R/ceplot.R
In markajoc/condvis: Conditional Visualization for Statistical Models
#' @title Interactive conditional expectation plot
#'
#' @description Creates an interactive conditional expectation plot, which
#' consists of two main parts. One part is a single plot depicting a section
#' through a fitted model surface, or conditional expectation. The other part
#' shows small data summaries which give the current condition, which can be
#' altered by clicking with the mouse.
#'
#' @param data A dataframe containing the data to plot
#' @param model A model object, or list of model objects
#' @param response Character name of response in \code{data}
#' @param sectionvars Character name of variable(s) from \code{data} on which to
#' take a section, can be of length 1 or 2.
#' @param conditionvars Character names of conditioning variables from
#' \code{data}. These are the predictors which we can set to single values in
#' order to produce a section. Can be a list of vectors of length 1 or 2. Can
#' be a character vector, which is then paired up using
#' \code{\link{arrangeC}}. If \code{NULL}, an attempt will be made to extract
#' all variable names which are not \code{response} or \code{sectionvars} from
#' \code{model}, and these will be arranged using \code{\link{arrangeC}}.
#' @param threshold This is a threshold distance. Points further than
#' \code{threshold} away from the current section will not be visible. Passed
#' to \code{\link{similarityweight}}.
#' @param lambda A constant to multiply by number of factor mismatches in
#' constructing a general dissimilarity measure. If left \code{NULL}, behaves
#' as though \code{lambda} is set greater than \code{threshold}, and so only
#' observations whose factor levels match the current section are visible.
#' Passed to \code{\link{similarityweight}}.
#' @param distance A character vector describing the type of distance measure to
#' use, either \code{"euclidean"} (default) or \code{"maxnorm"}.
#' @param type This specifies the type of interactive plot. \code{"default"}
#' places everything on one device. \code{"separate"} places condition
#' selectors on one device and the section on another. (These two options
#' require XQuartz on OS X). \code{"shiny"} produces a Shiny application.
#' @param view3d Logical; if \code{TRUE} plots a three-dimensional
#' regression surface if possible.
#' @param Corder Character name for method of ordering conditioning variables.
#' See \code{\link{arrangeC}}.
#' @param selectortype Type of condition selector plots to use. Must be
#' \code{"minimal"} if \code{type} is \code{"default"}. If \code{type} is
#' \code{"separate"}, can be \code{"pcp"} (see \code{\link{plotxc.pcp}}) or
#' \code{"full"} (see \code{\link{plotxc.full}}).
#' @param conf Logical; if \code{TRUE} plots confidence bounds (or equivalent)
#' for models which provide this.
#' @param probs Logical; if \code{TRUE}, shows predicted class probabilities
#' instead of just predicted classes. Only available if \code{S} specifies two
#' numeric predictors and the model's predict method provides this.
#' @param col Colour for observed data.
#' @param pch Plot symbols for observed data.
#' @param residuals Logical; if \code{TRUE}, plots a residual versus predictor
#' plot instead of the usual scale of raw response.
#' @param xsplotpar Plotting parameters for section visualisation as a list,
#' passed to \code{\link{plotxs}}. Can specify \code{xlim}, \code{ylim}.
#' @param modelpar Plotting parameters for models as a list, passed to
#' \code{\link{plotxs}}. Not used.
#' @param xcplotpar Plotting parameters for condition selector plots as a list,
#' passed to \code{\link{plotxc}}. Can specify \code{col} for highlighting
#' current section, \code{cex}, and \code{trim} (see \code{\link{plotxc}}).
