summary for modsem objects

Usage

# S3 method for class 'modsem_da'
summary(
  object,
  H0 = TRUE,
  verbose = interactive(),
  r.squared = TRUE,
  adjusted.stat = FALSE,
  digits = 3,
  scientific = FALSE,
  ci = FALSE,
  standardized = FALSE,
  monte.carlo = FALSE,
  mc.reps = 10000,
  loadings = TRUE,
  regressions = TRUE,
  covariances = TRUE,
  intercepts = TRUE,
  variances = TRUE,
  var.interaction = FALSE,
  ...
)

# S3 method for class 'modsem_mplus'
summary(
  object,
  scientific = FALSE,
  standardize = FALSE,
  ci = FALSE,
  digits = 3,
  loadings = TRUE,
  regressions = TRUE,
  covariances = TRUE,
  intercepts = TRUE,
  variances = TRUE,
  ...
)

# S3 method for class 'modsem_pi'
summary(
  object,
  H0 = TRUE,
  r.squared = TRUE,
  adjusted.stat = FALSE,
  digits = 3,
  scientific = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object: modsem object to summarized
H0: Should the baseline model be estimated, and used to produce comparative fit?
verbose: Should messages be printed?
r.squared: calculate R-squared
adjusted.stat: should sample size corrected/adjustes AIC and BIC be reported?
digits: Number of digits for printed numerical values
scientific: Should scientific format be used for p-values?
ci: print confidence intervals
standardized: print standardized estimates
monte.carlo: should Monte Carlo bootstrapped standard errors be used? Only relevant if standardized = TRUE. If FALSE delta method is used instead.
mc.reps: number of Monte Carlo repetitions. Only relevant if monte.carlo = TRUE, and standardized = TRUE.
loadings: print loadings
regressions: print regressions
covariances: print covariances
intercepts: print intercepts
variances: print variances
var.interaction: if FALSE variances for interaction terms will be removed from the output
...: arguments passed to lavaan::summary()
standardize: standardize estimates

Examples

if (FALSE) { # \dontrun{
m1 <- "
 # Outer Model
 X =~ x1 + x2 + x3
 Y =~ y1 + y2 + y3
 Z =~ z1 + z2 + z3

 # Inner model
 Y ~ X + Z + X:Z
"

est1 <- modsem(m1, oneInt, "qml")
summary(est1, ci = TRUE, scientific = TRUE)
} # }