similar to: error using "predict"

hi all i have found the follwoing way to choose a random sample by sample size (200): ten_per_T2000 <- F_T2000_All[sample(nrow(F_T2000_All), 200), ] but i wondered if there is a way to choose a sample size by precentage (10% etc..) thx ethan

On Oct 19, 2012, at 2:24 AM, Patrik Hägglund H <patrik.h.hagglund at ericsson.com> wrote: >> non-8-bit byte I'm a bit confused by this concept. I'm aware of the archaic meaning of the word byte, but it has meant 8 bits for the last 30 years. There's even an ISO/IEC standard. I know of architectures like Texas' C55x DSPs that address 16 bits at a time, but even their

On Fri, Oct 19, 2012 at 9:27 AM, Jakob Stoklund Olesen <stoklund at 2pi.dk> wrote: > > On Oct 19, 2012, at 2:24 AM, Patrik Hägglund H <patrik.h.hagglund at ericsson.com> wrote: > >>> non-8-bit byte > > I'm a bit confused by this concept. I'm aware of the archaic meaning of the word byte, but it has meant 8 bits for the last 30 years. There's even an

> Please start a thread on llvmdev about this functionality, and outline what other intrinsics will have to change to add non-8-bit byte support. Well, memset is the only we have seen so far (our back-end is ~50% finished for an initial release). We have our own front-end as well (we are currently not using the clang front-end), and currently don't use many llvm intrinsics (only

On Oct 19, 2012, at 10:47 AM, Eli Friedman <eli.friedman at gmail.com> wrote: > On Fri, Oct 19, 2012 at 9:27 AM, Jakob Stoklund Olesen <stoklund at 2pi.dk> wrote: >> >> On Oct 19, 2012, at 2:24 AM, Patrik Hägglund H <patrik.h.hagglund at ericsson.com> wrote: >> >>>> non-8-bit byte >> >> I'm a bit confused by this concept.

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Unless there is good reason not to, always cc the list -- there are lots of smarter folks than I on it who can help. I may or may not have time to look at this. Hopefully someone else will. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip

I have a table like the following. I want to fit Cm to Vm like this: Cm ~ Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, Vp2=200). But I always get 'singlular gradient' error like this. But in SigmaPlot I can get the result. How

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

I can't get my simultaneous equations to work using system fit. Please help. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) str(Empdata) Empdata$gnipc<-as.numeric(Empdata$gnipc) install.packages("systemfit") library("systemfit") pdata <- plm.data(Empdata,

Dear R-help, I'd like ask for your opinion on choosing the "right" strategy for a particular dataset. We conducted 24-hour electric field measurements on 90 subjects. They are grouped by job (2 categories) and location (3 categories). There are four exposure metrics assigned to each subject. An excerpt from the data: n job location M OA UE all 0 job1 dist_200 0.297 0.072 0.171

Hi R users: What is the difference between in the computation of the diag of the "hat" matrix in: "lm.influence" and the matrix operations with "solve()" and "t()"? I mean, this is my X matrix x1 x2 x3 x4 x5 [1,] 0.297 0.310 0.290 0.220 0.1560 [2,] 0.360 0.390 0.369 0.297 0.2050 [3,] 0.075 0.058 0.047 0.034 0.0230 [4,] 0.114 0.100

Hi Everybody, please can you help me how to call BayesX in R in order to see the graph already exist in BayesX Thanks ---------- Forwarded message ---------- From: <r-help-request@r-project.org> Date: Sat, Jan 22, 2011 at 5:00 AM Subject: R-help Digest, Vol 95, Issue 22 To: r-help@r-project.org Send R-help mailing list submissions to r-help@r-project.org To subscribe or

Recently there was a question on using lme with large data sets. As an experiment I fit a linear mixed-effects model to a data set with about 350,000 observations on 6 predictors, a numerical response, and a single grouping factor. The timings shown below were on a 1.2 GHz Athlon with 1 GB of PC133 memory and 2 GB of swap. The operating system is Debian 3.0 GNU/Linux. The kernel is 2.4.14.

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba