Combine the 'model_set()' results of two or more hypothesis models.
Arguments
- model_set_outputs
This must be a named list of the outputs of
model_set(). The names will be used as prefixes to name the models.- ...
Additional arguments to be passed to
model_set().
Value
A model_set-class object, which is
simply an output of model_set().
All methods and functions for the
output of model_set() will also
work on this object.
Details
There are cases in which users have more than one hypothesized model, each with its own set of neighboring models.
The function model_set_combined()
let users combine the model_set()
results two or more hypothesized model.
Users can then compare the BPPs
of these hypothesized models, as well
as their neighboring models. Equivalent
models will be removed in the process.
Examples
library(lavaan)
mod1 <-
"
x4 ~ x1
x7 ~ x4
"
mod2 <-
"
x1 ~ x4
x7 ~ x4
"
fit1 <- sem(mod1,
HolzingerSwineford1939,
fixed.x = FALSE)
fit2 <- sem(mod2,
HolzingerSwineford1939,
fixed.x = FALSE)
out1 <- model_set(fit1)
#>
#> Generate 1 less restrictive model(s):
#>
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
#>
#> Generate 2 more restrictive model(s):
#>
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
#>
#> Check for duplicated models (4 model[s] to check):
#>
|
| | 0%
|
|++++++++ | 17%
|
|+++++++++++++++++ | 33%
|
|+++++++++++++++++++++++++ | 50%
|
|+++++++++++++++++++++++++++++++++ | 67%
|
|++++++++++++++++++++++++++++++++++++++++++ | 83%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
#>
#> Fit the 4 model(s) (duplicated models removed):
#>
#> Check for nested models (6 pair[s] of models to check):
#>
|
| | 0%
|
|++++++++ | 17%
|
|+++++++++++++++++ | 33%
|
|+++++++++++++++++++++++++ | 50%
|
|+++++++++++++++++++++++++++++++++ | 67%
|
|++++++++++++++++++++++++++++++++++++++++++ | 83%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
out2 <- model_set(fit2)
#>
#> Generate 3 more restrictive model(s):
#>
| | 0 % ~calculating
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
#>
#> Check for duplicated models (4 model[s] to check):
#>
|
| | 0%
|
|++++++++ | 17%
|
|+++++++++++++++++ | 33%
|
|+++++++++++++++++++++++++ | 50%
|
|+++++++++++++++++++++++++++++++++ | 67%
|
|++++++++++++++++++++++++++++++++++++++++++ | 83%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
#>
#> Fit the 4 model(s) (duplicated models removed):
#>
#> Check for nested models (6 pair[s] of models to check):
#>
|
| | 0%
|
|++++++++ | 17%
|
|+++++++++++++++++ | 33%
|
|+++++++++++++++++++++++++ | 50%
|
|+++++++++++++++++++++++++++++++++ | 67%
|
|++++++++++++++++++++++++++++++++++++++++++ | 83%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
out1
#>
#> Call:
#> model_set(sem_out = fit1)
#>
#> Number of model(s) fitted : 4
#> Number of model(s) converged : 4
#> Number of model(s) passed post.check: 4
#>
#> The models (sorted by BPP):
#> model_df df_diff Prior BIC BPP cfi rmsea srmr
#> original 1 0 0.250 2770.178 0.814 1.000 0.000 0.001
#> drop: x7~x4 2 -1 0.250 2773.706 0.139 0.859 0.110 0.076
#> add: x7~x1 0 1 0.250 2775.883 0.047 1.000 0.000 0.000
#> drop: x4~x1 2 -1 0.250 2809.487 0.000 0.161 0.267 0.155
#>
#> Note:
#> - BIC: Bayesian Information Criterion.
#> - BPP: BIC posterior probability.
#> - model_df: Model degrees of freedom.
#> - df_diff: Difference in df compared to the original/target model.
#> - To show cumulative BPPs, call print() with 'cumulative_bpp = TRUE'.
