DFBA.Rcheck/DFBA-Ex.R
In danbarch/dfba: What the Package Does (One Line, Title Case)
pkgname <- "DFBA"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
base::assign(".ExTimings", "DFBA-Ex.timings", pos = 'CheckExEnv')
base::cat("name\tuser\tsystem\telapsed\n", file=base::get(".ExTimings", pos = 'CheckExEnv'))
base::assign(".format_ptime",
function(x) {
if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
options(OutDec = '.')
format(x[1L:3L], digits = 7L)
},
pos = 'CheckExEnv')
### * </HEADER>
library('DFBA')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("dfba_bayes_vs_t_power")
### * dfba_bayes_vs_t_power
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_bayes_vs_t_power
### Title: Simulated Distribution-Free Bayesian Power and _t_ Power
### Aliases: dfba_bayes_vs_t_power
### ** Examples
# Note: these examples have long runtimes due to Monte Carlo sampling;
# please feel free to run them in the console.
# Examples for two data sets sampled from standard normal distributions with
# no blocking effect
## Not run:
##D dfba_bayes_vs_t_power(n_min = 40,
##D delta = .45,
##D model = "normal",
##D design = "paired",
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 40,
##D delta = .45,
##D model = "normal",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D # Examples with Weibull-distributed variates with no blocking effect
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design ="paired",
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D # Examples with Weibull-distributed variates with a blocking effect
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "independent",
##D shape1 = .8,
##D shape2 = .8,
##D block_max = 2.3,
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "paired",
##D shape1 = .8,
##D shape2 = .8,
##D block_max = 2.3,
##D hide_progress = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_bayes_vs_t_power", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_bayes_factor")
### * dfba_beta_bayes_factor
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_bayes_factor
### Title: Bayes Factor for Posterior Beta Distribution
### Aliases: dfba_beta_bayes_factor
### ** Examples
## Examples with the default uniform prior
dfba_beta_bayes_factor(a_post = 17,
b_post = 5,
method = "interval",
H0 = c(0, .5)
)
dfba_beta_bayes_factor(a_post = 377,
b_post = 123,
method = "point",
H0 = .75)
# An example with the Jeffreys prior
dfba_beta_bayes_factor(a_post = 377.5,
b_post = 123.5,
method = "point",
H0 = .75,
a0 = .5,
b0 = .5
)
dfba_beta_bayes_factor(a_post = 273,
b_post = 278,
method = "interval",
H0 = c(.4975,
.5025)
)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_bayes_factor", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_contrast")
### * dfba_beta_contrast
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_contrast
### Title: Bayesian Contrasts
### Aliases: dfba_beta_contrast
### ** Examples
## Suppose there are four conditions from a factorial design
# where the conditions labels are A1B1, A2B1, A1B2, and A2B2
# where the frequencies for success for the binomial variate are:
n1_vec <- c(22, 15, 13, 21)
# and the frequencies for failures per condition are:
n2_vec <- c(18, 25, 27, 19)
# Let us test the following three orthogonal contrasts
contrast.B1vsB2 <- c(.5, .5, -.5, -.5)
contrast.A1vsA2 <- c(.5, -.5, .5, -.5)
contrast.ABinter <- c(.5, -.5, -.5, .5)
dfba_beta_contrast(n1_vec = n1_vec,
n2_vec = n2_vec,
contrast_vec = contrast.B1vsB2)
dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.A1vsA2)
dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.ABinter)
# Plot the cumulative distribution for AB interaction
testABinteraction<-dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.ABinter)
