NaNs produced during model fitting in dynr leading to errors in dynr.taste2.

Good day. I am working on a model using the dynr package and encountering several issues related to NaNs being produced during the fitting process. Below is the code I am using, along with the warnings and errors I keep encountering.

// Load necessary libraries
library(dynr)
dat <- temp

mean_x <- mean(dat)
sd_x <- sd(dat)
var_x <- var(dat)
cov_x <- cov(dat,dat)

// Prepare the data for dynr
data_shk <- dynr.data(dat)

// Measurement preparation
meas_shk <- prep.measurement(
  values.load = matrix(c(1), ncol = 1, byrow = TRUE),
  params.load = matrix(c('l_11'), ncol = 1, byrow = TRUE),
  state.names = c('eta_1'),
  obs.names = c('x')
)

// Noise preparation using the variance of the observed variable
nois_shk <- prep.noise(
  values.latent = matrix(c(var_x), ncol = 1, byrow = TRUE), # Variance of latent variable
  params.latent = matrix(c('psi_11'), ncol = 1, byrow = TRUE),
  values.observed = diag(var_x*0.5, ncol = 1, nrow = 1),        # Variance of observed variable
  params.observed = diag(paste0('e_', 1:1), 1)
)

// Initial conditions for state and latent using the mean and variance of 'x'
init_shk <- prep.initial(
  values.inistate = c(mean_x),  # Mean as starting value
  params.inistate = c('mu_1'),
  values.inicov = matrix(c(var_x*), ncol = 1, byrow = TRUE),  # Variance as initial covariance
  params.inicov = matrix(c('c_11'), ncol = 1, byrow = TRUE)
)

// Dynamics preparation 
dynm_shk <- prep.matrixDynamics(
  values.dyn = matrix(c(1), ncol = 1, byrow = TRUE),  # Transition matrix
  params.dyn = matrix(c('b_11'), ncol = 1, byrow = TRUE),
  isContinuousTime = FALSE
)

// Prepare the model with the updated initial conditions and noise parameters
model_shk <- dynr.model(
  dynamics = dynm_shk,
  measurement = meas_shk,
  noise = nois_shk,
  initial = init_shk,
  data = data_shk,
  outfile = paste0("tempk.c")
)

// Update options to increase the number of evaluations
opt <- model_shk$options
opt$maxeval <- 10000
model_shk@options <- opt

// Fit the model (run KL and FIS)
cook_shk <- dynr.cook(model_shk, debug_flag = TRUE)

//Print the model results or debug information
print(cook_shk)

When running the above code, I consistently receive the following warning messages:

Warning message in sqrt(diag(iHess)):
"NaNs produced"
Warning message in sqrt(diag(x$inv.hessian)):
"NaNs produced"
Warning message:
"These parameters may have untrustworthy standard errors: e_1, c_11."

I proceed with the second part of the analysis, which involves computing shocks and chi-square using dynr.taste, followed by re-fitting the state-space model using dynr.taste2.

taste_shk <- dynr.taste(model_shk, cook_shk, conf.level = .99) #compute shocks and chi-square

taste_plot <- autoplot(taste_shk)

taste2_shk <- dynr.taste2(#Re-fit state-space model using the estimated outliers.
  model_shk, cook_shk, taste_shk,
  newOutfile = "taste2_shk.c"
)

When I run this second part, I get the following warning and error messages:

Warning message in qt(conf_level, nrow(t_df_inn_plot_i) - obs_n):
"NaNs produced"
Warning message in qt(conf_level, t_add_df_i - obs_n):
"NaNs produced"
Error in deltaLat[, begT:endT] <- cbind(rep(0, lat_n), deltaLat[, begT:(endT - : number of items to replace is not a multiple of replacement length
Traceback:
1. dynr.taste2(model_shk, cook_shk, taste_shk, newOutfile = "taste2_shk.c")

What I Have Tried:

Adjusting starting values: I've changed the starting values several times, especially for e_1 and c_11, but the warnings and errors persist.
Testing with simple data: I even tried using a simple dataset generated from a normal distribution to check if it was an issue with the data, but I still encountered the same warnings and errors.
Modifying noise and initial condition values: I've adjusted the variance scaling, but no success so far.

Has anyone experienced similar NaN issues with dynr?
Any advice on what could be causing the errors in dynr.taste2?
Suggestions on how to resolve the NaNs in the Hessian matrix? Thank you for your time, and I appreciate any guidance you can provide.

The following could be issues with your model.

// Initial conditions for state and latent using the mean and variance of 'x'
init_shk <- prep.initial(
  values.inistate = c(mean_x),  # Mean as starting value
  params.inistate = c('mu_1'),
  values.inicov = matrix(c(var_x*), ncol = 1, byrow = TRUE),  # Variance as initial covariance
  params.inicov = matrix(c('c_11'), ncol = 1, byrow = TRUE)
)

The above code freely estimates the initial mean and variance. For single-subject data, these parameters are not identified. You cannot estimate them for single-subject data. For multi-subject data you can estimate them. A typical solution for single-subject data is to have a fixed initial mean of zero and a fixed initial variance of some reasonably large value (i.e., diffuse initial conditions).

// Dynamics preparation 
dynm_shk <- prep.matrixDynamics(
  values.dyn = matrix(c(1), ncol = 1, byrow = TRUE),  # Transition matrix
  params.dyn = matrix(c('b_11'), ncol = 1, byrow = TRUE),
  isContinuousTime = FALSE
)

The above code starts the autoregressive dynamics at the boundary of stability. The AR coefficient is 1.0 (i.e. a random walk). This is usually a bad spot to start your parameter optimization. I suggest an AR starting value in the range of -.3 to +.3.

I'd also recommend setting lower and upper bounds on most of your free parameters.

Finally for dynr.taste, if the initial model does not yield reasonable parameter estimates and standard errors, then I would not proceed with dynr.taste. I'm not sure what exactly is causing your errors, but the warnings are because the standard errors are all messed up.

mhunter1 / dynr

NaNs produced during model fitting in dynr leading to errors in dynr.taste2. #279