#'
#' @examples
#' \dontrun{
#' ## Example 1: Multivariate regression, xs one continuous predictor
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#'
#' library(mgcv)
#' model1 <- list(
#' quadratic = lm(mpg ~ cyl + hp + wt + I(wt^2), data = mtcars),
#' additive = mgcv::gam(mpg ~ cyl + hp + s(wt), data = mtcars))
#'
#' conditionvars1 <- list(c("cyl", "hp"))
#'
#' ceplot(data = mtcars, model = model1, response = "mpg", sectionvars = "wt",
#' conditionvars = conditionvars1, threshold = 0.3, conf = T)
#'
#' ## Example 2: Binary classification, xs one categorical predictor
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$am <- as.factor(mtcars$am)
#'
#' library(e1071)
#' model2 <- list(
#' svm = svm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"),
#' glm = glm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model2, sectionvars = "wt", threshold = 1,
#' type = "shiny")
#'
#' ## Example 3: Multivariate regression, xs both continuous
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$gear <- as.factor(mtcars$gear)
#'
#' library(e1071)
#' model3 <- list(svm(mpg ~ wt + qsec + cyl + hp + gear,
#' data = mtcars, family = "binomial"))
#'
#' conditionvars3 <- list(c("cyl","gear"), "hp")
#'
#' ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
#' threshold = 1, conditionvars = conditionvars3)
#'
#' ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
#' threshold = 1, type = "separate", view3d = T)
#'
#' ## Example 4: Multi-class classification, xs both categorical
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$vs <- as.factor(mtcars$vs)
#' mtcars$am <- as.factor(mtcars$am)
#' mtcars$gear <- as.factor(mtcars$gear)
#' mtcars$carb <- as.factor(mtcars$carb)
#'
#' library(e1071)
#' model4 <- list(svm(carb ~ ., data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model4, sectionvars = c("cyl", "gear"),
#' threshold = 3)
#'
#' ## Example 5: Multi-class classification, xs both continuous
#'
#' data(wine)
#' wine$Class <- as.factor(wine$Class)
#' library(e1071)
#'
#' model5 <- list(svm(Class ~ ., data = wine, probability = TRUE))
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, probs = TRUE)
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, type = "separate")
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, type = "separate", selectortype = "pcp")
#'
#' ## Example 6: Multi-class classification, xs with one categorical predictor,
#' ## and one continuous predictor.
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$carb <- as.factor(mtcars$carb)
#'
#' library(e1071)
#' model6 <- list(svm(cyl ~ carb + wt + hp, data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model6, threshold = 1, sectionvars = c("carb",
#' "wt"), conditionvars = "hp")
#' }
#'
#' @seealso \code{\link{condtour}}, \code{\link{similarityweight}}
#'
#' @references O'Connell M, Hurley CB and Domijan K (2017). ``Conditional
#' Visualization for Statistical Models: An Introduction to the
#' \strong{condvis} Package in R.''\emph{Journal of Statistical Software},
#' \strong{81}(5), pp. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
ceplot <-
function (data, model, response = NULL, sectionvars = NULL, conditionvars = NULL
, threshold = NULL, lambda = NULL, distance = c("euclidean", "maxnorm"), type
= c("default", "separate", "shiny"), view3d = FALSE, Corder = "default",
selectortype = "minimal", conf = FALSE, probs = FALSE, col = "black", pch =
NULL, residuals = FALSE, xsplotpar = NULL, modelpar = NULL, xcplotpar = NULL)
{
## Rename for internal
S <- sectionvars
C <- conditionvars
sigma <- threshold
## Check for optional inputs
cex.axis <- xcplotpar$cex.axis
cex.lab <- xcplotpar$cex.lab
tck <- xcplotpar$tck
select.colour <- if (is.null(xcplotpar$col))
"blue"
else xcplotpar$col
select.cex <- if (is.null(xcplotpar$cex))
1
else xcplotpar$cex
## Prepare variables
data <- na.omit(data)
model <- if (!inherits(model, "list"))
list(model)
else model
model.name <- if (!is.null(names(model)))
names(model)
else paste("model", 1:length(model), sep = "_")
varnamestry <- try(getvarnames(model[[1]]), silent = TRUE)
response <- if (is.null(response))
if (class(varnamestry) != "try-error")
varnamestry$response[1L]
else stop("could not extract response from 'model'.")
