modsem (version 1.0.11)

summary.modsem_da: summary for modsem objects

Description

summary for modsem objects

summary for modsem objects

summary for modsem objects

Usage

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

centered

print mean centered 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) {
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)
}