README.md
In animalSymbolicum/assumptions: Look up, plot, simulate and test statistical assumptions
The purpose of the ‘assumptions’ R-package is threefold:
-
It tries to raise awareness of statistical assumptions. Before
conducting a statistical test, its assumptions are printed to the
console and the user is asked for confirmation after a graphical
assessment.
-
It collects tests and graphs to assess statistical assumptions and
simplifies looking up specific assumptions.
-
All the tests are made available for simulations including complex
decision strategies for picking a statistical test for different
diagnosed assumption violations.
Install package
To install ‘assumptions’ R-package from github run:
require(remotes)
remotes::install_github("animalSymbolicum/assumptions")
library(assumptions)
Hypothesis Tests
The assumptions package seeks to expand common statistical methods with
the notice of their assumptions for educational purpose. To conduct a
parametric or non-parametric hypothesis test call:
# t-test
asm_ttest(rnorm(20), color = F)
#> The asm_ttest makes three assumptions for the input variable(s) x (and y):
#> I ) the sample distribution is normal or the sample size is big enough that
#> the distribution of the sample means likely converge to normal.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> ***********************************************************************************
#> If you confirm to acknowledge that under violation(s) of the assumptions the t-test
#> the t-test results might be wrong.
#> Press [enter] to confirm
#> Press [enter] for the next plot
#> Press [enter] for the next plot
#> Press [enter] to run Welch-Test - if you are convinced your data fulfills the assumptions.
#>
#> One Sample t-test
#>
#> data: x
#> t = 0.35822, df = 19, p-value = 0.7241
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> -0.3260519 0.4607036
#> sample estimates:
#> mean of x
#> 0.06732585
# wilcoxon test
asm_wilcox(rnorm(20), color = F)
#> If paired = FALSE -
#> the asm_wilcox makes three assumptions for the input variable(s) x (and y):
#> I ) are at least ordinal scaled.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> If paired = TRUE -
#> the asm_wilcox makes four assumptions for the input variable(s) x (and y):
#> I ) are at least interval scaled.
#> II ) the distribution of the calculated differences is symmetric.
#> III) are identically distributed, which means each value comes from the same distribution.
#> IV ) are independent, which means each value is not influenced by any other value.
#> ******************************************************************************************
#> If you confirm to acknowledge that under violation(s) of the assumptions the wilcoxon test
#> the results might be wrong.
#> Press [enter] to confirm
#> Press [enter] for the next plot
#> Press [enter] for the next plot
#> Press [enter] to run wilcoxon test - if you convinced your data fulfills the assumptions
#>
#> Wilcoxon signed rank exact test
#>
#> data: x
#> V = 129, p-value = 0.3884
#> alternative hypothesis: true location is not equal to 0
Look Up assumptions
Common statistical methods assumptions can be looked up with:
# get assumptions of t.test
assumptions(t.test, color = F)
#> The t.test makes three assumptions for the input variable(s) x (and y):
#> I ) the sample distribution is normal or the sample size is big enough that
#> the distribution of the sample means likely converge to normal.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> As two variables are used, the two variables can have two different distributions if
#> the Welch Test is used (var.equal=F).
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
# get assumptions of wilcox.test
assumptions(wilcox.test, color = F)
#> If paired = FALSE -
#> the wilcox.test makes three assumptions for the input variable(s) x (and y):
#> I ) are at least ordinal scaled.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> If paired = TRUE -
#> the wilcox.test makes four assumptions for the input variable(s) x (and y):
#> I ) are at least interval scaled.
#> II ) the distribution of the calculated differences is symmetric.
#> III) are identically distributed, which means each value comes from the same distribution.
#> IV ) are independent, which means each value is not influenced by any other value.
