Package 'bayes4psy'

Description

A user-friendly implementation of Bayesian statistical methods commonly used in social sciences. All used models are pre-compiled, meaning that users only need to call appropriate functions using their data.

References

Stan Development Team (NA) - the Stan framework and RStan interface. John Kruschke - mcmc_hdi function Rasmus Bååth - Easy Bayesian Bootstrap in R

Description

Performs a Bayesian bootstrap and returns a sample of size n1 representing the posterior distribution of the statistic. Returns a vector if the statistic is one-dimensional (like for mean(...)) or a data.frame if the statistic is multi-dimensional (like for the coefficients of lm).

Usage

b_bootstrap(
  data,
  statistic,
  n1 = 1000,
  n2 = 1000,
  use_weights = FALSE,
  weight_arg = NULL,
  ...
)

Arguments

Value

A data frame containing bootstrap samples.

Author(s)

Rasmus Baath

References

https://www.sumsar.net/blog/2015/07/easy-bayesian-bootstrap-in-r/

Rubin, D. B. (1981). The Bayesian Bootstrap. The annals of statistics, 9(1), 130-134.

Examples

# linear function of seqence vs. response
lm_statistic <- function(data) {
  lm(sequence ~ response, data)$coef
}

# load data
data <- adaptation_level_small

# bootstrap
data_bootstrap <- b_bootstrap(data, lm_statistic, n1 = 1000, n2 = 1000)

Description

Bayesian model for comparing colors.

Usage

b_color(
  colors,
  priors = NULL,
  hsv = FALSE,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Arguments

Value

An object of class 'color_class'

Examples

# priors for rgb
mu_prior <- b_prior(family="uniform", pars=c(0, 255))
sigma_prior <- b_prior(family="uniform", pars=c(0, 100))

# attach priors to relevant parameters
priors_rgb <- list(c("mu_r", mu_prior),
                   c("sigma_r", sigma_prior),
                   c("mu_g", mu_prior),
                   c("sigma_g", sigma_prior),
                   c("mu_b", mu_prior),
                   c("sigma_b", sigma_prior))


# generate data (rgb)
r <- as.integer(rnorm(100, mean=250, sd=20))
r[r > 255] <- 255
r[r < 0] <- 0

g <- as.integer(rnorm(100, mean=20, sd=20))
g[g > 255] <- 255
g[g < 0] <- 0

b <- as.integer(rnorm(100, mean=40, sd=20))
b[b > 255] <- 255
b[b < 0] <- 0

colors_rgb <- data.frame(r=r, g=g, b=b)

# fit
fit_rgb <- b_color(colors=colors_rgb, priors=priors_rgb, chains=1)


# priors for hsv
h_prior <- b_prior(family="uniform", pars=c(0, 2*pi))
sv_prior <- b_prior(family="uniform", pars=c(0, 1))
kappa_prior <- b_prior(family="uniform", pars=c(0, 500))
sigma_prior <- b_prior(family="uniform", pars=c(0, 1))

# attach priors to relevant parameters
priors_hsv <- list(c("mu_h", h_prior),
                   c("kappa_h", kappa_prior),
                   c("mu_s", sv_prior),
                   c("sigma_s", sigma_prior),
                   c("mu_v", sv_prior),
                   c("sigma_v", sigma_prior))

# generate data (hsv)
h <- rnorm(100, mean=2*pi/3, sd=0.5)
h[h > 2*pi] <- 2*pi
h[h < 0] <- 0

s <- rnorm(100, mean=0.9, sd=0.2)
s[s > 1] <- 1
s[s < 0] <- 0

v <- rnorm(100, mean=0.9, sd=0.2)
v[v > 1] <- 1
v[v < 0] <- 0

colors_hsv <- data.frame(h=h, s=s, v=v)

# fit
fit_hsv <- b_color(colors=colors_hsv, hsv=TRUE, priors=priors_hsv, chains=1)

Description

Bayesian model for fitting a linear normal model to data.

Usage

b_linear(
  x,
  y,
  s,
  priors = NULL,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Arguments

Value

An object of class 'linear_class'.

Examples

# priors
mu_prior <- b_prior(family="normal", pars=c(0, 100))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))

# attach priors to relevant parameters
priors <- list(c("mu_a", mu_prior),
               c("sigma_a", sigma_prior),
               c("mu_b", mu_prior),
               c("sigma_b", sigma_prior),
               c("mu_s", sigma_prior),
               c("sigma_s", sigma_prior))

# generate data
x <- vector()
y <- vector()
s <- vector()
for (i in 1:5) {
  x <- c(x, rep(1:10, 2))
  y <- c(y, rnorm(20, mean=1:10, sd=2))
  s <- c(s, rep(i, 20))
}

fit <- b_linear(x=x, y=y, s=s, priors=priors, chains=1)

Description

An S4 class for defining priors for Bayesian models.

Slots

family

Prior family - \"uniform\", \"normal\", \"gamma\" or \"beta\".

pars

Parameters of the prior - a vector of two numerical values.

Description

Bayesian model for comparing reaction times.

Usage

b_reaction_time(
  t,
  s,
  priors = NULL,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Arguments

Value

An object of class 'reaction_time_class'

Examples

# priors
mu_prior <- b_prior(family="normal", pars=c(0, 100))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))
lambda_prior <- b_prior(family="uniform", pars=c(0.05, 5))

# attach priors to relevant parameters
priors <- list(c("mu_m", mu_prior),
              c("sigma_m", sigma_prior),
              c("mu_s", sigma_prior),
              c("sigma_s", sigma_prior),
              c("mu_l", lambda_prior),
              c("sigma_l", sigma_prior))

# generate data
s <- rep(1:5, 20)
rt <- emg::remg(100, mu=10, sigma=1, lambda=0.4)

# fit
fit <- b_reaction_time(t=rt, s=s, priors=priors, chains=1)

Description

Parent S4 class for declaring shared function generics.

Description

Bayesian model for comparing test success rate.

Usage

b_success_rate(
  r,
  s,
  priors = NULL,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Arguments

Value

An object of class 'success_rate_class'.

Examples

# priors
p_prior <- b_prior(family="beta", pars=c(1, 1))
tau_prior <- b_prior(family="uniform", pars=c(0, 500))

# attach priors to relevant parameters
priors <- list(c("p", p_prior),
               c("tau", tau_prior))

# generate data
s <- rep(1:5, 20)
data <- rbinom(100, size=1, prob=0.6)

# fit
fit <- b_success_rate(r=data, s=s, priors=priors, chains=1)

Description

Bayesian t-test.

Usage

b_ttest(
  data,
  priors = NULL,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Arguments

Value

An object of class 'ttest_class'.

Examples

# priors
mu_prior <- b_prior(family="normal", pars=c(0, 1000))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))
nu_prior <- b_prior(family="normal", pars=c(2000, 1000))

# attach priors to relevant parameters
priors <- list(c("mu", mu_prior),
               c("sigma", sigma_prior),
               c("nu", nu_prior))

# generate some data
data  <- rnorm(20, mean=150, sd=20)

# fit
fit <- b_ttest(data=data, priors=priors, chains=1)

Datasets for bayes4psy examples Example datasets for use in rstanarm examples and vignettes. The datasets were extracted from the internal MBLab http://www.mblab.si repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia.

Description

Datasets for bayes4psy examples Example datasets for use in rstanarm examples and vignettes. The datasets were extracted from the internal MBLab http://www.mblab.si repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia.

Format

adaptation_level_small

Small dataset on subjects picking up weights and determining their weights from 1..10.

Source: Internal MBLab repository.

50 obs. of 3 variables

• sequence sequence index.

• weight actual weight of the object.

• response subject's estimation of weight.

adaptation_level

Data on subjects picking up weights and determining their weights from 1..10.

Source: Internal MBLab repository.

2900 obs. of 6 variables

• subject subject index.

• group group index.

• part first or second part of the experiment.

• sequence sequence index.

• weight actual weight of the object.

• response subject's estimation of weight.

#'

after_images_opponent_process

Colors predicted by the opponent process theory.

Source: Internal MBLab repository.

6 obs. of 7 variables

• stimuli name of the color stimuli.

• r value of the R component in the RGB model.

• g value of the G component in the RGB model.

• b value of the B component in the RGB model.

• h value of the H component in the HSV model.

• s value of the S component in the HSV model.

• v value of the V component in the HSV model.

#'

after_images_opponent_stimuli

Stimuli used in the after images experiment.

Source: Internal MBLab repository.

6 obs. of 7 variables

• r_s value of the R component in the RGB model.

• g_s value of the G component in the RGB model.

• b_s value of the B component in the RGB model.

• stimuli name of the color stimuli.

• h_s value of the H component in the HSV model.

• s_s value of the S component in the HSV model.

• v_s value of the V component in the HSV model.

#'

after_images_trichromatic

Colors predicted by the trichromatic theory.

Source: Internal MBLab repository.

6 obs. of 7 variables

• stimuli name of the color stimuli.

• r value of the R component in the RGB model.

• g value of the G component in the RGB model.

• b value of the B component in the RGB model.

• h value of the H component in the HSV model.

• s value of the S component in the HSV model.

• v value of the V component in the HSV model.

#'

after_images

Data gathered by the after images experiment.

Source: Internal MBLab repository.

1311 obs. of 12 variables

• subject subject index.

• rt reaction time.

• r value of the R component in the RGB model of subject's response.

• g value of the G component in the RGB model of subject's response.

• b value of the B component in the RGB model of subject's response.

• stimuli name of the color stimuli.

• r_s value of the R component in the RGB model of the shown stimulus

• g_s value of the G component in the RGB model of the shown stimulus

• b_s value of the B component in the RGB model of the shown stimulus

• h_s value of the H component in the HSV model of the shown stimulus

• s_s value of the S component in the HSV model of the shown stimulus

• v_s value of the V component in the HSV model of the shown stimulus

#'

flanker

Data gathered by the flanker experiment.

Source: Internal MBLab repository.

8256 obs. of 5 variables

• subject subject index.

• group group index.

• congruencty type of stimulus.

• result was subject's reponse correct or wrong?

• rt reaction time.

#'

stroop_extended

All the data gathered by the Stroop experiment.

Source: Internal MBLab repository.

41068 obs. of 5 variables

• subject subject ID.

• cond type of condition.

• rt reaction time.

• acc was subject's reponse correct or wrong?

• age age of subject.

#'

stroop_simple

All the data gathered by the Stroop experiment.

Source: Internal MBLab repository.

61 obs. of 5 variables

• subject subject ID.

• reading_neutral average response time for reading neutral stimuli.

• naming_neutral average response time for naming neutral stimuli.

• reading_incongruent average response time for reading incongruent stimuli.

• naming_incongruent average response time for naming incongruent stimuli.

Examples

# Example of Bayesian bootstraping on 'adaptation_level_small' dataset
# linear function of seqence vs. response
lm_statistic <- function(data) {
  lm(sequence ~ response, data)$coef
}

# load data
data <- adaptation_level_small

# bootstrap
data_bootstrap <- b_bootstrap(data, lm_statistic, n1=1000, n2=1000)

Description

An S4 class for storing results of Bayesian color model.

Functions

summary('color_class'): prints a summary of the fit.

print('color_class'): prints a more detailed summary of the fit

show('color_class'): prints a more detailed summary of the fit.

plot('color_class'): plots fitted model against the data. Use this function to explore the quality of your fit. You can compare fit with underlying data only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_fit('color_class'): plots fitted model against the data. Use this function to explore the quality of your fit. You can compare fit with underlying data only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_trace('color_class'): traceplot for main fitted model parameters.

get_parameters('color_class'): returns a dataframe with values of fitted parameters.

compare_means('color_class', fit2='color_class'): prints color difference between two fits. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

compare_means('color_class', rgb='vector'): prints color difference between a fit and a color defined with rgb components. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

compare_means('color_class', hsv='vector'): prints color difference between a fit and a color defined with hsv components. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means_difference('color_class', fit2='color_class'): a visualization of the difference between two fits You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means_difference('color_class', rgb='vector'): a visualization of the difference between a fit and a color defined with rgb components. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means_difference('color_class', hsv='vector'): a visualization of the difference between a fit and a color defined with hsv components. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means('color_class'): plots density of means. You can also visualize the density only for chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means('color_class', fit2='color_class'): plots density for the first and the second group means. You can also visualize the density only for chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means('color_class', rgb='vector'): plots density for the first and a color defined with rgb components. You can also visualize the density only for chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_means('color_class', hsv='vector'): plots density for the first and a color defined with hsv components. You can also visualize the density only for chosen color components (r, g, b, h, s, v) by using the pars parameter.

compare_distributions('color_class', fit2='color_class'): draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

compare_distributions('color_class', rgb='vector'): draws samples from distribution of the first group and compares them against a color defined with rgb components. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

compare_distributions('color_class', hsv='vector'): draws samples from distribution of the first group and compares them against a color defined with hsv components. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions('color_class'): a visualization of the fitted distribution. You can visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions('color_class', fit2='color_class'): a visualization of two fitted distributions. You can visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions('color_class', rgb='vector'): a visualization of the fitted distribution and a color defined with rgb components. You can visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions('color_class', hsv='vector'): a visualization of the fitted distribution and a color defined with hsv components. You can visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions_difference('color_class', fit2='color_class'): a visualization of the difference between the distribution of the first fit and the second fit. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions_difference('color_class', rgb='vector'): a visualization of the difference between the distribution of the first fit and a color defined with rgb components. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_distributions_difference('color_class', hsv='vector'): a visualization of the difference between the distribution of the first fit and a color defined with hsv components. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through chosen color components (r, g, b, h, s, v) by using the pars parameter.

plot_hsv('color_class'): plots fitted model against the data. Use this function to explore the quality of your fit thorough a circular visualization of hsv color components.

plot_fit_hsv('color_class'): plots fitted model against the data. Use this function to explore the quality of your fit thorough a circular visualization of hsv color components.

plot_means_hsv('color_class'): a visualization of the difference between means of two fits through a circular visualization of hsv color components. You can also compare fit means with colors defined through rgb or hsv components (as points or as lines on the visualization).

plot_distributions_hsv('color_class'): a visualization of distributions of one or two fits thorough a circular visualization of hsv color components. You can also compare fit means with colors defined through rgb or hsv components (as points or as lines on the visualization).

