search for: 0.264

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I have the following data: > y [1] 0.000 0.004 0.008 0.016 0.024 0.032 0.044 0.064 0.072 0.088 0.108 0.140 [13] 0.156 0.180 0.208 0.236 0.264 0.296 0.320 0.360 0.408 0.444 0.472 0.524 [25] 0.576 > x [1] 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 [16] 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 I'd

Hi all R-users, I?m trying to assign colors on those p-value in my table output that fall above a certain critical value, let?s say a p-value >0.05. My table looks like this: Assets ADF-Level P-Value ADF-First D P-Value ADF-Second D P-Value [1,] Liabilities -2.3109 0.1988 -3.162 0.025 -6.0281

Why am I getting this error if running from Netbeans or just from command line with executing blah_spec.rb? If I run with spec bla blah blah_spec.rb then it''s fine. . Finished in 0.264 seconds 1 example, 0 failures c:/ruby/lib/ruby/gems/1.8/gems/rspec-1.1.4/lib/spec.rb:27:in `exit?'': undefined method `run?'' for Test::Unit:Module (NoMethodError) from

Hi all I have a bit of a problem. I want to make a barplot of some data. My data is of a score that is separated by year and by a limit (above 3 and below 3 to calculate the score). Year Limit HSS 1999 ALT 0.675 1999 VFR 0.521 2000 ALT 0.264 2000 VFR 0.295 I would like to have a barplot with year on the x axis and HSS on the y axis and the two limits as two different colors to show the

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

Dear Sirs: I've been working with several variables in a dataframe that serve as part of a calculation that I need to perform in a different way depending on its value. Let me explain: The main dataframe is called llmcc llmcc : 'data.frame': 283 obs. of 11 variables: $ Area : num 308.8 105.6 51.4 51.4 52.9 ... $ mFondo : num 30.1 10 10.2 10.2 40.4 ... $ mFachada :

On 8/7/20 12:00 PM, Rowland penny via samba wrote: > On 07/08/2020 19:46, Simon Matthews via samba wrote: >> I have a network with a Samba server (Samba 4, but running as an old >> NT-style domain), Windows and Linux clients. > You really should consider upgrading to AD, >> Is there any way to have the Windows client access map to just "user", >> with its

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

On 8/7/20 12:58 PM, Rowland penny via samba wrote: > On 07/08/2020 20:12, Simon Matthews wrote: >> >> The client is running CentOS 7: >> >> # cat /etc/redhat-release >> CentOS Linux release 7.8.2003 (Core) >> >> After another attempt, I have successfully joined the linux client to >> the domain: >> >> # net rpc join MEMBER -S

> -----Original Message----- > From: William Dunlap > Sent: Tuesday, November 25, 2008 9:16 AM > To: 'johannes_graumann at web.de' > Subject: Re: [R] Efficient passing through big data.frame and > modifying select fields > > > Johannes Graumann johannes_graumann at web.de > > Tue Nov 25 15:16:01 CET 2008 > > > > Hi all, > > > >

Dear list, I am currently working with a rather large data set on body temperature regulation in wintering birds. My original model contains quite a few dependent variables, but I do not (of course) wish to keep them all in my final model. I've fitted the following model to the data: >

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

RFC 4731: " IMAP4 Extension to SEARCH Command for Controlling What Kind of Information Is Returned" https://tools.ietf.org/html/rfc4731 I do this (telnet session): """ a select proveedores/TUHS * FLAGS (\Answered \Flagged \Deleted \Seen \Draft) * OK [PERMANENTFLAGS (\Answered \Flagged \Deleted \Seen \Draft \*)] Flags permitted. * 8330 EXISTS * 0 RECENT

On 07/08/2020 20:12, Simon Matthews wrote: > > The client is running CentOS 7: > > # cat /etc/redhat-release > CentOS Linux release 7.8.2003 (Core) > > After another attempt, I have successfully joined the linux client to > the domain: > > # net rpc join MEMBER? -S raidserver -U root%<password> > Using short domain name -- BLUE > Joined 'TURQUOISE'

Hello, I'm trying to analyse data from an incomplete design with four factor : - fr : number of the batch - op : ID of operator - meth : method used - mat : nature of the material used and one variable - mv : mesure - trmv : transformed mesure str(matvol) `data.frame': 120 obs. of 6 variables: $ fr : Factor w/ 30 levels "1","2","3","4",..:

Dear all, I'm using the latest version of the package lme4 and R version 2.7.2 (2008-08-25). After I run the model, I get the results of the model (cf. below). Then, I run an ANOVA using the "anova" function and I get the following message "Error in anova(lmer1) : Calculated PWRSS for a LMM is negative". I went trough the R-mailing list and a similar error message was

How can I from the summary function, decide which glm (fit1, fit2 or fit3) fits to data best? I don't know what to look after, so I would please explain the important output. > fit1 <- glm(Y~X, family=gaussian(link="identity")) > fit2 <- glm(Y~X, family=gaussian(link="log")) > fit3 <- glm(Y~X, family=Gamma(link="log")) > summary(fit1) Call:

Running R 2.5.1 and a newly downloaded lme4 package on WinXP I'm trying to work my way through Everitt and Hothorn's "Handbook of Statistical Analyses Using R," c 2006. (No, it's not homework.) Chapter 10 discusses linear mixed effects models for longitudinal data. I've called my long data frame BtheB.long Here's the model from the book, which I run. lmer1 <-

Hello R-help subscribers, I am analyzing a data set using a mixed logit model, and I have recently discovered some curious behavior. I am hoping you all can help. I first ran the following model in December 2012. lmer(Response.binary ~ ItemType.c * Block + (1 | Subject) + (1 | Word), data=lexdec, family="binomial") I then took a break from the data for the holidays. I returned to