plot(testABinteraction)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_contrast", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_descriptive")
### * dfba_beta_descriptive
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_descriptive
### Title: Descriptive Statistics for a Beta Distribution
### Aliases: dfba_beta_descriptive
### ** Examples
dfba_beta_descriptive(a = 38,
b = 55)
dfba_beta_descriptive(38,
55,
prob_interval=.99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_descriptive", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_binomial")
### * dfba_binomial
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_binomial
### Title: Bayesian Binomial Rate Parameter Inference
### Aliases: dfba_binomial
### ** Examples
# Example using defaults of a uniform prior and 95% interval estimates
dfba_binomial(n1 = 16,
n2 = 2)
# Example with the Jeffreys prior and 99% interval estimates
dfba_binomial(n1 = 16,
n2 = 2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_binomial", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_bivariate_concordance")
### * dfba_bivariate_concordance
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_bivariate_concordance
### Title: Bayesian Distribution-Free Correlation and Concordance
### Aliases: dfba_bivariate_concordance
### ** Examples
x <- c(47, 39, 47, 42, 44, 46, 39, 37, 29, 42, 54, 33, 44, 31, 28, 49, 32, 37, 46, 55, 31)
y <- c(36, 40, 49, 45, 30, 38, 39, 44, 27, 48, 49, 51, 27, 36, 30, 44, 42, 41, 35, 49, 33)
dfba_bivariate_concordance(x, y)
## A goodness-of-fit example for a hypothetical case of fitting data in a
## yobs vector with prediction model
p = seq(.05,.95,.05)
ypred= 17.332 - (50.261*p) + (48.308*p^2)
# Note the coefficients in the ypred equation were found first via a
# polynomial regression
yobs<-c(19.805, 10.105, 9.396, 8.219, 6.110, 4.543, 5.864, 4.861, 6.136,
5.789, 5.443, 5.548, 4.746, 6.484, 6.185, 6.202, 9.804, 9.332,
14.408)
dfba_bivariate_concordance(x = yobs,
y = ypred,
fitting.parameters = 3)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_bivariate_concordance", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_gamma")
### * dfba_gamma
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_gamma
### Title: Goodman-Kruskal Gamma
### Aliases: dfba_gamma
### ** Examples
# Example with matrix input
N <- matrix(c(38, 4, 5, 0, 6, 40, 1, 2, 4, 8, 20, 30),
ncol = 4,
byrow = TRUE)
colnames(N) <- c('C1', 'C2', 'C3', 'C4')
rownames(N) <- c('R1', 'R2', 'R3')
dfba_gamma(N)
# Sample problem with table input
NTable <- as.table(N)
dfba_gamma(NTable)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_gamma", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_mann_whitney")
### * dfba_mann_whitney
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_mann_whitney
### Title: Independent Samples Test (Mann Whitney U)
### Aliases: dfba_mann_whitney
### ** Examples
# Note: examples with method = "small" have long runtimes due to Monte Carlo
# sampling; please feel free to run them in the console.
# Examples with large n per group
# The data for each condition are presorted only for the user convenience if
# checking the U stats by hand
groupA <- c(43, 45, 47, 50, 54, 58, 60, 63, 69, 84, 85, 91, 99, 127, 130,
147, 165, 175, 193, 228, 252, 276)
groupB <- c(0, 01, 02, 03, 05, 14, 15, 23, 23, 25, 27, 32, 57, 105, 115, 158,
161, 181, 203, 290)
dfba_mann_whitney(E = groupA,
C = groupB)
# The following uses a Jeffreys prior instead of a default flat prior:
dfba_mann_whitney(E = groupA,
C = groupB,
a0 = .5,
b0 =.5)
# The following also uses a Jeffreys prior but the analysis reverses the
# variates:
dfba_mann_whitney(E = groupB,
C = groupA,
a0 = .5,
b0 = .5)
