Title: Interactive Conditional Visualization for Supervised and Unsupervised Models in Shiny
Version: 0.1.2
Description: Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package 'condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
Encoding: UTF-8
Imports: shiny, RColorBrewer, ggplot2,scales, cluster, DendSer,methods,plyr, colorspace, gower
RoxygenNote: 7.2.1
Suggests: knitr, rmarkdown, hdrcde, scagnostics, keras, kernlab, mclust, MASS, ks, mgcv, randomForest, parsnip, mlr, C50, bartMachine, BART, caret, e1071, gbm, glmnet, glmnetUtils, mlr3, nnet, rpart, tree, testthat
VignetteBuilder: knitr
BugReports: https://github.com/cbhurley/condvis2/issues
URL: https://cbhurley.github.io/condvis2/, https://github.com/cbhurley/condvis2
NeedsCompilation: no
Packaged: 2022-09-14 13:08:20 UTC; catherine
Author: Catherine Hurley [aut, cre], Mark OConnell [aut], Katarina Domijan [aut]
Maintainer: Catherine Hurley <catherine.hurley@mu.ie>
Repository: CRAN
Date/Publication: 2022-09-14 13:30:02 UTC

A predict generic function for condvis

Description

A predict generic function for condvis

Usage

CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## Default S3 method:
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lm'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'glm'
CVpredict(
  fit,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lda'
CVpredict(
  fit,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'qda'
CVpredict(
  fit,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'nnet'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'randomForest'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'ranger'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'rpart'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'tree'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'C5.0'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'svm'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'gbm'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  n.trees = fit$n.trees,
  ptrans = NULL
)

## S3 method for class 'loess'
CVpredict(fit, newdata = NULL, ...)

## S3 method for class 'ksvm'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'glmnet'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  s = NULL,
  makex = NULL
)

## S3 method for class 'cv.glmnet'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  makex = NULL
)

## S3 method for class 'glmnet.formula'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  s = NULL
)

## S3 method for class 'cv.glmnet.formula'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'keras.engine.training.Model'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  batch_size = 32,
  response = NULL,
  predictors = NULL
)

## S3 method for class 'kde'
CVpredict(fit, newdata = fit$x, ..., scale = TRUE)

## S3 method for class 'densityMclust'
CVpredict(
  fit,
  newdata = NULL,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  scale = TRUE
)

## S3 method for class 'MclustDA'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'MclustDR'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'Mclust'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'train'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'bartMachine'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'wbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'pbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'bart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'model_fit'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## S3 method for class 'WrappedModel'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## S3 method for class 'Learner'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

Arguments

fit

A fitted model

newdata

Where to calculate predictions.

...

extra arguments to predict

ptype

One of "pred","prob" or "probmatrix"

pthreshold

Used for calculating classes from probs, in the two class case

ylevels

The levels of the response, when it is a factor

ptrans

A function to apply to the result

pinterval

NULL, "confidence" or "prediction". Only for lm, parsnip, mlr(regression, confidence only)

pinterval_level

Defaults to 0.95

type

For some predict methods

n.trees

Used by CVpredict.gbm, passed to predict

s

Used by CVpredict.glmnet and CVpredict.cv.glmnet, passed to predict

makex

Used by CVpredict.glmnet and CVpredict.cv.glmnet. A function to construct xmatrix for predict.

batch_size

Used by CVpredict.keras.engine.training.Model, passed to predict

response

Used by CVpredict.keras.engine.training.Model. Name of response (optional)

predictors

Used by CVpredict.keras.engine.training.Model. Name of predictors

scale

Used by CVpredict for densities. If TRUE (default) rescales the conditional density to integrate to 1.

Details

This is a wrapper for predict used by condvis. When the model response is numeric, the result is a vector of predictions. When the model response is a factor the result depends on the value of ptype. If ptype="pred", the result is a factor. If also threshold is numeric, it is used to threshold a numeric prediction to construct the factor when the factor has two levels. For ptype="prob", the result is a vector of probabilities for the last factor level. For ptype="probmatrix", the result is a matrix of probabilities for each factor level.

Value

a vector of predictions, or a matrix when type is "probmatrix"

Methods (by class)

Examples

#Fit a model. 
f <- lm(Fertility~ ., data=swiss)
CVpredict(f)

#Fit a model with a factor response
swiss1 <- swiss
swiss1$Fertility <-  cut(swiss$Fertility, c(0,80,100))
levels(swiss1$Fertility)<- c("lo", "hi")
f <- glm(Fertility~ ., data=swiss1, family="binomial")
CVpredict(f) # by default gives a factor
CVpredict(f, ptype="prob") # gives prob of level hi
CVpredict(f, ptype="probmatrix") # gives prob of both levels

Constructs tours of data space based on centers of clusters

Description

Constructs tours of data space based on centers of clusters

Usage

kmeansPath(data, length = 10, reorder = TRUE, conditionvars = NULL, ...)

pamPath(
  data,
  length = 10,
  reorder = TRUE,
  conditionvars = NULL,
  maxn = 4000,
  ...
)

claraPath(data, length = 10, reorder = TRUE, conditionvars = NULL, ...)

medoidPath(data, cl, reorder = FALSE)

centroidPath(data, cl, reorder = FALSE)

Arguments

data

A dataframe

length

Path length, defaults to 10

reorder

If TRUE uses DendSer to reorder the path dser

conditionvars

A vector of variable names. The returned tour is for this subset of variables.

...

ignored

maxn

(pamPath only) For datasets with more than maxn rows, use maxn randomly selected rows.

cl

A vector specifying cluster membership for rows of data.

Value

A dataframe with the path

Functions

Examples

kmeansPath(mtcars,length=4)
pamPath(mtcars,length=4)
claraPath(mtcars,length=4)
medoidPath(mtcars,cl=rep(1:3, length.out=nrow(mtcars)))

Plots a conditionPlot.

Description

Plots a conditionPlot, showing one, two or many predictors. The predictor setting in varVal is drawn in magenta.

Usage

conditionPlot(
  CVdata,
  var,
  varVal,
  pointColor = "steelblue",
  sim = NULL,
  resetpar = TRUE,
  plotrows = NULL
)

Arguments

CVdata

the dataset used for the fit

var

one more condition vars. Draws a parallel coordinate plot for more than two.

varVal

the current setting of the conditionvars, shown in magenta.

pointColor

a color, vector of colors,or the name of variable to be used for coloring

sim

If non-NULL should be a vector of similarity weights.

resetpar

For use withing shiny app.

plotrows

If non-NULL should be a vector of case indices

Examples

conditionPlot(mtcars, c("wt","hp"), c("wt"=3, "hp"=200), pointColor="am")

conditionPlot(mtcars, c("wt","hp"), mtcars[1,], pointColor="am")

#Calculate similarity using wt, hp observations from first case
sim <- similarityweight(mtcars[1, c("wt","hp")], mtcars[, c("wt","hp")], threshold=1)

# Marks points with black border with positive sim values. These are points within 1 (threshold) sd
#of pink cross.
conditionPlot(mtcars, c("wt","hp"), mtcars[1,], pointColor="am", sim=sim)

sim <- similarityweight(mtcars[1, ], mtcars, threshold=2)
conditionPlot(mtcars, names(mtcars), mtcars[1,], sim=sim)

Creates Condvis Shiny app

Description

Creates Condvis Shiny app

Usage

condvis(
  data,
  model = NULL,
  response = NULL,
  sectionvars = NULL,
  conditionvars = NULL,
  predsInit = NULL,
  pointColor = c("steelblue", "grey0"),
  cPlotPCP = FALSE,
  cPlotn = 1000,
  orderConditionVars = "default",
  threshold = 1,
  thresholdmax = NULL,
  linecols = NULL,
  showsim = NULL,
  theta3d = 45,
  phi3d = 20,
  dataplot = "pcp",
  tours = NULL,
  predictArgs = NULL,
  xlim = NULL,
  ylim = NULL,
  zlim = NULL,
  density = FALSE,
  showdata = density == FALSE,
  displayHeight = 950
)

Arguments

data

the dataset used for the fit. Should not have NAs for response, sectionvars or conditionvars.

model

A fitted model or list of models. May be NULL.

response

name of response variable. If null, tries to extract from model.

sectionvars

names of sectionvars. If null, extracts from data.

conditionvars

names of condition vars. If null, extracts from data.

predsInit

Optionally provide starting value for some predictors. Defaults to medoid.

pointColor

a color, or the name of variable to be used for coloring. If the named variable is numeric, it is first converted to a factor with 3 levels.

cPlotPCP

if TRUE, conditionplots are drawn as a single PCP (for more than two conditionvars)

cPlotn

Defaults to 1000. Shows a sample of this number of points in conditionplots. Non-numeric values are ignored.

orderConditionVars

If supplied, a function to order the Condition Vars

threshold

used for similarity weights, defaults to 1.

thresholdmax

maximum value allowed of threshold.

linecols

vector of colors to be used for fits

showsim

if TRUE, shows sim in conditionplots with points/lines. Defaults to TRUE with 150 or fewer cases.

theta3d, phi3d

Angles defining the viewing direction for 3d surface. theta3d gives the azimuthal direction and phi3d the colatitude. See persp.

dataplot

"pcp" or "pairs". Used when there is no response, or more than two sectionvars.

tours

A list of pre-calculated tours

predictArgs

a list with one entry per fit, giving arguments for CVpredict

xlim

passed on to sectionplot

ylim

passed on to sectionplot

zlim

passed on to sectionplot

density

default FALSE. Use TRUE if model is a density function.

showdata

defaults to density==TRUE. If FALSE, data on section not shown.

displayHeight

supply a value for the display height

Examples

fit <- lm(mpg ~ wt+hp+am, data=mtcars)
if(interactive()){
condvis(mtcars,fit, response="mpg",sectionvars="wt", conditionvars=c("am", "hp"), pointColor ="red")
}

Constructs tours of data space based on fits.

Description

Constructs tours of data space based on fits.

Usage

lofPath(
  data,
  fits,
  length = 10,
  reorder = TRUE,
  conditionvars = NULL,
  predictArgs = NULL,
  response = NULL,
  ...
)

diffitsPath(
  data,
  fits,
  length = 10,
  reorder = TRUE,
  conditionvars = NULL,
  predictArgs = NULL,
  ...
)

hiresponsePath(
  data,
  response = NULL,
  length = 10,
  reorder = TRUE,
  conditionvars = NULL,
  ...
)

loresponsePath(
  data,
  response = NULL,
  length = 10,
  reorder = TRUE,
  conditionvars = NULL,
  ...
)

Arguments

data

A dataframe

fits

A model fit or list of fits

length

Path length, defaults to 10

reorder

If TRUE (default) uses DendSer to reorder the path dser

conditionvars

A vector of variable names. The returned tour is for this subset of variables.

predictArgs

Extra inputs to CVpredict

response

The name of the response variable

...

ignored

Value

A dataframe with the path

Functions

Examples

fit1 <- lm(mpg ~ wt+hp+am, data=mtcars)
fit2 <- lm(mpg ~ wt, data=mtcars)
lofPath(mtcars,fit1, response="mpg")
diffitsPath(mtcars,list(fit1,fit2))

Finds medoid of data

Description

Finds medoid of data

Usage

medoid(data, maxn = 4000)

Arguments

data

A dataframe

maxn

For datasets with more than maxn rows, use maxn randomly selected rows.

Value

A dataframe with one row, which is the medoid of the data, based on (standardised) daisy dist

Examples

medoid(mtcars)

Interpolation

Description

Interpolation

Usage

pathInterpolate(x, ninterp = 4)

## Default S3 method:
pathInterpolate(x, ninterp = 4L)

## S3 method for class 'factor'
pathInterpolate(x, ninterp = 4L)

## S3 method for class 'data.frame'
pathInterpolate(x, ninterp = 4L)

Arguments

x

a numeric or factor vector or dataframe

ninterp

number of interpolated steps

Value

interpolated version of x

Methods (by class)

Plots diagnostics for the tour supplied

Description

Plots diagnostics for the tour supplied

Usage

plotTourDiagnostics(
  path,
  data,
  pathlen = nrow(path),
  threshold = 1,
  which = 1:3,
  ...
)

Arguments

path

the tour

data

the dataset

pathlen

the pathlength

threshold

used for similarityweight

which

subset of 1:3

...

other args for similarityweight

Details

The first plot shows approximately how much data are visible on each section, the second shows what proportion of data are visited by the tour, and the third a density estimate of max similarity values.

Value

Table of max sims attained.

Plots the main condvis display

Description

The section plot relates a fit or fits to one or two predictors (sectionvar), for fixed values of other predictors in conditionvals.

Usage

sectionPlot(
  CVdata,
  CVfit = NULL,
  response = NULL,
  preds,
  sectionvar,
  conditionvals,
  pointColor = "steelblue",
  sim = NULL,
  threshold = 1,
  linecols = NULL,
  dataplot = "pcp",
  gridsize = 50,
  probs = FALSE,
  view3d = FALSE,
  theta3d = 45,
  phi3d = 20,
  xlim = NULL,
  ylim = NULL,
  zlim = NULL,
  pointSize = 1.5,
  predictArgs = NULL,
  resetpar = TRUE,
  density = FALSE,
  showdata = density == FALSE,
  returnInfo = FALSE,
  pointColorFromResponse = FALSE,
  pcolInfo = NULL
)

Arguments

CVdata

the dataset used for the fit

CVfit

a fit or list of fits

response

name of response variable

preds

names of predictors

sectionvar

section variable, or variables.

conditionvals

conditioning values. A vector/list or dataframe with one row

pointColor

a color, vector of colors,or the name of variable to be used for coloring

sim

vector of similarity weights

threshold

used for similarity weights, defaults to 1.

linecols

vector of line colours

dataplot

"pcp" or "pairs". Used when there is no response, or more than two sectionvars.

gridsize

used to construct grid of fitted values.

probs

Logical; if TRUE, shows predicted class probabilities instead of just predicted classes. Only available with two numeric sectionvars and the model's predict method provides this.

view3d

Logical; if TRUE plots a three-dimensional regression surface if possible.

theta3d, phi3d

Angles defining the viewing direction. theta3d gives the azimuthal direction and phi3d the colatitude. Seepersp.

xlim

passed on to plot

ylim

passed on to plot

zlim

passed on to plot

pointSize

used for points

predictArgs

a list with one entry per fit, giving arguments for predict

resetpar

When TRUE (the default) resets pars after drawing.

density

default FALSE. Use TRUE if model is a density function.

showdata

If FALSE, data on section not shown.

returnInfo

If TRUE, returns coordinates for some plots

pointColorFromResponse

ignore–For interactive use only

pcolInfo

ignore–For interactive use only

Details

The type of plot depends on the fit and the section variables. Observations with non zero values of the similarity weights sim are shown. If no fit is provided, the data are shown as a parallel coordinate plot or pairs plot, depending on dataplot. The fit could also be a density estimate.

Value

plotted coordinates, for some plots

Examples

#Fit a model. 
f <- lm(Fertility~ ., data=swiss)
svar <- "Education"
preds <- variable.names(f)[-1]
sectionPlot(swiss,f, "Fertility",preds,svar, swiss[12,])
sectionPlot(swiss,f, "Fertility",preds,svar, apply(swiss,2,median))
sectionPlot(swiss,f, "Fertility",preds,preds[1:2], apply(swiss,2,median))
sectionPlot(swiss,f, "Fertility",preds,preds[1:2], apply(swiss,2,median), view3d=TRUE)

# PCP of swiss data, showing only cases whose percent catholic and infant.mortality are
# similar to those of the first case
sectionPlot(swiss,preds=names(swiss),
           sectionvar= names(swiss)[1:4],conditionvals=swiss[1,] )     
# Use dataplot="pairs" to switch to a pairs plot

# A density estimate example
 ## Not run: 
 library(ks)
fde <-kde(iris[,1:3])
sectionPlot(iris,list(kde=fde), response=NULL,
           preds=names(iris)[1:3],
           sectionvar=names(iris)[1],
           conditionvals=iris[1,],density=TRUE)
 
## End(Not run)

Calculate the similarity weight for a set of observations

Description

Calculate the similarity weight for a set of observations, based on their distance from some arbitrary points in data space. Observations which are very similar to the point under consideration are given weight 1, while observations which are dissimilar to the point are given weight zero.

Usage

similarityweight(
  x,
  data,
  threshold = 1,
  distance = "euclidean",
  lambda = NULL,
  scale = TRUE
)

Arguments

x

A dataframe describing arbitrary points in the space of the data (i.e., with same colnames as data).

data

A dataframe representing observed data.

threshold

Threshold distance outside which observations will be assigned similarity weight zero. This is numeric and should be > 0. Defaults to 1.

distance

The type of distance measure to be used, currently just three types of Minkowski distance: "euclidean" (default), "maxnorm", "manhattan" and also "gower"

lambda

A constant to multiply by the number of categorical mismatches, before adding to the Minkowski distance, to give a general dissimilarity measure. If left NULL, behaves as though lambda is set larger than threshold, meaning that one factor mismatch guarantees zero weight.

scale

defaults to TRUE, in which case numeric variables are scaled to unit sd.

Details

Similarity weight is assigned to observations based on their distance from a given point. The distance is calculated as Minkowski distance between the numeric elements for the observations whose categorical elements match, or else the Gower distance.

Value

A numeric vector or matrix, with values from 0 to 1. The similarity weights for the observations in data arranged in rows for each row in x.

References

O'Connell M, Hurley CB and Domijan K (2017). “Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R.”Journal of Statistical Software, 81(5), pp. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.

Examples

## Say we want to find observations similar to the first observation.
## The first observation is identical to itself, so it gets weight 1. The
## second observation is similar, so it gets some weight. The rest are more
## different, and so get zero weight.

data(mtcars)
similarityweight(x = mtcars[1, ], data = mtcars)

## By increasing the threshold, we can find observations which are more
## approximately similar to the first row. Note that the second observation
## now has weight 1, so we lose some ability to discern how similar
## observations are by increasing the threshold.

similarityweight(x = mtcars[1, ], data = mtcars, threshold = 5)

## Can provide a number of points to 'x'. Here we see that the Mazda RX4 Wag
## is more similar to the Merc 280 than the Mazda RX4 is.

similarityweight(mtcars[1:2, ], mtcars, threshold = 3)

Constructs a various tours of data space

Description

Constructs a various tours of data space

Usage

randomPath(data, length = 10, reorder = TRUE, conditionvars = NULL, ...)

seqPath(data, length = 10, reorder = FALSE, conditionvars = NULL, ...)

alongPath(data, var, length = 10, current = NULL, ...)

Arguments

data

A dataframe

length

Path length, defaults to 10

reorder

If TRUE (default) uses DendSer to reorder the path dser

conditionvars

A vector of variable names. The returned tour is for this subset of variables.

...

ignored

var

A variable name for alongPath

current

Default value of variables for alongPath

Value

A dataframe with the path

Functions

Examples

randomPath(mtcars,length=5)
seqPath(mtcars,length=5)
alongPath(mtcars,var="mpg", length=5, current=mtcars[1,])

Fade colours according to a weight vector

Description

The colours whose weights are less than 1 are diluted. Colours whose weight is zero are returned as white, other weights are grouped in nlevels groups and colours diluted proportionally.

Usage

weightcolor(col, weights, nlevels = 5)

Arguments

col

A vector of colour

weights

A vector of weights, values between 0 and 1

nlevels

The number of groups

Value

A vector of colours