Print method for a
'lav_betaselect' object, which
is the output of
lav_betaselect().
Arguments
- x
A
lav_betaselect-class object, such as the output oflav_betaselect().- ...
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
"lavaan.printer", and the results will be printed in a format similar to the printout of the output of thesummary-method of a 'lavaan'-class object. If set to"table", the results are printed in a table format similar to that oflavaan::parameterEstimates()withoutputset to"data.frame".- 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.- show_Bs.by
Logical. If
TRUEandoutputis"lavaan.printer", then the column"Bs.by"is shown, indicating, for each parameter, the variables standardized. This column is not shown ifoutputis not"lavaan.printer".- by_group
If
TRUE, the default, and the model has more than one group, sections will be grouped by groups first, as in the print out ofsummary()inlavaan. IfFALSE, then the sections will be grouped by sections first.- na_str
The string to be used for cells with
NA. Default is" ", a whitespace.- sig_stars
If
TRUE, the default, symbols such as asterisks (*,**,***) will be used to denote whether a beta-select is significant.- ci_sig
If
TRUE, the default, a beta-select will be denoted as significant or not significant based on its confidence interval.
Details
The default format of the printout,
"lavaan.printer",
is similar to that of the summary()
of a lavaan object.
Users can also select whether
only the standardized solution is
printed or whether
the standardized solution is appended
to the right of the printout.
If output is set to "table"' the format is that of [lavaan::parameterEstimates()] with output = "data.frame"`,
which is compact but not easy to
read.
See also
lav_betaselect(). This
function is adapted from
semhelpinghands::print.std_solution_boot().
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
# Need to mean-center iv and mod
data_test_medmod$iv <- data_test_medmod$iv - mean(data_test_medmod$iv)
data_test_medmod$mod <- data_test_medmod$mod - mean(data_test_medmod$mod)
mod <-
"
med ~ iv + mod + iv:mod
dv ~ med + iv
"
fit <- sem(mod,
data_test_medmod,
fixed.x = TRUE)
summary(fit)
#> lavaan 0.6-19 ended normally after 1 iteration
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 7
#>
#> Number of observations 200
#>
#> Model Test User Model:
#>
#> Test statistic 2.685
#> Degrees of freedom 2
#> P-value (Chi-square) 0.261
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> med ~
#> iv 0.661 0.217 3.047 0.002
#> mod 0.325 0.128 2.535 0.011
#> iv:mod 0.286 0.039 7.248 0.000
#> dv ~
#> med 0.093 0.011 8.298 0.000
#> iv 0.229 0.039 5.917 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .med 61.851 6.185 10.000 0.000
#> .dv 2.104 0.210 10.000 0.000
#>
fit_beta <- lav_betaselect(fit,
to_standardize = c("iv", "dv"))
fit_beta
#>
#> Selected Standardization:
#>
#> Standard Error: Nil
#>
#> Parameter Estimates Settings:
#>
#> Standard errors: Standard
#> Information: Expected
#> Information saturated (h1) model: Structured
#>
#> Regressions:
#> BetaSelect
#> med ~
#> iv 1.845
#> mod 0.325
#> iv:mod 0.797
#> dv ~
#> med 0.049
#> iv 0.333
#>
#> Covariances:
#> BetaSelect
#> iv ~~
#> mod 1.894
#> iv:mod 0.630
#> mod ~~
#> iv:mod 5.733
#>
#> Variances:
#> BetaSelect
#> .med 61.851
#> .dv 0.574
#> iv 1.000
#> mod 23.129
#> iv:mod 27.015
#>
#> Footnote:
#> - Variable(s) standardized: dv, iv
#> - Call 'print()' and set 'standardized_only' to 'FALSE' to print both
#> original estimates and betas-select.