# Note that BF10 from the above analysis is 1/BF10 from the original order
# of the variates.
# The next analysis constructs 99% interval estimates with the Jeffreys
# prior.
dfba_mann_whitney(E = groupA,
C = groupB,
a0 = .5,
b0 = .5,
prob_interval=.99)
# The following forces a discrete approach with a flat prior for a case with
# large n:
## Not run:
##D dfba_mann_whitney(E = groupA,
##D C = groupB,
##D method = "small",
##D hide_progress = FALSE)
## End(Not run)
#Examples with small n per group
groupC <- c(96.49, 96.78, 97.26, 98.85, 99.75, 100.14, 101.15, 101.39,
102.58, 107.22, 107.70, 113.26)
groupD <- c(101.16, 102.09, 103.14, 104.70, 105.27, 108.22, 108.32, 108.51,
109.88, 110.32, 110.55, 113.42)
## Not run:
##D CDex1<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D hide_progress = FALSE)
##D
##D CDex1
##D
##D CDex2<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D samples = 50000,
##D hide_progress = FALSE)
##D CDex2
##D
##D
##D CDex3<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D hide_progress = FALSE)
##D
##D CDex3
##D
## End(Not run)
# Note that CDex1 and CDex2 are replication analyses for the discrete approach.
# The variability is due to the different outcomes from the Monte Carlo
# sampling.
# Plot output
# with prior and posterior curves
## Not run:
##D
##D plot(CDex1)
##D
##D # with only posterior curve
##D plot(CDex2,
##D plot.prior = FALSE)
##D
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_mann_whitney", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_mcnemar")
### * dfba_mcnemar
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_mcnemar
### Title: Bayesian Repeated-Measures McNemar Test for Change
### Aliases: dfba_mcnemar
### ** Examples
## Examples with default value for a0, b0 and prob_interval
dfba_mcnemar(n_01 = 17,
n_10 = 2)
## Using the Jeffreys prior and .99 equal-tail interval
dfba_mcnemar(n_01 = 17,
n_10 = 2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_mcnemar", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_median_test")
### * dfba_median_test
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_median_test
### Title: Bayesian Median Test
### Aliases: dfba_median_test
### ** Examples
## Example with the default uniform prior
group1 <- c(12.90, 10.84, 22.67, 10.64, 10.67, 10.79, 13.55, 10.95, 12.19,
12.76, 10.89, 11.02, 14.27, 13.98, 11.52, 13.49, 11.22, 15.07,
15.74, 19.00)
group2 <- c(4.63, 58.64, 5.07, 4.66, 4.13, 3.92, 3.39, 3.57, 3.56, 3.39)
dfba_median_test(E = group1,
C = group2)
## Example with the Jeffreys prior
dfba_median_test(group1,
group2,
a0 = .5,
b0 = .5)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_median_test", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_power_curve")
### * dfba_power_curve
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_power_curve
### Title: Power Curves
### Aliases: dfba_power_curve
### ** Examples
# Note: these examples have long runtimes due to Monte Carlo sampling;
# please feel free to run them in the console.
## Not run:
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "paired",
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "paired",
##D delta_step = .03,
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "independent",
##D delta_step = .06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "paired",
##D delta_step =.06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
##D
##D # Using the Jeffreys prior rather than default flat prior
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "independent",
##D a0 = .5,
##D b0 = .5,
##D delta_step = .06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_power_curve", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_sign_test")
### * dfba_sign_test
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_sign_test
### Title: Bayesian Sign Test
### Aliases: dfba_sign_test
### ** Examples
measure_1 <- c(1.49, 0.64, 0.96, 2.34, 0.78, 1.29, 0.72, 1.52,
0.62, 1.67, 1.19, 0.860)
measure_2 <- c(0.53, 0.55, 0.58, 0.97, 0.60, 0.22, 0.05, 13.14,
0.63, 0.33, 0.91, 0.37)
dfba_sign_test(Y1 = measure_1,
Y2 = measure_2)
dfba_sign_test(measure_1,
measure_2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_sign_test", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_sim_data")
### * dfba_sim_data
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_sim_data
### Title: Simulated Data Generator and Inferential Comparison
### Aliases: dfba_sim_data
### ** Examples
# Example of two paired normal distributions where the s.d. of the two
# conditions are 1 and 4.
dfba_sim_data(n = 50,
model = "normal",
design = "paired",
delta = .4,
shape1 = 1,
shape2 = 4)
# Example of two independent Weibull variates with their shape parameters =.8
# and with a .25 offset
dfba_sim_data(n = 80,
model = "weibull",
design = "independent",
delta = .25,
shape1 = .8,
shape2 = .8)
# Example of two independent Weibull variates with their shape
# parameters = .8 and with a .25 offset along with some block differences
# with the max block effect being 1.5
dfba_sim_data(n = 80,
model = "weibull",
design = "independent",
delta = .25,
shape1 = .8,
shape2 = .8,
block_max = 1.5)
# Example of two paired Cauchy variates with a .4 offset
dfba_sim_data(n = 50,
model = "cauchy",
design = "paired",
delta = .4)
# Example of two paired Cauchy variates with a .4 offset where the Bayesian
# analysis uses the Jeffreys prior
dfba_sim_data(n = 50,
a0 = .5,
b0 = .5,
model = "cauchy",
design = "paired",
delta=.4)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_sim_data", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_wilcoxon")
### * dfba_wilcoxon
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_wilcoxon
### Title: Repeated-Measures Test (Wilcoxon Signed-Ranks Test)
### Aliases: dfba_wilcoxon
### ** Examples
# Note: examples with method = "small" have long runtimes due to Monte Carlo
# sampling; please feel free to run them in the console.
## Examples with a small number of pairs
conditionA <- c(1.49, 0.64, 0.96, 2.34, 0.78, 1.29, 0.72, 1.52, 0.62, 1.67,
1.19, 0.86)
conditionB <- c(0.53, 0.55, 0.58, 0.97, 0.60, 0.22, 0.05, 13.14, 0.63, 0.33,
0.91, 0.37)
## Not run:
##D dfba_wilcoxon(Y1 = conditionA,
##D Y2 = conditionB,
##D hide_progress = FALSE)
## End(Not run)
# Note the results for this method="small" analysis differs from
# the previously run. These differences are the differences from
# different Monte Carlo sampling
# Using the Jeffreys prior for the same two conditions.
## Not run:
##D dfba_wilcoxon(conditionA,
##D conditionB,
##D a0 = .5,
##D b0 = .5,
##D hide_progress = FALSE)
## End(Not run)
# Using 99% interval estimates and with 50000 Monte Carlo samples per
# candidate phi_w
## Not run:
##D
##D dfba_wilcoxon(conditionA,
##D conditionB,
##D prob_interval=.99,
##D samples=50000,
##D hide_progress = FALSE)
## End(Not run)
# Examples with large sample size
E <- c(6.45, 5.65, 4.34, 5.92, 2.84, 13.06, 6.61, 5.47, 4.49, 6.39, 6.63,
3.55, 3.76, 5.61, 7.45, 6.41, 10.16, 6.26, 8.46, 2.29, 3.16, 5.68,
4.13, 2.94, 4.87, 4.44, 3.13, 8.87)
C <- c(2.89, 4.19, 3.22, 6.50, 3.10, 4.19, 5.13, 3.77, 2.71, 2.58, 7.59,
2.68, 4.98, 2.35, 5.15, 8.46, 3.77, 8.83, 4.06, 2.50, 5.48, 2.80,
8.89, 3.19, 9.36, 4.58, 2.94, 4.75)
BW<-dfba_wilcoxon(Y1 = E,
Y2 = C)
BW
plot(BW)
# Forcing the method="small" despite a sufficiently large n value
## Not run:
##D CW<-dfba_wilcoxon(Y1 = E,
##D Y2 = C,
##D method = "small",
##D hide_progress = FALSE)
##D CW
##D plot(CW)
##D plot(CW, plot.prior = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_wilcoxon", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
danbarch/dfba documentation built on Jan. 30, 2024, 6:51 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
pkgname <- "DFBA"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
base::assign(".ExTimings", "DFBA-Ex.timings", pos = 'CheckExEnv')
base::cat("name\tuser\tsystem\telapsed\n", file=base::get(".ExTimings", pos = 'CheckExEnv'))
base::assign(".format_ptime",
function(x) {
if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
options(OutDec = '.')
