| Type: | Package |
| Title: | Power Analysis via Monte Carlo Simulation for Correlated Data |
| Version: | 0.1.0 |
| Description: | A flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <doi:10.48550/arXiv.2209.08036>. |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | abind, boot, dplyr, doSNOW, foreach, ggplot2, MASS, magrittr, parallel, purrr, snow, sbgcop, rlang, reshape2, tibble, tidyr, tidyselect |
| RoxygenNote: | 7.2.1 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | BMA, bkmr, bws, infinitefactor, knitr, NHANES, qgcomp, rmarkdown, rstan, testthat, openxlsx |
| NeedsCompilation: | no |
| Packaged: | 2022-09-20 18:32:25 UTC; phuc |
| Author: | Phuc H. Nguyen 
[aut, cre] |
| Maintainer: | Phuc H. Nguyen <phuc.nguyen.rcran@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-09-21 08:50:05 UTC |
Statistical model that returns significance criterion
Description
This function creates a wrapper function for a statistical model and its applicable significance criterion. It finds relationships between a matrix of predictors and a vector of outcomes using the statistical model, and determines if the relationships are 'significant' according to pre-specified criterion for that model.
Usage
InferenceModel(model, name = NULL, ...)
Arguments
model |
A string of the name of a built-in statistical model or a function implements a statistical model and returns a list of significance criteria for each predictor. Built-in options include 'bma', 'bkmr', 'mixselect', 'bws', 'qgc', 'fin', 'glm'. |
name |
A string, name of the statistical model. Default to string input of model. |
... |
Additional keyword arguments for the statistical model |
Value
An InferenceModel object.
model |
a function that takes matrices of predictors and outcomes and returns a list of significance criteria. |
model_name |
a string. |
Examples
imod <- mpower::InferenceModel(model = 'glm', family = 'gaussian',
formula = y ~ Poverty*(poly(Age, 2) + HHIncome + HomeOwn + Education))
Correlated predictors generator
Description
This function creates a generative model for the correlated, mixed-scale predictors.
Usage
MixtureModel(
method = "estimation",
data = NULL,
G = NULL,
m = 100,
nudge = 1e-09,
sbg_args = list(nsamp = 1000),
cvine_marginals = list(),
cvine_dtypes = list(),
resamp_prob = NULL
)
Arguments
method |
A string, one of the three options "resampling", "estimation", or "cvine". Default is "estimation". See Details. |
data |
A dataframe or matrix, required for resampling" and "estimation" method. |
G |
A guesstimate pairwise correlation matrix for "cvine" method. See Details. |
m |
A positive number indicating uncertainty in the guesstimate G, larger means more uncertainty. Default is 100. |
nudge |
A number, default 10e-10 to add to the diagonal of the covariance matrix for numerical stability. |
sbg_args |
A list of named arguments, except Y, for function 'sbgcop.mcmc()'. |
cvine_marginals |
A named list describing the univariate distribution of each predictor. See Details. |
cvine_dtypes |
A named list describing the data type of each variable. |
resamp_prob |
A vector of sampling probability for each observation in data. Must sums to 1. |
Value
A MixtureModel object.
Details
There are three methods to generate data:
1. Resampling: if we have enough data of the predictors, we can resample to get realistic joint distributions and dependence among them.
2. Estimation: if we have a small sample from, for example, a pilot study, we can sample from a semi-parametric copula model (Hoff 2007) after learning the dependence and univariate marginals of the predictors.
3. C-vine: if no pilot data exists, we can still set rough guesstimate of the dependence and univariate marginals. The C-vine algorithm (Joe 2006) generates positive semi-definite correlation matrix given the guesstimate G. The guesstimate G is a symmetric p x p matrix whose ij-th item is between -1 and 1 and is the guesstimate correlation between predictor ith and jth. G doesn't need to be a valid correlation matrix. The method works well when values in G are not extreme (i.e., 0.999, -0.999). Built-in functions for univariate marginals include: 'qbeta' , 'qbinom', 'qcauchy', 'qchisq', 'qexp', 'qf', 'qgamma', 'qgeom', 'qhyper', 'qlogis', 'qlnorm', 'qmultinom', 'qnbinom', 'qnorm', 'qpois', 'qt', 'qunif', 'qweibull'.
