
Extract Model Statistics for fits_ids_dm Object
Source:R/extended_s3_methods.R
logLik.fits_ids_dm.RdThese methods are wrappers to extract specific model fit statistics
(log-likelihood, AIC, BIC) for each model in a fits_ids_dm object.
Arguments
- object
a
fits_ids_dmobject (see estimate_model_ids)- ...
additional arguments (currently not used)
- k
numeric; penalty parameter for the AIC calculation. Defaults to 2 (standard AIC).
Value
An object of type fit_stats containing the respective statistic in
one column (named Log_Like, AIC, or BIC) and a corresponding ID
column. If any of the statistics can't be calculated, the function returns
NULL.
Details
Each function retrieves the relevant statistics by calling
calc_stats with type = "fit_stats" and selects the columns
for ID and the required statistic.
Examples
# get an auxiliary fits_ids object for demonstration purpose;
# such an object results from calling load_fits_ids
all_fits <- get_example_fits("fits_ids_dm")
# AICs
AIC(all_fits)
#> Type of Statistic: fit_stats
#>
#> ID AIC
#> 1 1 -784.163
#> 2 2 -737.576
#> 3 3 -931.294
#>
#> (access the data.frame's columns/rows as usual)
# BICs
BIC(all_fits)
#> Type of Statistic: fit_stats
#>
#> ID BIC
#> 1 1 -757.443
#> 2 2 -710.898
#> 3 3 -904.574
#>
#> (access the data.frame's columns/rows as usual)
# Log-Likelihoods
logLik(all_fits)
#> Type of Statistic: fit_stats
#>
#> ID Log_Like
#> 1 1 399.081
#> 2 2 375.788
#> 3 3 472.647
#>
#> (access the data.frame's columns/rows as usual)
# All unique and free parameters
coef(all_fits)
#> Object Type: coefs_dm
#>
#> ID muc b non_dec sd_non_dec tau A alpha
#> 1 1 4.551 0.446 0.341 0.032 0.035 0.103 7.386
#> 2 2 4.174 0.387 0.292 0.040 0.067 0.079 7.736
#> 3 3 5.652 0.585 0.319 0.014 0.101 0.180 3.840
#>
#> (access the data.frame's columns/rows as usual)
# Or all parameters across all conditions
coef(all_fits, select_unique = FALSE)
#> Object Type: coefs_dm
#>
#> ID Cond muc b non_dec sd_non_dec tau a A alpha peak_l
#> 1 1 comp 4.551 0.446 0.341 0.032 0.035 2 0.103 7.386 0.035
#> 2 1 incomp 4.551 0.446 0.341 0.032 0.035 2 -0.103 7.386 0.035
#> 3 2 comp 4.174 0.387 0.292 0.040 0.067 2 0.079 7.736 0.067
#> 4 2 incomp 4.174 0.387 0.292 0.040 0.067 2 -0.079 7.736 0.067
#> 5 3 comp 5.652 0.585 0.319 0.014 0.101 2 0.180 3.840 0.101
#> 6 3 incomp 5.652 0.585 0.319 0.014 0.101 2 -0.180 3.840 0.101
#>
#> (access the data.frame's columns/rows as usual)