format(x[1L:3L], digits = 7L)
},
pos = 'CheckExEnv')
### * </HEADER>
library('DFBA')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("dfba_bayes_vs_t_power")
### * dfba_bayes_vs_t_power
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_bayes_vs_t_power
### Title: Simulated Distribution-Free Bayesian Power and _t_ Power
### Aliases: dfba_bayes_vs_t_power
### ** Examples
# Note: these examples have long runtimes due to Monte Carlo sampling;
# please feel free to run them in the console.
# Examples for two data sets sampled from standard normal distributions with
# no blocking effect
## Not run:
##D dfba_bayes_vs_t_power(n_min = 40,
##D delta = .45,
##D model = "normal",
##D design = "paired",
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 40,
##D delta = .45,
##D model = "normal",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D # Examples with Weibull-distributed variates with no blocking effect
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design ="paired",
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D # Examples with Weibull-distributed variates with a blocking effect
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "independent",
##D shape1 = .8,
##D shape2 = .8,
##D block_max = 2.3,
##D hide_progress = FALSE)
##D
##D dfba_bayes_vs_t_power(n_min = 50,
##D delta = .45,
##D model = "weibull",
##D design = "paired",
##D shape1 = .8,
##D shape2 = .8,
##D block_max = 2.3,
##D hide_progress = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_bayes_vs_t_power", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_bayes_factor")
### * dfba_beta_bayes_factor
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_bayes_factor
### Title: Bayes Factor for Posterior Beta Distribution
### Aliases: dfba_beta_bayes_factor
### ** Examples
## Examples with the default uniform prior
dfba_beta_bayes_factor(a_post = 17,
b_post = 5,
method = "interval",
H0 = c(0, .5)
)
dfba_beta_bayes_factor(a_post = 377,
b_post = 123,
method = "point",
H0 = .75)
# An example with the Jeffreys prior
dfba_beta_bayes_factor(a_post = 377.5,
b_post = 123.5,
method = "point",
H0 = .75,
a0 = .5,
b0 = .5
)
dfba_beta_bayes_factor(a_post = 273,
b_post = 278,
method = "interval",
H0 = c(.4975,
.5025)
)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_bayes_factor", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_contrast")
### * dfba_beta_contrast
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_contrast
### Title: Bayesian Contrasts
### Aliases: dfba_beta_contrast
### ** Examples
## Suppose there are four conditions from a factorial design
# where the conditions labels are A1B1, A2B1, A1B2, and A2B2
# where the frequencies for success for the binomial variate are:
n1_vec <- c(22, 15, 13, 21)
# and the frequencies for failures per condition are:
n2_vec <- c(18, 25, 27, 19)
# Let us test the following three orthogonal contrasts
contrast.B1vsB2 <- c(.5, .5, -.5, -.5)
contrast.A1vsA2 <- c(.5, -.5, .5, -.5)
contrast.ABinter <- c(.5, -.5, -.5, .5)
dfba_beta_contrast(n1_vec = n1_vec,
n2_vec = n2_vec,
contrast_vec = contrast.B1vsB2)
dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.A1vsA2)
dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.ABinter)
# Plot the cumulative distribution for AB interaction
testABinteraction<-dfba_beta_contrast(n1_vec,
n2_vec,
contrast_vec = contrast.ABinter)
plot(testABinteraction)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_contrast", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_beta_descriptive")