Slots

extract

Extract from Stan fit.

fit

Stan fit.

data

Data on which the fit is based.

Examples

# priors for rgb
mu_prior <- b_prior(family="uniform", pars=c(0, 255))
sigma_prior <- b_prior(family="uniform", pars=c(0, 100))

# attach priors to relevant parameters
priors_rgb <- list(c("mu_r", mu_prior),
                   c("sigma_r", sigma_prior),
                   c("mu_g", mu_prior),
                   c("sigma_g", sigma_prior),
                   c("mu_b", mu_prior),
                   c("sigma_b", sigma_prior))


# generate data (rgb) and fit
r <- as.integer(rnorm(100, mean=250, sd=20))
r[r > 255] <- 255
r[r < 0] <- 0

g <- as.integer(rnorm(100, mean=20, sd=20))
g[g > 255] <- 255
g[g < 0] <- 0

b <- as.integer(rnorm(100, mean=40, sd=20))
b[b > 255] <- 255
b[b < 0] <- 0

colors <- data.frame(r=r, g=g, b=b)

fit1 <- b_color(colors=colors, priors=priors_rgb, chains=1)


# priors for hsv
h_prior <- b_prior(family="uniform", pars=c(0, 2*pi))
sv_prior <- b_prior(family="uniform", pars=c(0, 1))
kappa_prior <- b_prior(family="uniform", pars=c(0, 500))
sigma_prior <- b_prior(family="uniform", pars=c(0, 1))

# attach priors to relevant parameters
priors_hsv <- list(c("mu_h", h_prior),
                   c("kappa_h", kappa_prior),
                   c("mu_s", sv_prior),
                   c("sigma_s", sigma_prior),
                   c("mu_v", sv_prior),
                   c("sigma_v", sigma_prior))

# generate data (hsv) and fit
h <- rnorm(100, mean=2*pi/3, sd=0.5)
h[h > 2*pi] <- 2*pi
h[h < 0] <- 0

s <- rnorm(100, mean=0.9, sd=0.2)
s[s > 1] <- 1
s[s < 0] <- 0

v <- rnorm(100, mean=0.9, sd=0.2)
v[v > 1] <- 1
v[v < 0] <- 0

colors <- data.frame(h=h, s=s, v=v)

fit2 <- b_color(colors=colors, hsv=TRUE, priors=priors_hsv, chains=1)


# a short summary of fitted parameters
summary(fit1)

# a more detailed summary of fitted parameters
print(fit1)
show(fit1)

# plot the fitted distribution against the data
plot(fit1)
plot_fit(fit1)

# plot the fitted distribution against the data,
# specify only a subset of parameters for visualization
plot(fit1, pars=c("h", "s", "v"))
plot_fit(fit1, pars=c("h", "s", "v"))

# traceplot of the fitted parameters
plot_trace(fit1)

# extract parameter values from the fit
parameters <- get_parameters(fit1)

# compare means between two fits
compare_means(fit1, fit2=fit2)

# compare means between two fits,
# specify only a subset of parameters for comparison
compare_means(fit1, fit2=fit2, pars=c("h", "s", "v"))

# compare means of a fit with an rgb defined color
compare_means(fit1, rgb=c(255, 0, 0))

# compare means of a fit with an hsv defined color
compare_means(fit1, hsv=c(pi/2, 1, 1))

# visualize difference in means between two fits
plot_means_difference(fit1, fit2=fit2)

# visualize difference in means between two fits,
# specify only a subset of parameters for comparison, use a rope interval
plot_means_difference(fit1, fit2=fit2, pars=c("r", "g", "b"), rope=10)

# visualize difference in means between a fit and an rgb defined color
plot_means_difference(fit1, rgb=c(255, 0, 0))

# visualize difference in means between a fit and an hsv defined color
plot_means_difference(fit1, hsv=c(pi/2, 1, 1))

# visualize means of a single fit
plot_means(fit1)

# visualize means of two fits
plot_means(fit1, fit2=fit2)

# visualize means of two fits,
# specify only a subset of parameters for visualization
plot_means(fit1, fit2=fit2, pars=c("h", "s", "v"))

# visualize means of a single fit and an rgb defined color
plot_means(fit1, rgb=c(255, 0, 0))

# visualize means of a single fit and an an hsv defined color
plot_means(fit1, hsv=c(pi/2, 1, 1))

# draw samples from distributions underlying two fits and compare them
compare_distributions(fit1, fit2=fit2)

# draw samples from distributions underlying two fits and compare them,
# specify only a subset of parameters for comparison, use a rope interval
compare_distributions(fit1, fit2=fit2, pars=c("r", "g", "b"), rope=10)

# draw samples from a distribution underlying the fits,
# compare them with an rgb defined color
compare_distributions(fit1, rgb=c(255, 0, 0))

# draw samples from a distribution underlying the fits,
# compare them with an hsv defined color
compare_distributions(fit1, hsv=c(pi/2, 1, 1))

# visualize the distribution underlying a fit
plot_distributions(fit1)

# visualize distributions underlying two fits
plot_distributions(fit1, fit2=fit2)

# visualize distributions underlying two fits,
# specify only a subset of parameters for visualization
plot_distributions(fit1, fit2=fit2, pars=c("h", "s", "v"))

# visualize the distribution underlying a fit, and an rgb defined color
plot_distributions(fit1, rgb=c(255, 0, 0))

# visualize the distribution underlying a fit, and an hsv defined color
plot_distributions(fit1, hsv=c(pi/2, 1, 1))

# visualize difference between distributions underlying two fits
plot_distributions_difference(fit1, fit2=fit2)

# visualize difference between distributions underlying two fits
# specify only a subset of parameters for comparison, use a rope interval
plot_distributions_difference(fit1, fit2=fit2, pars=c("r", "g", "b"), rope=10)

# visualize difference between the distributions underlyin a fit,
# and an rgb defined color
plot_distributions_difference(fit1, rgb=c(255, 0, 0))

# visualize difference between the distributions underlyin a fit,
# and an hsv defined color
plot_distributions_difference(fit1, hsv=c(pi/2, 1, 1))

# plot the fitted distribution for hue against the hue data
plot_hsv(fit1)

# plot the fitted distribution for hue against the hue data
plot_fit_hsv(fit1)

# visualize hue means of a single fit
plot_means_hsv(fit1)

# visualize hue means of two fits
plot_means_hsv(fit1, fit2=fit2)

# visualize hue means of two fits, add annotation points and lines,
# hsv parameter determines whether annotations are defined in hsv or rgb
lines <- list()
lines[[1]] <- c(2*pi, 1, 1)
lines[[2]] <- c(pi/2, 0.5, 0.5)

points <- list()
points[[1]] <- c(pi, 1, 1)

plot_means_hsv(fit1, fit2=fit2, points=points, lines=lines, hsv=TRUE)

# visualize the hue distribution underlying a fit
plot_distributions_hsv(fit1)

# visualize hue distributions underlying two fits
plot_distributions_hsv(fit1, fit2=fit2)

# visualize hue distributions of two fits, add annotation points and lines,
# hsv parameter determines whether annotations are defined in hsv or rgb
lines <- list()
lines[[1]] <- c(2*pi, 1, 1)
lines[[2]] <- c(pi/2, 0.5, 0.5)

points <- list()
points[[1]] <- c(pi, 1, 1)

plot_distributions_hsv(fit1, fit2=fit2, points=points, lines=lines, hsv=TRUE)

Description

compare_distributions draws samples from distribution of the first fit and compares them against samples drawn from the distribution of the second fit, or against samples from multiple fits.

