optim_model_space | R Documentation |
This function calculates model space, values of the maximized likelihood function, BICs, and standard deviations of the parameters that will be used in Bayesian model averaging.
optim_model_space(
df,
timestamp_col,
entity_col,
dep_var_col,
init_value,
exact_value = FALSE,
cl = NULL,
control = list(trace = 0, maxit = 10000, fnscale = -1, REPORT = 100, scale = 0.05)
)
df |
Data frame with data for the analysis. |
timestamp_col |
The name of the column with time stamps |
entity_col |
Column with entities (e.g. countries) |
dep_var_col |
Column with the dependent variable |
init_value |
The value with which the model space will be initialized. This will be the starting point for the numerical optimization. |
exact_value |
Whether the exact value of the likelihood should be
computed ( |
cl |
An optional cluster object. If supplied, the function will use this
cluster for parallel processing. If |
control |
a list of control parameters for the optimization which are
passed to optim. Default is
|
List with two objects:
params - table with parameters of all estimated models
stats - table with the value of maximized likelihood function, BIC, and standard errors for all estimated models
## Not run:
library(magrittr)
data_prepared <- bdsm::economic_growth[, 1:5] %>%
bdsm::feature_standardization(
excluded_cols = c(country, year, gdp)
) %>%
bdsm::feature_standardization(
group_by_col = year,
excluded_cols = country,
scale = FALSE
)
optim_model_space(
df = data_prepared,
dep_var_col = gdp,
timestamp_col = year,
entity_col = country,
init_value = 0.5
)
## End(Not run)
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