References
Hoff P (2007). 'Extending the rank likelihood for semiparametric copula estimation.' Ann. Appl. Stat, 1(1), 265-283.
Joe H (2006). “Generating random correlation matrices based on partial correlations.”Journal of Multivariate Analysis, 97, 2177-2189.
Examples
data("nhanes1518")
xmod <- mpower::MixtureModel(nhanes1518, method = "resampling")
Outcome generator
Description
This function creates a generative model of the outcome given a matrix of predictors.
Usage
OutcomeModel(f, family = "gaussian", sigma = 1, f_args = list())
Arguments
f |
A string that describes the relationships between the predictors and
outcome or a function that takes an input matrix and returns a vector of
outcome: |
family |
A string, 'gaussian', 'binomial', or 'poisson' for continuous, binary, or count outcomes. |
sigma |
A number, Gaussian noise standard deviation if applicable. |
f_args |
A named list of additional arguments to f |
Value
An OutcomeModel object. Attributes:
f |
mean function. |
sigma |
a number for the Gaussian observation noise. |
family |
a string 'gaussian' or 'binomial'. |
Examples
# Define BMI as a ratio of weight and height plus random Gaussian error with standard deviation 1.
bmi_model <- mpower::OutcomeModel(f = 'weight/(height^2)', sigma = 1, family = 'gaussian')
Fits a BKMR model with significance criteria: PIP and group-wise PIP
Description
Fits a BKMR model with significance criteria: PIP and group-wise PIP
Usage
bkmr_wrapper(y, x, args = list())
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments, see R function 'bkmr::kmbayes()' |
Value
A list of vectors whose values are between 0 and 1
pip |
PIP for component-wise selection or conditional (with-in group) PIP for hierarchical variable selection. |
group_pip |
PIP for group-specific selection. |
time |
elapsed time to fit the model. |
Reference
Bobb JF, Henn BC, Valeri L, Coull BA (2018). “Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression.”Environ-mental Health,17(67).doi:10.1186/s12940-018-0413-y.
Fits a linear model with Bayesian model selection with significance criteria: PIP and posterior probability of nonzero coefficients being on one side of zero.
Description
Fits a linear model with Bayesian model selection with significance criteria: PIP and posterior probability of nonzero coefficients being on one side of zero.
Usage
bma_wrapper(y, x, args = list())
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments see R function 'BMA::bic.glm()'. |
Value
A list of vectors whose values are between 0 and 1
beta |
The smaller posterior probability of the coefficients being to one side of zero: min(Pr(beta >0), Pr(beta<0)). |
pip |
PIP of the effect not being zero. |
time |
elapsed time to fit the model. |
Reference
Raftery A, Hoeting J, Volinsky C, Painter I, Yeung KY (2021).BMA: Bayesian model averaging. R package version 3.18.15.
Fits a Bayesian weighted sums
Description
Fits a Bayesian weighted sums
Usage
bws_wrapper(y, x, args = list(iter = 2000))
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments see R 'bws::bws()“ function. |
Value
A list
beta |
The smaller posterior probability of the combined overall effect being to one side of zero: min(Pr(beta >0), Pr(beta<0)). The same for all predictor. |
weights |
The 95% CI of the contribution of each predictor to the overall effect. Between 0 and 1. |
time |
elapsed time to fit the model. |
Reference
Hamra GB, MacLehose RF, Croen L, Kauffman EM, Newschaffer C (2021). “Bayesian weighted sums: a flexible approach to estimate summed mixture effects.” International Journal of Environmental Research and Public Health, 18(4), 1373.
Convert a correlation matrix into a partial correlation matrix
Description
Convert a correlation matrix into a partial correlation matrix
Usage
cor2partial(r)
Arguments
r |
A correlation matrix |
Value
A partial correlation matrix
Citation: Daniel Lewandowski, Dorota Kurowicka, Harry Joe, Generating random correlation matrices based on vines and extended onion method, Journal of Multivariate Analysis, Volume 100, Issue 9, 2009, Pages 1989-2001, ISSN 0047-259X, https://doi.org/10.1016/j.jmva.2009.04.008.