#> - Since Version 0.1.3.5, the default values of exclude_feedback and
#> exclude_xy_cov changed to TRUE. Set them to FALSE to reproduce
#> results from previous versions.
out2
#>
#> Call:
#> model_set(sem_out = fit2)
#>
#> Number of model(s) fitted : 4
#> Number of model(s) converged : 4
#> Number of model(s) passed post.check: 4
#>
#> The models (sorted by BPP):
#> model_df df_diff Prior BIC BPP cfi rmsea srmr
#> drop: x1~~x7 1 -1 0.250 2770.178 0.937 1.000 0.000 0.001
#> original 0 0 0.250 2775.883 0.054 1.000 0.000 0.000
#> drop: x7~x4 1 -1 0.250 2779.412 0.009 0.839 0.165 0.076
#> drop: x1~x4 1 -1 0.250 2815.193 0.000 0.141 0.382 0.154
#>
#> Note:
#> - BIC: Bayesian Information Criterion.
#> - BPP: BIC posterior probability.
#> - model_df: Model degrees of freedom.
#> - df_diff: Difference in df compared to the original/target model.
#> - To show cumulative BPPs, call print() with 'cumulative_bpp = TRUE'.
#> - Since Version 0.1.3.5, the default values of exclude_feedback and
#> exclude_xy_cov changed to TRUE. Set them to FALSE to reproduce
#> results from previous versions.
outb <- model_set_combined(
list(fit1 = out1,
fit2 = out2))
#>
#> Check for nested models (28 pair[s] of models to check):
#>
|
| | 0%
|
|++ | 4%
|
|++++ | 7%
|
|+++++ | 11%
|
|+++++++ | 14%
|
|+++++++++ | 18%
|
|+++++++++++ | 21%
|
|++++++++++++ | 25%
|
|++++++++++++++ | 29%
|
|++++++++++++++++ | 32%
|
|++++++++++++++++++ | 36%
|
|++++++++++++++++++++ | 39%
|
|+++++++++++++++++++++ | 43%
|
|+++++++++++++++++++++++ | 46%
|
|+++++++++++++++++++++++++ | 50%
|
|+++++++++++++++++++++++++++ | 54%
|
|+++++++++++++++++++++++++++++ | 57%
|
|++++++++++++++++++++++++++++++ | 61%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|++++++++++++++++++++++++++++++++++++ | 71%
|
|++++++++++++++++++++++++++++++++++++++ | 75%
|
|+++++++++++++++++++++++++++++++++++++++ | 79%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 89%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 93%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
outb
#>
#> Call:
#> model_set(sem_out = all_fits)
#>
#> Number of model(s) fitted : 6
#> Number of model(s) converged : 6
#> Number of model(s) passed post.check: 6
#>
#> The models (sorted by BPP):
#> model_df df_diff Prior BIC BPP cfi rmsea srmr
#> fit1_original 1 NA 0.167 2770.178 0.807 1.000 0.000 0.001
#> fit1_drop: x7~x4 2 NA 0.167 2773.706 0.138 0.859 0.110 0.076
#> fit2_original 0 NA 0.167 2775.883 0.047 1.000 0.000 0.000
#> fit2_drop: x7~x4 1 NA 0.167 2779.412 0.008 0.839 0.165 0.076
#> fit1_drop: x4~x1 2 NA 0.167 2809.487 0.000 0.161 0.267 0.155
#> fit2_drop: x1~x4 1 NA 0.167 2815.193 0.000 0.141 0.382 0.154
#>
#> Models that are equivalent:
#> Cluster
#> 1 fit2_original, fit1_add: x7~x1
#> 2 fit1_original, fit2_drop: x1~~x7
#>
#> Equivalent model(s) excluded from the analysis:
#> fit1_add: x7~x1, fit2_drop: x1~~x7
#>
#> Note:
#> - BIC: Bayesian Information Criterion.
#> - BPP: BIC posterior probability.
#> - model_df: Model degrees of freedom.
#> - df_diff: Difference in df compared to the original/target model.
#> - To show cumulative BPPs, call print() with 'cumulative_bpp = TRUE'.
#> - Since Version 0.1.3.5, the default values of exclude_feedback and
#> exclude_xy_cov changed to TRUE. Set them to FALSE to reproduce
#> results from previous versions.