Nothing
R/CoefQuartVarCI.R
In cvcqv: Coefficient of Variation (CV) with Confidence Intervals (CI)
#' @title R6 Confidence Intervals for the Coefficient of Quartile Variation
#' (cqv)
#' @name CoefQuartVarCI
#' @description The R6 class \code{CoefQuartVarCI} for the confidence intervals
#' of coefficient of quartile variation (cqv)
#' @param x An \code{R} object. Currently there are methods for numeric vectors
#' @param na.rm a logical value indicating whether \code{NA} values should be
#' stripped before the computation proceeds.
#' @param digits integer indicating the number of decimal places to be used.
#' @param methods the available computation methods of confidence intervals are:
#' "bonett_ci", "norm_ci", "basic_ci", "perc_ci", "bca_ci" or "all_ci".
#' @param R integer indicating the number of bootstrap replicates.
#' @details \describe{ \item{\strong{Coefficient of Quartile Variation}}{ The
#' \emph{cqv} is a measure of relative dispersion that is based on
#' interquartile range \emph{(iqr)}. Since \eqn{cqv} is unitless, it is useful
#' for comparison of variables with different units. It is also a measure of
#' homogeneity \code{[1, 2]}. } }
#' @return An object of type "list" which contains the estimate, the intervals,
#' and the computation method. It has two components:
#' @return \describe{ \item{$method}{ A description of statistical method used
#' for the computations. } \item{$statistics}{ A data frame representing three
#' vectors: est, lower and upper limits of 95\% confidence interval
#' \code{(CI)}: \cr \cr \strong{est:}{ \code{cqv*100} } \cr \cr \strong{Bonett
#' 95\% CI:}{ It uses a centering adjustment which helps to equalize the tail
#' error probabilities \code{[1, 2]}. } \cr \cr \strong{Normal approximation
#' 95\% CI:}{ The intervals calculated by the normal approximation \code{[3,
#' 4]}, using \link[boot]{boot.ci}. } \cr \cr \strong{Basic bootstrap 95\%
#' CI:}{ The intervals calculated by the basic bootstrap method \code{[3, 4]},
#' using \link[boot]{boot.ci}. } \cr \cr \strong{Bootstrap percentile 95\%
#' CI:}{ The intervals calculated by the bootstrap percentile method \code{[3,
#' 4]}, using \link[boot]{boot.ci}. } \cr \cr \strong{Adjusted bootstrap
#' percentile (BCa) 95\% CI:}{ The intervals calculated by the adjusted
#' bootstrap percentile (BCa) method \code{[3, 4]}, using
#' \link[boot]{boot.ci}. } } }
#' @examples
#' y <- c(
#' 0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
#' 4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
#' )
#' CoefQuartVarCI$new(x = y)$bonett_ci()
#' cqv_y <- CoefQuartVarCI$new(
#' x = y,
#' alpha = 0.05,
#' R = 1000,
#' digits = 2
#' )
#' cqv_y$bonett_ci()
#' R6::is.R6(cqv_y)
#' @references \code{[1]} Bonett, DG., 2006, Confidence interval for a
#' coefficient of quartile variation, Computational Statistics & Data
#' Analysis, 50(11), 2953-7, DOI:
#' \href{http://doi.org/10.1016/j.csda.2005.05.007}{http://doi.org/10.1016/j.csda.2005.05.007}
#'
#' @references \code{[2]} Altunkaynak, B., Gamgam, H., 2018, Bootstrap
#' confidence intervals for the coefficient of quartile variation, Simulation
#' and Computation, 1-9, DOI:
#' \href{http://doi.org/10.1080/03610918.2018.1435800}{http://doi.org/10.1080/03610918.2018.1435800}
#'
#' @references \code{[3]} Canty, A., & Ripley, B, 2017, boot: Bootstrap R
#' (S-Plus) Functions. R package version 1.3-20.