### * dfba_beta_descriptive
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_beta_descriptive
### Title: Descriptive Statistics for a Beta Distribution
### Aliases: dfba_beta_descriptive
### ** Examples
dfba_beta_descriptive(a = 38,
b = 55)
dfba_beta_descriptive(38,
55,
prob_interval=.99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_beta_descriptive", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_binomial")
### * dfba_binomial
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_binomial
### Title: Bayesian Binomial Rate Parameter Inference
### Aliases: dfba_binomial
### ** Examples
# Example using defaults of a uniform prior and 95% interval estimates
dfba_binomial(n1 = 16,
n2 = 2)
# Example with the Jeffreys prior and 99% interval estimates
dfba_binomial(n1 = 16,
n2 = 2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_binomial", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_bivariate_concordance")
### * dfba_bivariate_concordance
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_bivariate_concordance
### Title: Bayesian Distribution-Free Correlation and Concordance
### Aliases: dfba_bivariate_concordance
### ** Examples
x <- c(47, 39, 47, 42, 44, 46, 39, 37, 29, 42, 54, 33, 44, 31, 28, 49, 32, 37, 46, 55, 31)
y <- c(36, 40, 49, 45, 30, 38, 39, 44, 27, 48, 49, 51, 27, 36, 30, 44, 42, 41, 35, 49, 33)
dfba_bivariate_concordance(x, y)
## A goodness-of-fit example for a hypothetical case of fitting data in a
## yobs vector with prediction model
p = seq(.05,.95,.05)
ypred= 17.332 - (50.261*p) + (48.308*p^2)
# Note the coefficients in the ypred equation were found first via a
# polynomial regression
yobs<-c(19.805, 10.105, 9.396, 8.219, 6.110, 4.543, 5.864, 4.861, 6.136,
5.789, 5.443, 5.548, 4.746, 6.484, 6.185, 6.202, 9.804, 9.332,
14.408)
dfba_bivariate_concordance(x = yobs,
y = ypred,
fitting.parameters = 3)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_bivariate_concordance", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_gamma")
### * dfba_gamma
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_gamma
### Title: Goodman-Kruskal Gamma
### Aliases: dfba_gamma
### ** Examples
# Example with matrix input
N <- matrix(c(38, 4, 5, 0, 6, 40, 1, 2, 4, 8, 20, 30),
ncol = 4,
byrow = TRUE)
colnames(N) <- c('C1', 'C2', 'C3', 'C4')
rownames(N) <- c('R1', 'R2', 'R3')
dfba_gamma(N)
# Sample problem with table input
NTable <- as.table(N)
dfba_gamma(NTable)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_gamma", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_mann_whitney")
### * dfba_mann_whitney
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_mann_whitney
### Title: Independent Samples Test (Mann Whitney U)
### Aliases: dfba_mann_whitney
### ** Examples
# Note: examples with method = "small" have long runtimes due to Monte Carlo
# sampling; please feel free to run them in the console.
# Examples with large n per group
# The data for each condition are presorted only for the user convenience if
# checking the U stats by hand
groupA <- c(43, 45, 47, 50, 54, 58, 60, 63, 69, 84, 85, 91, 99, 127, 130,
147, 165, 175, 193, 228, 252, 276)
groupB <- c(0, 01, 02, 03, 05, 14, 15, 23, 23, 25, 27, 32, 57, 105, 115, 158,
161, 181, 203, 290)
dfba_mann_whitney(E = groupA,
C = groupB)
# The following uses a Jeffreys prior instead of a default flat prior:
dfba_mann_whitney(E = groupA,
C = groupB,
a0 = .5,
b0 =.5)
# The following also uses a Jeffreys prior but the analysis reverses the
# variates:
dfba_mann_whitney(E = groupB,
C = groupA,
a0 = .5,
b0 = .5)
