similar to: predict and arima

When I do ARMA(2,2) using one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std.

Hi there! I´m a noob when it comes to R and I´m using it to run statisc analysis. With the code for ARIMA below I´m getting this error: Error in solve.default(res$hessian * n.used) :Lapack routine dgesv: system is exactly singular The code is: > s.ts <- ts(x[,7], start = 2004, fre=12) > get.best.arima <- function (x.ts, maxord=c(1,1,1,1,1,1)) + { + best.aic <- 1e8 + n <-

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 <-

Dear R-helpers, I have a time series analysis problem in R: I want to analyse the output of my simulation model which is proportional cover of shrubs in a savanna plot for each of 500 successive years. I have run the model (which includes stochasticity, especially in the initial conditions) 17 times generating 17 time series of shrub cover. I am interested in a possible periodicity of shrub

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

The purpose of this email is to ask for pre-built procedures or techniques for smoothing and interpolating missing time series data. I've made some headway on my problem in my spare time. I started with an irregular time series with lots of missing data. It even had duplicated data. Thanks to zoo, I've cleaned that up -- now I have a regular time series with lots of NA's.

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Thanks for everyone's help with zoo -- I think I've got my data set ready. (The data consists of surface weather temperatures, from 2002 to 2005, one observation per hour. Some values are missing... i.e. NA) I have three goals: GOAL #1:Get the data in proper time series form, preserving frequency information: > w4.ts <- as.ts( w3.zoo, frequency=(1/3600) ) I hope that 1/3600

Hi everyone, I have recently experienced a strange behavior (strange from my knowledge) in rails. In my controllers ''new'' action, I am creating a few instance variables in the following manner : @controllerModel = ControllerModel.new @model1 = Model1.all @model2 = Model2.all in my ''new'' view, I am using the @controllerModel to create the form for new and I

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

Hi R-Experts, Currently I'm using an univariate time series in which I'm going to apply KalmanLike(),KalmanForecast (),KalmanSmooth(), KalmanRun(). For I use it before makeARIMA () but I don't understand and i don't know to include the seasonal coefficients. Can anyone help me citing a suitable example? Thanks in advance. ------------------------------------------

Hello R-Help, ----------------------------------------------------------------------------------------------------------------------------------------- Issues (there are 2): 1) Possible bug when using lmtest::encomptest() with a linear model created using nlme::lmList() 2) Possible modification to lmtest::encomptest() to fix confusing fail when models provided are, in fact, nested. I have

Hi, I'm trying to compare models, one of which has all parameters fixed using offsets. The log-likelihoods seem reasonble in all cases except the model in which there are no free parameters (model3 in the toy example below). Any help would be appreciated. Cheers, Jarrod x<-rnorm(100) y<-rnorm(100, 1+x) model1<-lm(y~x) logLik(model1) sum(dnorm(y, predict(model1),

Full_Name: Menelaos Stavrinides Version: 1.3. 1 OS: Windows 98 Submission from: (NULL) (193.129.76.90) When model simplification is used in glm (binomial errors) and anova is used two compare two competitive models one can use either an "F" or a "Chi" test. R always performs an F test (Although when test="Chi" the test is labeled as Chi, there isn't any

Hi all, I have been trying to calculate Type III SS in R for an unbalanced two-way anova. However, the Type III SS are lower for the first factor compared to type I but higher for the second factor (see below). I have the impression that Type III are always lower than Type I - is that right? And a clarification about how to fit Type III SS. Fitting model<-aov(y~a*b) in the base package and

I am using anova.lm to compare 3 linear models. Model 1 has 1 variable, model 2 has 2 variables and model 3 has 3 variables. All models are fitted to the same data set. anova.lm(model1,model2) gives me: Res.Df RSS Df Sum of Sq F Pr(>F) 1 135 245.38 2 134 184.36 1 61.022 44.354 6.467e-10 *** anova.lm(model1,model2,model3) gives

Hello, I want to understand how to tell if a model is significant. For example I have vectX1 and vectY1. I seek first what model is best suited for my vectors and then I want to know if my result is significant. I'am doing like this: model1 <- lm(vectY1 ~ vectX1, data= d), model2 <- nls(vectY1 ~ a*(1-exp(-vectX1/b)) + c, data= d, start = list(a=1, b=3, c=0)) aic1 <- AIC(model1)

Dear All I am attempting some power analyses, based on simulated data. My experimental set up is thus: Bleach: main effect, three levels (control, med, high), Fixed. Temp: main effect, two levels (cold, hot), Fixed. Main effect interactions, six levels (fixed) For each main-effect combination I have three replicates. Within each replicate I can take varying numbers of measurements (response

Hello, I have a simple theorical question about regresion... Let's suppose I have this: Model 1: Y = B0 + B1*X1 + B2*X2 + B3*X3 and Model 2: Y = B0 + B2*X2 + B3*X3 I.E. Model1 = lm(Y~X1+X2+X3) Model2 = lm(Y~X2+X3) The Ajusted R-Square for Model1 is 0.9 and the Ajusted R-Square for Model2 is 0.99, among many other significant improvements. And I want to do the anova test to choose the best

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]