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README.md
In semFromKeys: Run 'lavaan' Models from Keys Lists
semFromKeys
The ‘semFromKeys’ package was designed to streamline running ‘lavaan’
models with similar structures using keys lists to generate model code
instead of writing out the code for models manually. For confirmatory
factor analyses (CFAs) and bifactor models, the code creates and runs a
series of models based on keys indicating each of the factors in the
models. For exploratory factor analyses (EFAs) keys list are used to
create a target rotation for a single EFA. For exploratory structural
equation models (ESEM), the code takes a fitted EFA model and fitted CFA
and/or bifactor models and runs an ESEM for each CFA or bifactor model
input. In the ESEM, the EFA factors predict a series of latent variables
in separate models using Burt’s (1976) 2-stage procedure to prevent
interpretational confounding. The ESEM models were designed to run
analyses equivalent to that of Bainbridge, Ludeke, and Smillie (2022).
Although the package might be of most use to those running ESEM similar
to those of Bainbridge and colleagues (2022), it could also be very
helpful to anyone wanting to estimate a CFA measurement model for each
scale in a sample to either check measurement characteristics before
proceeding with further analyses or to simply compute measurement model
based reliability statistics.
For more sets of models that take a long time to run, code has been
included to allow the first run to save outputs that can be checked
against in subsequent runs. If nothing has changed, then the previous
outputs are returned, saving the time (and energy) of running them
again. To get this feature to work, the R version has to be 4.0 or later
and a cache directory will have to be set with the cache.setup()
function, which, by default, configures a cache directory in the users’
cache as determined by the operating system. It can alternatively be set
as a subdirectory within the current project or, if not using a project,
the current working directory. Once the cache is set, save_out = TRUE
can be included in function calls to save the relevant outputs, and
check = TRUE can be included to look for previous outputs and only run
models where something has changed.
Given that the package enables creating files in a cache directory, the
cache.clean() function has also been included to help clean up files.
To comply with CRAN policies, the cache directory is set as a temporary
environment variables, so it has to be set for each session when
required.
Installation
You can install the development version of ‘semFromKeys’ from
GitHub with:
# install.packages("pak")
pak::pak("timbainbridge/semFromKeys")
You can install the stable CRAN version with:
install.packages("semFromKeys")
Example
The following example generates keys, runs CFAs and an EFA using these
keys, and uses outputs from these to run ESEMs.
CFAs
In this case, keys can be created from names in the dataset but they can
also be created with simple code to generate a list.
library(semFromKeys)
keys0 <- c("grit_c", "grit_p", "hope_a", "hope_p")
keys <- sapply(
keys0, function(x) names(BFIGritHope)[grep(x, names(BFIGritHope))]
)
The lists should look something like this:
keys
#> $grit_c
#> [1] "grit_c_1" "grit_c_2" "grit_c_3" "grit_c_4" "grit_c_5" "grit_c_6"
#>
#> $grit_p
#> [1] "grit_p_1" "grit_p_2" "grit_p_3" "grit_p_4" "grit_p_5" "grit_p_6"
#>
#> $hope_a
#> [1] "hope_a_1" "hope_a_2" "hope_a_3" "hope_a_4"
#>
#> $hope_p
#> [1] "hope_p_1" "hope_p_2" "hope_p_3" "hope_p_4"
Once keys are created, the CFAs can be run. The function produces
messages of progress. These can help identify which models produced
errors or warnings or to keep track of progress for collections of
models with long run times.
cfa_fit <- cfa.from.keys(keys, BFIGritHope, fit_save = TRUE)
#> Fitting models
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating parameter estimates
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating model fit statistics
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
Results can be examined. For example, standard ‘lavaan’ summaries:
lavaan::summary(cfa_fit$fit$grit_c)
#> lavaan 0.6-21 ended normally after 12 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 18
#>
#> Number of observations 388
#> Number of missing patterns 1
#>
#> Model Test User Model:
#>
#> Test statistic 64.001
#> Degrees of freedom 9
#> P-value (Chi-square) 0.000
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Observed
#> Observed information based on Hessian
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> grit_c =~
#> grit_c_1 0.904 0.053 17.204 0.000
#> grit_c_2 0.832 0.057 14.560 0.000
#> grit_c_3 0.685 0.059 11.656 0.000
#> grit_c_4 0.794 0.056 14.089 0.000
#> grit_c_5 0.879 0.057 15.317 0.000
#> grit_c_6 0.762 0.063 12.173 0.000
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> .grit_c_1 3.235 0.058 55.482 0.000
#> .grit_c_2 2.856 0.061 47.163 0.000
#> .grit_c_3 3.088 0.059 52.185 0.000
#> .grit_c_4 3.219 0.059 54.439 0.000
#> .grit_c_5 2.938 0.062 47.644 0.000
#> .grit_c_6 3.376 0.064 52.736 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .grit_c_1 0.501 0.052 9.722 0.000
#> .grit_c_2 0.731 0.064 11.400 0.000
#> .grit_c_3 0.889 0.072 12.431 0.000
#> .grit_c_4 0.726 0.063 11.539 0.000
#> .grit_c_5 0.704 0.064 11.070 0.000
#> .grit_c_6 1.010 0.081 12.452 0.000
#> grit_c 1.000
And selected fit measures:
cfa_fit$fit_measures[, c("cfi", "rmsea")]
#> cfi rmsea
#> grit_c 0.9328014 0.12550173
#> grit_p 0.9143581 0.12213711
#> hope_a 0.9796273 0.12148480
#> hope_p 0.9978190 0.03467277
These models can be used to examine the measurement characteristics of
the scales or to calculate latent variable model-based reliability
scores (e.g., with
sapply(cfa_fit$fit, function(x) semTools::compRelSEM(x)[[1]]) for
composite reliability, Jöreskog, 1971).
