Invalid domain for l_serialaxes

[chriskuchar]

Invalid domain for l_serialaxes

[rwoldford]

The default range is always going to be from 0 to 1 on each axis. You might want to rescale your data appropriately before calling l_serialaxes().

I am adding the following to the examples section on l_serialaxes() man page which might be helpful to you. Most likely the scaling = "none" choice at the bottom is the most relevant to you if I have understood your request.

#'
#' #######
#' #
#' # Effect of the choice of the argument "scaling"
#' #
#' # To illustrate we will look at the four measurements of
#' # 150 iris flowers from the iris data of Edgar Anderson made
#' # famous by R.A. Fisher.
#' #
#' # First separate the measurements
#' irisFlowers <- iris[, 1:4]
#' # from their species
#' species <- iris[,5]
#' # and get some identifiers for the individual flowers
#' flowerIDs <- paste(species, 1:50)
#' #
#' # Now create parallel axes plots of the measurements
#' # using different scaling values.
#'
#' #
#' # scaling = "variable"
#' #
#' # This is the standard scaling of most serial axes plots,
#' # scaling each axis from the minimum to the maximum of that variable.
#' # Hence it is the default scaling.
#' #
#' # More precisely, it maps the minimum value in each column (variable) to
#' # zero and the maximum to one. The result is every parallel
#' # axis will have a point at 0 and a point at 1.
#' #
#' # This scaling highlights the relationships (e.g. correlations)
#' # between the variables (removes the effect of the location and scale of
#' # each variable).
#' #
#' # For the iris data, ignoring species we see for example that
#' # Sepal.Length and Sepal.Width are negatively correlated (lots of
#' # crossings) across species but more positively correlated (mostly
#' # parallel lines) within each species (colour).
#' #
#' sa_var <- l_serialaxes(irisFlowers,
#' scaling = "variable", # scale within column
#' axesLayout = "parallel",
#' color = species,
#' linewidth = 2,
#' itemLabel = flowerIDs,
#' showItemLabels = TRUE,
#' title = "scaling = variable (initially)",
#' linkingGroup = "irisFlowers data")
#'
#' #
#' # scaling = "observation"
#' #
#' # This maps the minimum value in each row (observation) to
#' # zero and the maximum value in each row to one.
#' #
#' # The result is that every observation (curve in the parallel
#' # coordinate plot) will touch 0 on at least one axis and touch
#' # 1 on another.
#' #
#' # This scaling highlights the differences between observations (rows)
#' # in terms of the relative measurements across the variables for each
#' # observation.
#' #
#' # For example, for the iris data we can see that for every flower (row)
#' # the Sepal.Length is the largest measurement and the Petal.Width
#' # is the smallest. Each curve gives some sense of the shape of each
#' # flower without regard to its size. Two species (versicolor and
#' # virginica) have similar shaped flowers (relatively long but narrow
#' # sepals and petals), whereas the third (setosa) has relatively large
#' # sepals compared to small petals.
#' #
#' sa_obs <- l_serialaxes(irisFlowers,
#' scaling = "observation", # scale within row
#' axesLayout = "parallel",
#' color = species,
#' linewidth = 2,
#' itemLabel = flowerIDs,
#' showItemLabels = TRUE,
#' title = "scaling = observation (initially)",
#' linkingGroup = "irisFlowers data")
#'
#' #
#' # scaling = "data"
#' #
#' # This maps the minimum value in the whole dataset (over all elements)
#' # to zero and the maximum value in the whole dataset to one.
#' #
#' # The result is that every measurement is on the same numeric (if not
#' # measurement) scale. Highlighting the relative magnitudes of all
#' # numerical values in the data set, each curve shows the relative magnitudes
#' # without rescaling by variable.
#' #
#' # This is most sensible data such as the iris flower where all four measurements
#' # appear to have been taken on the same measuring scale.
#' #
#' # For example, for the iris data full data scaling preserves the size
#' # and shape of each flower. Again virginica is of roughly the same
#' # shape as versicolor but has distinctly larger petals.
#' # Setosa in contrast is quite differently shaped in both sepals and petals
#' # but with sepals more similar in size to the two other flowers and
#' # with significantly smaller petals.
#' sa_dat <- l_serialaxes(irisFlowers,
#' scaling = "data", # scale using all data
#' axesLayout = "parallel",
#' color = species,
#' linewidth = 2,
#' itemLabel = flowerIDs,
#' showItemLabels = TRUE,
#' title = "scaling = data (initially)",
#' linkingGroup = "irisFlowers data")
#'
#' #
#' # scaling = "none"
#' #
#' # Sometimes we might wish to choose a min and max to use
#' # for the whole data set; or perhaps a separate min and max
#' # for each variable.
#'
#' # This would be done outside of the construction of the plot
#' # and displayed by having scaling = "none" in the plot.
#' #
#' # For example, for the iris data, we might choose scales so that
#' # the minimum and the maximum values within the data set do not
#' # appear at the end points 0 and 1 of the axes but instead inside.
#' #
#' # Suppose we choose the following limits for all variables
#' lower_lim <- -3 ; upper_lim <- max(irisFlowers) + 1
#'
#' # These are the limits we want to use to define the end points of
#' # the axes for all variables.
#' # We need only scale the data as
#' irisFlowers_0_1 <- (irisFlowers - lower_lim)/(upper_lim - lower_lim)
#' # Or alternatively using the built-in scale function
#' # (which allows different scaling for each variable)
#' irisFlowers_0_1 <- scale(irisFlowers,
#' center = rep(lower_lim, 4),
#' scale = rep((upper_lim - lower_lim), 4))
#'
#' # Different scales for different
#' # And instruct the plot to not scale the data but plot it on the 0-1 scale
#' # for all axes. (Note any rescaled date outside of [0,1] will not appear.)
#' #
#' sa_none <- l_serialaxes(irisFlowers_0_1,
#' scaling = "none", # do not scale
#' axesLayout = "parallel",
#' color = species,
#' linewidth = 2,
#' itemLabel = flowerIDs,
#' showItemLabels = TRUE,
#' title = "scaling = none (initially)",
#' linkingGroup = "irisFlowers data")
#'
#' # This is particularly useful for "radial" axes to keep the polygons away from
#' # the centre of the display.
#' # For example
#' sa_none["axesLayout"] <- "radial"
#' # now displays each flower as a polygon where shapes and sizes are easily
#' # compared.
#' #
#' # NOTE: rescaling the data so that all values are within [0,1] is perhaps
#' # the best way to proceed (especially if there are natural lower and
#' # upper limits for each variable).
#' # Then scaling can always be changed via the inspector.