#' @references \code{[4]} Davison, AC., & Hinkley, DV., 1997, Bootstrap Methods
#' and Their Applications. Cambridge University Press, Cambridge. ISBN
#' 0-521-57391-2
#' @export
#' @import dplyr SciViews boot R6 utils
NULL
#' @importFrom stats quantile sd qchisq qnorm
#' @importFrom MBESS conf.limits.nct
NULL
CoefQuartVarCI <- R6::R6Class(
classname = "CoefQuartVarCI",
inherit = CoefQuartVar,
public = list(
# ---------------- determining defaults for arguments -----------------
x = NA,
na.rm = FALSE,
digits = 1,
R = 1000,
alpha = 0.05,
# --------------------- adding some internal fields -------------------
zzz = NA,
f1square = NA,
f3square = NA,
D = NA,
S = NA,
v = NA,
ccc = NA,
bootcqv = NA,
# --------- determining constructor defaults for arguments ------------
initialize = function(
x = NA,
na.rm = FALSE,
digits = 1,
R = 1000,
alpha = 0.05,
...
) {
# ---------------------- check NA or NAN -------------------------
if (missing(x) || is.null(x)) {
stop("object 'x' not found")
} else if (!missing(x)) {
self$x <- x
}
if (!missing(na.rm)) {
self$na.rm <- na.rm
}
if (self$na.rm == TRUE) {
self$x <- x[!is.na(x)]
} else if (anyNA(x) & self$na.rm == FALSE) {
stop(
"missing values and NaN's not allowed if 'na.rm' is FALSE"
)
}
# ------------- stop if input x vector is not numeric -------------
if (!is.numeric(x)) {
stop("argument is not a numeric vector")
}
# ------------------- set digits with user input ------------------
if (!missing(digits)) {
self$digits <- digits
}
# ---- set the number of bootstrap replicates with user input -----
if (!missing(R)) {
self$R <- R
}
# ------ set the probability of type I error with user input ------
if (!missing(alpha)) {
self$alpha <- alpha
}
# ------------- initialize zzz() i.e., z(1 - alpha/2) -------------
self$zzz = function(...) {
qnorm((1 - ((1 - super$alphastar())/2)))
}
# --------------- initialize internal function f1^2 ---------------
self$f1square = function(...) {
(3 * (self$zzz())^2)/(
4 * length(self$x) * ((super$Yb() - super$Ya())^2))
}
# --------------- initialize internal function f3^2 ---------------
self$f3square = function(...) {
(3 * (self$zzz())^2)/(
4 * length(self$x) * ((super$Yd() - super$Yc())^2))
}
# ----------- initialize internal function D = q3 - q1 ------------
self$D = function(...) {
super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.75,
names = FALSE
) - super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.25,
names = FALSE
)
}
# ----------- initialize internal function S = q3 + q1 ------------
self$S = function(...) {
super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.75,
names = FALSE
) + super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.25,
names = FALSE
)
}
# -------- initialize internal function v = var{ln(D/S)} ----------
self$v = function(...) {
(
(1/(16 * length(self$x))) * (
(((3/self$f1square()) + (3/self$f3square()) - (
2/sqrt(self$f1square() * self$f3square()))) /
self$D()^2) +
(((3/self$f1square()) + (3/self$f3square()) +
(2/sqrt(self$f1square() * self$f3square()))) /
self$S()^2) -
((2 * ((3/self$f3square()) - (3/self$f1square()))) /
(self$D()*self$S()))
)
)
}
# ----------- initialize internal function c = n/(n-1) ------------
# ---------------- which is a centering adjustment ----------------
self$ccc = function(...) {length(self$x)/(length(self$x) - 1)}
# ---- initialize the internal functions for public methods -------
self$bootcqv = function(...) {
return(
super$super_$initialize(
x = self$x,
na.rm = self$na.rm,
R = self$R,
alpha = self$alpha
))
# invisible(self)
}
self$bootcqv()
# invisible(self)
},
# -------------- public method bonett_ci() i.e., Bonett CI ------------
bonett_ci = function(...) {
return(
list(
method = "cqv with Bonett CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = (
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) -
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
)
),
upper = (
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) +
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
)
),
row.names = c(" ")
)
)
)
},
# -- public method norm_ci() i.e., Normal Approximation Bootstrap CI --
norm_ci = function(...) {
return(
list(
method = "cqv with normal approximation bootstrap CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_norm_ci()$normal[2],