EFAs
As for CFAs, an EFA can be run from a keys list. In this case, the keys
list indicates factor that items are expected to load on rather than
separate models. These are used to generate a target rotation to help
ensure the EFA matches expectations.
keys_e0 <- paste0("bfi_", c("e", "a", "c", "n", "o"))
keys_e <- sapply(
keys_e0,
function(x) names(BFIGritHope)[grep(x, names(BFIGritHope))],
simplify = FALSE
)
After the keys list has been created, the model can be run similarly to
the CFAs. When running the model, fit measures can be restricted to
speed up estimation if not all are required (as for ‘lavaan’s’
lavaan::fitMeasures() function).
efa_fit <- efa.from.keys(
keys_e, BFIGritHope, check = FALSE, fit_save = TRUE,
fit_measures = c("chisq", "df", "pvalue", "bic")
)
#> Fitting models
#> 1 / 1 efa
#> Generating parameter estimates
#> 1 / 1 efa
#> Generating model fit statistics
#> 1 / 1 efa
EFA results can be examined in a similar way to the CFAs.
# Not run due to length
# lavaan::summary(efa_fit$fit$efa) # Standard lavaan summary
efa_fit$fit_measures # Fit measures
#> chisq df pvalue bic
#> efa 4808.621 1480 0 62887.71
ESEM
Finally, outputs from these models can be used as inputs into ESEMs
where the scales of the CFAs are regressed on the EFA factors.
esem_fit <- esem.from.mods(
efa_fit$fit$efa, cfa_fit$fit, data = BFIGritHope, fit_save = FALSE
)
#> Fitting models
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating parameter estimates
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
The function provides standard ‘lavaan’ outputs, as well as r-squared
values and regression parameters.
# Not run due to length
# lavaan::summary(esem_fit$fit$grit_c) # Standard lavaan summary
round(esem_fit$r2, 3)
#> R2 se ci.lower ci.upper
#> grit_c 0.508 0.041 0.428 0.588
#> grit_p 0.731 0.035 0.663 0.799
#> hope_a 0.782 0.030 0.724 0.840
#> hope_p 0.610 0.040 0.532 0.688
esem_fit$b$grit_c
#> rhs est.std se z pvalue ci.lower ci.upper
#> 388 bfi_e -0.155 0.046 -3.369 0.001 -0.244 -0.065
#> 389 bfi_a 0.077 0.048 1.607 0.108 -0.017 0.171
#> 390 bfi_c 0.432 0.048 8.995 0.000 0.338 0.526
#> 391 bfi_n -0.367 0.048 -7.606 0.000 -0.461 -0.272
#> 392 bfi_o 0.100 0.048 2.112 0.035 0.007 0.193
To take advantage of functions’ time-saving check = TRUE for
subsequent running of code, a cache directory will need to be set. To
see how to do this, see ?cache.setup.
References
Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022). Evaluating
the Big Five as an organizing framework for commonly used psychological
trait scales. Journal of Personality and Social Psychology, 122(4),
749-777. https://doi.org/10.1037/pspp0000395.
Burt, R. S. (1976). Interpretational confounding of unobserved variables
in Structural Equation Models. Sociological Methods & Research, 5(1),
3-52. https://doi.org/10.1177/004912417600500101.
Jöreskog, K. G. (1971). Statistical Analysis of Sets of Congeneric
Tests. Psychometrika, 36(2), 109-133.
https://doi.org/10.1007/BF02291393.