Description
Citation: Daniel Lewandowski, Dorota Kurowicka, Harry Joe, Generating random correlation matrices based on vines and extended onion method, Journal of Multivariate Analysis, Volume 100, Issue 9, 2009, Pages 1989-2001, ISSN 0047-259X, https://doi.org/10.1016/j.jmva.2009.04.008.
Usage
cvine(d, alpha = 10, beta = 10, S = NULL, m = 100)
Arguments
d |
Number of dimension |
alpha |
Parameter for Beta distribution |
beta |
Parameter for Beta distribution |
S |
A 'guess' of the correlation matrix |
m |
A number that indicates how much the random matrices vary from S |
Value
A random positive-definite correlation matrix
Monte Carlo approximation of the SNR
Description
Monte Carlo approximation of the SNR
Usage
estimate_snr(ymod, xmod, m = 5000, R = 100)
Arguments
ymod |
A OutcomeModel object |
xmod |
A MixtureModel object |
m |
Number of MC samples |
R |
Number of bootstrap replicates |
Value
An estimate SNR and 95-percent CI.
Fits a Bayesian factor model with interactions
Description
Fits a Bayesian factor model with interactions
Usage
fin_wrapper(y, x, args = list(nrun = 2000))
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments see R function ‘infinitefactor::interactionDL()' in ’infinitefactor' package. |
Value
A list of vectors whose values are between 0 and 1
beta |
The smallest posterior probability of the coefficients being to one side of zero for either main effect or interaction: min(Pr(beta >0), Pr(beta<0)). |
linear_beta |
The smaller of posterior probability of the main effects being to one side of zero. |
interact_beta |
Same as linear_beta but for pair-wise interactions. |
time |
elapsed time to fit the model. |
Reference
Ferrari F, Dunson DB (2020). “Bayesian factor analysis for inference on interactions.”Journal of the American Statistical Association, 0(0), 1–12.
Fits the model to given data and gets a list of significance criteria
Description
Fits the model to given data and gets a list of significance criteria
Usage
fit(mod, x, y)
Arguments
mod |
An InferenceModel object |
x |
A matrix of predictors |
y |
A vector of outcome |
Value
A list of some of the following significance criteria:
beta |
The smaller posterior probability of being to one side of zero for linear term, given either the main effect or interaction is non-zero. Applicable to 'bma', 'bws', 'fin', and 'mixselect' model. |
interact_beta |
Same as linear_beta but for pair-wise interactions. Applicable to 'fin' model. |
pip |
Posterior inclusion probability (PIP) of either a linear or non-linear effect. Applicable to 'bma', 'bkmr', and 'mixselect' model. |
group_pip |
PIP of either a linear or non-linear effect. Applicable to 'bkmr' model. |
linear_pip |
PIP of a linear effect. Applicable to 'mixselect' model. |
gp_pip |
PIP of a non-linear effect. Applicable to 'mixselect' model. |
pval |
The p-value of the combined effect, the same for all predictors. Applicable to 'glm', and 'qgc' model. |
time |
elapsed time to fit the model. |
Generates a matrix of n observations of p predictors
Description
Generates a matrix of n observations of p predictors
Usage
genx(obj, n)
Arguments
obj |
A MixtureModel object. |
n |
An integer, number of observations to generate. |
Value
A (n x p) dataframe.
Generates a vector of outcomes
Description
Generates a vector of outcomes
Usage
geny(obj, x)
Arguments
obj |
An OutcomeModel object |
x |
An (n x p) matrix of predictors |
Value
An n-vector of outcomes
Fits a generalized linear model
Description
Fits a generalized linear model
Usage
glm_wrapper(y, x, args = list())
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments see R 'glm' function. |
Value
A list
pval |
The p-value of the linear main effect. |
time |
elapsed time to fit the model. |
Visualize marginals and Gaussian copula correlations of simulated data
Description
Visualize marginals and Gaussian copula correlations of simulated data
Usage
mplot(obj, split = TRUE)
Arguments
obj |
A MixtureModel object. |
split |
A logical, whether to display numbers on half of the covariance matrix. |
Value
A 'ggplot2' graphics.
mpower: Power analysis using Monte Carlo for correlated predictors.