digits = self$digits),
upper = round(
self$boot_norm_ci()$normal[3],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# ---------- public method basic_ci() i.e., Basic Bootstrap CI --------
basic_ci = function(...) {
return(
list(
method = "cqv with basic bootstrap CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_basic_ci()$basic[4],
digits = self$digits),
upper = round(
self$boot_basic_ci()$basic[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# ----- public method perc_ci() i.e., Bootstrap Percentile CI ---------
perc_ci = function(...) {
return(
list(
method = "cqv with bootstrap percentile CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_perc_ci()$percent[4],
digits = self$digits),
upper = round(
self$boot_perc_ci()$percent[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# - public method bca_ci() i.e., Adjusted Bootstrap Percentile (BCa) CI
bca_ci = function(...) {
return(
list(
method = "cqv with adjusted bootstrap percentile (BCa) CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_bca_ci()$bca[4],
digits = self$digits),
upper = round(
self$boot_bca_ci()$bca[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# --------- public method all_ci() i.e., All Bootstrap CIs ------------
all_ci = function(...) {
return(
list(
method = "All methods",
statistics = data.frame(
row.names = c(
"bonett",
"norm",
"basic",
"percent",
"bca"
),
est = c(
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits)
),
lower = c(
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) -
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
),
round(
self$boot_norm_ci()$normal[2],
digits = self$digits),
round(
self$boot_basic_ci()$basic[4],
digits = self$digits),
round(
self$boot_perc_ci()$percent[4],
digits = self$digits),
round(
self$boot_bca_ci()$bca[4],
digits = self$digits)
),
upper = c(
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) +
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
),
round(
self$boot_norm_ci()$normal[3],
digits = self$digits),
round(
self$boot_basic_ci()$basic[5],
digits = self$digits),
round(
self$boot_perc_ci()$percent[5],
digits = self$digits),
round(
self$boot_bca_ci()$bca[5],
digits = self$digits)
),
description = c(
"cqv with Bonett CI",
"cqv with normal approximation CI",
"cqv with basic bootstrap CI",
"cqv with bootstrap percentile CI",
"cqv with adjusted bootstrap percentile (BCa) CI"
)
)
)
)
}
)
)
Try the cvcqv package in your browser
Any scripts or data that you put into this service are public.
cvcqv documentation built on Aug. 6, 2019, 5:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @title R6 Confidence Intervals for the Coefficient of Quartile Variation
#' (cqv)
#' @name CoefQuartVarCI
#' @description The R6 class \code{CoefQuartVarCI} for the confidence intervals
#' of coefficient of quartile variation (cqv)
#' @param x An \code{R} object. Currently there are methods for numeric vectors
#' @param na.rm a logical value indicating whether \code{NA} values should be
#' stripped before the computation proceeds.
#' @param digits integer indicating the number of decimal places to be used.
#' @param methods the available computation methods of confidence intervals are:
#' "bonett_ci", "norm_ci", "basic_ci", "perc_ci", "bca_ci" or "all_ci".
#' @param R integer indicating the number of bootstrap replicates.
#' @details \describe{ \item{\strong{Coefficient of Quartile Variation}}{ The
#' \emph{cqv} is a measure of relative dispersion that is based on
#' interquartile range \emph{(iqr)}. Since \eqn{cqv} is unitless, it is useful
#' for comparison of variables with different units. It is also a measure of
#' homogeneity \code{[1, 2]}. } }
#' @return An object of type "list" which contains the estimate, the intervals,
#' and the computation method. It has two components:
#' @return \describe{ \item{$method}{ A description of statistical method used
#' for the computations. } \item{$statistics}{ A data frame representing three
#' vectors: est, lower and upper limits of 95\% confidence interval
#' \code{(CI)}: \cr \cr \strong{est:}{ \code{cqv*100} } \cr \cr \strong{Bonett
#' 95\% CI:}{ It uses a centering adjustment which helps to equalize the tail
#' error probabilities \code{[1, 2]}. } \cr \cr \strong{Normal approximation