else if (is.integer(response)){
colnames(data)[response]
} else response
type <- match.arg(type)
## Need to check here if XQuartz is available when on OS X platform.
## capabilities() is hanging up my R session when called with no XQuartz
## installed. The problem seems to be that .Internal(capabilitiesX11()) just
## keeps waiting for X11 to be found, instead of returning FALSE, when some X11
## files are installed, but X11.app/XQuartz.app is missing.
## Resorting to a rough system call of ls on the Utilities directory.
if (identical(.Platform$OS.type, "unix")){
if (grepl("^darwin", R.version$os)){
# if (!capabilities("X11"))
if (!any(c("XQuartz.app", "X11.app") %in% system2(command = "ls", args =
"/Applications/Utilities/", stdout = TRUE))){
warning("no X11 available, setting 'type' to \"shiny\"")
type <- "shiny"
}
}
}
## If no section variables have been specified, just try and pick the first
## predictor out of the model.
# S <- if(is.null(S)){
# if (!inherits(varnamestry, "try-error")){
# varnamestry$predictors[1L]
# } else {
# setdiff(colnames(data), response)[1L]
# }
# } else if (is.integer(S)){
# colnames(data)[S]
# } else S
## Hierarchy for specifying C
## 1. If user supplies list, use that exactly (maybe chop length).
## 2. If user supplies vector, use that but order/group it.
## 3. If user supplies nothing, try to extract from model, then order.
## 4. Else bung in everything from the data that isn't 'response' or 'S' and
## then try to order that and show the top 20.
if (is.list(C)){
C <- C[1:min(length(C), 20L)]
} else if (is.vector(C)){
C <- arrangeC(data[, setdiff(C, S), drop = FALSE], method = Corder)
} else if (!inherits(varnamestry, "try-error")){
possibleC <- unique(unlist(lapply(lapply(model, getvarnames), `[[`, 2)))
C <- arrangeC(data[, setdiff(intersect(possibleC, colnames(data)), S), drop
= FALSE], method = Corder)
} else {
C <- arrangeC(data[, setdiff(colnames(data), c(S, response)), drop = FALSE],
method = Corder)
}
C <- C[1:min(length(C), 20L)]
uniqC <- unique(unlist(C))
## A few checks on choices of response, S and C.
if (any(response %in% uniqC))
stop("cannot have 'response' variable in 'C'")
if (any(response %in% S))
stop("cannot have 'response' variable in 'S'")
if (!identical(length(intersect(S, uniqC)), 0L))
stop("cannot have variables common to both 'S' and 'C'")
## Set up col so it is a vector with length equal to nrow(data). Default pch to
## 1, or 21 for using background colour to represent observed values.
nr.data <- nrow(data)
col <- rep(col, length.out = nr.data)
pch <- if (is.null(pch)){
if (identical(length(S), 2L))
rep(21, nr.data)
else rep(1, nr.data)
} else rep(pch, length.out = nr.data)
## Make the appropriate call to an internal ceplot function
if (identical(type, "default")){
ceplot.interactive(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis =
cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder,
conf = conf, separate = FALSE, select.colour = select.colour, select.cex =
select.cex, probs = probs, col = col, pch = pch, residuals = residuals,
xsplotpar = xsplotpar, modelpar = modelpar, xcplotpar = xcplotpar)
} else if (identical(type, "separate")){
ceplot.interactive(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis =
cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder,
conf = conf, separate = TRUE, select.colour = select.colour, select.cex =
select.cex, probs = probs, col = col, pch = pch, select.type =
selectortype, residuals = residuals, xsplotpar = xsplotpar, modelpar =
modelpar, xcplotpar = xcplotpar)
} else if (identical(type, "shiny")){
ceplot.shiny(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis
= cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder
, conf = conf, separate = FALSE, select.colour = select.colour, select.cex
= select.cex, probs = probs, col = col, pch = pch, residuals = residuals,
xsplotpar = xsplotpar, modelpar = modelpar, xcplotpar = xcplotpar)
}
}
markajoc/condvis documentation built on May 21, 2019, 11:48 a.m.