# Note that BF10 from the above analysis is 1/BF10 from the original order
# of the variates.
# The next analysis constructs 99% interval estimates with the Jeffreys
# prior.
dfba_mann_whitney(E = groupA,
C = groupB,
a0 = .5,
b0 = .5,
prob_interval=.99)
# The following forces a discrete approach with a flat prior for a case with
# large n:
## Not run:
##D dfba_mann_whitney(E = groupA,
##D C = groupB,
##D method = "small",
##D hide_progress = FALSE)
## End(Not run)
#Examples with small n per group
groupC <- c(96.49, 96.78, 97.26, 98.85, 99.75, 100.14, 101.15, 101.39,
102.58, 107.22, 107.70, 113.26)
groupD <- c(101.16, 102.09, 103.14, 104.70, 105.27, 108.22, 108.32, 108.51,
109.88, 110.32, 110.55, 113.42)
## Not run:
##D CDex1<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D hide_progress = FALSE)
##D
##D CDex1
##D
##D CDex2<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D samples = 50000,
##D hide_progress = FALSE)
##D CDex2
##D
##D
##D CDex3<-dfba_mann_whitney(E = groupC,
##D C = groupD,
##D hide_progress = FALSE)
##D
##D CDex3
##D
## End(Not run)
# Note that CDex1 and CDex2 are replication analyses for the discrete approach.
# The variability is due to the different outcomes from the Monte Carlo
# sampling.
# Plot output
# with prior and posterior curves
## Not run:
##D
##D plot(CDex1)
##D
##D # with only posterior curve
##D plot(CDex2,
##D plot.prior = FALSE)
##D
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_mann_whitney", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_mcnemar")
### * dfba_mcnemar
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_mcnemar
### Title: Bayesian Repeated-Measures McNemar Test for Change
### Aliases: dfba_mcnemar
### ** Examples
## Examples with default value for a0, b0 and prob_interval
dfba_mcnemar(n_01 = 17,
n_10 = 2)
## Using the Jeffreys prior and .99 equal-tail interval
dfba_mcnemar(n_01 = 17,
n_10 = 2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_mcnemar", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_median_test")
### * dfba_median_test
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_median_test
### Title: Bayesian Median Test
### Aliases: dfba_median_test
### ** Examples
## Example with the default uniform prior
group1 <- c(12.90, 10.84, 22.67, 10.64, 10.67, 10.79, 13.55, 10.95, 12.19,
12.76, 10.89, 11.02, 14.27, 13.98, 11.52, 13.49, 11.22, 15.07,
15.74, 19.00)
group2 <- c(4.63, 58.64, 5.07, 4.66, 4.13, 3.92, 3.39, 3.57, 3.56, 3.39)
dfba_median_test(E = group1,
C = group2)
## Example with the Jeffreys prior
dfba_median_test(group1,
group2,
a0 = .5,
b0 = .5)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_median_test", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_power_curve")
### * dfba_power_curve
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_power_curve
### Title: Power Curves
### Aliases: dfba_power_curve
### ** Examples
# Note: these examples have long runtimes due to Monte Carlo sampling;
# please feel free to run them in the console.
## Not run:
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "independent",
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "paired",
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 85,
##D model = "normal",
##D design = "paired",
##D delta_step = .03,
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "independent",
##D delta_step = .06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "paired",
##D delta_step =.06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
##D
##D # Using the Jeffreys prior rather than default flat prior
##D
##D dfba_power_curve(n = 30,
##D model = "lognormal",
##D design = "independent",
##D a0 = .5,
##D b0 = .5,
##D delta_step = .06,
##D block_max = 3,
##D samples = 2500,
##D hide_progress = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_power_curve", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_sign_test")
### * dfba_sign_test
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_sign_test
### Title: Bayesian Sign Test
### Aliases: dfba_sign_test
### ** Examples
measure_1 <- c(1.49, 0.64, 0.96, 2.34, 0.78, 1.29, 0.72, 1.52,
0.62, 1.67, 1.19, 0.860)
measure_2 <- c(0.53, 0.55, 0.58, 0.97, 0.60, 0.22, 0.05, 13.14,
0.63, 0.33, 0.91, 0.37)
dfba_sign_test(Y1 = measure_1,
Y2 = measure_2)
dfba_sign_test(measure_1,
measure_2,
a0 = .5,
b0 = .5,
prob_interval = .99)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_sign_test", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_sim_data")