Usage

compare_distributions(object, ...)

Arguments

Description

compare_distributions draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group or against a color defined with rgb or hsv components. You can also provide the rope parameter or execute the comparison only through chosen color components (r, g, b, h, s, v).

Usage

## S4 method for signature 'color_class'
compare_distributions(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

compare_distributions draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group.

Usage

## S4 method for signature 'linear_class'
compare_distributions(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

compare_distributions draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group or from samples drawn from distributions of multiple groups.

Usage

## S4 method for signature 'reaction_time_class'
compare_distributions(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

compare_distributions draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group or from samples drawn from distributions of multiple groups.

Usage

## S4 method for signature 'success_rate_class'
compare_distributions(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

compare_distributions draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group, against samples drawn from distributions of multiple groups, against a mean value or against samples from a normal distribution with a defined mean value and variance.

Usage

## S4 method for signature 'ttest_class'
compare_distributions(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

compare_means prints difference between means of two or multiple fits.

Usage

compare_means(object, ...)

Arguments

Description

compare_means prints difference in colors between two fits or a fit and a color.

Usage

## S4 method for signature 'color_class'
compare_means(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

compare_means prints difference in intercept and slope between two groups.

Usage

## S4 method for signature 'linear_class'
compare_means(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

compare_means prints difference in reaction times between two groups or multiple groups.

Usage

## S4 method for signature 'reaction_time_class'
compare_means(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

compare_means prints difference in success rate between two groups or multiple groups.

Usage

## S4 method for signature 'success_rate_class'
compare_means(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

compare_means prints difference/equality of the first group against the second group, against multiple groups, against a mean value or against a normal distribution with a defined mean value and variance.

Usage

## S4 method for signature 'ttest_class'
compare_means(object, ...)

Arguments

Value

Comparison results or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

get_parameters(object)

Arguments

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

## S4 method for signature 'color_class'
get_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

## S4 method for signature 'linear_class'
get_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

## S4 method for signature 'reaction_time_class'
get_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

## S4 method for signature 'success_rate_class'
get_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

get_parameters returns a dataframe with values of fitted parameters.

Usage

## S4 method for signature 'ttest_class'
get_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

get_prior_id returns an integer id of prior's family (1 = uniform, 2 = normal, 3 = gamma, 4 = beta).

Usage

get_prior_id(object)

## S4 method for signature 'b_prior'
get_prior_id(object)

Arguments

Description

get_subject_parameters returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

Usage

get_subject_parameters(object)

Arguments

Description

get_subject_parameters returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

Usage

## S4 method for signature 'linear_class'
get_subject_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

get_subject_parameters returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

Usage

## S4 method for signature 'reaction_time_class'
get_subject_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

get_subject_parameters returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

Usage

## S4 method for signature 'success_rate_class'
get_subject_parameters(object)

Arguments

Value

A data frame with parameter values.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

An S4 class for storing results of normal linear model.

Functions

summary('linear_class'): prints a summary of the fit.

print('linear_class'): prints a more detailed summary of the fit

show('linear_class'): prints a more detailed summary of the fit.

plot('linear_class'): plots fitted model against the data. Use this function to explore the quality of your fit. Fit will be plotted on the subject level.

plot(‘linear_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the subjects level (subjects=FALSE).

plot_fit('linear_class'): plots fitted model against the data. Use this function to explore the quality of your fit. Fit will be plotted on the subject level.

plot_fit(‘linear_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the subjects level (subjects=FALSE).

plot_trace('linear_class'): traceplot for main fitted model parameters.

get_parameters('linear_class'): returns a dataframe with values of fitted parameters.

get_subject_parameters('linear_class'): returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

compare_means('linear_class', fit2='linear_class'): prints difference in slope and intercept between two groups. You can also provide the rope parameter.

plot_means_difference('linear_class', fit2='linear_class'): a visualization of the difference between two groups. You can plot only slope or intercept by using the par parameter. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_means('linear_class'): plots density of means. You can plot only slope or intercept by using the par parameter.

plot_means('linear_class', fit2='linear_class'): plots density for the first and the second group means. You can plot only slope or intercept by using the par parameter.

compare_distributions('linear_class', fit2='linear_class'): draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group.

plot_distributions('linear_class'): a visualization of the fitted distribution.

plot_distributions('linear_class', fit2='linear_class'): a visualization of two fitted distribution.

plot_distributions_difference('linear_class', fit2='linear_class'): a visualization of the difference between the distribution of the first group and the second group. You can plot only slope or intercept by using the par parameter. You can also provide the rope and bins (number of bins in the histogram) parameters.

Slots

extract

Extract from Stan fit.

fit

Stan fit.

data

Raw data for the tested group.

Examples

# priors
mu_prior <- b_prior(family="normal", pars=c(0, 100))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))

# attach priors to relevant parameters
priors <- list(c("mu_a", mu_prior),
               c("sigma_a", sigma_prior),
               c("mu_b", mu_prior),
               c("sigma_b", sigma_prior),
               c("mu_s", sigma_prior),
               c("sigma_s", sigma_prior))


# generate data and fit
x <- vector()
y <- vector()
s <- vector()
for (i in 1:5) {
  x <- c(x, rep(1:10, 2))
  y <- c(y, rnorm(20, mean=1:10, sd=2))
  s <- c(s, rep(i, 20))
}

fit1 <- b_linear(x=x, y=y, s=s, priors=priors, chains=1)

fit2 <- b_linear(x=x, y=-2*y, s=s, priors=priors, chains=1)

# a short summary of fitted parameters
summary(fit1)

# a more detailed summary of fitted parameters
print(fit1)
show(fit1)

# plot the fitted distribution against the data
plot(fit1)
plot_fit(fit1)

# plot the fitted distribution against the data,
# plot on the top (group) level
plot(fit1, subjects=FALSE)
plot_fit(fit1, subjects=FALSE)

# traceplot of the fitted parameters
plot_trace(fit1)
# extract parameter values from the fit
parameters <- get_parameters(fit1)

# extract parameter values on the bottom (subject) level from the fit
subject_parameters <- get_subject_parameters(fit1)

# compare means between two fits
compare_means(fit1, fit2=fit2)

# compare means between two fits, use a rope interval for intercept and slope
compare_means(fit1, fit2=fit2, rope_intercept=0.5, rope_slope=0.2)

# visualize difference in means between two fits
plot_means_difference(fit1, fit2=fit2)

# visualize difference in means between two fits,
# use a rope interval for intercept and slope,
# set the number of bins in the histogram
plot_means_difference(fit1, fit2=fit2, rope_intercept=0.5, rope_slope=0.2, bins=20)

