linLIR (version 1.1)

idf: Interval data frame

Description

Create an interval data frame (idf-object), summarize its content and visualize subsets of two variables.

Usage

idf.create(dat, var.labels = NULL)
"summary"(object, ...)
"plot"(x, y=NULL, ..., var = NULL, typ="hist", k.x = 1, k.y = 1, inf.margin=10, p.cex=1, col.lev=15, plot.grid=FALSE, x.adj = 0.5, x.padj = 3, y.las = 0, y.adj = 1, y.padj = 0, x.lim = c(0, 0), y.lim = c(0, 0), x.lab = "X", y.lab = "Y")

Arguments

dat

A data.frame containing 2 neighboring columns for each variable, the first column for the left endpoints of the interval observations, the second for the right endpoints.

var.labels

Names of the variables corresponding to the interval-valued observations in the data.frame.

object

The idf-object to be summarized.

...

Argument of the generic functions plot and summary: Other parameters.

x

Argument of the generic function plot. Here x is the idf-object to be plotted.

y

Argument of the generic function plot. Here y=NULL.

var

Names of the two variables out of the idf-object to be plotted. (Optional)

typ

Type of the plot. Possible values are "hist": plot 2-dim. histogram (default) and "draft".

k.x

Particular plot function parameter. 1/k.x is the step width along the abscissa.

k.y

Particular plot function parameter. 1/k.y is the step width along the ordinate.

inf.margin

Particular parameter for plot type "draft". inf.margin is the number of steps that the infinite observations are drawn beyond the limits of the plot.

p.cex

Particular parameter for plot type "draft". p.cex is the point size to fill the rectangles with grey color.

col.lev

Particular parameter for plot type "hist" indicating the number of different grey levels in the 2-dim. histogram.

plot.grid

Logical for plot type "hist". If plot.grid=TRUE dashed lines are added to the plot to indicate the location of the interval endpoints. This is particularly useful for categorized data.

x.adj

Horizontal position of the text for the abscissa.

x.padj

Vertical position of the text for the abscissa.

y.las

Orientation of the text for the ordinate. y.las=1 will turn the axis labels and the text in reading direction.

y.adj

y.adj regulates the position of the text for the ordinate in reading direction, i.e. if y.las=0 it sets the vertical position and if y.las=1 the horizontal position.

y.padj

y.padj regulates the position of the text for the ordinate orthogonal to the reading direction, i.e. if y.las=0 it sets the horizontal position and if y.las=1 the vertical position.

x.lim

The limits for the abscissa of the plot.

y.lim

The limits for the ordinate of the plot.

x.lab

Title of the abscissa.

y.lab

Title of the ordinate.

Value

An idf-object of m variables, which is a list of m+1 entries.

Var1 ... varm

m different data.frames with 2 columns each, one for each of the 1st to mth variables.

n

Number of observations.

Details

Within the LIR framework all types of interval data are possible, including the particular cases of actually precise data (i.e., lower endpoint = upper endpoint) or missing data (i.e., in case of a real valued variable, lower endpoint = -Inf and upper endpoint = Inf). For the LIR analysis it makes practically no difference if the intervals are closed or not, therefore, the created idf-object does not contain this information.

References

M. Cattaneo, A. Wiencierz (2012c). On the implementation of LIR: the case of simple linear regression with interval data. Technical Report No. 127. Department of Statistics. LMU Munich.

A. Wiencierz, M. Cattaneo (2012b). An exact algorithm for Likelihood-based Imprecise Regression in the case of simple linear regression with interval data. In: R. Kruse et al. (Eds.). Advances in Intelligent Systems and Computing. Vol. 190. Springer. pp. 293-301.

M. Cattaneo, A. Wiencierz (2012a). Likelihood-based Imprecise Regression. International Journal of Approximate Reasoning. Vol. 53. pp. 1137-1154.

Examples

data('toy.smps')
toy.idf <- idf.create(toy.smps, var.labels=c("x","y"))

summary(toy.idf)

plot(toy.idf, typ="draft", k.x=10, k.y=10, p.cex=1.5, y.las=1, y.adj=6) 
plot(toy.idf, typ="draft", k.x=10, k.y=10, x.adj=0.7, y.las=1, y.adj=6, y.padj=-3)
plot(toy.idf, k.x=10, k.y=10, x.adj=0.7, x.padj=4, y.adj=0.7, y.padj=-4)

data('pm10')
pm.idf <- idf.create(pm10)

summary(pm.idf)

plot(pm.idf, typ="draft", k.x=10, k.y=20, p.cex=0.35, x.adj=0.5, x.padj=4, y.las=0, y.adj=0.5, y.padj=-4, x.lab="temperature", y.lab="particulate matter concentration")