Overview of assumptions assessment tests (pre-tests)
All tests and graphs to assess specific assumptions such as the
distribution, independence, identical distribution (homogeneity) or
randomness are collected in a central catalogue table.
asm_library()
#> Assumption Name Package Help
#> 1: distribution Lilliefors PoweR <list[1]>
#> 2: distribution Anderson-Darling PoweR <list[1]>
#> 3: distribution D'Agostino-Pearson PoweR <list[1]>
#> 4: distribution Jarque-Bera PoweR <list[1]>
#> 5: distribution Doornik-Hansen PoweR <list[1]>
#> 6: distribution Gel-Gastwirth PoweR <list[1]>
#> 7: distribution 1st Hosking PoweR <list[1]>
#> 8: distribution 2nd Hosking PoweR <list[1]>
#> 9: distribution 3rd Hosking PoweR <list[1]>
#> 10: distribution 4th Hosking PoweR <list[1]>
#> 11: distribution 1st Bontemps-Meddahi PoweR <list[1]>
#> 12: distribution 2nd Bontemps-Meddahi PoweR <list[1]>
#> 13: distribution Brys-Hubert-Struyf PoweR <list[1]>
#> 14: distribution Bonett-Seier PoweR <list[1]>
#> 15: distribution Brys-Hubert-Struyf & Bonett-Seier PoweR <list[1]>
#> 16: distribution Shapiro-Wilk PoweR <list[1]>
#> 17: distribution Shapiro-Francia PoweR <list[1]>
#> 18: distribution Gel-Miao-Gastwirth PoweR <list[1]>
#> 19: distribution R_n PoweR <list[1]>
#> 20: distribution X_{APD} PoweR <list[1]>
#> 21: distribution Z_{EPD} PoweR <list[1]>
#> 22: randomness Bartels Rank randtests <list[1]>
#> 23: randomness Cox Stuart randtests <list[1]>
#> 24: randomness Difference Sign randtests <list[1]>
#> 25: randomness Rank randtests <list[1]>
#> 26: randomness Runs randtests <list[1]>
#> 27: homogeneity F-Test stats <list[1]>
#> 28: homogeneity Bartlett ExpDes <list[1]>
#> 29: homogeneity Layard ExpDes <list[1]>
#> 30: homogeneity Levene ExpDes <list[1]>
#> 31: independence Box-Pierce stats <list[1]>
#> 32: independence Ljung-Pierce stats <list[1]>
#> 33: iid Spanos Auxiliary Regression assumptions <list[1]>
#> 34: homogeneity Graph Boxplot assumptions <list[1]>
#> 35: distribution Graph Dotplot assumptions <list[1]>
#> 36: distribution Graph ecdf assumptions <list[1]>
#> 37: distribution Graph Histogram assumptions <list[1]>
#> 38: distribution Graph Mean Increase assumptions <list[1]>
#> 39: distribution Graph P-P-Plot assumptions <list[1]>
#> 40: distribution Graph Q-Q-plot assumptions <list[1]>
#> 41: randomness Graph Randruns assumptions <list[1]>
#> 42: randomness Graph Differencesign assumptions <list[1]>
#> 43: independence Graph t-Plot assumptions <list[1]>
#> 44: distribution Graph Var Increase assumptions <list[1]>
#> 45: homogeneity Graph Window assumptions <list[1]>
#> Assumption Name Package Help
#> SimulationName
#> 1: pre_K_S
#> 2: pre_AD
#> 3: pre_K_2
#> 4: pre_JB
#> 5: pre_DH
#> 6: pre_RJB
#> 7: pre_T_Lmom
#> 8: pre_T_Lmom_1
#> 9: pre_T_Lmom_2
#> 10: pre_T_Lmom_3
#> 11: pre_BM_3_4
#> 12: pre_BM_3_6
#> 13: pre_T_MC_LR
#> 14: pre_T_w
#> 15: pre_T_MC_LR_T_w
#> 16: pre_W
#> 17: pre_W_Quote
#> 18: pre_R_sJ
#> 19: pre_R_n
#> 20: pre_X_APD
#> 21: pre_Z_EPD
#> 22: pre_bartels.rank
#> 23: pre_cox.stuart
#> 24: pre_difference.sign
#> 25: pre_rank
#> 26: pre_runs
#> 27: pre_ftest
#> 28: pre_bartlett
#> 29: pre_levene
#> 30: pre_oneillmathews
#> 31: pre_Box.Pierce
