Basic print of objects ctsmTMB fit objects
Usage
# S3 method for class 'ctsmTMB.fit'
print(x, ...)Examples
library(ctsmTMB)
model <- ctsmTMB$new()
# create model
model$addSystem(dx ~ theta * (mu+u-x) * dt + sigma_x*dw)
model$addObs(y ~ x)
model$setVariance(y ~ sigma_y^2)
model$addInput(u)
model$setParameter(
theta = c(initial = 1, lower=1e-5, upper=50),
mu = c(initial=1.5, lower=0, upper=5),
sigma_x = c(initial=1, lower=1e-10, upper=30),
sigma_y = 1e-2
)
model$setInitialState(list(1,1e-1))
# fit model to data
fit <- model$estimate(Ornstein)
#> Checking model components...
#> Checking and setting data...
#> Constructing objective function and derivative tables...
#> Minimizing the negative log-likelihood...
#> 0: 160.35328: 1.00000 1.50000 1.00000
#> 10: 89.603625: 2.58436 2.91384 1.15608
#> Optimization finished!:
#> Elapsed time: 0.013 seconds.
#> The objective value is: 7.387879e+01
#> The maximum gradient component is: 9.9e-08
#> The convergence message is: relative convergence (4)
#> Iterations: 19
#> Evaluations: Fun: 29 Grad: 20
#> See stats::nlminb for available tolerance/control arguments.
#> Returning results...
#> Finished!
# print fit
print(fit)
#> Coefficent Matrix
#> Estimate Std. Error t value Pr(>|t|)
#> theta 5.140485 0.539245 9.5327 < 2.2e-16 ***
#> mu 3.005177 0.056661 53.0380 < 2.2e-16 ***
#> sigma_x 1.271765 0.071374 17.8183 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1