Description
This package provides tools to set up simulations for power calculation
NHANES data from 2015-2016 and 2017-2018 cycles
Description
Combined NHANES data from the 2015-2016 and 2017-2018 cycles The weights have been adjusted according to https://wwwn.cdc.gov/nchs/nhanes/tutorials/module3.aspx
Usage
nhanes1518
Format
Data with the following variables:
- SEQN
Respondent sequence number
- WTINT4YR
Full sample 4 year interview weight
- WTMEC4YR
Full sample 4 year MEC exam weight
- WTSB4YR
Environmental B 4-year weights
- RIDSTATR
Interview/Examination status
- RIAGENDR
Gender of the participant
- RIDAGEYR
Age in years of the participant at the time of screening. Individuals 80 and over are top-coded at 80 years of age
- INDFMPIR
A ratio of family income to poverty guidelines
- RIDRETH1
Race/Hispanic origin
- INDHHIN2
Total household income (reported as a range value in dollars)
- BMXBMI
Body Mass Index (kg/m**2)
- BMXWAIST
Waist Circumference (cm)
- BMXWT
Weight (kg)
- BMXHT
Standing Height (cm)
- URXUCR
Creatinine, urine (mg/dL)
- URXCNP
MCNP Mono(carboxyisononyl) phthalate (ng/mL), LLOD = 0.2
- URXCOP
MCOP Mono(carboxyisoctyl) phthalate (ng/mL), LLOD = 0.3
- URXECP
MECPP Mono-2-ethyl-5-carboxypentyl phthalate (ng/mL), LLOD = 0.4
- URXHIBP
MHIBP phthalate (ng/mL), LLOD = 0.4
- URXMBP
MnBP Mono-n-butyl phthalate (ng/mL), LLOD = 0.4
- URXMC1
MCPP Mono-(3-carboxypropyl) phthalate (ng/mL), LLOD = 0.4
- URXMCOH
MCOCH phthalate (ng/mL), LLOD = 0.5
- URXMEP
MEP Mono-ethyl phthalate (ng/mL), LLOD = 1.2
- URXMHBP
Mono-3-hydroxy-n-butyl phthalate (ng/mL), LLOD = 0.4
- URXMHH
MEHHP Mono-(2-ethyl-5-hydroxyhexyl) phthalate (ng/mL), LLOD = 0.4
- URXMHNC
Cyclohexane 1,2-dicarboxylic acid monohydroxy isononyl ester (ng/mL), LLOD = 0.4
- URXMHP
MEHP Mono-(2-ethyl)-hexyl phthalate (ng/mL), LLOD = 0.8
- URXMIB
MiBP Mono-isobutyl phthalate (ng/mL), LLOD = 0.8
- URXMNP
MCNP Mono-isononyl phthalate (ng/mL), LLOD = 0.9
- URXMOH
MEOHP Mono-(2-ethyl-5-oxohexyl) phthalate (ng/mL), LLOD = 0.2
- URXMZP
MBzP Mono-benzyl phthalate (ng/mL), LLOD = 0.3
- URDCNPLC, URDCOPLC, URDECPLC, URDHIBLC, URDMBPLC, URDMC1LC, URDMCOLC, URDMEPLC, URDMHBLC, URDMHHLC, URDMCHLC, URDMHPLC, URDMIBLC, URDMNPLC, URDMOHLC, URDMZPLC
Phthalates comment code for whether the measurement is under the limit of detection
Source
Detailed documentation of the phthalates variables can be found here:
Partial correlations between elements in x and elements in y
Description
Partial correlations between elements in x and elements in y
Usage
partial(r, x, y)
Arguments
r |
A correlation matrix |
x |
A vector of indices |
y |
A vector of indices |
Value
A partial correlation matrix
Plot summaries of power simulation
Description
Plot summaries of power simulation
Usage
plot_summary(sim, crit, thres, digits = 3, how = "greater")
Arguments
sim |
A Sim or a SimCurve object, output from 'sim_power()' or 'sim_curve()'. |
crit |
A string specifying the significance criteria. |
thres |
A number or vector of numbers specifying the thresholds of "significance". |
digits |
An integer for the number of decimal points to display. |
how |
A string, whether to compare the criterion 'greater' or 'lesser' than the threshold. |
Value
A 'ggplot2' graphics.