Try the semFromKeys package in your browser
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semFromKeys documentation built on July 24, 2026, 5:07 p.m.
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semFromKeys
The ‘semFromKeys’ package was designed to streamline running ‘lavaan’ models with similar structures using keys lists to generate model code instead of writing out the code for models manually. For confirmatory factor analyses (CFAs) and bifactor models, the code creates and runs a series of models based on keys indicating each of the factors in the models. For exploratory factor analyses (EFAs) keys list are used to create a target rotation for a single EFA. For exploratory structural equation models (ESEM), the code takes a fitted EFA model and fitted CFA and/or bifactor models and runs an ESEM for each CFA or bifactor model input. In the ESEM, the EFA factors predict a series of latent variables in separate models using Burt’s (1976) 2-stage procedure to prevent interpretational confounding. The ESEM models were designed to run analyses equivalent to that of Bainbridge, Ludeke, and Smillie (2022).
Although the package might be of most use to those running ESEM similar to those of Bainbridge and colleagues (2022), it could also be very helpful to anyone wanting to estimate a CFA measurement model for each scale in a sample to either check measurement characteristics before proceeding with further analyses or to simply compute measurement model based reliability statistics.
For more sets of models that take a long time to run, code has been
included to allow the first run to save outputs that can be checked
against in subsequent runs. If nothing has changed, then the previous
outputs are returned, saving the time (and energy) of running them
again. To get this feature to work, the R version has to be 4.0 or later
and a cache directory will have to be set with the cache.setup()
function, which, by default, configures a cache directory in the users’
cache as determined by the operating system. It can alternatively be set
as a subdirectory within the current project or, if not using a project,
the current working directory. Once the cache is set, save_out = TRUE
can be included in function calls to save the relevant outputs, and
check = TRUE can be included to look for previous outputs and only run
models where something has changed.
Given that the package enables creating files in a cache directory, the
cache.clean() function has also been included to help clean up files.
To comply with CRAN policies, the cache directory is set as a temporary
environment variables, so it has to be set for each session when
required.
Installation
You can install the development version of ‘semFromKeys’ from GitHub with:
# install.packages("pak")
pak::pak("timbainbridge/semFromKeys")
You can install the stable CRAN version with:
install.packages("semFromKeys")
Example
The following example generates keys, runs CFAs and an EFA using these keys, and uses outputs from these to run ESEMs.
CFAs
In this case, keys can be created from names in the dataset but they can also be created with simple code to generate a list.
library(semFromKeys)
keys0 <- c("grit_c", "grit_p", "hope_a", "hope_p")
keys <- sapply(
keys0, function(x) names(BFIGritHope)[grep(x, names(BFIGritHope))]
)
The lists should look something like this:
keys
#> $grit_c
#> [1] "grit_c_1" "grit_c_2" "grit_c_3" "grit_c_4" "grit_c_5" "grit_c_6"
#>
#> $grit_p
#> [1] "grit_p_1" "grit_p_2" "grit_p_3" "grit_p_4" "grit_p_5" "grit_p_6"
#>
#> $hope_a
#> [1] "hope_a_1" "hope_a_2" "hope_a_3" "hope_a_4"
#>
#> $hope_p
#> [1] "hope_p_1" "hope_p_2" "hope_p_3" "hope_p_4"
Once keys are created, the CFAs can be run. The function produces messages of progress. These can help identify which models produced errors or warnings or to keep track of progress for collections of models with long run times.
cfa_fit <- cfa.from.keys(keys, BFIGritHope, fit_save = TRUE)
#> Fitting models
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating parameter estimates
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating model fit statistics
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
Results can be examined. For example, standard ‘lavaan’ summaries:
lavaan::summary(cfa_fit$fit$grit_c)
#> lavaan 0.6-21 ended normally after 12 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 18
#>
#> Number of observations 388
#> Number of missing patterns 1
#>
#> Model Test User Model:
#>
#> Test statistic 64.001
#> Degrees of freedom 9
#> P-value (Chi-square) 0.000
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Observed
#> Observed information based on Hessian
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> grit_c =~
#> grit_c_1 0.904 0.053 17.204 0.000
#> grit_c_2 0.832 0.057 14.560 0.000
#> grit_c_3 0.685 0.059 11.656 0.000
#> grit_c_4 0.794 0.056 14.089 0.000
#> grit_c_5 0.879 0.057 15.317 0.000
#> grit_c_6 0.762 0.063 12.173 0.000
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> .grit_c_1 3.235 0.058 55.482 0.000
#> .grit_c_2 2.856 0.061 47.163 0.000
#> .grit_c_3 3.088 0.059 52.185 0.000
#> .grit_c_4 3.219 0.059 54.439 0.000
#> .grit_c_5 2.938 0.062 47.644 0.000
#> .grit_c_6 3.376 0.064 52.736 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .grit_c_1 0.501 0.052 9.722 0.000
#> .grit_c_2 0.731 0.064 11.400 0.000
#> .grit_c_3 0.889 0.072 12.431 0.000
#> .grit_c_4 0.726 0.063 11.539 0.000
#> .grit_c_5 0.704 0.064 11.070 0.000
#> .grit_c_6 1.010 0.081 12.452 0.000
#> grit_c 1.000
And selected fit measures:
cfa_fit$fit_measures[, c("cfi", "rmsea")]
#> cfi rmsea
#> grit_c 0.9328014 0.12550173
#> grit_p 0.9143581 0.12213711
#> hope_a 0.9796273 0.12148480
#> hope_p 0.9978190 0.03467277
These models can be used to examine the measurement characteristics of
the scales or to calculate latent variable model-based reliability
scores (e.g., with
sapply(cfa_fit$fit, function(x) semTools::compRelSEM(x)[[1]]) for
composite reliability, Jöreskog, 1971).
