search for: phi3

...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('tau3', 'phi2', 'phi3') }...

...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~TRAT),...

...t))[2, 3] teststat <- as.matrix(t(c(tau, phi1))) colnames(teststat) <- c("tau2", "phi1") } if (type == "trend") { result <- lm(z.diff ~ z.lag.1 + 1 + tt) phi2.reg <- lm(z.diff ~ -1) phi3.reg <- lm(z.diff ~ 1) phi2 <- anova(phi2.reg, result)$F[2] phi3 <- anova(phi3.reg, result)$F[2] tau <- coef(summary(result))[2, 3] teststat <- as.matrix(t(c(tau, phi2, phi3))) colnames(teststat) <- c("tau3", &qu...

...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 significant...

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 corresponding to t...

...at = 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 + exp(...

...eta<-(-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,t]*phi(u,i) } X<-X } })) X2<-lapply(1:n, function(t)local({force(t)...

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

...ect? 2/ I would like to see critical values also, so I tried with ur.df > summary(ur.df(x, "trend", lag=0)) <snip> Value of test-statistic is: -31.8629 338.4156 507.6231 Critical values for test statistics: 1pct 5pct 10pct tau3 -3.96 -3.41 -3.12 phi2 6.09 4.68 4.03 phi3 8.27 6.25 5.34 Here if I understand correctly, as my first critical value is significantly less than the 1% critical value I reject the null hypothesis that x has a unit root, so x is stationary and then mean reverting. Thanks, -Arnaud

Dear R users, I am applying the augmented-Dickey-Fuller Unit Root Test (ur.df function of the urca package) to a time series of approximately 50 values. To be sure I understood what was going on with the ur.df function, I checked the critical values of the 3 test statistics (tau, phi2 and phi3 if a trend is included) or the 2 test statistics (tau and phi1 if only a drift is included) with the values found in Hamilton (1994) and Dickey and Fuller (1981). The ur.df function critical values correspond to Hamilton's critical values for a sample of size 100. I was surprised that it does...

...l standard error: NaN on 0 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: NaN F-statistic: NaN on 2 and 0 DF, p-value: NA Value of test-statistic is: NaN NaN NaN Critical values for test statistics: 1pct 5pct 10pct tau3 -4.38 -3.60 -3.24 phi2 8.21 5.68 4.67 phi3 10.61 7.24 5.91 " Can anyone tell me what is wrong with the codes???The code used is given below. Thanx t=1:length(x) plot(t,x) trend = lm(x~t) abline(lm(x~t)) summary(trend) library(urca) x = ts(x, start=1, end = length(x), frequency=1) x.ct = ur.df(x,lags=0,type='trend') plot(x....

...at",] ### 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 &lt...