#> 32: pre_Ljung.Box
#> 33: pre_asm_preJoint
#> 34: -
#> 35: -
#> 36: -
#> 37: -
#> 38: -
#> 39: -
#> 40: -
#> 41: -
#> 42: -
#> 43: -
#> 44: -
#> 45: -
#> SimulationName
Single assessment of an assumption
Single or combinations of assumptions can be assessed by calls like:
# only first test selected as example
asm_distribution(rnorm(10), 1)
#> Name Help pvalue
#> 1: Lilliefors <list[1]> 0.8682802
asm_independence(rnorm(10), 1)
#> Name Help pvalue
#> 1: Box-Pierce <list[1]> 0.7579546
asm_randomness(rnorm(10), 1)
#> Name Help pvalue
#> 1: Bartels Rank <list[1]> 0.9004949
asm_homogeneity(list(A=rnorm(10), B=rnorm(10)), 1)
#> Name Help pvalue
#> 1: F-Test <list[1]> 0.8583127
# a combination of tests
asm_preTests(rnorm(10), c("Shapiro-Wilk", "Rank", "Ljung-Pierce"))
#> Name Help pvalue
#> 1: Shapiro-Wilk <list[1]> 0.35093862
#> 2: Rank <list[1]> 0.53124999
#> 3: Ljung-Pierce <list[1]> 0.05997327
Simulations
These tests are also available for simulations. Simulation can be
defined and summarized by:
# minimal example
minSim <- asm_simulate(100, pre_selection = c("Shapiro-Wilk", "Rank", "Ljung-Pierce"))
asm_reportSim(minSim, report = "result")
#> $result
#> sim_func sim_n_X sim_n_Y sim_cell pre_W_X pre_W_Y pre_rank_X pre_rank_Y
#> 1: asm_simData 10 30 1 0.04 0.04 0.04 0.05
#> pre_Ljung.Box_X pre_Ljung.Box_Y post_welch post_ttest post_wilcox
#> 1: 0.09 0.09 0.04 0.02 0.01
#>
#> $cross
#> NULL
#>
#> $description
#> NULL
Complex decision strategies can be defined in the simulation call or
afterwards on simulation result:
# minimal example
asm_simStrategy(minSim,
list(
simple = quote(ifelse(pre_W_X & pre_W_Y, post_ttest, post_wilcox)),
complex = quote(ifelse(sim_n_X < 11 & pre_W_X_pvalue <= 0.001 & !descr_skew_X > 1, post_ttest, post_wilcox))
)
)
asm_reportSim(minSim, report = "result")
#> $result
#> sim_func sim_n_X sim_n_Y sim_cell pre_W_X pre_W_Y pre_rank_X pre_rank_Y
#> 1: asm_simData 10 30 1 0.04 0.04 0.04 0.05
#> pre_Ljung.Box_X pre_Ljung.Box_Y post_welch post_ttest post_wilcox
#> 1: 0.09 0.09 0.04 0.02 0.01
#> strat_simple strat_complex
#> 1: 0.01 0.01
#>
#> $cross
#> NULL
#>
#> $description
#> NULL
A simulation study like ‘The two-sample t test: pre-testing its
assumptions does not pay
off’ could be replicated
with:
require(miceadds)
asm_simulate(
simulations = 100000,
sim_n = list(c(10,30,10,30,30), c(10,30,30,10,100)),
sim_func = rep("asm_simData", 24),
sim_args = mapply(function(distr, sig, delta) {
list(
distr = c("fleishman_sim", "fleishman_sim"),
dots = list(
list(mean = delta, sd = sig, skew = distr[1], kurt = distr[2]),
list(skew = distr[3], kurt = distr[4])
)
)
},
rep(
list(c(0,0,0,0), c(0,15,0,15), c(0.5,15,0.5,15), c(1,15,1,15), c(3,15,3,15), c(0,0,3,15)),
4
),
as.list(rep(rep(1:2, each = 6), 2)),
as.list(rep(c(0, 1), each = 12)),
SIMPLIFY=FALSE
)
)
animalSymbolicum/assumptions documentation built on Dec. 19, 2021, 3:37 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
The purpose of the ‘assumptions’ R-package is threefold:
-
It tries to raise awareness of statistical assumptions. Before conducting a statistical test, its assumptions are printed to the console and the user is asked for confirmation after a graphical assessment.
