Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for lavaan
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A lavaan
object, such as those returned from lavaan::cfa()
,
and lavaan::sem()
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
The confidence level to use for the confidence interval
if conf.int = TRUE
. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Additional arguments passed to lavaan::parameterEstimates()
.
Cautionary note: Misspecified arguments may be silently ignored.
A tibble::tibble()
with one row for each estimated parameter and
columns:
The result of paste(lhs, op, rhs)
The operator in the model syntax (e.g. ~~
for covariances, or
~
for regression parameters)
The group (if specified) in the lavaan model
The parameter estimate (may be standardized)
The z value returned by lavaan::parameterEstimates()
Standardized estimates based on the variances of the (continuous) latent variables only
Standardized estimates based on both the variances of both (continuous) observed and latent variables.
Standardized estimates based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.
tidy()
, lavaan::cfa()
, lavaan::sem()
,
lavaan::parameterEstimates()
Other lavaan tidiers:
glance.lavaan()
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("lavaan", quietly = TRUE)) {
# load libraries for models and data
library(lavaan)
cfa.fit <- cfa("F =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9",
data = HolzingerSwineford1939, group = "school"
)
tidy(cfa.fit)
}
#> # A tibble: 58 × 11
#> term op block group estimate std.error statistic p.value std.lv std.all
#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 F =~ … =~ 1 1 1 0 NA NA 0.567 0.480
#> 2 F =~ … =~ 1 1 0.333 0.190 1.76 7.89e-2 0.189 0.154
#> 3 F =~ … =~ 1 1 0.400 0.182 2.20 2.80e-2 0.227 0.196
#> 4 F =~ … =~ 1 1 1.66 0.280 5.92 3.28e-9 0.941 0.819
#> 5 F =~ … =~ 1 1 1.92 0.323 5.95 2.60e-9 1.09 0.835
#> 6 F =~ … =~ 1 1 1.48 0.247 5.98 2.23e-9 0.837 0.848
#> 7 F =~ … =~ 1 1 0.453 0.173 2.61 8.96e-3 0.257 0.238
#> 8 F =~ … =~ 1 1 0.376 0.155 2.43 1.51e-2 0.213 0.219
#> 9 F =~ … =~ 1 1 0.422 0.159 2.66 7.80e-3 0.240 0.242
#> 10 x1 ~~… ~~ 1 1 1.07 0.127 8.47 0 1.07 0.769
#> # … with 48 more rows, and 1 more variable: std.nox <dbl>