Fits a linear Quantile G-Computation model with no interactions
Description
Fits a linear Quantile G-Computation model with no interactions
Usage
qgcomp_lin_wrapper(y, x, args = list())
Arguments
y |
A vector of outcome |
x |
A matrix of predictors |
args |
A list of arguments see R function 'qgcomp::qgcomp.noboot()'. |
Value
A list
pval |
The p-value of the combined effect, the same for all predictors. |
pos_weights |
Positive weights. See 'qgcomp' package. |
neg_weights |
Negative weights. See 'qgcomp' package. |
time |
elapsed time to fit the model. |
Reference
Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ (2020). “A Quantile-based g-computation approach to addressing the effects of exposure mixtures.”Environmental Health Perspectives, 128(4).
Quantile function for the multinomial distribution, size = 1
Description
Quantile function for the multinomial distribution, size = 1
Usage
qmultinom(p, probs)
Arguments
p |
A quantile. |
probs |
A vector of probabilities for each level. |
Value
Gives the quantile function
Convert R-squared value to the SNR
Description
Convert R-squared value to the SNR
Usage
rsq2snr(r)
Arguments
r |
R-squared value |
Rescale the mean function of an OutcomeModel to meet a given SNR
Description
Rescale the mean function of an OutcomeModel to meet a given SNR
Usage
scale_f(snr, ymod, xmod, m = 5000)
Arguments
snr |
A SNR |
ymod |
A OutcomeModel object to modify |
xmod |
A MixtureModel object |
m |
Number of MC samples to estimate the SNR of a proposed noise variance |
Value
A new OutcomeModel object
Rescale the noise variance of a Gaussian OutcomeModel to meet a given SNR
Description
Rescale the noise variance of a Gaussian OutcomeModel to meet a given SNR
Usage
scale_sigma(snr, ymod, xmod, m = 5000)
Arguments
snr |
A SNR |
ymod |
A OutcomeModel object to modify |
xmod |
A MixtureModel object |
m |
Number of MC samples to estimate the SNR of a proposed noise variance |
Value
A new OutcomeModel object
This function updates values in an OutcomeModel object
Description
This function updates values in an OutcomeModel object
Usage
set_value(obj, name, value)
Arguments
obj |
An OutcomeModel object |
name |
A string for name of the attribute to be changed |
value |
An appropriate data type |
Power curve using Monte Carlo simulation
Description
This function can be used to create power curves by calling sim_power() on combinations of many sample sizes and signal-to-noise ratio (SNR).
Usage
sim_curve(
xmod,
ymod,
imod,
s = 100,
n = 100,
cores = 1,
file = NULL,
errorhandling = "stop",
snr_iter = 10000,
cluster_export = c()
)
Arguments
xmod |
A MixtureModel object. |
ymod |
One or a list of OutcomeModel object(s). |
imod |
An InferenceModel object. |
s |
An integer for the number of Monte Carlo simulations. |
n |
An integer or a vector of sample sizes. |
cores |
An integer for the number of processing cores. When cores > 1, parallelism is automatically applied. |
file |
A string, a file name with no extension to write samples to periodically. By default write to an RDS file. |
errorhandling |
A string "remove", "stop", or "pass". If an error occurs in any iteration, remove that iteration (remove), return the error message verbatim in the output (pass), or terminate the loop (stop). Default is "remove". See R package 'foreach' for more details. |
snr_iter |
An integer for number of Monte Carlo samples to estimate SNR. |
cluster_export |
A vector of functions to pass to the parallel-processing clusters. |
Value
A SimCurve object with the following attributes:
s |
a number of simulations. |
snr |
a real number or array of real numbers for SNR of each OutcomeModel. |
n |
