Confirmatory Factor Analysis After Correlation Synthesis
cfa_from_synthesis.RdConvenience wrapper around path_model for latent
(=~) model syntax using synthesized correlation matrices.
Usage
cfa_from_synthesis(
mars_object,
model,
num_obs = NULL,
method_null = "sem",
synthesis_method = c("model", "average", "weighted"),
synthesis_transform = c("none", "fisher_z"),
missing_corr = c("available", "pairwise", "em"),
attenuation = c("none", "correct"),
reliability = NULL,
reliability_missing = c("error", "skip", "impute_mean", "assume_1"),
pd_adjust = c("none", "eigen_clip", "nearpd"),
pd_tol = 1e-08,
...
)
# S3 method for class 'cfa_mars'
print(x, ...)Arguments
- mars_object
A fitted object returned by
mars.- model
Lavaan-style latent model syntax.
- num_obs
Optional sample size.
- method_null
Null model method passed to
path_model.- synthesis_method
Correlation synthesis method:
"model","average", or"weighted".- synthesis_transform
Optional pooling transform for
"average"/"weighted":"none"or"fisher_z".- missing_corr
Missing-correlation handling for synthesized matrices. One of
"available","pairwise", or"em".- attenuation
Attenuation-correction mode for synthesized correlations. One of
"none"or"correct".- reliability
Reliability input used when
attenuation = "correct".- reliability_missing
Strategy for missing reliability values. One of
"error","skip","impute_mean", or"assume_1".- pd_adjust
Positive-definite repair for synthesized correlations:
"none","eigen_clip", or"nearpd".- pd_tol
Minimum eigenvalue tolerance used by
pd_adjust = "eigen_clip".- ...
Additional arguments passed to downstream methods.
- x
An object of class
"cfa_mars".
Examples
# \donttest{
fit <- mars(
data = becker09,
studyID = "ID",
effectID = "numID",
sample_size = "N",
effectsize_type = "cor",
varcov_type = "weighted",
variable_names = c(
"Cognitive_Performance",
"Somatic_Performance",
"Selfconfidence_Performance",
"Somatic_Cognitive",
"Selfconfidence_Cognitive",
"Selfconfidence_Somatic"
)
)
cfa_from_synthesis(fit, "Performance =~ Cognitive + Somatic + Selfconfidence")
#> Results generated with MARS:v 0.5.3
#> Wednesday, June 17, 2026
#>
#> Model Type:
#> multivariate
#>
#> Average Correlation Matrix:
#> Performance Cognitive Somatic Selfconfidence
#> Performance 1.00000000 -0.02286058 -0.06819054 0.2348581
#> Cognitive -0.02286058 1.00000000 0.54220432 -0.4447191
#> Somatic -0.06819054 0.54220432 1.00000000 -0.3886975
#> Selfconfidence 0.23485808 -0.44471906 -0.38869747 1.0000000
#>
#> Synthesis options:
#> method: model
#> transform: none
#> missing_corr: available
#> attenuation: none
#> pd_adjust: none
#> pd_adjusted: FALSE
#> min eigen (before/after): 0.4407 / 0.4407
#> fit index notes: bounded indices applied (raw values available in fit_index_raw)
#> SE note: Latent-model delta SEs are conditional on the synthesized correlation matrix and are not reported. Use se_method = "simulation" or "bootstrap" to propagate correlation uncertainty.
#>
#>
#> Model Fitted:
#> Performance =~ Cognitive + Somatic + Selfconfidence
#>
#> Fixed Effects:
#> predictor outcome estimate
#> Performance -> Cognitive Performance Cognitive 1.0000000
#> Performance -> Somatic Performance Somatic -0.0681892
#> Performance -> Selfconfidence Performance Selfconfidence 0.2348490
#> standard_errors test_statistic p_value
#> Performance -> Cognitive NA NA NA
#> Performance -> Somatic NA NA NA
#> Performance -> Selfconfidence NA NA NA
#>
#>
#> Fit Statistics:
#> Type Value
#> 1 Model Chi-Square 839.253 (4), 0
#> 2 Null Model Chi-Square 425.703 (6)
#> 3 CFI 0
#> 4 TLI 0
#> 5 RMSEA <NA>
#> 6 SRMR 0.788
#> 7 CFI (raw) -0.99
#> 8 TLI (raw) -1.985
# }