Combine the outputs
of sim_data(), fit_model(),
and optionally gen_mc() and/or
gen_boot() to one
single object.
Arguments
- data_all
The output of
sim_data().- ...
Named arguments of objects to be added to each replication under the element
extra. For example, if set tofit = fit_all, wherefit_allis the output offit_model(), thendata_all[[1]]$extra$fitwill be set to the first output infit_all.- x
The
sim_outobject to be printed.- digits
The numbers of digits displayed after the decimal.
- digits_descriptive
The number of digits displayed after the decimal for the descriptive statistics table.
- fit_to_all_args
A named list of arguments to be passed to
lavaan::sem()when the model is fitted to a sample combined from
Value
The function sim_out() returns a sim_out object, which
is a list of length equal to the
length of data_all. Each element
of the list is a sim_data object
with the element extra added to
it. Other named elements will be
added under this name. For example.
the output of fit_model()
for this replication can be added
to fit, under extra. See
the description of the argument
... for details.
The print method of sim_out
returns x invisibly. Called for
its side effect.
Details
It merges into one object the output
of sim_data(), which is a list of
nrep simulated datasets,
fit_model(), which is a list of the
lavaan::sem() output for the nrep
datasets, and optionally the output
of gen_mc() or gen_boot(), which is a list of the
R sets of Monte Carlo or bootstrap estimates
based on the results of
fit_model(). The list has nrep
elements, each element with the data,
the model fit
results, and optionally the Monte
Carlo estimates matched.
This object can then be used for testing effects of interests, which are further processed to estimate the power of this test.
The function sim_out() is used by
the all-in-one function
power4test(). Users usually do not
call this function directly, though
developers can use this function to
develop other functions for power
analysis, or to build their own
workflows to do the power analysis.
Examples
# Specify the model
mod <-
"m ~ x
y ~ m + x"
# Specify the population values
es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"
# Generate the simulated datasets
dats <- sim_data(nrep = 5,
model = mod,
pop_es = es,
n = 100,
iseed = 1234)
# Fit the population model to each dataset
fits <- fit_model(dats)
# Combine the results to one object
sim_out_all <- sim_out(data_all = dats,
fit = fits)
sim_out_all
#>
#> ====================== Model Information ======================
#>
#> == Model on Factors/Variables ==
#> m ~ x
#> y ~ m + x
#> == Model on Variables/Indicators ==
#> m ~ x
#> y ~ m + x
#> ====== Population Values ======
#>
#> Regressions:
#> Population
#> m ~
#> x 0.300
#> y ~
#> m 0.300
#> x 0.000
#>
#> Variances:
#> Population
#> .m 0.910
#> .y 0.910
#> x 1.000
#>
#> (Computing indirect effects for 2 paths ...)
#>
#> == Population Conditional/Indirect Effect(s) ==
#>
#> == Indirect Effect(s) ==
#>
#> ind
#> x -> m -> y 0.090
#> x -> y 0.000
#>
#> - The 'ind' column shows the indirect effect(s).
#>
#> ======================= Data Information =======================
#>
#> Number of Replications: 5
#> Sample Sizes: 100
#>
#> ==== Descriptive Statistics ====
#>
#> vars n mean sd skew kurtosis se
#> m 1 500 -0.03 1.00 -0.09 -0.03 0.04
#> y 2 500 -0.01 0.98 0.03 0.41 0.04
#> x 3 500 0.01 0.94 -0.21 0.17 0.04
#>
#> ===== Parameter Estimates Based on All 5 Samples Combined =====
#>
#> Total Sample Size: 500
#>
#> ==== Standardized Estimates ====
#>
#> Variances and error variances omitted.
#>
#> Regressions:
#> est.std
#> m ~
#> x 0.323
#> y ~
#> m 0.277
#> x -0.098
#>
# Verify that the elements of fits are set to extra$fit
library(lavaan)
#> This is lavaan 0.6-19
#> lavaan is FREE software! Please report any bugs.
parameterEstimates(fits[[1]])
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 m ~ x 0.382 0.099 3.837 0.000 0.187 0.577
#> 2 y ~ m 0.266 0.103 2.591 0.010 0.065 0.467
#> 3 y ~ x 0.108 0.109 0.990 0.322 -0.106 0.322
#> 4 m ~~ m 0.903 0.128 7.071 0.000 0.653 1.153
#> 5 y ~~ y 0.951 0.134 7.071 0.000 0.687 1.214
#> 6 x ~~ x 0.913 0.000 NA NA 0.913 0.913
parameterEstimates(sim_out_all[[1]]$extra$fit)
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 m ~ x 0.382 0.099 3.837 0.000 0.187 0.577
#> 2 y ~ m 0.266 0.103 2.591 0.010 0.065 0.467
#> 3 y ~ x 0.108 0.109 0.990 0.322 -0.106 0.322
#> 4 m ~~ m 0.903 0.128 7.071 0.000 0.653 1.153
#> 5 y ~~ y 0.951 0.134 7.071 0.000 0.687 1.214
#> 6 x ~~ x 0.913 0.000 NA NA 0.913 0.913
parameterEstimates(fits[[2]])
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 m ~ x 0.383 0.108 3.559 0.000 0.172 0.594
#> 2 y ~ m 0.208 0.104 1.995 0.046 0.004 0.413
#> 3 y ~ x -0.172 0.119 -1.442 0.149 -0.406 0.062
#> 4 m ~~ m 0.988 0.140 7.071 0.000 0.714 1.262
#> 5 y ~~ y 1.078 0.153 7.071 0.000 0.780 1.377
#> 6 x ~~ x 0.854 0.000 NA NA 0.854 0.854
parameterEstimates(sim_out_all[[2]]$extra$fit)
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 m ~ x 0.383 0.108 3.559 0.000 0.172 0.594
#> 2 y ~ m 0.208 0.104 1.995 0.046 0.004 0.413
#> 3 y ~ x -0.172 0.119 -1.442 0.149 -0.406 0.062
#> 4 m ~~ m 0.988 0.140 7.071 0.000 0.714 1.262
#> 5 y ~~ y 1.078 0.153 7.071 0.000 0.780 1.377
#> 6 x ~~ x 0.854 0.000 NA NA 0.854 0.854