search for: phi2

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 == "drift") {...

...s.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 signif...

...teststat <- as.matrix(t(c(tau, phi1))) colnames(teststat) <- c('tau2', 'phi1') } if (type == "trend") { result <- lm(z.diff ~ z.lag.1 + 1 + tt + z.diff.lag) tau <- coef(summary(result))[2, 3] phi2.reg <- lm(z.diff ~ -1 + z.diff.lag) phi3.reg <- lm(z.diff ~ z.diff.lag) phi2 <- anova(phi2.reg, result)$F[2] phi3 <- anova(phi3.reg, result)$F[2] teststat <- as.matrix(t(c(tau, phi2, phi3))) colnames(teststat) <- c('t...

...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,phi3~TRA...

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 Model: y ~...

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 datasets...

...ep(data,t1): invalid number of copies in "rep". This happens no matter what values that I give the parameters when I call the function. Any suggestions on clearing this up would be appreciated! The function is printed below. Many Thanks, Monnie McGee mcmcmd <- function(d, w, mean, phi2, varwt, ntimes) { # A function to run a multi dimensional MCMC for a bivariate # normal distribution # d = dimension of problem # w = weight assigned to larger of two modes in target distribution # mu = mean of second mode # phi2 = multiplier of variance factor 2.38 # varwt = weight (0 < varw...

...NGLISH :ekiga cannot subscribe I don't know where is the solution. Can anybody help me ? thanks. ========== Here are the significant lines of my sip.conf ,---- | [general] | context=default | bindport=5060 | bindaddr=0.0.0.0 | srvlookup=yes | disallow=all | | register => phi2:6641ekiganet@ekiga.net/1234 | | [authentication] | | [ekiganet-out] | type=peer | secret=6641ekiganet | username=phi2 | host=ekiga.net | call-limit=5 | context=from-ekiganet | | [ekiga1] | type=friend | host=dynamic | username=ekiga1 | secret=6641ekiga1 | disallow=all...

...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 / (1 +...

...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]]

Dear all, I need to extract the covariance matrix of my quantile regression estimation to use in a test. My regression is: qf2_1 <- summary(rq(wb2 ~ apv2 + vol2, tau = phi2[1]), cov = TRUE) I can extract the covaraince matrix by using: qf2_1 [3]. However, if I try to use it in the test, it does not work. I only need to transform qf2_1[3] in a matrix 3x3. I have already tried: matrix(qf2_1[3], nrow=3, ncol=3) but it also does not work. By using the latter, I find:...

...roduced 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" ##################################...

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

...alization of an AR(1) process. xi<-matrix(0,nrow=d,ncol=n) for (i in 1:d){ theta<-(-1)^i * (.9 - .5*i/d) xi[i,]<-arima.sim(model=list(ar=theta), n, rand.gen = rnorm) } rm(i, theta) #Defining the deterministic base functions 'phi' phi<-function(u,i){ sqrt(2)*cos(pi*i*u) } phi2<-function(i){force(i); function(u){ sqrt(2)*cos(pi*outer(i,u)) } } phi3<-function(u,i){ sqrt(2)*cos(pi*outer(i,u)) } # Building the random functional process 'X' X<-lapply(1:n, function(t)local({force(t); function(u){ X<-0 for (i in 1:d){ X<-X+xi[i,...

Finally I kind of modified llvm to generate assembly that can run on AMDGPU pro drivers. One problem is the performance of the code generated by llvm is about 10% slower than amdgpu's online compiler. Anything I can tune the performance up the performance of llvm?\ Thanks! On Tue, Sep 4, 2018 at 9:23 AM 董昌道 <dongchangdao at gmail.com> wrote: > I am writing a miner of crypto

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

...1), 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(..., form...

...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(..., form...

...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(..., form...

...2712 422 7357 +2782 456 4669 Generalized least squares fit by maximum likelihood Model: log(y) ~ log(x) + d1 + d3 Data: NULL AIC BIC logLik -166.2344 -147.8469 92.1172 Correlation Structure: ARMA(2,2) Formula: ~1 Parameter estimate(s): Phi1 Phi2 Theta1 Theta2 0.65960193 0.26221313 -0.02561896 -0.37995110 Coefficients: Value Std.Error t-value p-value (Intercept) -2.4304969 0.8078837 -3.008474 0.004 log(x) 1.0558775 0.0684389 15.428027 0.000 d1 0.2105713 0.0110250...