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#' @title Interactive conditional expectation plot
#'
#' @description Creates an interactive conditional expectation plot, which
#' consists of two main parts. One part is a single plot depicting a section
#' through a fitted model surface, or conditional expectation. The other part
#' shows small data summaries which give the current condition, which can be
#' altered by clicking with the mouse.
#'
#' @param data A dataframe containing the data to plot
#' @param model A model object, or list of model objects
#' @param response Character name of response in \code{data}
#' @param sectionvars Character name of variable(s) from \code{data} on which to
#' take a section, can be of length 1 or 2.
#' @param conditionvars Character names of conditioning variables from
#' \code{data}. These are the predictors which we can set to single values in
#' order to produce a section. Can be a list of vectors of length 1 or 2. Can
#' be a character vector, which is then paired up using
#' \code{\link{arrangeC}}. If \code{NULL}, an attempt will be made to extract
#' all variable names which are not \code{response} or \code{sectionvars} from
#' \code{model}, and these will be arranged using \code{\link{arrangeC}}.
#' @param threshold This is a threshold distance. Points further than
#' \code{threshold} away from the current section will not be visible. Passed
#' to \code{\link{similarityweight}}.
#' @param lambda A constant to multiply by number of factor mismatches in
#' constructing a general dissimilarity measure. If left \code{NULL}, behaves
#' as though \code{lambda} is set greater than \code{threshold}, and so only
#' observations whose factor levels match the current section are visible.
#' Passed to \code{\link{similarityweight}}.
#' @param distance A character vector describing the type of distance measure to
#' use, either \code{"euclidean"} (default) or \code{"maxnorm"}.
#' @param type This specifies the type of interactive plot. \code{"default"}
#' places everything on one device. \code{"separate"} places condition
#' selectors on one device and the section on another. (These two options
#' require XQuartz on OS X). \code{"shiny"} produces a Shiny application.
#' @param view3d Logical; if \code{TRUE} plots a three-dimensional
#' regression surface if possible.
#' @param Corder Character name for method of ordering conditioning variables.
#' See \code{\link{arrangeC}}.
#' @param selectortype Type of condition selector plots to use. Must be
#' \code{"minimal"} if \code{type} is \code{"default"}. If \code{type} is
#' \code{"separate"}, can be \code{"pcp"} (see \code{\link{plotxc.pcp}}) or
#' \code{"full"} (see \code{\link{plotxc.full}}).
#' @param conf Logical; if \code{TRUE} plots confidence bounds (or equivalent)
#' for models which provide this.
#' @param probs Logical; if \code{TRUE}, shows predicted class probabilities
#' instead of just predicted classes. Only available if \code{S} specifies two
#' numeric predictors and the model's predict method provides this.
#' @param col Colour for observed data.
#' @param pch Plot symbols for observed data.
#' @param residuals Logical; if \code{TRUE}, plots a residual versus predictor
#' plot instead of the usual scale of raw response.
#' @param xsplotpar Plotting parameters for section visualisation as a list,
#' passed to \code{\link{plotxs}}. Can specify \code{xlim}, \code{ylim}.
#' @param modelpar Plotting parameters for models as a list, passed to
#' \code{\link{plotxs}}. Not used.
#' @param xcplotpar Plotting parameters for condition selector plots as a list,
#' passed to \code{\link{plotxc}}. Can specify \code{col} for highlighting
#' current section, \code{cex}, and \code{trim} (see \code{\link{plotxc}}).
