histgbs {gbs} | R Documentation |
The function {histgbs()} produces a histogram and a box-plot of the data. Also, the estimated pdf may be sketched on the histogram.
histgbs(x, kernel = "normal", boxPlot = "TRUE", densityLine = "FALSE", mainTitle = "Histogram and boxplot", xLabel = "Data", yLabel = "Frequency", yRange = NULL, colourHistogram = "blue", colourDensity = "black", colourBoxPlot = "blue")
x |
Vector of observations. |
kernel |
Kernel of the pdf of the associated symmetrical distribution by means of which the GBSD is obtained. The kernels: laplace, logistic, normal and t are available. |
boxPlot |
Logical; if TRUE (default), the boxplot is plotted, otherwise not. |
densityLine |
Logical; if TRUE, the pdf is sketched on the histogram, otherwise not. |
mainTitle |
Main title of the graph. |
xLabel |
A title for the x axis. |
yLabel |
A title for the x axis. |
yRange |
Limit for the y axis. |
colourHistogram |
Color inside the histogram. |
colourDensity |
Color of the estimated pdf curve. |
colourBoxPlot |
Color inside the boxplot. |
The function {histgbs()} simultaneously produces a box-plot and a histogram for the data. The box-plot may be supressed by the instruction {boxPlot = FALSE}. Also, the estimated pdf may be sketched on the histogram adding the instruction {densityLine = TRUE}.
The function {histgbs()} carries out an exploratory graphical analysis and can be also useful as a simple goodness-of-fit tool.
Barros, Michelli <michelli.karinne@gmail.com>
Leiva, Victor <victor.leiva@uv.cl, victor.leiva@yahoo.com>
Paula, Gilberto A. <giapaula@ime.usp.br>
Diaz-Garcia, J.A., Leiva, V. (2005) A new family of life distributions based on elliptically contoured distributions. J. Stat. Plan. Infer. 128:445-457 (Erratum: J. Stat. Plan. Infer. 137:1512-1513).
Leiva, V., Barros, M., Paula, G.A., Sanhueza, A. (2008) Generalized Birnbaum-Saunders distributions applied to air pollutant concentration. Environmetrics 19:235-249.
Sanhueza, A., Leiva, V., Balakrishnan, N. (2008) The generalized Birnbaum-Saunders distribution and its theory, methodology and application. Comm. Stat. Theory and Meth. 37:645-670.
## Generates a sample from the GBSD with normal kernel x <- rgbs(300, alpha = 1.0, beta = 1.0, nu = 1.0, kernel = "normal") ## Produces a histogram for the GBSD with normal kernel from the data x histgbs(x, mainTitle = "", xLabel = "Data", kernel = "normal", colourHistogram = 4, colourBoxPlot = 4)