# visualize difference in means between two fits, compare only slope
plot_means_difference(fit1, fit2=fit2, par="slope")

# visualize means of a single fit
plot_means(fit1)

# visualize means of two fits
plot_means(fit1, fit2=fit2)

# visualize means of two fits, plot slope only
plot_means(fit1, fit2=fit2, par="slope")

# draw samples from distributions underlying two fits and compare them,
# use a rope interval for intercept and slope
compare_distributions(fit1, fit2=fit2, rope_intercept=0.5, rope_slope=0.2)

# visualize the distribution underlying a fit
plot_distributions(fit1)

# visualize distributions underlying two fits
plot_distributions(fit1, fit2=fit2)

# visualize distributions underlying two fits, plot slope only
plot_distributions(fit1, fit2=fit2, par="slope")

# visualize difference between distributions underlying two fits
plot_distributions_difference(fit1, fit2=fit2)

# visualize difference between distributions underlying two fits,
# use a rope interval for intercept and slope,
# set the number of bins in the histogram
plot_distributions_difference(fit1, fit2=fit2, rope_intercept=0.5, rope_slope=0.2, bins=20)

# visualize difference between distributions underlying two fits, plot slope only
plot_distributions_difference(fit1, fit2=fit2, par="slope")

Description

A function for calculating the HDI (highest density interval) of a vector of values.

Usage

mcmc_hdi(samples, cred_mass = 0.95)

Arguments

Value

Boundaries of the HDI.

Author(s)

John Kruschke

Description

plot_distributions visualizes fitted distributions.

Usage

plot_distributions(object, ...)

Arguments

Description

plot_distributions_difference a visualization of the difference between the distributions of two or more fits.

Usage

plot_distributions_difference(object, ...)

Arguments

Description

plot_distributions_difference a visualization of the difference between the distribution of the first group and the second group.

Usage

## S4 method for signature 'color_class'
plot_distributions_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_distributions_difference visualizes the difference between two groups.

Usage

## S4 method for signature 'linear_class'
plot_distributions_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_distributions_difference a visualization of the difference between the distribution of the first group and the second group or between multiple groups.

Usage

## S4 method for signature 'reaction_time_class'
plot_distributions_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_distributions_difference a visualization of the difference between the distribution of the first group and the second group or between multiple groups.

Usage

## S4 method for signature 'success_rate_class'
plot_distributions_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_distributions_difference a visualization of the difference between the distribution of the first group, the distribution or a constant value for the second group or between multiple distributions.

Usage

## S4 method for signature 'ttest_class'
plot_distributions_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot_distributions_hsv a visualization of distributions of one or two fits thorough a circular visualization of hsv color components. You can also compare fit means with colors defined through rgb or hsv components (as points or as lines on the visualization).

Usage

plot_distributions_hsv(object, ...)

## S4 method for signature 'color_class'
plot_distributions_hsv(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_distributions a visualization of the fitted distributions or constant colors.

Usage

## S4 method for signature 'color_class'
plot_distributions(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_distributions a visualization of the fitted distribution, for one or two fits.

Usage

## S4 method for signature 'linear_class'
plot_distributions(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_distributions a visualization of the fitted distribution, for one, two or multiple fits.

Usage

## S4 method for signature 'reaction_time_class'
plot_distributions(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_distributions a visualization of the fitted distribution, for one, two or multiple fits.

Usage

## S4 method for signature 'success_rate_class'
plot_distributions(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_distributions visualizes distributions underlying tested groups.

Usage

## S4 method for signature 'ttest_class'
plot_distributions(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit.

Usage

plot_fit(object, ...)

Arguments

Description

plot_fit_hsv plots fitted model against the data. Use this function to explore the quality of your fit thorough a circular visualization of hsv color components.

Usage

plot_fit_hsv(object)

## S4 method for signature 'color_class'
plot_fit_hsv(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit. You can compare fit with underlying data only through chosen color components (r, g, b, h, s, v).

Usage

## S4 method for signature 'color_class'
plot_fit(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'linear_class'
plot_fit(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'reaction_time_class'
plot_fit(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'success_rate_class'
plot_fit(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_fit plots fitted model against the data. Use this function to explore the quality of your fit.

Usage

## S4 method for signature 'ttest_class'
plot_fit(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot_hsv plots fitted model against the data. Use this function to explore the quality of your fit thorough a circular visualization of hsv color components.

Usage

plot_hsv(object)

## S4 method for signature 'color_class'
plot_hsv(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_means plots means for one or multiple fits.

Usage

plot_means(object, ...)

Arguments

Description

plot_means_difference plots difference between means of two or multiple fits.

Usage

plot_means_difference(object, ...)

Arguments

Description

plot_means_difference a visualization of the difference between two fits

Usage

## S4 method for signature 'color_class'
plot_means_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_means_difference plots difference between two groups.

Usage

## S4 method for signature 'linear_class'
plot_means_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_means_difference a visualization of the difference between two groups or multiple groups.

Usage

## S4 method for signature 'reaction_time_class'
plot_means_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_means_difference a visualization of the difference between two groups or multiple groups.

Usage

## S4 method for signature 'success_rate_class'
plot_means_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_means_difference a visualization of the difference of the first group against the second group, against multiple groups, against a mean value or against a normal distribution with a defined mean value and variance.

Usage

## S4 method for signature 'ttest_class'
plot_means_difference(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot_means_hsv a visualization of the difference between means of two fits through a circular visualization of hsv color components. You can also compare fit means with colors defined through rgb or hsv components (as points or as lines on the visualization).

Usage

plot_means_hsv(object, ...)

## S4 method for signature 'color_class'
plot_means_hsv(object, ...)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_means plots density of means, the first and the second group means or a constant values in case second group is defined as rgb or hsv color.

Usage

## S4 method for signature 'color_class'
plot_means(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_means plots means or the first and the second group means.

Usage

## S4 method for signature 'linear_class'
plot_means(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_means plots density of means for one, two or multiple groups.

Usage

## S4 method for signature 'reaction_time_class'
plot_means(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_means plots density of means for one, two or multiple groups.

Usage

## S4 method for signature 'success_rate_class'
plot_means(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_means plots density of means, the first and the second group means, means of multiple groups or a mean value in case second group is defined as a constant.

Usage

## S4 method for signature 'ttest_class'
plot_means(object, ...)

Arguments

Value

A ggplot visualization or an error if something went wrong.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot_trace traceplot for main fitted model parameters.

Usage

plot_trace(object)

Arguments

Description

plot_trace traceplot for main fitted model parameters.

Usage

## S4 method for signature 'color_class'
plot_trace(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot_trace traceplot for main fitted model parameters.

Usage

## S4 method for signature 'linear_class'
plot_trace(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot_trace traceplot for main fitted model parameters.

Usage

## S4 method for signature 'reaction_time_class'
plot_trace(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot_trace traceplot for main fitted model parameters.

Usage

## S4 method for signature 'success_rate_class'
plot_trace(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot_trace traceplot for main fitted model parameters.

Usage

## S4 method for signature 'ttest_class'
plot_trace(object)

Arguments

Value

A ggplot visualization.

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

plot plots fitted model against the data. Use this function to explore the quality of your fit. You can compare fit with underlying data only through chosen color components (r, g, b, h, s, v).

