similar to: Big thanks

All, I have just returned to R after a decade of absence, and it is good to see that R has become such a great success! I'm trying to bring Design of Experiments into some aspects of software performance evaluation, and to teach myself that, I picked up "Experiments: Planning, Analysis and Optimization" by Wu and Hamada. I try to reproduce an analysis in the book using lm, but

: A message that you sent could not be delivered to one or more of its : recipients. This is a permanent error. The following address(es) failed: : : openssh-announce at zax.half.pint-stowp.cx : unknown local-part "openssh-announce" in domain "zax.half.pint-stowp.cx" [I seem to be having an unlucky day today]. See below for the correction. ------ This is a copy of the

: A message that you sent could not be delivered to one or more of its : recipients. This is a permanent error. The following address(es) failed: : : openssh-announce at zax.half.pint-stowp.cx : unknown local-part "openssh-announce" in domain "zax.half.pint-stowp.cx" [I seem to be having an unlucky day today]. See below for the correction. ------ This is a copy of the

Hi All. I have R code whose functionality is being replicated within a C+ program. The outputs are to be compared to validate the conversion somewhat - however (as is always the case) I have stuffed my code with random number calls. Random uniform numbers in C+ are being produced using the (Boost) mersenne-twister generators (mt11213b & mt19937) - which is the default type of generator

Hi all, I am trying to make a script that prints all possible models from a model I've created using nls(). It is a logisitc model which in total includes 13 variables. So its >8000 models I need to create, which I don't want to do manually. I've tried modify scripts made for linear models with no results. I've tried these scripts on a two variable model (c,a1 and a2 is what I

I don't know what do you (devels) fixed (maybe that what i read in WWN 287), but alsa works NICE on my system. So, I step in here to wish you luck with the project. I'm deeply impressed that this works :P -- Pawe? R??a?ski

Hi, I came into a crash when using 32-bit `speexdec` and found that there's an address overflow in function `print_comments()`: static void print_comments(char *comments, int length) { char *c=comments; int len, i, nb_fields; char *end; if (length<8) { fprintf (stderr, "Invalid/corrupted comments\n"); return; } end = c+length;

Dear R .Call() insiders, Can someone enlighten me how to properly finalize external pointers in C code (R-2.5.1 win)? What is the relation between R_ClearExternalPtr and the finalizer set in R_RegisterCFinalizer? I succeeded registering a finalizer that works when an R object containing an external pointer is garbage collected. However, I have some difficulties figuring out how to do that in an

Pointers are unsigned so this shouldn't be an issue. I suspect you're being hit by something else. That or your compiler is really broken. Cheers, Jean-Marc On 02/09/2018 04:42 AM, Ruikai Liu wrote: > Hi, > > I came into a crash when using 32-bit `speexdec` and found that there's > an address overflow in function `print_comments()`: > >

I am having trouble figuring out the right form for the nlme arguments. I do have examples in Modern and Applied Statistics with S and from other sources, but I still can't figure it out. I am trying to estimate species richness (sr) in streams across minnesota. My predictor variables are depth (d), habitat diversity (habdiv), drainage area (da) and an indicator variable representing the

I have grades data. I read them from a csv in letter-grade format. I then converted them to levels levels(grades$grade)=c('A+','A','A-','B+','B','B-','C+','C','C-','D+','D','D-') And then to numbers grades$gp=grades$grade levels(grades$gp)=c(4.3,4.0,3.7, 3.3,3.0,2.7, 2.3,2.0,1.7, 1.3,1.0,0.7)

Hello all, I am using the hurdle model for fitting my count data using the pscl package which is working fine. However, I am stuck with the problem of calculating the percent correctly predicted (PCP) zeros for hurdle model. The method I am trying to use to achieve this is 'hitmiss' in the pscl package (ref: http://www.inside-r.org/packages/cran/pscl/docs/hitmiss). When I do: >

I ran a negative binomial logit hurdle model and am now trying to plot the effects of a continuous predictor variable (the only variable in my model) on the count and zero component and the overall mean response. I'm confused because for some values, the predicted overall mean is higher than the mean of the non-zero counts (range of predicted overall means=2.2-11.0; range of non-zero count

Good afternoon, I?m in need of an advice regarding a proper use of glm.nb, zeroinfl or hurdle with my dataframe. I can not provide a self-contained example, since I need an advice on this current dataset and its ?contradictory? results. So.... i have a dataset which contains 1309 cases and 11 variables, highly right-skewed and heavily zeroinflated (with over 1100 cases that have 0 value

Hi all, We're having a meeting this coming Thursday, June 1, 7:00 at Pint on Punt (same as last time). Peter Fern has graciously volunteered to lead a discussion on ENUM and DUNDi: What they are, how they work, and how they work with Asterisk. Of course there will also be time to talk about phones and what's new in the Asterisk community. Date: Thursday June 1 Time: 7:00pm Place:

The following is legal LLVM code in which ptr, ptr2, and ptr3 are all aliases: %struct = type { int, int } implementation int %p() { %ptr1 = alloca %struct %ptr2 = getelementptr %struct* %ptr1 %ptr3 = getelementptr %struct* %ptr2, uint 0 %pint = getelementptr %struct* %ptr3, uint 0, ubyte 0 %rval = load int* %pint ret int %rval } Should our pass a) ignore this, not replace %ptr1,

Hi, I swear I have read almost all the posted messages about this issue, but it's evident I couldn't find an answer (surely esay) to my problem. What I want is the following: Make 8 days aggregates from a daily series like this (dput output): structure(c(6.91777181625366, 0.79051125049591, 9.00625133514404, 9.86966037750244, 14.4326181411743, 3.70155477523804, 9.67768573760986,

Hello, I'm using pscl to run a hurdle model. Everything works great until I get to the point of making predictions. All of my "count" predictions are lower than my actual data, and lower than the "response" predictions, similar to the issue described here ( https://stat.ethz.ch/pipermail/r-help/2012-August/320426.html) and here (

We were trying to normalize bands that didn't actually exist (e.g., the last band with 320-sample frames at 32kHz). Thanks to John Ridges for the report. --- libcelt/celt.c | 24 +++++++++++++++++------- 1 files changed, 17 insertions(+), 7 deletions(-) diff --git a/libcelt/celt.c b/libcelt/celt.c index 31d35f8..287c720 100644 --- a/libcelt/celt.c +++ b/libcelt/celt.c @@ -1137,6 +1137,7

Dear All, I am wondering about the fitting negative binomial(NB) hurdle model with random effect using REML estimation method in R. We can fit regular hurdle model without random effect using ML method as following. hurdle(pkg).... But, I couldn't figure out how I can fit NB hurdle model with random effect using REML in R. Please give me a tip. Thank you so much. Sincerely, SK