Print method for an
'std_solution_boot' object, which
is the output of
standardizedSolution_boot_ci().
Arguments
- x
Object of the class
std_solution_boot.- ...
Optional arguments to be passed to
print()methods.- nd
The number of digits after the decimal place. Default is 3.
- output
String. How the results are printed. Default is
"table"and the results are printed in a table format similar to that oflavaan::standardizedSolution(). If"text", the results will be printed in a text format similar to the printout of the output ofsummary()of a 'lavaan'-class object.- standardized_only
Logical. If
TRUE, the default, only the results for the standardized solution will be printed. IfFALSE, then the standardized solution is printed alongside the unstandardized solution, as in the printout of the output ofsummary()of a 'lavaan'-class object.
Details
The default format of the printout
is that of lavaan::standardizedSolution(),
which is compact but not easy to
read. Users can request a format
similar to that of the printout
of the summary of a lavaan output
by setting output to "text".
For the "text" format, users can
also select whether
only the standardized solution is
printed (the default) or whether
the standardized solution is appended
to the right of the printout.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
set.seed(5478374)
n <- 50
x <- runif(n) - .5
m <- .40 * x + rnorm(n, 0, sqrt(1 - .40))
y <- .30 * m + rnorm(n, 0, sqrt(1 - .30))
dat <- data.frame(x = x, y = y, m = m)
model <-
'
m ~ a*x
y ~ b*m
ab := a*b
'
# Should set bootstrap to at least 2000 in real studies
fit <- sem(model, data = dat, fixed.x = FALSE,
se = "boot",
bootstrap = 50)
std_out <- standardizedSolution_boot_ci(fit)
std_out
#> lhs op rhs label est.std se z pvalue ci.lower ci.upper boot.ci.lower
#> 1 m ~ x a 0.229 0.117 1.955 0.051 -0.001 0.458 -0.025
#> 2 y ~ m b 0.198 0.121 1.644 0.100 -0.038 0.434 -0.005
#> 3 m ~~ m 0.948 0.053 17.729 0.000 0.843 1.053 0.786
#> 4 y ~~ y 0.961 0.048 20.110 0.000 0.867 1.054 0.758
#> 5 x ~~ x 1.000 0.000 NA NA 1.000 1.000 NA
#> 6 ab := a*b ab 0.045 0.037 1.240 0.215 -0.026 0.117 -0.006
#> boot.ci.upper boot.se
#> 1 0.462 0.121
#> 2 0.492 0.112
#> 3 1.000 0.055
#> 4 0.999 0.056
#> 5 NA NA
#> 6 0.151 0.038
print(std_out, output = "text")
#>
#> Standardized Estimates Only
#>
#> Standard errors Bootstrap
#> Confidence interval Bootstrap
#> Confidence Level 95.0%
#> Standardization Type std.all
#> Number of requested bootstrap draws 50
#> Number of successful bootstrap draws 50
#>
#> Regressions:
#> Standardized Std.Err ci.lower ci.upper
#> m ~
#> x (a) 0.229 0.121 -0.025 0.462
#> y ~
#> m (b) 0.198 0.112 -0.005 0.492
#>
#> Variances:
#> Standardized Std.Err ci.lower ci.upper
#> .m 0.948 0.055 0.786 1.000
#> .y 0.961 0.056 0.758 0.999
#> x 1.000 NA NA NA
#>
#> Defined Parameters:
#> Standardized Std.Err ci.lower ci.upper
#> ab 0.045 0.038 -0.006 0.151
#>
print(std_out, output = "text", standardized_only = FALSE)
#>
#> Parameter Estimates:
#>
#> Standard errors Bootstrap
#> Number of requested bootstrap draws 50
#> Number of successful bootstrap draws 50
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> m ~
#> x (a) 0.569 0.293 1.942 0.052 -0.056 1.165
#> y ~
#> m (b) 0.219 0.147 1.490 0.136 -0.002 0.725
#> Standardized ci.std.lower ci.std.upper Std.Err.std
#>
#> 0.229 -0.025 0.462 0.121
#>
#> 0.198 -0.005 0.492 0.112
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> .m 0.460 0.083 5.556 0.000 0.247 0.593
#> .y 0.570 0.109 5.229 0.000 0.341 0.792
#> x 0.078 0.013 5.924 0.000 0.052 0.102
#> Standardized ci.std.lower ci.std.upper Std.Err.std
#> 0.948 0.786 1.000 0.055
#> 0.961 0.758 0.999 0.056
#> 1.000 NA NA NA
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> ab 0.125 0.107 1.160 0.246 -0.019 0.440
#> Standardized ci.std.lower ci.std.upper Std.Err.std
#> 0.045 -0.006 0.151 0.038
#>