Usage

## S4 method for signature 'color_class,missing'
plot(x, y, ...)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

plot plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'linear_class,missing'
plot(x, y, ...)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

plot plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'reaction_time_class,missing'
plot(x, y, ...)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

plot plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

Usage

## S4 method for signature 'success_rate_class,missing'
plot(x, y, ...)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

plot plots fitted model against the data. Use this function to explore the quality of your fit.

Usage

## S4 method for signature 'ttest_class,missing'
plot(x)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

An S4 class for storing results of reaction time Bayesian model.

Functions

summary('reaction_time_class'): prints a summary of the fit.

print('reaction_time_class'): prints a more detailed summary of the fit

show('reaction_time_class'): prints a more detailed summary of the fit.

plot('reaction_time_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot(‘reaction_time_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the group level (subjects=FALSE).

plot_fit('reaction_time_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot_fit(‘reaction_time_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subject level (subjects=TRUE) or on the group level (subjects=FALSE).

plot_trace('reaction_time_class'): traceplot for main fitted model parameters.

get_parameters('reaction_time_class'): returns a dataframe with values of fitted parameters.

get_subject_parameters('reaction_time_class'): returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

compare_means('reaction_time_class', fit2='reaction_time_class'): returns difference in reaction times between two groups. You can also provide the rope parameter or execute the comparison only through a chosen parameter - mu or lambda.

compare_means('reaction_time_class', fits='list'): returns difference in reaction times between multiple groups. You can also provide the rope parameter. You can also provide the rope parameter or execute the comparison only through a chosen parameter - mu or lambda.

plot_means_difference('reaction_time_class', fit2='reaction_time_class'): a visualization of the difference between two groups. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through a chosen parameter - mu or lambda.

plot_means_difference('reaction_time_class', fits='list'): a visualization of the difference between multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison only through a chosen parameter - mu or lambda.

plot_means('reaction_time_class'): plots density of the means. You can also visualize the density only for a chosen parameter - mu or lambda.

plot_means('reaction_time_class', fit2='reaction_time_class'): plots density for the first and the second group means. You can also visualize the density only for a chosen parameter - mu or lambda.

plot_means('reaction_time_class', fits='list'): plots density for means of multiple groups. You can also visualize the density only for a chosen parameter - mu or lambda.

compare_distributions('reaction_time_class', fit2='reaction_time_class'): draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group. You can also provide the rope parameter.

compare_distributions('reaction_time_class', fits='lists'): draws and compares samples from distributions of multiple groups. You can also provide the rope parameter.

plot_distributions('reaction_time_class'): a visualization of the fitted distribution.

plot_distributions('reaction_time_class', fit2='reaction_time_class'): a visualization of the distribution for two fits.

plot_distributions('reaction_time_class', fits='list'): a visualization of the distribution for multiple fits.

plot_distributions_difference('reaction_time_class', fit2='reaction_time_class'): a visualization of the difference between the distribution of the first group and the second group. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_distributions_difference('reaction_time_class', fits='list'): a visualization of the difference between the distributions of multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters.

Slots

extract

Extract from Stan fit.

fit

Stan fit.

data

Data on which the fit is based.

Examples

# priors
mu_prior <- b_prior(family="normal", pars=c(0, 100))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))
lambda_prior <- b_prior(family="uniform", pars=c(0.05, 5))

# attach priors to relevant parameters
priors <- list(c("mu_m", mu_prior),
               c("sigma_m", sigma_prior),
               c("mu_s", sigma_prior),
               c("sigma_s", sigma_prior),
               c("mu_l", lambda_prior),
               c("sigma_l", sigma_prior))


# subjects
s <- rep(1:5, 20)

# generate data and fit
rt1 <- emg::remg(100, mu=10, sigma=1, lambda=0.4)
fit1 <- b_reaction_time(t=rt1, s=s, priors=priors, chains=1)

rt2 <- emg::remg(100, mu=10, sigma=2, lambda=0.1)
fit2 <- b_reaction_time(t=rt2, s=s, priors=priors, chains=1)

rt3 <- emg::remg(100, mu=20, sigma=2, lambda=1)
fit3 <- b_reaction_time(t=rt3, s=s, priors=priors, chains=1)

rt4 <- emg::remg(100, mu=15, sigma=2, lambda=0.5)
fit4 <- b_reaction_time(t=rt4, s=s, priors=priors, chains=1)

# fit list
fit_list <- list(fit2, fit3, fit4)

# a short summary of fitted parameters
summary(fit1)

# a more detailed summary of fitted parameters
print(fit1)
show(fit1)

# plot the fitted distribution against the data
plot(fit1)
plot_fit(fit1)

# plot the fitted distribution against the data,
# plot on the top (group) level
plot(fit1, subjects=FALSE)
plot_fit(fit1, subjects=FALSE)

# traceplot of the fitted parameters
plot_trace(fit1)

# extract parameter values from the fit
parameters <- get_parameters(fit1)

# extract parameter values on the bottom (subject) level from the fit
subject_parameters <- get_subject_parameters(fit1)

# compare means between two fits, use a rope interval
compare_means(fit1, fit2=fit2, rope=0.5)

# compare means between two fits,
# use only the mu parameter of the exponentially modified gaussian distribution
compare_means(fit1, fit2=fit2, par="mu")

# compare means between multiple fits
compare_means(fit1, fits=fit_list)

# visualize difference in means between two fits,
# specify number of histogram bins and rope interval
plot_means_difference(fit1, fit2=fit2, bins=20, rope=0.5)

# visualize difference in means between two fits,
# use only the mu parameter of the exponentially modified gaussian distribution
plot_means_difference(fit1, fit2=fit2, par="mu")

# visualize difference in means between multiple fits
plot_means_difference(fit1, fits=fit_list)

# visualize means of a single fit
plot_means(fit1)

# visualize means of two fits
plot_means(fit1, fit2=fit1)

# visualize means of two fits,
# use only the mu parameter of the exponentially modified gaussian distribution
plot_means(fit1, fit2=fit2, par="mu")

# visualize means of multiple fits
plot_means(fit1, fits=fit_list)

# draw samples from distributions underlying two fits and compare them,
# use a rope interval
compare_distributions(fit1, fit2=fit2, rope=0.5)

# draw samples from distributions underlying multiple fits and compare them
compare_distributions(fit1, fits=fit_list)

# visualize the distribution underlying a fit
plot_distributions(fit1)

# visualize distributions underlying two fits
plot_distributions(fit1, fit2=fit2)

# visualize distributions underlying multiple fits
plot_distributions(fit1, fits=fit_list)

# visualize difference between distributions underlying two fits,
# use a rope interval
plot_distributions_difference(fit1, fit2=fit2, rope=0.05)

# visualize difference between distributions underlying multiple fits
plot_distributions_difference(fit1, fits=fit_list)

Description

show prints a more detailed summary of the Bayesian color fit.

Usage

## S4 method for signature 'color_class'
show(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

show prints a more detailed summary of the Bayesian linear model fit.

Usage

## S4 method for signature 'linear_class'
show(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

show prints a more detailed summary of the Bayesian reaction time fit.

Usage

## S4 method for signature 'reaction_time_class'
show(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

show prints a more detailed summary of the Bayesian success rate fit.

Usage

## S4 method for signature 'success_rate_class'
show(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

show prints a more detailed summary of the Bayesian ttest fit.

