search for: phi1

Hello, I am using the ur.df function from the {arca} package to run the augmented Dickey-Fuller unit root test on several time series. However; I do not understand the econometric interpretation of the the "phi1" and "phi2" test-statisitc which are output if you choose a "trend" or "drift" model. I looked at the source code for the function but I do not quite understand the code (which I have included below). Any help would be much appreciate. Thanks, Max if (type == &...

...Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A) > fm2 <- nls(Biomass~SSmicmen(P, phi1, phi2), data=Ca.P.Biomass.A) > fm3 <- lm(Biomass~P, data = Ca.P.Biomass.A) I hope to compare the difference among the three models, and I using anova(). As for the example here, the three models seem not have...

...coef(summary(result))[1, 3] teststat <- as.matrix(tau) colnames(teststat) <- 'tau1' } if (type == "drift") { result <- lm(z.diff ~ z.lag.1 + 1 + z.diff.lag) tau <- coef(summary(result))[2, 3] phi1.reg <- lm(z.diff ~ -1 + z.diff.lag) phi1 <- anova(phi1.reg, result)$F[2] teststat <- as.matrix(t(c(tau, phi1))) colnames(teststat) <- c('tau2', 'phi1') } if (type == "trend") { result <- lm(z....

...es of the parameters those obtained from the nls fit. Is a problem of the initial estimates of the parameters that I get the error or could be something else? The code for the nls fit was: options(contrasts=c("contr.helmert","contr.poly")) VA1.lis<-nlsList(DRAM~SSlogis(MED,phi1,phi2,phi3)|TRAT, data=VA1,na.action=na.omit) The code for the gnls fit was (using a 'difference parameterization' like SAS): options(contrasts=c("contr.SAS","contr.poly")) VA1.gnls<-gnls(DRAM~SSlogis(MED,phi1,phi2,phi3), data=VA1,params=list(phi1~TRAT,phi2~TRAT,phi...

...e error message that is produced by the optim function. can anybody help??? ie: [[1]]$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" can anyone help? ########################################################################### SK.FIT(XDATA=a,XDATAname="a",PHI1=1,v=5,vlo=2,vhi=300,phi2lo=.01) [[1]] [[1]]$par [1] -0.01377906 0.83859445 0.34675230 300.00000000 [[1]]$value [1] 90.59185 [[1]]$counts function gradient 53 53 [[1]]$convergence [1] 0 [[1]]$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" #########...

...er compiler's intermediate code, and I'm working on the PHI instruction. Here's an example phi instruction to help explain what I'm trying to do: %inc25 = phi i32 [ 1, %entry ], [ %inc, %for.body ] What I would want to do here is allocate some memory memory (i'm trying to use %phi1 = alloca i32, allign 4 ). Then, I want to go to the %entry block and say "store i32 1, i32* %phi1", and in the %for.body I want to add "store i32 1, i32* %phi1". So first, I'm iterating through my function and basicblock: int phi = 0; Function::iterator func_bblo...

...ion structure of order two (the data along time are given below). I use 'gls' from package 'nlme': Calib.gls <- gls(Y~X, data=my.data, correlation=corARMA(form=~Time,p=2,q=0), method="ML") For reasons I do not understand, gls results in parameter estimate(s): Phi1 Phi2 1.3066817 -0.3898277 Phi1 is larger than one... but how is this possible? When I run the same regression with those parameters Calib.gls <- gls(Y~X, data=my.data, correlation=corARMA(value=c(1.3066817,-0.3898277),form=~Time,p=2,q=0,fixed=T), method="ML") I get the fol...

Hi: The gls function I used in my code is the following fm<-gls(y~x,correlation=corARMA(p=2) ) My question is how to extact the AR(2) parameters from "fm". The object "fm" is the following. How can I extract the correlation parameters Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm". Thanks a lot! liu -------------------------------------------------------------------------------------------------------------- > fm Generalized least squares fit by REML Mo...

...er compiler's intermediate code, and I'm working on the PHI instruction. Here's an example phi instruction to help explain what I'm trying to do: %inc25 = phi i32 [ 1, %entry ], [ %inc, %for.body ] What I would want to do here is allocate some memory memory (i'm trying to use %phi1 = alloca i32, allign 4 ). Then, I want to go to the %entry block and say "store i32 1, i32* %phi1", and in the %for.body I want to add "store i32 1, i32* %phi1". So first, I'm iterating through my function and basicblock: int phi = 0; Function::iterator func_bblo...

...tions concerning nlme library 3.1-65 (running on R 2.2.0 / Win XP Pro). The first one is related to augPred function. Ive been working with a nonlinear mixed model with no problems so far. However, when the parameters of the model are specified in terms of some other covariates, say treatment (i.e. phi1~trt1+trt2, etc) the augPred function give me the following error: "Error in predict.nlme(object, value[1:(nrow(value)/nL),,drop=FALSE], : Levels 0,1 not allowed for trt1, trt2". The same model specification as well as the augPred function under SPlus 2000 run without problems. The second...

