Tidy a(n) lavaan object

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.

Usage

# S3 method for class 'lavaan'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x: A lavaan object, such as those returned from lavaan::cfa(), and lavaan::sem().
conf.int: Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level: 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.

Value

A tibble::tibble() with one row for each estimated parameter and columns:

term: The result of paste(lhs, op, rhs)
op: The operator in the model syntax (e.g. ~~ for covariances, or ~ for regression parameters)
group: The group (if specified) in the lavaan model
estimate: The parameter estimate (may be standardized)
std.error
statistic: The z value returned by lavaan::parameterEstimates()
p.value
conf.low
conf.high
std.lv: Standardized estimates based on the variances of the (continuous) latent variables only
std.all: Standardized estimates based on both the variances of both (continuous) observed and latent variables.
std.nox: Standardized estimates based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.

Examples


# 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 × 10
#>    term     op    block group estimate std.error statistic  p.value std.lv
#>    <chr>    <chr> <int> <int>    <dbl>     <dbl>     <dbl>    <dbl>  <dbl>
#>  1 F =~ x1  =~        1     1    1         0         NA    NA        0.567
#>  2 F =~ x2  =~        1     1    0.333     0.190      1.76  7.89e-2  0.189
#>  3 F =~ x3  =~        1     1    0.400     0.182      2.20  2.80e-2  0.227
#>  4 F =~ x4  =~        1     1    1.66      0.280      5.92  3.28e-9  0.941
#>  5 F =~ x5  =~        1     1    1.92      0.323      5.95  2.60e-9  1.09 
#>  6 F =~ x6  =~        1     1    1.48      0.247      5.98  2.23e-9  0.837
#>  7 F =~ x7  =~        1     1    0.453     0.173      2.61  8.96e-3  0.257
#>  8 F =~ x8  =~        1     1    0.376     0.155      2.43  1.51e-2  0.213
#>  9 F =~ x9  =~        1     1    0.422     0.159      2.66  7.80e-3  0.240
#> 10 x1 ~~ x1 ~~        1     1    1.07      0.127      8.47  0        1.07 
#> # ℹ 48 more rows
#> # ℹ 1 more variable: std.all <dbl>

Usage

Arguments

Value

See also

Examples