Displaying 4 results from an estimated 4 matches for "gamma0".
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2008 Jan 04
2
R2WinBUGS sending variables as factors
...82
model
{
#Centre variables
mSeaW <- mean(SeaW[])
s_dSeaW <- sd(SeaW[])
#normalise Variables
nSeaWiFS <- mSeaW/s_dSeaW
for(i in 1:N) {
log(lambda[i]) <- delta0 + alpha1 * Month[i] + alpha2 * Lat[i] +
beta1 * (SeaW[i] - nSeaW)
logit(p[i]) <- gamma0
mu[i, 1] <- 0
mu[i, 2] <- lambda[i]
mu.i[i] <- mu[i, index[i]]
index[i] ~ dcat(theta[i, 1:2])
theta[i, 1] <- p[i]
theta[i, 2] <- 1 - p[i]
# mixture
YFTCPUE[i] ~ dpois(mu.i[i])
}
# recalculate the original intercept term
Intercept <- delta0 - bet...
2009 Nov 05
1
Simulate data for spline/piecewise regression model
...PLINE MODEL")
#Data Simulation Routine#
subjects = 30 #Sets the sample of subjects for whom the data is to be generated#
knot = 8 #Specifies the value of knot/change point#
beta0 = 3 #Specifies the value of intercept for x <= knot#
beta1 = 8 #Specifies the value of slope for x <= knot#
gamma0 = 6 #Specifies the value of intercept for x > knot#
gamma1 = 5 #Specifies the value of slope for x > knot#
#The following intializes empty (NA) vectors#
X = rep(NA,subjects)
Y = rep(NA, subjects)
DATA = rep(NA, subjects)
#The following sample x.values from uniform distribution#
X = runif(...
2011 Dec 01
1
Estimation of AR(1) Model with Markov Switching
...i-1] + rnorm(1)
}
}
# convert into time series object
y <- ts(y, start = 1, freq = 1)
# construct negative conditional likelihood function
neg.logl <- function(theta, data) {
# construct parameters
beta_s0 <- theta[1:2]
beta_s1 <- theta[3:4]
sigma2 <- exp(theta[5])
gamma0 <- theta[6]
gamma1 <- theta[7]
# construct probabilities
#probit specification
p_s0_s0 <- pnorm(gamma_s0)
p_s0_s1 <- pnorm(gamma_s1)
p_s1_s0 <- 1-pnorm(gamma_s0)
p_s1_s1 <- 1-pnorm(gamma_s1)
# create data matrix
X <- cbind(1,y)
# assume erogodicity of the markov chain...
2006 May 19
0
how to estimate adding-regression GARCH Model
...I have a question in using fSeries package--the funciton garchFit and
garchOxFit
if adding a regression to the mean formula, how to estimate the model in
R? using garchFit or garchOxFit?
For example, Observations is {x,y}_t,there may be some relation between x
and y.
the model is
y_t=gamma0 + *gamma1*x_t*+psi*e_{t-1}+e_t the gamma1*x_t is
regression.
e_t=sqrt(h_t)*N(0,1)
h_t=alpha0+alpha1*e_t^2+beta*h_{t_1}~~~~~~~GARCH(1,1).
I didn't know how to estimate the model using function garchFit or
garchOxFit or other functions? because the argument in
garchFit/garch...