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power4test(),
x_from_power(), and Related FunctionsAdded an argument rejection_rates_args to
power4test(). When calling power4test(), users
can in advance some settings for rejection rates, such as collapsing all
tests into one. They will be used when calling
rejection_rates(). They will also be stored internally, and
used by power4test_by_n(), x_from_power(), and
similar functions that used a power4test object as an
input. (0.1.1.39)
Updated x_from_power() and related functions to
allow users specifying how tests will be collapsed ("none"
is not allowed), by setting the argument
rejection_rates_args. (0.1.1.39, 0.1.1.41)
To simplify the workflows for some common mediation models, a set of “quick” functions have been added. Users can do common tasks using just one function.
Added q_power_mediation() and friends for common
mediation models. Users can do most steps in one call. (0.1.1.18,
0.1.1.19)
Updated the quick functions (q_power_mediation() and
friends) to have one more mode, "n". Probabilistic
bisection is the default algorithm for this mode. Other methods have
been updated for this mode. (0.1.1.56)
Changed the default method for rejection rate confidence
intervals to Wilson’s (1927) method. For backward compatibility, use
options(power4mome.ci_method = "norm") to set the default
method to normal approximation. (0.1.1.2)
Updated summarize_tests() and
rejection_rates() to use the extrapolation method by Boos
and Zhang (2000) if the number of resamples for bootstrapping or Monte
Carlo is of the supported values. (0.1.1.5, 0.1.1.6, 0.1.1.7)
Added merge_all_tests to
rejection_rates() to support merging all tests into one.
The argument collapse can then be used for collapsing
several different tests, not just for one test with several results.
(0.1.1.28)
Added the p_adjust_method argument to some tests, as
well as the rejection_rates method and
summarize_tests(). Users can adjust p-values using
p.adjust() when there are more than one test in a test
function set to test_fun, or when merging several tests in
summarize_tests(). This feature is used to estimate power
when multiple-comparison adjustment is used, such as false discover rate
(FDR) or Bonferroni correction. (0.1.1.31)
Added two levels of effects, sm for
small-to-moderate, and ml for moderate-to-large.
(0.1.1.11)
Added loading_difference and reference
for sim_data(). The population standardized factor loadings
of indicators of a factor can have different loadings, and the first
indicator (the default reference indicator) can be the indicator with
the strongest, weakest, or medium loadings. (0.1.1.70)
Added the test_method argument for tests of indirect
effects and their variants to use asymmetric p-values to do the
tests. This change is for using the efficient method by Boos and Zhang
(2000) to estimate the rejection rates for resampling methods.
(0.1.1.3)
Updated test_k_indirect_effects() to support
computing and testing the total indirect effect. (0.1.1.65)
When calling the function power4test(), the generated
data can now be further processed by data processors: functions
to manipulate the generated data, such as introducing missing values or
computing scale scores. Some built-in processors are included, but
user-supplied functions can also be used.
Added scale_scores() for replacing the indicator
scores by the corresponding scale scores before fitting a model. This
allows estimating power when the data generating model is a latent
factor model with indicators but the model will be fitted to the scale
scores instead of the indicators. (0.1.1.43)
Added missing_values() for generating missing
values. This allows sample size determination that takes into account
probable missing data. This also allows comparing the levels of power
between two different methods to handle missing data, such as full
information maximum likelihood versus listwise deletion.
(0.1.1.66)
Added ordinal_variables() for converting continuous
indicator variables to ordinal variables. This allows estimating power
when the indicators are ordinal variables, such as binary items or
5-point Likert scales. This also allows comparing the levels of power
between different numbers of response options. (0.1.1.67,
0.1.1.69)
The argument sim_data_name of
process_data is now default to "data". It is
not a required argument. (0.1.1.43)
Support for multigroup models have been substantially improved. Users can generate data from a multigroup models. Some tests can also be used for estimating power in detecting group differences, such as the group difference in an indirect effect.