#'
#' @examples
#' \dontrun{
#' ## Example 1: Multivariate regression, xs one continuous predictor
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#'
#' library(mgcv)
#' model1 <- list(
#' quadratic = lm(mpg ~ cyl + hp + wt + I(wt^2), data = mtcars),
#' additive = mgcv::gam(mpg ~ cyl + hp + s(wt), data = mtcars))
#'
#' conditionvars1 <- list(c("cyl", "hp"))
#'
#' ceplot(data = mtcars, model = model1, response = "mpg", sectionvars = "wt",
#' conditionvars = conditionvars1, threshold = 0.3, conf = T)
#'
#' ## Example 2: Binary classification, xs one categorical predictor
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$am <- as.factor(mtcars$am)
#'
#' library(e1071)
#' model2 <- list(
#' svm = svm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"),
#' glm = glm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model2, sectionvars = "wt", threshold = 1,
#' type = "shiny")
#'
#' ## Example 3: Multivariate regression, xs both continuous
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$gear <- as.factor(mtcars$gear)
#'
#' library(e1071)
#' model3 <- list(svm(mpg ~ wt + qsec + cyl + hp + gear,
#' data = mtcars, family = "binomial"))
#'
#' conditionvars3 <- list(c("cyl","gear"), "hp")
#'
#' ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
#' threshold = 1, conditionvars = conditionvars3)
#'
#' ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
#' threshold = 1, type = "separate", view3d = T)
#'
#' ## Example 4: Multi-class classification, xs both categorical
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$vs <- as.factor(mtcars$vs)
#' mtcars$am <- as.factor(mtcars$am)
#' mtcars$gear <- as.factor(mtcars$gear)
#' mtcars$carb <- as.factor(mtcars$carb)
#'
#' library(e1071)
#' model4 <- list(svm(carb ~ ., data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model4, sectionvars = c("cyl", "gear"),
#' threshold = 3)
#'
#' ## Example 5: Multi-class classification, xs both continuous
#'
#' data(wine)
#' wine$Class <- as.factor(wine$Class)
#' library(e1071)
#'
#' model5 <- list(svm(Class ~ ., data = wine, probability = TRUE))
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, probs = TRUE)
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, type = "separate")
#'
#' ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
#' threshold = 3, type = "separate", selectortype = "pcp")
#'
#' ## Example 6: Multi-class classification, xs with one categorical predictor,
#' ## and one continuous predictor.
#'
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' mtcars$carb <- as.factor(mtcars$carb)
#'
#' library(e1071)
#' model6 <- list(svm(cyl ~ carb + wt + hp, data = mtcars, family = "binomial"))
#'
#' ceplot(data = mtcars, model = model6, threshold = 1, sectionvars = c("carb",
#' "wt"), conditionvars = "hp")
#' }
#'
#' @seealso \code{\link{condtour}}, \code{\link{similarityweight}}
#'
#' @references O'Connell M, Hurley CB and Domijan K (2017). ``Conditional
#' Visualization for Statistical Models: An Introduction to the
#' \strong{condvis} Package in R.''\emph{Journal of Statistical Software},
#' \strong{81}(5), pp. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
ceplot <-
function (data, model, response = NULL, sectionvars = NULL, conditionvars = NULL
, threshold = NULL, lambda = NULL, distance = c("euclidean", "maxnorm"), type
= c("default", "separate", "shiny"), view3d = FALSE, Corder = "default",
selectortype = "minimal", conf = FALSE, probs = FALSE, col = "black", pch =
NULL, residuals = FALSE, xsplotpar = NULL, modelpar = NULL, xcplotpar = NULL)
{
## Rename for internal
S <- sectionvars
C <- conditionvars
sigma <- threshold
## Check for optional inputs
cex.axis <- xcplotpar$cex.axis
cex.lab <- xcplotpar$cex.lab
tck <- xcplotpar$tck
select.colour <- if (is.null(xcplotpar$col))
"blue"
else xcplotpar$col
select.cex <- if (is.null(xcplotpar$cex))
1
else xcplotpar$cex
## Prepare variables
data <- na.omit(data)
model <- if (!inherits(model, "list"))
list(model)
else model
model.name <- if (!is.null(names(model)))
names(model)
else paste("model", 1:length(model), sep = "_")
varnamestry <- try(getvarnames(model[[1]]), silent = TRUE)
response <- if (is.null(response))
if (class(varnamestry) != "try-error")
varnamestry$response[1L]
else stop("could not extract response from 'model'.")
