Dear R users,
When running the program below I receive the following error message:
fit <- optim(parm, objective, yt = tyield, hessian = TRUE)
Error in as.vector(data) :
no method for coercing this S4 class to a vector
I can't figure out what the problem is exactly. I imagine that it has
something to do with "tyield" being a matrix. Any help on explaining what's
going on and how to solve this is much appreciated.
Thank you,
Kristian
library(FKF) #loading Fast Kalman Filter package
library(Matrix) # matrix exponential package
K_1 = 0.1156
K_2 = 0.17
sigma_1 = 0.1896
sigma_2 = 0.2156
lambda_1 = 0
lambda_2 = -0.5316
theta_1 = 0.1513
theta_2 = 0.2055
#test data
tyield <- matrix(data = rnorm(200), nrow =2, ncol =100)
# defining dimensions
m <- 2 # m is the number of state variables
n <- 100 # is the length of the observed sample
d <- 2 # is the number of observed variables.
theta <- c(theta_1, theta_2)
h <- t <- 1/52 # time between observations
## creating state space representation of 2-factor CIR model follwing
Driessen and Geyer et al.
CIR2ss <- function(K_1, K_2, sigma_1, sigma_2, lambda_1, lambda_2, theta_1,
theta_2) {
## defining auxilary parameters
phi_11 <- sqrt((K_1+lambda_1)^2+2*sigma_1^2)
phi_21 <- sqrt((K_1+lambda_2)^2+2*sigma_2^2)
phi_12 <- K_1+lambda_1+phi_11
phi_22 <- K_2+lambda_2+phi_12
phi_13 <- -2*K_1*theta_1/sigma_1^2
phi_23 <- -2*K_2*theta_2/sigma_2^2
phi_14 <- 2*phi_11+phi_21*(exp(phi_11*t)-1)
phi_24 <- 2*phi_12+phi_22*(exp(phi_12*t)-1)
phi <- array(c(phi_11, phi_21, phi_12, phi_22, phi_13, phi_23, phi_14,
phi_24), c(4,2))
a <- array(0, c(d,n))
for(t in n:1){
a[,n-(t+1)] <-
-phi_13/(n-(t+1))*log(2*phi_11*exp(phi_12*(n-(t+1))/2)/phi_14)-phi_23/(n-(t+1))*log(2*phi_21*exp(phi_22*(n-(t+1))/2)/phi_24)
}
b <- array(c(1,0,0,1,0), c(d,m,n))
j <- -array(c(K_1, 0, 0, K_2), c(2,2))*h
explh <- expm(j)
Tt <- array(explh, c(m,m,n)) #array giving the factor of the transition
equation
Zt <- b #array giving the factor of the measurement equation
ct <- a #matrix giving the intercept of the measurement equation
dt <- (diag(m)-expm(-array(c(K_1, 0, 0, K_2), c(2,2))*h))*theta #matrix
giving the intercept of the transition equation
GGt <- array(c(1,0,0,1), c(d,d,n)) #array giving the variance of the
disturbances of the measurement equation
HHt <- array(c(1,0,0,1), c(m,m,n)) #array giving the variance of the
innovations of the transition equation
a0 <- c(0, 0) #vector giving the initial value/estimation of the state
variable
P0 <- matrix(1e6, nrow = 2, ncol = 2) # matrix giving the variance of a0
return(list(a0 = a0, P0 = P0, ct = ct, dt = dt, Zt = Zt, Tt = Tt, GGt =
GGt,
HHt = HHt))
}
## Objective function passed to optim
objective <- function(parm, yt) {
sp <- CIR2ss(parm["K_1"], parm["K_2"], parm["sigma_1"], parm["sigma_2"],
parm["lambda_1"], parm["lambda_2"],
parm["theta_1"], parm["theta_2"])
ans <- fkf(a0 = sp$a0, P0 = sp$P0, dt = sp$dt, ct = sp$ct, Tt = sp$Tt,
Zt = sp$Zt, HHt = sp$HHt, GGt = sp$GGt, yt = yt)
return(-ans$loglik)
}
parm <- c(K_1 = 0.1156, K_2 = 0.17, sigma_1 = 0.1896, sigma_2 = 0.2156,
lambda_1 = 0, lambda_2 = -0.5316, theta_1 = 0.1513, theta_2 =
0.2055) # initial parameters
##optimizing objective function
fit <- optim(parm, objective, yt = tyield, hessian = TRUE)
print(fit)
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