Usage

## S4 method for signature 'ttest_class'
show(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

An S4 class for storing results of successes (true/false) Bayesian model.

Functions

summary('success_rate_class'): prints a summary of the fit.

print('success_rate_class'): prints a more detailed summary of the fit

show('success_rate_class'): prints a more detailed summary of the fit.

plot('success_rate_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot(‘success_rate_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

plot_fit('success_rate_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot_fit(‘success_rate_class', subjects=’boolean'): plots fitted model against the data. Use this function to explore the quality of your fit. You can plot on the subjects level (subjects=TRUE) or on the group level (subjects=FALSE).

plot_trace('success_rate_class'): traceplot for main fitted model parameters.

get_parameters('success_rate_class'): returns a dataframe with values of fitted parameters.

get_subject_parameters('success_rate_class'): returns a dataframe with values of fitted parameters for each subject in the hierarchical model.

compare_means('success_rate_class', fit2='success_rate_class'): returns difference in success rate between two groups. You can also provide the rope parameter.

compare_means('success_rate_class', fits='list'): returns difference in success rate between multiple groups. You can also provide the rope parameter.

plot_means_difference('success_rate_class', fit2='success_rate_class'): a visualization of the difference between two groups. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_means_difference('success_rate_class', fits='list'): a visualization of the difference between multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_means('success_rate_class'): plots density for the first group means.

plot_means('success_rate_class', fit2='success_rate_class'): plots density for the first and the second group means.

plot_means('success_rate_class', fits='list'): plots density for multiple

compare_distributions('success_rate_class', fit2='success_rate_class'): draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group. You can also provide the rope parameter.

compare_distributions('success_rate_class', fits='list'): draws and compares samples from distributions of multiple groups. You can also provide the rope parameter.

plot_distributions('success_rate_class'): a visualization of the fitted distribution.

plot_distributions('success_rate_class', fit2='success_rate_class'): a visualization of the distribution for two fits.

plot_distributions('success_rate_class', fits='list'): a visualization of the distribution for multiple fits.

plot_distributions_difference('success_rate_class', fit2='success_rate_class'): a visualization of the difference between the distribution of the first group and the second group. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_distributions_difference('success_rate_class', fits='list'): a visualization of the difference between the distributions of multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_fit('success_rate_class'): plots fitted model against the data. Use this function to explore the quality of your fit. Fit will be plotted on the group level.

Slots

extract

Extract from Stan fit.

fit

Stan fit.

data

Data on which the fit is based.

Examples

# priors
p_prior <- b_prior(family = "beta", pars = c(1, 1))
tau_prior <- b_prior(family = "uniform", pars = c(0, 500))

# attach priors to relevant parameters
priors <- list(
  c("p", p_prior),
  c("tau", tau_prior)
)

# subjects
s <- rep(1:5, 20)

# generate data and fit
data1 <- rbinom(100, size = 1, prob = 0.6)
fit1 <- b_success_rate(r = data1, s = s, priors = priors, chains = 1)

data2 <- rbinom(100, size = 1, prob = 0.1)
fit2 <- b_success_rate(r = data2, s = s, priors = priors, chains = 1)

data3 <- rbinom(100, size = 1, prob = 0.5)
fit3 <- b_success_rate(r = data3, s = s, priors = priors, chains = 1)

data4 <- rbinom(100, size = 1, prob = 0.9)
fit4 <- b_success_rate(r = data4, s = s, priors = priors, chains = 1)

# fit list
fit_list <- list(fit2, fit3, fit4)

# a short summary of fitted parameters
summary(fit1)

# a more detailed summary of fitted parameters
print(fit1)
show(fit1)

# plot the fitted distribution against the data
plot(fit1)
plot_fit(fit1)

# plot the fitted distribution against the data,
# plot on the top (group) level
plot(fit1, subjects = FALSE)
plot_fit(fit1, subjects = FALSE)

# traceplot of the fitted parameters
plot_trace(fit1)

# extract parameter values from the fit
parameters <- get_parameters(fit1)

# extract parameter values on the bottom (subject) level from the fit
subject_parameters <- get_subject_parameters(fit1)

# compare means between two fits, use a rope interval
compare_means(fit1, fit2 = fit2, rope = 0.05)

# compare means between multiple fits
compare_means(fit1, fits = fit_list)

# visualize difference in means between two fits,
# specify number of histogram bins and rope interval
plot_means_difference(fit1, fit2 = fit2, bins = 40, rope = 0.05)

# visualize difference in means between multiple fits
plot_means_difference(fit1, fits = fit_list)

# visualize means of a single fit
plot_means(fit1)

# visualize means of two fits
plot_means(fit1, fit2 = fit2)

# visualize means of multiple fits
plot_means(fit1, fits = fit_list)

# draw samples from distributions underlying two fits and compare them,
# use a rope interval
compare_distributions(fit1, fit2 = fit2, rope = 0.05)

# draw samples from distributions underlying multiple fits and compare them
compare_distributions(fit1, fits = fit_list)

# visualize the distribution underlying a fit
plot_distributions(fit1)

# visualize distributions underlying two fits
plot_distributions(fit1, fit2 = fit2)

# visualize distributions underlying multiple fits
plot_distributions(fit1, fits = fit_list)

# visualize difference between distributions underlying two fits,
# use a rope interval
plot_distributions_difference(fit1, fit2 = fit2, rope = 0.05)

# visualize difference between distributions underlying multiple fits
plot_distributions_difference(fit1, fits = fit_list)

Description

summary prints summary of the Bayesian color fit.

Usage

## S4 method for signature 'color_class'
summary(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?color_class

Description

summary prints a summary of the Bayesian linear model fit.

Usage

## S4 method for signature 'linear_class'
summary(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?linear_class

Description

summary prints a summary of the Bayesian reaction time fit.

Usage

## S4 method for signature 'reaction_time_class'
summary(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?reaction_time_class

Description

summary prints a summary of the Bayesian success rate fit.

Usage

## S4 method for signature 'success_rate_class'
summary(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?success_rate_class

Description

summary prints a summary of the Bayesian ttest fit.

Usage

## S4 method for signature 'ttest_class'
summary(object)

Arguments

Examples

# to use the function you first have to prepare the data and fit the model
# see class documentation for an example of the whole process
# along with an example of how to use this function
?ttest_class

Description

An S4 class for storing results of Bayesian t-test results.

