color_class-class: color_class

In bayes4psy: User Friendly Bayesian Data Analysis for Psychology

color_class

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)

bayes4psy documentation built on Sept. 29, 2023, 5:08 p.m.