cfa.from.keys: Runs CFA models for multiple scales based on items in a keys...

In semFromKeys: Run 'lavaan' Models from Keys Lists

Runs CFA models for multiple scales based on items in a keys list.

Description

cfa.from.keys runs a confirmatory factor analysis (CFA) model for each element of a keys list. The keys list must be a named list of scales, where each element is an item from the corresponding scale. The function is designed to streamline running CFA models for all scales in a sample and to input model outputs into downstream functions.

Usage

cfa.from.keys(
  keys,
  data,
  fit_save = TRUE,
  fit_measures = "all",
  std.lv = TRUE,
  miss = "ML",
  est = "default",
  name = "cfa",
  check = FALSE,
  save_out = FALSE
)

Arguments

keys	A named list of keys. Names should be scale names, elements should a list of items included in each scale.
data	A dataframe or object coercible to a dataframe. Data must include all observed variables in any of the keys.
fit_save	Logical. TRUE indicates model fit measures should be included in the output; FALSE indicates model fit measures should not be included in the output. FALSE may be desirable when fit measures are of little interest and when they may take a long time to estimate. Fit can still be examined for individual models with, ⁠lavaan::fitMeasures(fit$fit$[model name])⁠.
fit_measures	A vector of fit measures to save or 'all' to select all fit measures, as per the fit.measures parameter from lavaan's lavaan::fitMeasures() function. Defaults to 'all'. Irrelevant if fit_save = FALSE.
std.lv	Logical. Sets the std.lv parameter, as per lavaan (see lavaan::lavOptions()). TRUE indicates that factor variances should be fixed to 1. FALSE indicates that loadings of the first items of factors should be fixed to 1. Defaults to TRUE.
miss	A string. Sets the missing parameter, as per lavaan (see lavaan::lavOptions()). Defaults to 'ML'.
est	A string. Sets the estimator parameter, as per lavaan (see lavaan::lavOptions()). The default ('default') uses the lavaan default for the model being run.
name	A string indicating a subdirectory where model outputs will be saved when save_out = TRUE and checked against when check = TRUE. Defaults to 'cfa'. Irrelevant if both save_out = FALSE and check = FALSE. The name should be unique for each set of models or outputs from calls with the same name will be overwritten.
check	Logical. TRUE indicates that current inputs should be compared to previous inputs if they exist and that the model should not be rerun if nothing has changed; FALSE indicates that these checks should not be made and the model should be run regardless of the existence of previous inputs.
save_out	Logical. TRUE indicates that model code, a hash of the data, important input parameter values, and output will be saved; FALSE indicates that nothing will be saved. Selecting save_out = TRUE enables the function to not rerun models next time if check = TRUE the next time the code is run and nothing has changed in the meantime.

Details

The model relies on sem.check() for the back-end of running the models. This enables saving inputs and outputs from model runs (with save_out = TRUE) and checking to see if anything has changed from prior runs before running again (with check = TRUE). The functionality was included for a number of very slow models or a lot of faster models, such that time spent rerunning them would be onerous. For further details on how this works, see the sem.check() function documentation.

The function does not provide any warnings for poor fit beyond those provided by lavaan. If any CFA models have poor fit, there is currently no capability to update them beyond removing items by omitting them from the keys. Any other changes to CFA models have to be made manually currently. (Note that with save_out = TRUE, model code is saved in ⁠file.path(out_dir, name, paste0(name, _mod.rds)⁠, which could help with manually updating models.)

Value

Returns a list of length 2 (if fit_save = FALSE) or 3 (if fit_save = TRUE). The elements of the list are: a list of lavaan model output objects; a list of parameter estimates from the models (standardized if std = TRUE); and, if fit_save = TRUE, a matrix of fit measures for each model.

See Also

sem.check(), which cfa.from.keys() uses for all the back-end, and lavaan::sem(), which is used to estimate the models.

Examples

# Create CFA keys
keys0 <- c("grit_c", "grit_p", "hope_a", "hope_p")
keys <- sapply(
  keys0, function(x) names(BFIGritHope)[grep(x, names(BFIGritHope))]
)
# Run models
cfa_fit <- cfa.from.keys(keys, BFIGritHope, check = FALSE, fit_save = TRUE)
# Examine some results
summary(cfa_fit$fit$grit_c)                # Standard lavaan summary
cfa_fit$fit_measures[, c("cfi", "rmsea")]  # Fit measures

semFromKeys documentation built on July 24, 2026, 5:07 p.m.