### * dfba_sim_data
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_sim_data
### Title: Simulated Data Generator and Inferential Comparison
### Aliases: dfba_sim_data
### ** Examples
# Example of two paired normal distributions where the s.d. of the two
# conditions are 1 and 4.
dfba_sim_data(n = 50,
model = "normal",
design = "paired",
delta = .4,
shape1 = 1,
shape2 = 4)
# Example of two independent Weibull variates with their shape parameters =.8
# and with a .25 offset
dfba_sim_data(n = 80,
model = "weibull",
design = "independent",
delta = .25,
shape1 = .8,
shape2 = .8)
# Example of two independent Weibull variates with their shape
# parameters = .8 and with a .25 offset along with some block differences
# with the max block effect being 1.5
dfba_sim_data(n = 80,
model = "weibull",
design = "independent",
delta = .25,
shape1 = .8,
shape2 = .8,
block_max = 1.5)
# Example of two paired Cauchy variates with a .4 offset
dfba_sim_data(n = 50,
model = "cauchy",
design = "paired",
delta = .4)
# Example of two paired Cauchy variates with a .4 offset where the Bayesian
# analysis uses the Jeffreys prior
dfba_sim_data(n = 50,
a0 = .5,
b0 = .5,
model = "cauchy",
design = "paired",
delta=.4)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_sim_data", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("dfba_wilcoxon")
### * dfba_wilcoxon
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: dfba_wilcoxon
### Title: Repeated-Measures Test (Wilcoxon Signed-Ranks Test)
### Aliases: dfba_wilcoxon
### ** Examples
# Note: examples with method = "small" have long runtimes due to Monte Carlo
# sampling; please feel free to run them in the console.
## Examples with a small number of pairs
conditionA <- c(1.49, 0.64, 0.96, 2.34, 0.78, 1.29, 0.72, 1.52, 0.62, 1.67,
1.19, 0.86)
conditionB <- c(0.53, 0.55, 0.58, 0.97, 0.60, 0.22, 0.05, 13.14, 0.63, 0.33,
0.91, 0.37)
## Not run:
##D dfba_wilcoxon(Y1 = conditionA,
##D Y2 = conditionB,
##D hide_progress = FALSE)
## End(Not run)
# Note the results for this method="small" analysis differs from
# the previously run. These differences are the differences from
# different Monte Carlo sampling
# Using the Jeffreys prior for the same two conditions.
## Not run:
##D dfba_wilcoxon(conditionA,
##D conditionB,
##D a0 = .5,
##D b0 = .5,
##D hide_progress = FALSE)
## End(Not run)
# Using 99% interval estimates and with 50000 Monte Carlo samples per
# candidate phi_w
## Not run:
##D
##D dfba_wilcoxon(conditionA,
##D conditionB,
##D prob_interval=.99,
##D samples=50000,
##D hide_progress = FALSE)
## End(Not run)
# Examples with large sample size
E <- c(6.45, 5.65, 4.34, 5.92, 2.84, 13.06, 6.61, 5.47, 4.49, 6.39, 6.63,
3.55, 3.76, 5.61, 7.45, 6.41, 10.16, 6.26, 8.46, 2.29, 3.16, 5.68,
4.13, 2.94, 4.87, 4.44, 3.13, 8.87)
C <- c(2.89, 4.19, 3.22, 6.50, 3.10, 4.19, 5.13, 3.77, 2.71, 2.58, 7.59,
2.68, 4.98, 2.35, 5.15, 8.46, 3.77, 8.83, 4.06, 2.50, 5.48, 2.80,
8.89, 3.19, 9.36, 4.58, 2.94, 4.75)
BW<-dfba_wilcoxon(Y1 = E,
Y2 = C)
BW
plot(BW)
# Forcing the method="small" despite a sufficiently large n value
## Not run:
##D CW<-dfba_wilcoxon(Y1 = E,
##D Y2 = C,
##D method = "small",
##D hide_progress = FALSE)
##D CW
##D plot(CW)
##D plot(CW, plot.prior = FALSE)
## End(Not run)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("dfba_wilcoxon", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.