Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## -----------------------------------------------------------------------------
# libraries
library(simts)
library(gmwmx)
## ---- eval=T, echo=T, message=F-----------------------------------------------
phase = 0.45
amplitude = 2.5
sigma2_wn = 15
sigma2_powerlaw = 10
d = 0.4
bias = 0
trend = 5/365.25
cosU = amplitude*cos(phase)
sinU = amplitude*sin(phase)
## ---- eval=T, echo=T, message=F-----------------------------------------------
n = 5*365
## ---- eval=F------------------------------------------------------------------
# install.packages("devtools")
# devtools::install_github("SMAC-Group/simts")
## ---- eval=F, echo=T, message=F-----------------------------------------------
# model_i = WN(sigma2 = sigma2_wn) + PLP(sigma2 = sigma2_powerlaw, d = d)
## ---- eval=T, echo=T, message=F-----------------------------------------------
# define time at which there are jumps
jump_vec = c(600, 1200)
jump_height = c(20, 30)
# define myseed
myseed=123
## ---- eval=F, echo=T, message=F-----------------------------------------------
# # generate residuals
# eps = simts::gen_gts(model = model_i, n= n)
## ---- evaL=T, echo=F----------------------------------------------------------
file_path_eps_simulate_data = system.file("extdata", "eps.rda", package = "gmwmx", mustWork = T)
load(file_path_eps_simulate_data)
## ---- eval=T, echo=T, message=F-----------------------------------------------
# add trend and sin
A = gmwmx::create_A_matrix(t_nogap = 1:length(eps), n_seasonal = 1, jumps = NULL)
# define beta
x_0 = c(bias, trend, cosU, sinU)
# create time series
deterministic_signal = A %*% x_0
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
plot(deterministic_signal, type="l")
plot(eps)
## -----------------------------------------------------------------------------
# add trend, gaps and sin
A = gmwmx::create_A_matrix(t_nogap = 1:length(eps), n_seasonal = 1, jumps = jump_vec)
# define beta
x_0 = c(bias, trend, cosU, sinU, jump_height)
# create time series
deterministic_signal = A %*% x_0
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
plot(deterministic_signal, type="l")
plot(eps)
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
yy = deterministic_signal + eps
plot(yy)
## ---- eval=T------------------------------------------------------------------
# save signal in temp
gnssts_obj = create.gnssts(t = 1:length(yy), y = yy, jumps = jump_vec)
## ---- eval=T------------------------------------------------------------------
class(gnssts_obj)
## ---- eval=F, echo=T----------------------------------------------------------
# write.gnssts(gnssts_obj, filename = "simulated_data.mom")
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
fit_gmwmx = gmwmx::estimate_gmwmx(x = gnssts_obj,
model_string = "wn+powerlaw",
n_seasonal = 1,
theta_0 = c(0.1, 0.1, 0.1),
k_iter = 1)
fit_gmwmx
plot(fit_gmwmx)
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
fit_gmwmx_2 = gmwmx::estimate_gmwmx(x = gnssts_obj,
model_string = "wn+powerlaw",
n_seasonal = 1,
theta_0 = c(0.1, 0.1, 0.1),
k_iter = 2)
fit_gmwmx_2
plot(fit_gmwmx_2)
## ---- eval=F, echo=T----------------------------------------------------------
# fit_mle_hector = gmwmx::estimate_hector(x = gnssts_obj,
# model_string = "wn+powerlaw",
# n_seasonal = 1
# )
#
#
## ---- eval=F, echo=F----------------------------------------------------------
# save(fit_mle_hector,file="fit_mle_hector.rda")
## ---- eval=T, echo=F----------------------------------------------------------
# fit_mle_hector is save in inst/extdata
file_path = system.file("extdata", "fit_mle_hector.rda", package = "gmwmx", mustWork = T)
load(file = file_path)
## ---- fig.height=8, fig.width=6, fig.align='center'---------------------------
fit_mle_hector
plot(fit_mle_hector)
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