Functions

summary('ttest_class'): prints a summary of the fit.

print('ttest_class'): prints a more detailed summary of the fit

show('ttest_class'): prints a more detailed summary of the fit.

plot('ttest_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot_fit('ttest_class'): plots fitted model against the data. Use this function to explore the quality of your fit.

plot_trace('ttest_class'): traceplot for main fitted model parameters.

get_parameters('ttest_class'): returns a dataframe with values of fitted parameters.

compare_means('ttest_class', fit2='ttest_class'): prints difference/equality of the first group against the second group. You can also provide the rope parameter or execute the comparison through the sigma or nu parameter.

compare_means('ttest_class', mu='numeric'): prints difference/equality of the first group against a mean value. You can also provide the rope parameter or execute the comparison through the sigma parameter.

compare_means(‘ttest_class', mu='numeric', sigma='numeric'): prints difference/equality of the first group against a normal distribution provided with mean value and standard deviation. Note here that sigma is used only in the Cohen’s d calculation. You can also provide the rope parameter or execute the comparison through the sigma or nu parameter.

compare_means('ttest_class', fits='list'): prints difference/equality of the first group and multiple other groups. You can also provide the rope parameter or execute the comparison through the sigma or nu parameter.

plot_means_difference('ttest_class', fit2='ttest_class'): a visualization of the difference between the first group and the second group. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison through the sigma or nu parameter.

plot_means_difference('ttest_class', mu='numeric'): a visualization of the difference between the first group and a constant value or a normal distribution with mean value mu. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison through the sigma or nu parameter.

plot_means_difference('ttest_class', fits='list'): a visualization of the difference between multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters or visualize the comparison through the sigma or nu parameter.

plot_means('ttest_class'): plots density of means. You can also visualize the density for the sigma or nu parameter.

plot_means('ttest_class', fit2='ttest_class'): plots density for the first and the second group means. You can also visualize the density for the sigma or nu parameter.

plot_means('ttest_class', mu='numeric'): plots density for the first group means and a mean value in case second group is defined as a normal distribution or as a constant. You can also visualize the density for the sigma or nu parameter.

plot_means('ttest_class', fits='list'): plots density for the first group means and means for multiple other groups. You can also visualize the density for the sigma or nu parameter.

compare_distributions('ttest_class', fit2='ttest_class'): draws samples from distribution of the first group and compares them against samples drawn from the distribution of the second group. You can also provide the rope parameter.

compare_distributions('ttest_class', mu='numeric'): draws samples from distribution of the first group and compares them against a mean value. You can also provide the rope parameter.

compare_distributions('ttest_class', mu='numeric', sigma='numeric'): draws samples from distribution of the first group and compares them against samples from a normal distribution with a defined mean value and variance. You can also provide the rope parameter.

compare_distributions('ttest_class', fits='list'): draws samples from distribution of the first group and compares them against samples drawn from multiple other groups. You can also provide the rope parameter.

plot_distributions('ttest_class'): a visualization of the fitted distribution.

plot_distributions('ttest_class', fit2='ttest_class'): a visualization of two fitted distributions.

plot_distributions('ttest_class', mu='numeric'): a visualization of the fitted distribution and a constant value.

plot_distributions('ttest_class', mu='numeric', sigma='numeric'): a visualization of the fitted distribution and the normal distribution defined with a mean value and a standard deviation.

plot_distributions('ttest_class', fits='list'): a visualization of multiple fitted distributions.

plot_distributions_difference('ttest_class', fit2='ttest_class'): a visualization of the difference between the distribution of the first group and the distribution of the second group. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_distributions_difference('ttest_class', mu='numeric'): a visualization of the difference between the distribution of the first group and a constant value. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_distributions_difference('ttest_class', mu='numeric', sigma='numeric'): a visualization of the difference between the distribution of the first group and the normal distribution defined with a mean value and standard deviation. You can also provide the rope and bins (number of bins in the histogram) parameters.

plot_distributions_difference('ttest_class', fits='list'): a visualization of the difference between multiple groups. You can also provide the rope and bins (number of bins in the histogram) parameters.

Slots

extract

Extract from Stan fit.

fit

Stan fit.

data

Raw data for the tested group.

Examples

# priors
mu_prior <- b_prior(family = "normal", pars = c(0, 1000))
sigma_prior <- b_prior(family = "uniform", pars = c(0, 500))
nu_prior <- b_prior(family = "normal", pars = c(2000, 1000))

# attach priors to relevant parameters
priors <- list(
  c("mu", mu_prior),
  c("sigma", sigma_prior),
  c("nu", nu_prior)
)

# generate data and fit
data1 <- rnorm(20, mean = 150, sd = 20)
fit1 <- b_ttest(data = data1, priors = priors, chains = 1)

data2 <- rnorm(20, mean = 200, sd = 20)
fit2 <- b_ttest(data = data2, priors = priors, chains = 1)

data3 <- rnorm(20, mean = 150, sd = 40)
fit3 <- b_ttest(data = data3, priors = priors, chains = 1)

data4 <- rnorm(20, mean = 50, sd = 10)
fit4 <- b_ttest(data = data4, priors = priors, chains = 1)

# fit list
fit_list <- list(fit2, fit3, fit4)

# a short summary of fitted parameters
summary(fit1)

# a more detailed summary of fitted parameters
print(fit1)
show(fit1)

# plot the fitted distribution against the data
plot(fit1)
plot_fit(fit1)

# traceplot of the fitted parameters
plot_trace(fit1)

# extract parameter values from the fit
parameters <- get_parameters(fit1)

# compare means between two fits
compare_means(fit1, fit2 = fit2)

# compare means between two fits, use a rope interval
compare_means(fit1, fit2 = fit2, rope = 2)

# compare means between a fit and a constant value
compare_means(fit1, mu = 150)

# compare means between a fit and a distribution,
# sigma is used for calculating Cohen's d
compare_means(fit1, mu = 150, sigma = 20)

# compare means between multiple fits
compare_means(fit1, fits = fit_list)

# visualize difference in means between two fits,
# specify number of histogram bins
plot_means_difference(fit1, fit2 = fit2, bins = 20)

# visualize difference in means between a fit and a constant value
plot_means_difference(fit1, mu = 150)

# visualize difference in means between multiple fits, use a rope interval
plot_means_difference(fit1, fits = fit_list, rope = 2)

# visualize means of a single fit
plot_means(fit1)

# visualize means of two fits
plot_means(fit1, fit2 = fit2)

# visualize means of a fit and a constant value
plot_means(fit1, mu = 150)

# visualize means of multiple fits
plot_means(fit1, fits = fit_list)

# draw samples from distributions underlying two fits and compare them
compare_distributions(fit1, fit2 = fit2)

# draw samples from a distribution underlying the fit
# and compare them with a constant, use a rope interval
compare_distributions(fit1, mu = 150, rope = 2)

# draw samples from a distribution underlying the fit and
# compare them with a user defined distribution
compare_distributions(fit1, mu = 150, sigma = 20)

# draw samples from distributions underlying multiple fits and compare them
compare_distributions(fit1, fits = fit_list)

# visualize the distribution underlying a fit
plot_distributions(fit1)

# visualize distributions underlying two fits
plot_distributions(fit1, fit2 = fit2)

# visualize the distribution underlying a fit and a constant value
plot_distributions(fit1, mu = 150)

# visualize the distribution underlying a fit and a user defined distribution
plot_distributions(fit1, mu = 150, sigma = 20)

# visualize distributions underlying multiple fits
plot_distributions(fit1, fits = fit_list)

# visualize difference between distributions underlying two fits,
# use a rope interval
plot_distributions_difference(fit1, fit2 = fit2, rope = 2)

# visualize difference between a distribution underlying the fit
# and a constant value
plot_distributions_difference(fit1, mu = 150)

# visualize difference between a distribution underlying the fits
# and a user defined distribution
plot_distributions_difference(fit1, mu = 150, sigma = 20)

# visualize difference between distributions underlying multiple fits
plot_distributions_difference(fit1, fits = fit_list)