#> - Product terms (iv:mod) have variables standardized before computing
#> them. The product term(s) is/are not standardized.
print(fit_beta)
#>
#> Selected Standardization:
#>
#> Standard Error: Nil
#>
#> Parameter Estimates Settings:
#>
#> Standard errors: Standard
#> Information: Expected
#> Information saturated (h1) model: Structured
#>
#> Regressions:
#> BetaSelect
#> med ~
#> iv 1.845
#> mod 0.325
#> iv:mod 0.797
#> dv ~
#> med 0.049
#> iv 0.333
#>
#> Covariances:
#> BetaSelect
#> iv ~~
#> mod 1.894
#> iv:mod 0.630
#> mod ~~
#> iv:mod 5.733
#>
#> Variances:
#> BetaSelect
#> .med 61.851
#> .dv 0.574
#> iv 1.000
#> mod 23.129
#> iv:mod 27.015
#>
#> Footnote:
#> - Variable(s) standardized: dv, iv
#> - Call 'print()' and set 'standardized_only' to 'FALSE' to print both
#> original estimates and betas-select.
#> - Product terms (iv:mod) have variables standardized before computing
#> them. The product term(s) is/are not standardized.
print(fit_beta, show_Bs.by = TRUE)
#>
#> Selected Standardization:
#>
#> Standard Error: Nil
#>
#> Parameter Estimates Settings:
#>
#> Standard errors: Standard
#> Information: Expected
#> Information saturated (h1) model: Structured
#>
#> Regressions:
#> BetaSelect Selected
#> med ~
#> iv 1.845 iv
#> mod 0.325
#> iv:mod 0.797 iv
#> dv ~
#> med 0.049 dv
#> iv 0.333 iv,dv
#>
#> Covariances:
#> BetaSelect Selected
#> iv ~~
#> mod 1.894 iv
#> iv:mod 0.630 iv
#> mod ~~
#> iv:mod 5.733 iv
#>
#> Variances:
#> BetaSelect Selected
#> .med 61.851
#> .dv 0.574 dv
#> iv 1.000 iv
#> mod 23.129
#> iv:mod 27.015 iv
#>
#> Footnote:
#> - Variable(s) standardized: dv, iv
#> - Call 'print()' and set 'standardized_only' to 'FALSE' to print both
#> original estimates and betas-select.
#> - The column 'Selected' lists variable(s) standardized when computing
#> the standardized coefficient of a parameter. ('NA' for user-defined
#> parameters because they are computed from other standardized
#> parameters.)
#> - Product terms (iv:mod) have variables standardized before computing
#> them. The product term(s) is/are not standardized.
print(fit_beta, output = "table")
#> lhs op rhs est se z pvalue ci.lower ci.upper std.p
#> 1 med ~ iv 0.661 0.217 3.047 0.002 0.236 1.086 1.845
#> 2 med ~ mod 0.325 0.128 2.535 0.011 0.074 0.577 0.325
#> 3 med ~ iv:mod 0.286 0.039 7.248 0.000 0.208 0.363 0.797
#> 4 dv ~ med 0.093 0.011 8.298 0.000 0.071 0.115 0.049
#> 5 dv ~ iv 0.229 0.039 5.917 0.000 0.153 0.304 0.333
#> 6 med ~~ med 61.851 6.185 10.000 0.000 49.728 73.974 61.851
#> 7 dv ~~ dv 2.104 0.210 10.000 0.000 1.692 2.517 0.574
#> 8 iv ~~ iv 7.795 0.000 NA NA 7.795 7.795 1.000
#> 9 iv ~~ mod 5.287 0.000 NA NA 5.287 5.287 1.894
#> 10 iv ~~ iv:mod 4.908 0.000 NA NA 4.908 4.908 0.630
#> 11 mod ~~ mod 23.129 0.000 NA NA 23.129 23.129 23.129
#> 12 mod ~~ iv:mod 16.006 0.000 NA NA 16.006 16.006 5.733
#> 13 iv:mod ~~ iv:mod 210.572 0.000 NA NA 210.572 210.572 27.015
#> std.p.by
#> 1 iv
#> 2
#> 3 iv
#> 4 dv
#> 5 iv,dv
#> 6
#> 7 dv
#> 8 iv
#> 9 iv
#> 10 iv
#> 11
#> 12 iv
#> 13 iv