similar to: calculation problem

Hi, I am doing a project on the simulation of glucose metabolism based on a pharmacokinetic modeling in which we have 4 differential equations. I did this in R by using the odesolve package. It works very well, but I have two questions: Here is the odemodel function _________________________________________________ Ogtt.Odemodel <- function(t, y, p) { absx <- c(-60, -45, -30,

Dear R Users, Suppose we want to creat a new vector ( x ) from a current vector (y) of length 1000. The current vector y includes negative, zero and positive values. We want our new vector x includes the negative values in y, otherwise NA with the same length as y. For this, we have x=y[y<0] . Now x includes a subset of y with shorter length than y. With x=match(y,x) we would

Hi list, I am brand new to this list and have come across an issue with one of my servers that I can't seem to figure out: First the specifics of the clients and server: Clients: OS - Windows XP PRO SP2 10/100 MB Ethernet adapters (different types in all units) --> Not sure what else you need to know here Server: Mac OS X Server (v10.4.3) running Samba version 3.0.10 User Home

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

Dear R-users, I am trying to get a simple plot for a linear model. The following function does the job of getting all the plots of the lm function but the "main" plot. multivarplot <- function(...){ x1 <- rnorm(10); x2 <- rnorm(10); x3 <- rnorm(10) y <- rnorm(10) multivarfit <- lm(y ~ x1 + x2 + x3) par(mfrow = c(3,2)) plot.lm(multivarfit) }

Hi, i have a weekly data for servers for 62 weeks. want to predict the cpu% for next 5 weeks.I am trying to forecast for many servers at once but with the code i am getting only one week of future forecast for all the servers. Also the week date for the predicted week is showing as the last week of the original data . Need help in two things How can i change the date for the predicted week, and

Hi I am trying to forecast for multiple server for CPU Utilization , But currently i am getting it for for one week , when ever i am trying to get more than one week it gives me error. Secondly in the one week forecast values i am getting the weekend date as the last week end in the original data set , where as it should be one week after that . Below is my code Let me know if anything else is

Dear R-experts, Doing cross-validation for 2 robust regressions (HBR and fast Tau). I can't get the 2 errors rates (RMSE and MAPE). The problem is to predict the response on the testing data. I get 2 error messages. Here below the reproducible (fictional example) R code. #install.packages("MLmetrics") # install.packages( "robustbase" ) # install.packages(

Hi List, I am running R 2.8.0 on a Windows XP machine, running ggplot2 version 0.8.1 I want to label the bars in a faceted grid barplot. Reproducible R code is given below: #### reproducible facet barplot ##### library(ggplot2) # Dataset from which to create the barplot ml <- rep(1:10,2) vals <- rnorm(20,mean = 10, sd=1) type <- c(rep("MAPE",10),rep("AIC",10))

I would like the text plotted with 'mtext' to be alighned like it is for printing on the console. Here is what I have: > print(emt) ME RMSE MAE MPE MAPE MASE original -1.034568e+07 1.097695e+08 2.433160e+07 -31.30554 37.47713 1.5100050 xreg 1.561235e+01 2.008599e+03 9.089473e+02 267.05490 280.66734

I installed CentOS 7 which supported by default a newer version of qemu-kvm (qemu-kvm-1.5.3-60.el7.x86_64.rpm) but this time I got new error: << error: internal error: unable to execute QEMU command 'migrate': this feature or command is not currently supported >> but it's kind of wired because qemu-kvm-1.0 supported already live migration, so a newer version should also

Hi again With arima0 the problem was solved but what are the diferences between arima and arima0? I have another question. I fit the model to the data and I make some predictions. But I also want to calculate MAPE based in the last 3 observations available. Is it possible? Can I obtain the fitted values from the model? thanks~ luis -- SAPO ADSL.PT, apanhe j? o comboio da Banda Larga. Kit

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hey, I have a data frame x which consists of say 10 vectors. I essentially want to find out the best fit exponential smoothing for each of the vectors. The problem while I'm getting results when i say > lapply(x,ets) I am getting an error when I say >> myprint function(x) { for(i in 1:length(x)) { ets(x[i],model="AZZ",opt.crit=c("amse")) } } The error message is

Hi, I would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model =

I tired to install newer version of qemu-kvm (2.1.0-2.9-x86_64) through rpms but the following rpms are not all available - qemu-2.1.0-2.9-x86_64.rpm - qemu-kvm-2.1.0-2.9-x86_64.rpm - qemu-common-2.1.0-2.9-x86_64.rpm - qemu-kvm-tools-2.1.0-2.9-x86_64.rpm - qemu-debuginfo-2.1.0-2.9-x86_64.rpm - qemu-system-x86-2.1.0-2.9-x86_64.rpm - qemu-guest-agent-2.1.0-2.9-x86_64.rpm -

Hello, I have the following data1 (index are chars): ?? ?index 1 ?008823 2 ?012689 3 ?004503 4 ?002991 5 ?012689 6 ?002845 7 ?012689 8 ?012395 9 ?012689 10 009302 11 002845 12 006669 13 008823 14 009302 15 025340 16 012689 and data2 in this format (index2 are chars): ?? ? ? index2 ? ? ?tic 1 ? ? 001003 ? ? ANTQ 2 ? ? 001004 ? ? AIR 3 ? ? 001009 ? ? ABSI 4 ? ? 001011 ? ? ACSE etc I am

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >

Dear Reseacher, i find this modified version of SMAPE at pag 13 formula "msSMAPE" to to avoid the possibility of an inflation caused by zero values in the series using a Si component in the denominator of the symmetric MAPE http://www.stat.iastate.edu/preprint/articles/2004-10.pdf I wrote a function but I wish eventually correct error, change or improve with your suggestions. Please