else if (is.integer(response)){
colnames(data)[response]
} else response
type <- match.arg(type)
## Need to check here if XQuartz is available when on OS X platform.
## capabilities() is hanging up my R session when called with no XQuartz
## installed. The problem seems to be that .Internal(capabilitiesX11()) just
## keeps waiting for X11 to be found, instead of returning FALSE, when some X11
## files are installed, but X11.app/XQuartz.app is missing.
## Resorting to a rough system call of ls on the Utilities directory.
if (identical(.Platform$OS.type, "unix")){
if (grepl("^darwin", R.version$os)){
# if (!capabilities("X11"))
if (!any(c("XQuartz.app", "X11.app") %in% system2(command = "ls", args =
"/Applications/Utilities/", stdout = TRUE))){
warning("no X11 available, setting 'type' to \"shiny\"")
type <- "shiny"
}
}
}
## If no section variables have been specified, just try and pick the first
## predictor out of the model.
# S <- if(is.null(S)){
# if (!inherits(varnamestry, "try-error")){
# varnamestry$predictors[1L]
# } else {
# setdiff(colnames(data), response)[1L]
# }
# } else if (is.integer(S)){
# colnames(data)[S]
# } else S
## Hierarchy for specifying C
## 1. If user supplies list, use that exactly (maybe chop length).
## 2. If user supplies vector, use that but order/group it.
## 3. If user supplies nothing, try to extract from model, then order.
## 4. Else bung in everything from the data that isn't 'response' or 'S' and
## then try to order that and show the top 20.
if (is.list(C)){
C <- C[1:min(length(C), 20L)]
} else if (is.vector(C)){
C <- arrangeC(data[, setdiff(C, S), drop = FALSE], method = Corder)
} else if (!inherits(varnamestry, "try-error")){
possibleC <- unique(unlist(lapply(lapply(model, getvarnames), `[[`, 2)))
C <- arrangeC(data[, setdiff(intersect(possibleC, colnames(data)), S), drop
= FALSE], method = Corder)
} else {
C <- arrangeC(data[, setdiff(colnames(data), c(S, response)), drop = FALSE],
method = Corder)
}
C <- C[1:min(length(C), 20L)]
uniqC <- unique(unlist(C))
## A few checks on choices of response, S and C.
if (any(response %in% uniqC))
stop("cannot have 'response' variable in 'C'")
if (any(response %in% S))
stop("cannot have 'response' variable in 'S'")
if (!identical(length(intersect(S, uniqC)), 0L))
stop("cannot have variables common to both 'S' and 'C'")
## Set up col so it is a vector with length equal to nrow(data). Default pch to
## 1, or 21 for using background colour to represent observed values.
nr.data <- nrow(data)
col <- rep(col, length.out = nr.data)
pch <- if (is.null(pch)){
if (identical(length(S), 2L))
rep(21, nr.data)
else rep(1, nr.data)
} else rep(pch, length.out = nr.data)
## Make the appropriate call to an internal ceplot function
if (identical(type, "default")){
ceplot.interactive(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis =
cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder,
conf = conf, separate = FALSE, select.colour = select.colour, select.cex =
select.cex, probs = probs, col = col, pch = pch, residuals = residuals,
xsplotpar = xsplotpar, modelpar = modelpar, xcplotpar = xcplotpar)
} else if (identical(type, "separate")){
ceplot.interactive(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis =
cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder,
conf = conf, separate = TRUE, select.colour = select.colour, select.cex =
select.cex, probs = probs, col = col, pch = pch, select.type =
selectortype, residuals = residuals, xsplotpar = xsplotpar, modelpar =
modelpar, xcplotpar = xcplotpar)
} else if (identical(type, "shiny")){
ceplot.shiny(data = data, model = model, response = response, S = S,
C = C, sigma = sigma, lambda = lambda, distance = distance, cex.axis
= cex.axis, cex.lab = cex.lab, tck = tck, view3d = view3d, Corder = Corder
, conf = conf, separate = FALSE, select.colour = select.colour, select.cex
= select.cex, probs = probs, col = col, pch = pch, residuals = residuals,
xsplotpar = xsplotpar, modelpar = modelpar, xcplotpar = xcplotpar)
}
}
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