-
It collects tests and graphs to assess statistical assumptions and simplifies looking up specific assumptions.
-
All the tests are made available for simulations including complex decision strategies for picking a statistical test for different diagnosed assumption violations.
Install package
To install ‘assumptions’ R-package from github run:
require(remotes)
remotes::install_github("animalSymbolicum/assumptions")
library(assumptions)
Hypothesis Tests
The assumptions package seeks to expand common statistical methods with the notice of their assumptions for educational purpose. To conduct a parametric or non-parametric hypothesis test call:
# t-test
asm_ttest(rnorm(20), color = F)
#> The asm_ttest makes three assumptions for the input variable(s) x (and y):
#> I ) the sample distribution is normal or the sample size is big enough that
#> the distribution of the sample means likely converge to normal.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> ***********************************************************************************
#> If you confirm to acknowledge that under violation(s) of the assumptions the t-test
#> the t-test results might be wrong.
#> Press [enter] to confirm
#> Press [enter] for the next plot
#> Press [enter] for the next plot
#> Press [enter] to run Welch-Test - if you are convinced your data fulfills the assumptions.
#>
#> One Sample t-test
#>
#> data: x
#> t = 0.35822, df = 19, p-value = 0.7241
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> -0.3260519 0.4607036
#> sample estimates:
#> mean of x
#> 0.06732585
# wilcoxon test
asm_wilcox(rnorm(20), color = F)
#> If paired = FALSE -
#> the asm_wilcox makes three assumptions for the input variable(s) x (and y):
#> I ) are at least ordinal scaled.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> If paired = TRUE -
#> the asm_wilcox makes four assumptions for the input variable(s) x (and y):
#> I ) are at least interval scaled.
#> II ) the distribution of the calculated differences is symmetric.
#> III) are identically distributed, which means each value comes from the same distribution.
#> IV ) are independent, which means each value is not influenced by any other value.
#> ******************************************************************************************
#> If you confirm to acknowledge that under violation(s) of the assumptions the wilcoxon test
#> the results might be wrong.
#> Press [enter] to confirm
#> Press [enter] for the next plot
#> Press [enter] for the next plot
#> Press [enter] to run wilcoxon test - if you convinced your data fulfills the assumptions
#>
#> Wilcoxon signed rank exact test
#>
#> data: x
#> V = 129, p-value = 0.3884
#> alternative hypothesis: true location is not equal to 0
Look Up assumptions
Common statistical methods assumptions can be looked up with:
# get assumptions of t.test
assumptions(t.test, color = F)
#> The t.test makes three assumptions for the input variable(s) x (and y):
#> I ) the sample distribution is normal or the sample size is big enough that
#> the distribution of the sample means likely converge to normal.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> As two variables are used, the two variables can have two different distributions if
#> the Welch Test is used (var.equal=F).
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
# get assumptions of wilcox.test
assumptions(wilcox.test, color = F)
#> If paired = FALSE -
#> the wilcox.test makes three assumptions for the input variable(s) x (and y):
#> I ) are at least ordinal scaled.
#> II ) are identically distributed, which means each value comes from the same distribution.
#> III) are independent, which means each value is not influenced by any other value.
#> As two paired variables are used (paired=T) the pairs are dependent but
#> values between pairs still must be independent.
#> If paired = TRUE -
#> the wilcox.test makes four assumptions for the input variable(s) x (and y):
#> I ) are at least interval scaled.
#> II ) the distribution of the calculated differences is symmetric.
#> III) are identically distributed, which means each value comes from the same distribution.
#> IV ) are independent, which means each value is not influenced by any other value.