Added n_ratio to power4test() and
related functions to supporting controlling the sample sizes of
multigroup models using one single value for n. This allows
functions such as n_from_power() and
n_region_from_power() to support multigroup models.
(0.1.1.44)
test_cond_indirect() and
test_cond_indirect_effects() now support multigroup models,
although test_cond_indirect_effects() only support either a
path with moderators (wlevels) or a path between groups,
but not both. (0.1.1.45)
Updated test_cond_indirect_effects() to support
computing and testing group differences in indirect effects for
multigroup models. (0.1.1.46)
Updated test_parameters() to support doing
likelihood ratio tests to test constraining pairs of parameters in
multigroup models, by using the argument compare_groups.
(0.1.1.47)
Added the argument exclude_var to
test_parameters() to exclude variances and error variances,
useful for multigroup models. (0.1.1.47)
Updated power4test() to support updating a parameter
in one group when the population model is a multigroup model. Use
"y ~ x.g2" to denote "y ~ x" in Group 2. If
the suffix is omitted, the parameter is assumed to be in Group 1. The
function power4test_by_es() now also support multigroup
models due to this change. (0.1.1.48)
Added test_group_equal() for testing equality
constraints between groups. (0.1.1.49)
If the model is a multigroup model, descriptive statistics for
the generated data are printed by group, using
psych::describeBy(). (0.1.1.76)
The probabilistic bisection algorithm proposed by Waeber et
al. (2013) has been added. For some scenarios, it allows for a more
efficient search for a sample size with sufficient power, especially
when a high precision is desired (by using a large value of
nrep).
"probabilistic_bisection" for
x_from_power() and friends. (0.1.1.51)Change the default of test_long to TRUE
for the print method of power4test and related
objects. (0.1.1.41)
For some functions, nrep will be included in the
output if its values vary across rows. (0.1.1.57)
The default values of n of q-functions now depends
on the mode and algorithm. (0.1.1.57)
Added some helpers, R_for_bz() and
Rs_bz_supported(), for users to use the Boos-Zhang-2000
method without remembering the number of resamples supported.
(0.1.1.61)
Updated the quick functions (q_power_mediation() and
friends). When mode is "n" or
"region", it is optional to set n. If not set,
it will be determined internally. (0.1.1.63)
Added the rejection_rates() method to
x_from_power, n_region_from_power, and
q_power_mediation objects. (0.1.1.71)
Moved delta_tol and last_k to
variants, such that users can change them through
control when calling x_from_power() and
friends. (0.1.1.72)
Changed the default algorithms to be used in
x_from_power(). (0.1.1.74)
Response proportions will be printed with
data_long = TRUE if a variable has 10 or fewer unique
values (e.g., ordinal variables), using
psych::responseFrequency(). (0.1.1.77)
Improved the function for extending the initial interval before doing a bisection search. (0.1.1.1)
Optimized the search by bisection, to make use of value already tried and store all values tried. (0.1.1.14)
The bisection algorithm has been improved in handling unusual intervals. (0.1.1.21)
Skipped the check for combining objects in the bisection algorithm because they must be identical in the model. (0.1.1.23)
Updated the bisection algorithm to use power_curve()
to assist finding the solution. If estimated solution inside an
interval, use it instead of the mean. (0.1.1.37)
More checks for the solution in the bisection algorithm. When extending an interval, the power curve will also be used. (0.1.1.38)
goal = "close_enough" and all three types of
what ("point", "lb", and
"ub"). (0.1.1.34)Adjust the initial_nrep in probabilistic bisection
such that the rejection rate will not be exactly equal to the target
power. (0.1.1.52)
For probabilistic bisection, the initial interval will no longer be adjusted. This algorithm should be used with a wide enough initial interval because the interval will not be adjusted during the search (for now). (0.1.1.56)
Increased delta_tol fo PBA (2 for n and
.002 for es). (0.1.1.57)
Added pba_diagnosis() to generate plots related to
the search history of probabilistic bisection algorithm. For advanced
users and developers. (0.1.1.58, 0.1.1.62)
Updated the help page of x_from_power() to describe
the probabilistic bisection method. (0.1.1.60)
Updated test functions that used manymome to store
the number of bootstrap or Monte Carlo samples and the number of
estimates less than zero. Used by the Boos-Zhang-2000 method.
