similar to: min() return factor class values

I have test environment to determine what would be best settings. I have been told that enabling zlib compression would be good to save iops on storage. But doing the test now, I get worse results. [@test2 ~]# pr -m -t mail04-mdbox-vdb-append-64kb-6.log mail04-mdbox-vdb-append-64kb-8.log |less Logi Sele Appe Logi Sele Appe 100% 100% 100% 100% 100%

Now that I have a working kvm setup, I thought I'd finally try how Zimbra works. This is mainly some microbenchmarking, so it may not have much to do with actual performance in real life. Setup: - 1GB memory given to kvm (from host's 2GB) - Intel Core 2 6600 (kvm uses only one CPU) - CentOS 5 - 15GB qcow2 image on XFS filesystem - Zimbra 5.0 RC2 RHEL5 x86_64 - Dovecot latest hg,

Estimados herreros, Sabéis si hay algún sitio donde pueda hacer una pregunta sobre estadística, en concreto un diseño experimental. Alguna lista, foros, etc. Ya se que este no es el sitio adecuado para preguntas de estadística, pero por si alguien se pica os la pongo a continuación. Dos tratamientos C y H. Se aplican a una serie de especies (p.e. 10) Se observa la respuesta de las especies a

I thought I'd try benchmarking with dbmail (v2.2.4) to see how much slower a SQL backend could actually be. Skip to bottom for the conclusions. Originally I ran the tests with the databases being in XFS filesystem. MySQL's performance was horrible. It went 3-7x faster with ext3. MySQL 5.0.30 backend (innodb): ./imaptest clients=1 - append=100 seed=1 secs=30 msgs=1000000 logout=0

We have done some benchmarking tests using dovecot 2.0.12 to find the best shared filesystem for hosting many users, here I share with you the results, notice the bad perfomance of all the shared filesystems against the local storage. Is there any specific optimization/tunning on dovecot for use GFS2 on rhel6??, we have configured the director to make the user mailbox persistent in a node, we will

I am fitting logistic regression models, by defining my own link function, and would like to get AICc values. Using the glm command gives a value for AIC, but I haven't been able to get R to convert that to AICc. Is there a code that has already been written for this? Right now I am just putting the AIC values into an excel spreadsheet and calculating AICc, likelihood, and AIC

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

Hi, I'm fitting poisson regression models to counts of birds in 1x1 km squares using several environmental variables as predictors. I do this in a stepwise way, using the stepAIC function. However the resulting models appear to be overparametrized, since too much variables were included. I would like to know if there is the possibility of fitting models by steps but using the AICc

Hola a todos, Escribo para preguntarles sobre cómo programar algo, que estoy segura es algo bastante sencillo para muchos de Uds que tienen amplia experiencia programando. Lo describo a continuación. Tengo una base de datos (file = datos) de especies (de plantas) en donde cada fila es un individuo de una especie con ciertos atributos (alrededor de 1500 filas = individuos). Quería anexar en esta

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

Buenos d?as. Tengo una duda: Trabajo con comundiades de especies, y mis datos se ordenan de tal forma que, cada especie corresponde a una fila de la base de datos, por lo que cada muestra tiene un n?mero de filas correspondiente al n?mero de especies. Mi objetivo es transformar esa base de datos a un formato de matriz donde, cada muestra sea una fila ?nica y las especies sean las columnas, con

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Estimados Quisiéramos que la salida final plot (z) fuera de tres colores, uno para cada especie, no encuentro el comando para ello. Apreciaría su ayuda Saludos Jose rm(list = ls()) library(MASS) data(iris) datos <- data.frame(iris[,1:4],clase=as.vector(iris[,5])) head(datos) especies <- iris[,5] # La columna cinco se refiere a las especies(clase) z <- lda(clase~.,datos)

Saludos, tengo una base de datos con tres variables: especie, tratamiento y abundancia. Quiero hacer comparaciones múltiples con Tukey. Ya había usado el comando TukeyHSD(aov(x~y)). La cuestión esque ahora tengo varios niveles del factor especie y varios niveles del factor tratamiento. Si hago TukeyHSD(aov(abundancia~especie:tratamiento)) me salen todas las comparaciones posibles, pero yo sólo

Hi all, I'm having trouble with the model generating 'dredge' function in the MuMIn 'Multi-model Inference' package. Here's the script: globalmodel<- glm(TB~lat+protocol+tested+ streams+goats+hay+cattle+deer, family="binomial") chat<- deviance(globalmodel)/59 #There we 59 residual degrees of freedom in this global model. models<- dredge(globalmodel,

http://dovecot.org/patches/1.1/maildir-dirty-syncs.diff This patch adds a new maildir_very_dirty_syncs setting. If set to "yes", Dovecot assumes it's the only one changing the cur/ directory (so other MDAs can add mails to new/ without problems). This makes it possible to avoid rescanning the cur/ directory all the time looking for filenames. It also looks like (in stress testing)

I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many

How can I extract K (number of parameters) from an AIC calculation, both to report K itself and to calculate AICc? I'm aware of the conversion from AIC -> AICc, where AICc = AIC + 2K(K+1)/(n-K-1), but not sure of how K is calculated or how to extract that value from either an AIC or logLik calculation. This is probably more of a basic statistics question than an R question, but I thank

Hi I get the following error when I try and get the AICc for a gls regression using qpcR: > AICc(gls1) Loading required package: nlme Error in n/(n - p - 1) : 'n' is missing My gls is like this: > gls1 Generalized least squares fit by REML Model: thercarnmax ~ therherbmax Data: NULL Log-restricted-likelihood: 2.328125 Coefficients: (Intercept) therherbmax 1.6441405