Overview of assumptions assessment tests (pre-tests)
All tests and graphs to assess specific assumptions such as the distribution, independence, identical distribution (homogeneity) or randomness are collected in a central catalogue table.
asm_library()
#> Assumption Name Package Help
#> 1: distribution Lilliefors PoweR <list[1]>
#> 2: distribution Anderson-Darling PoweR <list[1]>
#> 3: distribution D'Agostino-Pearson PoweR <list[1]>
#> 4: distribution Jarque-Bera PoweR <list[1]>
#> 5: distribution Doornik-Hansen PoweR <list[1]>
#> 6: distribution Gel-Gastwirth PoweR <list[1]>
#> 7: distribution 1st Hosking PoweR <list[1]>
#> 8: distribution 2nd Hosking PoweR <list[1]>
#> 9: distribution 3rd Hosking PoweR <list[1]>
#> 10: distribution 4th Hosking PoweR <list[1]>
#> 11: distribution 1st Bontemps-Meddahi PoweR <list[1]>
#> 12: distribution 2nd Bontemps-Meddahi PoweR <list[1]>
#> 13: distribution Brys-Hubert-Struyf PoweR <list[1]>
#> 14: distribution Bonett-Seier PoweR <list[1]>
#> 15: distribution Brys-Hubert-Struyf & Bonett-Seier PoweR <list[1]>
#> 16: distribution Shapiro-Wilk PoweR <list[1]>
#> 17: distribution Shapiro-Francia PoweR <list[1]>
#> 18: distribution Gel-Miao-Gastwirth PoweR <list[1]>
#> 19: distribution R_n PoweR <list[1]>
#> 20: distribution X_{APD} PoweR <list[1]>
#> 21: distribution Z_{EPD} PoweR <list[1]>
#> 22: randomness Bartels Rank randtests <list[1]>
#> 23: randomness Cox Stuart randtests <list[1]>
#> 24: randomness Difference Sign randtests <list[1]>
#> 25: randomness Rank randtests <list[1]>
#> 26: randomness Runs randtests <list[1]>
#> 27: homogeneity F-Test stats <list[1]>
#> 28: homogeneity Bartlett ExpDes <list[1]>
#> 29: homogeneity Layard ExpDes <list[1]>
#> 30: homogeneity Levene ExpDes <list[1]>
#> 31: independence Box-Pierce stats <list[1]>
#> 32: independence Ljung-Pierce stats <list[1]>
#> 33: iid Spanos Auxiliary Regression assumptions <list[1]>
#> 34: homogeneity Graph Boxplot assumptions <list[1]>
#> 35: distribution Graph Dotplot assumptions <list[1]>
#> 36: distribution Graph ecdf assumptions <list[1]>
#> 37: distribution Graph Histogram assumptions <list[1]>
#> 38: distribution Graph Mean Increase assumptions <list[1]>
#> 39: distribution Graph P-P-Plot assumptions <list[1]>
#> 40: distribution Graph Q-Q-plot assumptions <list[1]>
#> 41: randomness Graph Randruns assumptions <list[1]>
#> 42: randomness Graph Differencesign assumptions <list[1]>
#> 43: independence Graph t-Plot assumptions <list[1]>
#> 44: distribution Graph Var Increase assumptions <list[1]>
#> 45: homogeneity Graph Window assumptions <list[1]>
#> Assumption Name Package Help
#> SimulationName
#> 1: pre_K_S
#> 2: pre_AD
#> 3: pre_K_2
#> 4: pre_JB
#> 5: pre_DH
#> 6: pre_RJB
#> 7: pre_T_Lmom
#> 8: pre_T_Lmom_1
#> 9: pre_T_Lmom_2
#> 10: pre_T_Lmom_3
#> 11: pre_BM_3_4
#> 12: pre_BM_3_6
#> 13: pre_T_MC_LR
#> 14: pre_T_w
#> 15: pre_T_MC_LR_T_w
#> 16: pre_W
#> 17: pre_W_Quote
#> 18: pre_R_sJ
#> 19: pre_R_n
#> 20: pre_X_APD
#> 21: pre_Z_EPD
#> 22: pre_bartels.rank
#> 23: pre_cox.stuart