(0.1.1.4)
Updated all test functions to include p-values in the output. (0.1.1.30)
Test functions using manymome will now automatically
use "pvalues" as the test method if R is a
value supported by the Boos-Zhang-method. (0.1.1.53)
Improved x_from_power() and friends (e.g.,
n_from_power() and n_region_from_power()) to
make use of previous trials. (0.1.1.13)
x_from_power() now detects whether a test has more
than one result (e.g., testing two parameters but omnibus
is "none"). If yes, it will throw an error.
(0.1.1.10)
Updated extend_interval() to handle intervals with
nearly equal function values. (0.1.1.12)
Fixed duplicated values of x when extending the interval. (0.1.1.15)
Functions that print a call will replace object with
<hidden> if it is not a symbol. (0.1.1.16)
Functions that print a call will replace the function with the original function name if it is not a symbol. (0.1.1.17)
The arguments final_nrep and final_R of
x_from_power() and its wrappers will use stored values if
available. (0.1.1.20)
Revised c.power4test_by_n() to allow for minor
differences in error variances when they are determined by Monte Carlo
simulation. (0.1.1.24)
Properly support a model with only one latent variable. (0.1.1.25)
Vertically displace the labels of sample sizes in
plot.n_region_from_power() to prevent overlapping.
(0.1.1.26)
Fixed the printing of effects in a multigroup model with within-group moderation. (0.1.1.27)
The function power4test() now properly reuse
arguments such as parallel and ncores when
adding a new test to a power4test object.
(0.1.1.29)
Added one model to power_curve().
(0.1.1.34)
Added nls_options to power_curve() to
configure the use of nls(), such as when it should not be
attempted. (0.1.1.36)
Updated sim_data(). Lines for the indicators will
not be added to the model syntax if scale scores are used.
(0.1.1.43)
The attribute number_of_indicators will be added to
the generated data before passing to process_data. Used by
data processors such as scale_scores(). (0.1.1.43)
The arguments in control are now passed directly to
the entry point of the algorithms. Potential conflicts due to partial
matching should be prevented inside these algorithms (0.1.1.50)
Updated power4test() to make one cluster that will
be used in all stages. (0.1.1.55)
Load balancing is no longer used by default, to ensure the
results are reproducible. To enable load balancing, set the option
"power4mome.use_lb" to TRUE using
options(). (0.1.1.56)
Updated the internal functions
summarize_one_test_vector () and
summarize_one_test_data_frame() to handle failed
replications properly. (0.1.1.59)
The internal helper do_FUN() for parallel processing
now export functions defined in the global environment to clusters,
because they may be used in arguments such as process_data.
(0.1.1.64)
The internal helper do_FUN() will reproduce the
search path by loading the packages in the workers. (0.1.1.69)
Added and updated technical appendices for the three algorithms
used in x_from_power() and friends. (0.1.1.73)
Fixed the help for the argument mode of
q_power_mediation() and friends. (0.1.1.75)
Simplify some examples to meet the five-second requirement. (0.1.1.78)
Replaced a test using lavaan by a test using
lm. Also improved a test for stability. (0.2.1)
Fixed a bug in extending intervals in the bisection algorithm, and also improved the way intervals are extended. (0.1.1.35)
Fixed fix_many_lm_model() to handle model syntax
with a regression model spanning more than one line and fitted by
lm(). (0.1.1.54)
lavaan, 0.9-12. (0.1.1)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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