a number or vector of sample sizes. |
xmod |
the MixtureModel used. |
ymod |
the OutcomeModel used. |
imod |
the InferenceModel used. |
sims |
a list of simulation output matrices. |
Examples
data("nhanes1518")
chems <- c("URXCNP", "URXCOP", "URXECP", "URXHIBP", "URXMBP", "URXMC1",
"URXMCOH", "URXMEP","URXMHBP", "URXMHH", "URXMHNC", "URXMHP", "URXMIB",
"URXMNP", "URXMOH", "URXMZP")
chems_mod <- mpower::MixtureModel(nhanes1518[, chems], method = "resampling")
bmi_mod <- mpower::OutcomeModel(f = "0.2*URXCNP + 0.15*URXECP +
0.1*URXCOP*URXECP", family = "binomial")
logit_mod <- mpower::InferenceModel(model = "glm", family = "binomial")
logit_out <- mpower::sim_curve(xmod=chems_mod, ymod=bmi_mod, imod=logit_mod,
s=20, n=c(500, 1000), cores=2, snr_iter=1000)
logit_df <- summary(logit_out, crit="pval", thres=0.05, how="lesser")
plot_summary(logit_out, crit="pval", thres=0.05, how="lesser")
Power analysis using Monte Carlo simulation
Description
Power analysis using Monte Carlo simulation
Usage
sim_power(
xmod,
ymod,
imod,
s = 100,
n = 100,
cores = 1,
file = NULL,
errorhandling = "stop",
snr_iter = 10000,
cluster_export = c()
)
Arguments
xmod |
A MixtureModel object. |
ymod |
An OutcomeModel object. |
imod |
An InferenceModel object. |
s |
An integer for number of Monte Carlo simulations. |
n |
An integer for sample size in each simulation. |
cores |
An integer for number of processing cores. When cores > 1, parallelism is automatically applied. |
file |
A string, a file name with no extension to write samples to periodically. By default write to an RDS file. |
errorhandling |
A string "remove", "stop", or "pass". If an error occurs in any iteration, remove that iteration (remove), return the error message verbatim in the output (pass), or terminate the loop (stop). Default is "remove". See R package 'foreach' for more details. |
snr_iter |
An integer for number of Monte Carlo samples to estimate SNR |
cluster_export |
A vector of functions to pass to the parallel-processing clusters |
Value
A PowerSim object. Attributes:
s |
a number of simulations. |
snr |
a real number for SNR of the OutcomeModel. |
n |
a number of sample sizes. |
xmod |
the MixtureModel used. |
ymod |
the OutcomeModel used. |
imod |
the InferenceModel used. |
sims |
an output matrices. |
Examples
data("nhanes1518")
chems <- c("URXCNP", "URXCOP", "URXECP", "URXHIBP", "URXMBP", "URXMC1",
"URXMCOH", "URXMEP","URXMHBP", "URXMHH", "URXMHNC", "URXMHP", "URXMIB",
"URXMNP", "URXMOH", "URXMZP")
chems_mod <- mpower::MixtureModel(nhanes1518[, chems], method = "resampling")
bmi_mod <- mpower::OutcomeModel(f = "0.2*URXCNP + 0.15*URXECP +
0.1*URXCOP*URXECP", family = "binomial")
logit_mod <- mpower::InferenceModel(model = "glm", family = "binomial")
logit_out <- mpower::sim_power(xmod=chems_mod, ymod=bmi_mod, imod=logit_mod,
s=100, n=2000, cores=2, snr_iter=2000)
logit_df <- summary(logit_out, crit="pval", thres=0.05, how="lesser")
plot_summary(logit_out, crit="pval", thres=0.05, how="lesser")
Tabular summaries of power simulation
Description
Tabular summaries of power simulation
Usage
summary(sim, crit, thres, digits = 3, how = "greater")
Arguments
sim |
A Sim or a SimCurve object, output from 'sim_power()' or 'sim_curve()'. |
crit |
A string specifying the significance criteria. |
thres |
A number or vector of numbers specifying the thresholds of "significance". |
digits |
An integer for the number of decimal points to display. |
how |
A string, whether to compare the criterion 'greater' or 'lesser' than the threshold. |
Value
A data.frame summary of power for each predictor for each combination of thresholds, sample size, signal-to-noise ratios.