#' 95\% CI:}{ The intervals calculated by the normal approximation \code{[3,
#' 4]}, using \link[boot]{boot.ci}. } \cr \cr \strong{Basic bootstrap 95\%
#' CI:}{ The intervals calculated by the basic bootstrap method \code{[3, 4]},
#' using \link[boot]{boot.ci}. } \cr \cr \strong{Bootstrap percentile 95\%
#' CI:}{ The intervals calculated by the bootstrap percentile method \code{[3,
#' 4]}, using \link[boot]{boot.ci}. } \cr \cr \strong{Adjusted bootstrap
#' percentile (BCa) 95\% CI:}{ The intervals calculated by the adjusted
#' bootstrap percentile (BCa) method \code{[3, 4]}, using
#' \link[boot]{boot.ci}. } } }
#' @examples
#' y <- c(
#' 0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
#' 4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
#' )
#' CoefQuartVarCI$new(x = y)$bonett_ci()
#' cqv_y <- CoefQuartVarCI$new(
#' x = y,
#' alpha = 0.05,
#' R = 1000,
#' digits = 2
#' )
#' cqv_y$bonett_ci()
#' R6::is.R6(cqv_y)
#' @references \code{[1]} Bonett, DG., 2006, Confidence interval for a
#' coefficient of quartile variation, Computational Statistics & Data
#' Analysis, 50(11), 2953-7, DOI:
#' \href{http://doi.org/10.1016/j.csda.2005.05.007}{http://doi.org/10.1016/j.csda.2005.05.007}
#'
#' @references \code{[2]} Altunkaynak, B., Gamgam, H., 2018, Bootstrap
#' confidence intervals for the coefficient of quartile variation, Simulation
#' and Computation, 1-9, DOI:
#' \href{http://doi.org/10.1080/03610918.2018.1435800}{http://doi.org/10.1080/03610918.2018.1435800}
#'
#' @references \code{[3]} Canty, A., & Ripley, B, 2017, boot: Bootstrap R
#' (S-Plus) Functions. R package version 1.3-20.
#' @references \code{[4]} Davison, AC., & Hinkley, DV., 1997, Bootstrap Methods
#' and Their Applications. Cambridge University Press, Cambridge. ISBN
#' 0-521-57391-2
#' @export
#' @import dplyr SciViews boot R6 utils
NULL
#' @importFrom stats quantile sd qchisq qnorm
#' @importFrom MBESS conf.limits.nct
NULL
CoefQuartVarCI <- R6::R6Class(
classname = "CoefQuartVarCI",
inherit = CoefQuartVar,
public = list(
# ---------------- determining defaults for arguments -----------------
x = NA,
na.rm = FALSE,
digits = 1,
R = 1000,
alpha = 0.05,
# --------------------- adding some internal fields -------------------
zzz = NA,
f1square = NA,
f3square = NA,
D = NA,
S = NA,
v = NA,
ccc = NA,
bootcqv = NA,
# --------- determining constructor defaults for arguments ------------
initialize = function(
x = NA,
na.rm = FALSE,
digits = 1,
R = 1000,
alpha = 0.05,
...
) {
# ---------------------- check NA or NAN -------------------------
if (missing(x) || is.null(x)) {
stop("object 'x' not found")
} else if (!missing(x)) {
self$x <- x
}
if (!missing(na.rm)) {
self$na.rm <- na.rm
}
if (self$na.rm == TRUE) {
self$x <- x[!is.na(x)]
} else if (anyNA(x) & self$na.rm == FALSE) {
stop(
"missing values and NaN's not allowed if 'na.rm' is FALSE"
)
}
# ------------- stop if input x vector is not numeric -------------
if (!is.numeric(x)) {
stop("argument is not a numeric vector")
}
# ------------------- set digits with user input ------------------
if (!missing(digits)) {
self$digits <- digits
}
# ---- set the number of bootstrap replicates with user input -----
if (!missing(R)) {
self$R <- R
}
# ------ set the probability of type I error with user input ------
if (!missing(alpha)) {
self$alpha <- alpha
}
# ------------- initialize zzz() i.e., z(1 - alpha/2) -------------
self$zzz = function(...) {
qnorm((1 - ((1 - super$alphastar())/2)))
}
# --------------- initialize internal function f1^2 ---------------
self$f1square = function(...) {
(3 * (self$zzz())^2)/(
4 * length(self$x) * ((super$Yb() - super$Ya())^2))
}
# --------------- initialize internal function f3^2 ---------------
self$f3square = function(...) {
(3 * (self$zzz())^2)/(
4 * length(self$x) * ((super$Yd() - super$Yc())^2))
}
# ----------- initialize internal function D = q3 - q1 ------------
self$D = function(...) {
super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.75,
names = FALSE
) - super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.25,
names = FALSE
)
}
# ----------- initialize internal function S = q3 + q1 ------------
self$S = function(...) {
super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.75,
names = FALSE
