similar to: plotting gamm results in lattice

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

Dear all, I am writing as I would really need your help on the problem with gamm4. I have tried to find a solution online but I wasn't very successful. I am running a gamm4 model with an interaction between two variable using the tensor term, t2. I have a group variable (super end group) with six factors; I would like to run the model to see the how the interaction term varies across the

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )

hi dovecot: when i login 100 users with imap protocol ,Someusers appeared a BUG in maillog like : Sep 10 09:05:22 auth(default): Error: BUG: Worker sent reply with id 393, expected 394 Sep 10 09:05:22 auth(default): Error: worker-server(q82 at t.com,10.12.80.3): Aborted: Worker is buggy Sep 10 09:05:22 auth(default): Error: BUG: Worker sent reply with id 31, expected 32 Sep 10 09:05:22

Hello, I am trying to filter a data set like below so that the peaks in the Phase value are more obvious and can be identified by a peak finding function following the useful advise of Carl Witthoft. I have written the following for(i in length(data$Phase)){ newphase=if(abs(data$Phase[i+1]-data$Phase[i])>6){ data$Phase[i+1] }else{data$Phase[i] } } I get the following error which I have not

Hola Tal vez la pregunta que tengo tenga una respuesta muy f?cil, sin embargo lo entiendo. Tengo una data frame que se ve as?: R> df semana precios ventas preciosCan 1 11724 3.512441 2 33.53 2 11726 3.512441 1 33.53 3 11727 3.512441 2 33.53 4 11728 3.512441 1 33.53 5 11729 3.512441 4 33.53 6 11730 3.512441 3 33.53

http://dovecot.org/releases/2.2/dovecot-2.2.19.tar.gz http://dovecot.org/releases/2.2/dovecot-2.2.19.tar.gz.sig * pop3_deleted_flag has been broken since v2.2.10. Using it would cause buffer overflows, which could be exploitable. However, this bug would have become visible quite soon after users had deleted some POP3 mails, because the pop3 processes would have started crashing all the

http://dovecot.org/releases/2.2/dovecot-2.2.19.tar.gz http://dovecot.org/releases/2.2/dovecot-2.2.19.tar.gz.sig * pop3_deleted_flag has been broken since v2.2.10. Using it would cause buffer overflows, which could be exploitable. However, this bug would have become visible quite soon after users had deleted some POP3 mails, because the pop3 processes would have started crashing all the

Hi Aki! Thank you for your reply. Sure, here are the related lines within my conf files and sieve scripts: https://hastepaste.com/view/Erk8Ra Thank you so much, Regards. Aki Tuomi <aki.tuomi at open-xchange.com>, 5 Mar 2020 Per, 11:31 tarihinde ?unu yazd?: > > > On 5.3.2020 9.00, ?zg?r Kazancci wrote: > > Greetings everyone. > > > > My dovecot mail server

Hola Carlos Muchas gracias por tu respuesta. Saludos ________________________________ De: Carlos Ortega <cof en qualityexcellence.es> Enviado: jueves, 19 de abril de 2018 10:47:54 a. m. Para: Javier Nieto CC: r-help-es en r-project.org Asunto: Re: [R-es] Interpretaci?n NA's Pues que la peque?a variaci?n en las ventas no pueden explicarse con un precio constante. Hay otra variable

Valeri Galtsev wrote: > > On Tue, April 12, 2016 1:43 pm, Always Learning wrote: > <snip> > In US you can buy some laptops without MS Windows OS (read: with Linux, > most likely Ubuntu) from some small manufacturers... I believe, one <snip> I *think* you can buy Dell laptops with no o/s. mark

I'm encountering excruciatingly slow load times for character vectors in R 2.6.0-- up to 30sec for a 15K file that contains a no-attributes character vector of length ~1e4 and object size ~0.5MB. In R 2.5.1, repeated loads of the same set of files are near-instantaneous. The problem is proving tricky to reproduce consistently from scratch, so I have attached the 3 files used in the examples

Greetings, I am reasonably experienced with R but I recently tried to do some clustering using the "cluster" package, in order to see if it would help. I only tried this once with the 1.3.1 version and it worked (I don't quite remember which method I used). Now, I tried with the 1.4.0 version and no clustering function seems to work with matrices that contain NAs, even though

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

I am testing some kickstarts on ESXi virtual machine with pair of 16GB disks. Partitioning is lvm over raid. If i am using "logvol --grow i get "ValueError: not enough free space in volume group" Only workaround i can find is to add --maxsize=XXX where XXX is at least 640MB less than available. (10 extents or 320Mb per created logical volume) Following snippet is failing with

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

On 2016-05-25 19:13, Kelly Lesperance wrote: > Hdparm didn?t get far: > > [root at r1k1 ~] # hdparm -tT /dev/sda > > /dev/sda: > Timing cached reads: Alarm clock > [root at r1k1 ~] # Hi Kelly, Try running 'iostat -xdmc 1'. Look for a single drive that has substantially greater await than ~10msec. If all the drives except one are taking 6-8msec, but one is very

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in