#> 24: pre_difference.sign
#> 25: pre_rank
#> 26: pre_runs
#> 27: pre_ftest
#> 28: pre_bartlett
#> 29: pre_levene
#> 30: pre_oneillmathews
#> 31: pre_Box.Pierce
#> 32: pre_Ljung.Box
#> 33: pre_asm_preJoint
#> 34: -
#> 35: -
#> 36: -
#> 37: -
#> 38: -
#> 39: -
#> 40: -
#> 41: -
#> 42: -
#> 43: -
#> 44: -
#> 45: -
#> SimulationName
Single assessment of an assumption
Single or combinations of assumptions can be assessed by calls like:
# only first test selected as example
asm_distribution(rnorm(10), 1)
#> Name Help pvalue
#> 1: Lilliefors <list[1]> 0.8682802
asm_independence(rnorm(10), 1)
#> Name Help pvalue
#> 1: Box-Pierce <list[1]> 0.7579546
asm_randomness(rnorm(10), 1)
#> Name Help pvalue
#> 1: Bartels Rank <list[1]> 0.9004949
asm_homogeneity(list(A=rnorm(10), B=rnorm(10)), 1)
#> Name Help pvalue
#> 1: F-Test <list[1]> 0.8583127
# a combination of tests
asm_preTests(rnorm(10), c("Shapiro-Wilk", "Rank", "Ljung-Pierce"))
#> Name Help pvalue
#> 1: Shapiro-Wilk <list[1]> 0.35093862
#> 2: Rank <list[1]> 0.53124999
#> 3: Ljung-Pierce <list[1]> 0.05997327
Simulations
These tests are also available for simulations. Simulation can be defined and summarized by:
# minimal example
minSim <- asm_simulate(100, pre_selection = c("Shapiro-Wilk", "Rank", "Ljung-Pierce"))
asm_reportSim(minSim, report = "result")
#> $result
#> sim_func sim_n_X sim_n_Y sim_cell pre_W_X pre_W_Y pre_rank_X pre_rank_Y
#> 1: asm_simData 10 30 1 0.04 0.04 0.04 0.05
#> pre_Ljung.Box_X pre_Ljung.Box_Y post_welch post_ttest post_wilcox
#> 1: 0.09 0.09 0.04 0.02 0.01
#>
#> $cross
#> NULL
#>
#> $description
#> NULL
Complex decision strategies can be defined in the simulation call or afterwards on simulation result:
# minimal example
asm_simStrategy(minSim,
list(
simple = quote(ifelse(pre_W_X & pre_W_Y, post_ttest, post_wilcox)),
complex = quote(ifelse(sim_n_X < 11 & pre_W_X_pvalue <= 0.001 & !descr_skew_X > 1, post_ttest, post_wilcox))
)
)
asm_reportSim(minSim, report = "result")
#> $result
#> sim_func sim_n_X sim_n_Y sim_cell pre_W_X pre_W_Y pre_rank_X pre_rank_Y
#> 1: asm_simData 10 30 1 0.04 0.04 0.04 0.05
#> pre_Ljung.Box_X pre_Ljung.Box_Y post_welch post_ttest post_wilcox
#> 1: 0.09 0.09 0.04 0.02 0.01
#> strat_simple strat_complex
#> 1: 0.01 0.01
#>
#> $cross
#> NULL
#>
#> $description
#> NULL
A simulation study like ‘The two-sample t test: pre-testing its assumptions does not pay off’ could be replicated with:
require(miceadds)
asm_simulate(
simulations = 100000,
sim_n = list(c(10,30,10,30,30), c(10,30,30,10,100)),
sim_func = rep("asm_simData", 24),
sim_args = mapply(function(distr, sig, delta) {
list(
distr = c("fleishman_sim", "fleishman_sim"),
dots = list(
list(mean = delta, sd = sig, skew = distr[1], kurt = distr[2]),
list(skew = distr[3], kurt = distr[4])
)
)
},
rep(
list(c(0,0,0,0), c(0,15,0,15), c(0.5,15,0.5,15), c(1,15,1,15), c(3,15,3,15), c(0,0,3,15)),
4
),
as.list(rep(rep(1:2, each = 6), 2)),
as.list(rep(c(0, 1), each = 12)),
SIMPLIFY=FALSE
)
)
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