) + super$super_$super_$initialize(
x = self$x,
na.rm = self$na.rm,
probs = 0.25,
names = FALSE
)
}
# -------- initialize internal function v = var{ln(D/S)} ----------
self$v = function(...) {
(
(1/(16 * length(self$x))) * (
(((3/self$f1square()) + (3/self$f3square()) - (
2/sqrt(self$f1square() * self$f3square()))) /
self$D()^2) +
(((3/self$f1square()) + (3/self$f3square()) +
(2/sqrt(self$f1square() * self$f3square()))) /
self$S()^2) -
((2 * ((3/self$f3square()) - (3/self$f1square()))) /
(self$D()*self$S()))
)
)
}
# ----------- initialize internal function c = n/(n-1) ------------
# ---------------- which is a centering adjustment ----------------
self$ccc = function(...) {length(self$x)/(length(self$x) - 1)}
# ---- initialize the internal functions for public methods -------
self$bootcqv = function(...) {
return(
super$super_$initialize(
x = self$x,
na.rm = self$na.rm,
R = self$R,
alpha = self$alpha
))
# invisible(self)
}
self$bootcqv()
# invisible(self)
},
# -------------- public method bonett_ci() i.e., Bonett CI ------------
bonett_ci = function(...) {
return(
list(
method = "cqv with Bonett CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = (
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) -
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
)
),
upper = (
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) +
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
)
),
row.names = c(" ")
)
)
)
},
# -- public method norm_ci() i.e., Normal Approximation Bootstrap CI --
norm_ci = function(...) {
return(
list(
method = "cqv with normal approximation bootstrap CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_norm_ci()$normal[2],
digits = self$digits),
upper = round(
self$boot_norm_ci()$normal[3],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# ---------- public method basic_ci() i.e., Basic Bootstrap CI --------
basic_ci = function(...) {
return(
list(
method = "cqv with basic bootstrap CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_basic_ci()$basic[4],
digits = self$digits),
upper = round(
self$boot_basic_ci()$basic[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# ----- public method perc_ci() i.e., Bootstrap Percentile CI ---------
perc_ci = function(...) {
return(
list(
method = "cqv with bootstrap percentile CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_perc_ci()$percent[4],
digits = self$digits),
upper = round(
self$boot_perc_ci()$percent[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# - public method bca_ci() i.e., Adjusted Bootstrap Percentile (BCa) CI
bca_ci = function(...) {
return(
list(
method = "cqv with adjusted bootstrap percentile (BCa) CI",
statistics = data.frame(
est = round(super$est(), digits = self$digits),
lower = round(
self$boot_bca_ci()$bca[4],
digits = self$digits),
upper = round(
self$boot_bca_ci()$bca[5],
digits = self$digits),
row.names = c(" ")
)
)
)
},
# --------- public method all_ci() i.e., All Bootstrap CIs ------------
all_ci = function(...) {
return(
list(
method = "All methods",
statistics = data.frame(
row.names = c(
"bonett",
"norm",
"basic",
"percent",
"bca"
),
est = c(
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits),
round(super$est(), digits = self$digits)
),
lower = c(
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) -
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
),
round(
self$boot_norm_ci()$normal[2],
digits = self$digits),
round(
self$boot_basic_ci()$basic[4],
digits = self$digits),
round(
self$boot_perc_ci()$percent[4],
digits = self$digits),
round(
self$boot_bca_ci()$bca[4],
digits = self$digits)
),
upper = c(
round(
(100 * exp(
((SciViews::ln((self$D()/self$S())) *
self$ccc())) +
(self$zzz() * (self$v()^(0.5)))
)), digits = self$digits
),
round(
self$boot_norm_ci()$normal[3],
digits = self$digits),
round(
self$boot_basic_ci()$basic[5],
digits = self$digits),
round(
self$boot_perc_ci()$percent[5],
digits = self$digits),
round(
self$boot_bca_ci()$bca[5],
digits = self$digits)
),
description = c(
"cqv with Bonett CI",
"cqv with normal approximation CI",
"cqv with basic bootstrap CI",
"cqv with bootstrap percentile CI",
"cqv with adjusted bootstrap percentile (BCa) CI"
)
)
)
)
}
)
)
Try the cvcqv package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.