search for: negated

Hello After add some packagelist to puppet class. I get lots of warning message "appears to have a negative number of dependencies". My puppet master and agent version are 3.3.1. Here is the log out: [root@gpu022 ~]# puppet agent --test Info: Retrieving plugin Info: Loading facts in /var/lib/puppet/lib/facter/puppet_vardir.rb Info: Loading facts in

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i think my data is closer to negative binomial but i cant find any examples of

Dear all, I have a dataset. I want to make barplot from this data. Zero1 <- " V1 V2 V3 V4 V5 V6 V7 V8 V9 1 1 0 0 0 1 0 0 0 Positive 2 0 0 1 0 1 0 1 1 Negative 3 0 0 1 0 0 0 1 1 Positive 4 0 1 0 1 1 1 0 1 Negative 5 0 0 1 0 1 1 0 0 Positive 6 0 1 0 0 1 1 1 1 Negative 7 1 0 1 1 1 1 1 1 Negative 8 0 0 0 0 1 0 0 1

Hi; I am running generalized linear mixed models (GLMMs) with the lmer function from the lme4 package in R 2.6.2. My response variable is overdispersed, and I would like (if possible) to run a negative binomial GLMM with lmer if possible. I saw a posting from November 15, 2007 which indicated that there was a way to get lmer to work with negative binomial by assigning: family =

In x64 SMP Vista HVM guest (vcpus=2 in the configuration file), the wall clock time is 50% slower than that in the real world. The attached patch fixes the issue. -- Dexuan Signed-off-by: Dexuan Cui <dexuan.cui@intel.com> _______________________________________________ Xen-devel mailing list Xen-devel@lists.xensource.com http://lists.xensource.com/xen-devel

Dear list, I would like to know: 1. After I have used the R code (http://pscl.stanford.edu/zeroinfl.r) to fit a zero-inflated negative binomial model, what criteria I should follow to compare and select the best model (models with different predictors)? 2. How can I compare the model I get from question 1 (zero-inflated negative binomial) to other models like glm family models or a logistic

Dear List, Why do commonly used estimator functions (such as lm(), glm(), etc.) not allow negative case weights? I suspect that there is a good reason for this. Yet, I can see reasonable cases when one wants to use negative case weights. Take lm() for example: ### n <- 20 Y <- rnorm(n) X <- cbind(rep(1,n),runif(n),rnorm(n)) Weights <- rnorm(n) # Includes Pos and Neg Weights Weights

Hi, I would like to implement a inexpensive home PBX with Asterisk. I have an internal ISDN bus with 6 ISDN phones. I now thought, I connect a Fritz card to my Mehrgerateanschluss (Point-to-Multipoint) supplied by my provider and a second Fritz card to the internal bus. Will this work? Thanks for the help, Arik

Hi all, I have been beating my head against this problem for a bit, but I can't figure it out. I have a series of strings of variable length, and each will have one or more numbers, of varying format. E.g., I might have: tmpstr = "The first number is: 32. Another one is: 32.1. Here's a number in scientific format, 0.3523e10, and another, 0.3523e-10, and a negative,

The IEEE floating point standard allows for negative zero, but it's hard to know that you have one in R. One reliable test is to take the reciprocal. For example, > y <- 0 > 1/y [1] Inf > y <- -y > 1/y [1] -Inf The other day I came across one in complex numbers, and it took me a while to figure out that negative zero was what was happening: > x <-

Recently, I was working with some lagged designs where a vector of observations at one time was used to predict a vector of observations at another time using a lag 1 design. In the work, I noticed a lot of negative correlations, so I ran a simple simulation with 2 matched points. The crude simulation example below shows that the correlation can be -1 or +1, but interestingly if you do this

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

LLVM build without big problems in obj dir == src dir case (for example, last night tester build) But I have problem with building CVS version LLVM in obj dir != src dir case. ======= Finished building ModuleMaker debug executable (without symbols) ======= gmake[2]: Leaving directory `/usr/home/wanderer/pkg/build/llvm/obj/examples/ModuleMaker' gmake[1]: Leaving directory

Matrix indexing seems to give rather "variable" results when zeros or negative values are included among the indices (in terms of both error messages and in terms of the number of returned values when there is no error message). Is this the intended behavior? I couldn't see any comments about zeros or negative values in matrix indices in either ?"[" or Section 3.4.2

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not

Hi, In continuation of the discussion of melange comments,about negative value returned in matcher due to logarithm of probabilities. *I**f we make K suitably large, we could clamp each log(K.Pi) to be >= 0, and this change will only affect really low probability terms (those with Pi < 1/K, so you can adjust K to suit):* *W' = sum(i=1,...,n, max(log(K.Pi), 0))* Did you mean for low

I resend email with updated (after mass header move) log examples. > LLVM build without big problems in obj dir == src dir case (for example, > last night tester build) > But I have problem with building CVS version LLVM in obj dir != src dir > case. > gmake[1]: Entering directory `/usr/home/wanderer/pkg/build/llvm/obj/projects' gmake[2]: Entering directory

Dear R-ers! Asking for your help with building the stacked area chart for the following simple data (several variables - with date on the X axis): ### Creating a data set my.data<-data.frame(date=c(20080301,20080402,20080503,20090301,20090402,20090503,20100301,20100402,20100503), x=c(1.1,1.0,1.6,1,2,1.5,2.1,1.3,1.9),y=c(-4,-3,-6,-5,-7,-5.2,-6,-4,-4.9),

In Section 'Indexing by vectors' of 'R Language Definition' (http://cran.r-project.org/doc/manuals/r-release/R-lang.html#Indexing-by-vectors) it says: "Integer. All elements of i must have the same sign. If they are positive, the elements of x with those index numbers are selected. If i contains negative elements, all elements except those indicated are selected. If i is