EFAs
As for CFAs, an EFA can be run from a keys list. In this case, the keys list indicates factor that items are expected to load on rather than separate models. These are used to generate a target rotation to help ensure the EFA matches expectations.
keys_e0 <- paste0("bfi_", c("e", "a", "c", "n", "o"))
keys_e <- sapply(
keys_e0,
function(x) names(BFIGritHope)[grep(x, names(BFIGritHope))],
simplify = FALSE
)
After the keys list has been created, the model can be run similarly to
the CFAs. When running the model, fit measures can be restricted to
speed up estimation if not all are required (as for ‘lavaan’s’
lavaan::fitMeasures() function).
efa_fit <- efa.from.keys(
keys_e, BFIGritHope, check = FALSE, fit_save = TRUE,
fit_measures = c("chisq", "df", "pvalue", "bic")
)
#> Fitting models
#> 1 / 1 efa
#> Generating parameter estimates
#> 1 / 1 efa
#> Generating model fit statistics
#> 1 / 1 efa
EFA results can be examined in a similar way to the CFAs.
# Not run due to length
# lavaan::summary(efa_fit$fit$efa) # Standard lavaan summary
efa_fit$fit_measures # Fit measures
#> chisq df pvalue bic
#> efa 4808.621 1480 0 62887.71
ESEM
Finally, outputs from these models can be used as inputs into ESEMs where the scales of the CFAs are regressed on the EFA factors.
esem_fit <- esem.from.mods(
efa_fit$fit$efa, cfa_fit$fit, data = BFIGritHope, fit_save = FALSE
)
#> Fitting models
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
#> Generating parameter estimates
#> 1 / 4 grit_c
#> 2 / 4 grit_p
#> 3 / 4 hope_a
#> 4 / 4 hope_p
The function provides standard ‘lavaan’ outputs, as well as r-squared values and regression parameters.
# Not run due to length
# lavaan::summary(esem_fit$fit$grit_c) # Standard lavaan summary
round(esem_fit$r2, 3)
#> R2 se ci.lower ci.upper
#> grit_c 0.508 0.041 0.428 0.588
#> grit_p 0.731 0.035 0.663 0.799
#> hope_a 0.782 0.030 0.724 0.840
#> hope_p 0.610 0.040 0.532 0.688
esem_fit$b$grit_c
#> rhs est.std se z pvalue ci.lower ci.upper
#> 388 bfi_e -0.155 0.046 -3.369 0.001 -0.244 -0.065
#> 389 bfi_a 0.077 0.048 1.607 0.108 -0.017 0.171
#> 390 bfi_c 0.432 0.048 8.995 0.000 0.338 0.526
#> 391 bfi_n -0.367 0.048 -7.606 0.000 -0.461 -0.272
#> 392 bfi_o 0.100 0.048 2.112 0.035 0.007 0.193
To take advantage of functions’ time-saving check = TRUE for
subsequent running of code, a cache directory will need to be set. To
see how to do this, see ?cache.setup.
References
Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022). Evaluating the Big Five as an organizing framework for commonly used psychological trait scales. Journal of Personality and Social Psychology, 122(4), 749-777. https://doi.org/10.1037/pspp0000395.
Burt, R. S. (1976). Interpretational confounding of unobserved variables in Structural Equation Models. Sociological Methods & Research, 5(1), 3-52. https://doi.org/10.1177/004912417600500101.
Jöreskog, K. G. (1971). Statistical Analysis of Sets of Congeneric Tests. Psychometrika, 36(2), 109-133. https://doi.org/10.1007/BF02291393.
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