R2.3, WinXP Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different dat...

Dear all, 1. How can I compute the intersecting area between 2 polygons ? 2. I have polygons with coordinates in decimal degrees (i.e. 13 deg 30 min = 13.5 decimal degrees). I want to compute their area and get the results in square meters or square kiometers. Can anyone give me a conversion coefficient or a pointer where I can find this information (sorry for this off topic question) ? Thanks

...even a nasty hack. Okay, let descend from 5k' down to the ground:-) ================================================ Let us consider this snippet ---------------------- E1 = ... E2 = expression of E1 // sext/zext/trunc or other simple expression of E1. loop1(...) { r1 = phi1(..., E2) = r1 // use 1 } loop2(...) { r2 = phi2(... r1) = r2 // use 2; --------------------- o. At beginning, both SCEVs of use1 and use-2 are in the form of SCEV(use1) = ... E2 ... SCEV(use2) = ... E2 ... SE dose not dig into E2 b...

...Residual standard error: 0.9526 on 109 degrees of freedom Multiple R-squared: 0.2514, Adjusted R-squared: 0.2171 F-statistic: 7.321 on 5 and 109 DF, p-value: 5.989e-06 Value of test-statistic is: -2.4746 3.0877 Critical values for test statistics: 1pct 5pct 10pct tau2 -3.46 -2.88 -2.57 phi1 6.52 4.63 3.81 > CPILD4UR<-ur.df(x1$CPILD4[5:nr1], type ="drift", lags=12, selectlags ="BIC") > summary(CPILD4UR) ############################################### # Augmented Dickey-Fuller Test Unit Root Test # ############################################### Test r...

...returns incorrect covariance matrices in certain cases. Indeed, below is an example of a SARIMA(1,0,1)x(1,0,0)_12 where getQ0 gives a covariance matrix which possess negative eigenvalues ! Below, I obtain getQ0 results through makeARIMA(). Example: > s <- 12 > phis <- 0.95 > phi1 <- 0.0001 > phi <- c(phi1,rep(0,s-2),phis,-phi1*phis) > theta <- 0.7 > out <- makeARIMA(phi,theta,NULL) > min(eigen(out$Pn)$value) [1] -19.15890 There are consequences of this "bug" in the functions KalmanLike() and arima(). Indeed, arima() in its default behavi...

...tion) wheat = germination[germination$species == "wheat",] ### subset by wheat scatterplot(Prop ~ end|temp,data=wheat,box=FALSE,reg=FALSE) ### view the data wheat$temp = as.factor(wheat$temp) ### convert to factor ### First, try to use nlsList wheat.list <- nlsList(Prop ~ SSlogis(end,phi1,phi2,phi3)| temp,pool=FALSE,data=wheat) ### ### next, try to use lm to estimate starting parameters. wheat.list = list() for (i in 1:length(levels(wheat$temp))){ tmpDat = wheat[wheat$temp == levels(wheat$temp)[i],] tmp.lm <- lm(Prop ~ end,data = tmpDat) tmp.nls <- nls(Prop ~ theta1 /...

...I am not sure about the codes to combine these two on R. If anybody has any R example (code), please post it. My original model: log(Y(t))~constant+b1*Y(t-1)+b2*Y(t-2)+b3*(variable1)+b4*(variable2)+e I would like to construct a model: log(Y(t))~constant+b1*(variable1)+b2*(variable2)+X(t) X(t)~phi1*Xt-1+phi2*Xt-2+error where X(t) is the autoregressive lag effect of response. A.K. --------------------------------- [[alternative HTML version deleted]]

I have a question regarding the adf.test command in the tseries library. I have a vector of time series observations (2265 daily log prices for the OEX to be exact). I also have this same data in first-differenced form. I want to test both vectors individually for staionarity with an Augmented Dickey-Fuller test. I noticed when I use the adf.test command from the tseries library, the general

...I(age - 11), Orthodont, + correlation = corARMA(value = c(0,-.3), form = ~ 1 | Subject, p = 2, q = 0), + weights = varIdent(form = ~ 1 | age)) #-- Selected output follows----- Correlation Structure: ARMA(2,0) Formula: ~1 | Subject Parameter estimate(s): Phi1 Phi2 0.3269544 0.4897645 --------------------- End R-code & output -------------------------------- I cannot figure out how to restrict RHO1 = 0, while allowing estimation of RHO2. Maybe an answer lies in specifying a different ``position vector'' other than the default: corARMA...

...* I(age - 11), Orthodont, + correlation = corARMA(value = c(0,-.3), form = ~ 1 | Subject, p = 2, q = 0), + weights = varIdent(form = ~ 1 | age)) #-- Selected output follows----- Correlation Structure: ARMA(2,0) Formula: ~1 | Subject Parameter estimate(s): Phi1 Phi2 0.3269544 0.4897645 --------------------- End R-code & output -------------------------------- I cannot figure out how to restrict RHO1 = 0, while allowing estimation of RHO2. Maybe an answer lies in specifying a different ``position vector'' other than the default: corARMA...