a nested model in R and the lm output shows 47 not defined because of singularities and I have no idea why. Any help on why this is happening or how to fix this problem would be very much appreciated. Below is the output I received from R. Thanks and happy holidays! Call: lm(formula = Dist ~ Treatment/SiteL/Territory) Residuals: Min 1Q Median 3Q Max -6.646 -1.443

Hello, I am trying to run a linear regression analysis on my data set. For some reason most variables are removed due to singularities. My linear regression looks this way (I am using only partial data, which is selected by flags): fm<-lm(log(cplex6.time..sec..[flags]) ~ cplex6.cities[flags] + log(1/features.meanOver.frust[flags]) + log(1/features.meanOver.minDist[flags]) + [...]

I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

Hi! I was trying to implement a probit model on a dichotomous outcome variable and found that the predictions were outside the (0,1) interval that one should get. I later tried it with some simulated data with a similar result. Here is a toy program I wrote and I cant figure why I should be getting such odd predictions. x1<-rnorm(1000) x2<-rnorm(1000) x3<-rnorm(1000)

R 2.12 windows 7 I am summary data that I would like to make into a 2x2 table representing counts positive vs. negative counts: 28/289 20/276 My table should look something like the following: group1 group2 Positive 28 20 Negative 289 276 How can a (1) create the 2x2 table (2) run a chi square test on the table? I have tried the following code, but I

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I am having some trouble extracting results from the function by, used to average variables in a data.frame first by one factor (depth) and then by a second factor (station). The real data.frame is quite large > dim(data.2001) [1] 32049 11 Here is a

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking