Description Usage Arguments Details Value References Examples
Create an interval data frame (idf
-object), summarize its content and visualize subsets of two variables.
1 2 3 4 5 6 7 8 9 10 | idf.create(dat, var.labels = NULL)
## S3 method for class 'idf'
summary(object, ...)
## S3 method for class 'idf'
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")
|
dat |
A |
var.labels |
Names of the variables corresponding to the interval-valued observations in the |
object |
The |
... |
Argument of the generic functions |
x |
Argument of the generic function |
y |
Argument of the generic function |
var |
Names of the two variables out of the |
typ |
Type of the plot. Possible values are |
k.x |
Particular plot function parameter. 1/ |
k.y |
Particular plot function parameter. 1/ |
inf.margin |
Particular parameter for plot type |
p.cex |
Particular parameter for plot type |
col.lev |
Particular parameter for plot type |
plot.grid |
Logical for plot type |
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.adj |
|
y.padj |
|
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. |
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.
An idf
-object of m
variables, which is a list of m+1
entries.
Var1 ... varm |
|
n |
Number of observations. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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")
|
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