Package 'rpanel'

Description

rpanel provides a set of functions to build simple GUI controls for R functions. Uses include changing a parameter on a graph (and animating it) with a slider, or a "doublebutton", up to more sophisticated mini-applications. In addition to functions which create controls, a number of ‘cartoon’ functions built on these controls are also available.

Details

This package contains a number of functions (with help and examples) and several example scripts.

Cartoon functions:

rp.ci: Confidence intervals
rp.coefficients: Visualisation of the effects of the coefficients in a linear model or glm
rp.contingency: Visualisation of two-way contingency tables with uncertainties
rp.drop1: Visualisation of the results of dropping all single terms from a model
rp.firth: Sampling in a firth
rp.geosim: Simulation of spatial processes
rp.gulls: An interactive problem-solving exercise on deciding the sex of a herring gull
rp.likelihood: Exploration of one and two parameter likelihood functions
rp.lm: Visualisation of linear models with one or two explanatory variables
rp.logistic: Interactive display of logistic regression with a single covariate
rp.mururoa: Sampling in Mururoa Atoll
rp.normal: Fitting a normal distribution to a single sample
rp.plot3d: Interactive display of a plot of three variables
rp.plot4d: Interactive display of a plot of four variables
rp.power: Power calculations for a two-sample t-test
rp.rmplot: Plotting of repeated measurement data
rp.sample: Interactive exploration of sampling variation
rp.surface: Displaying the uncertainty in an estimate of a surface
rp.t_test: Visualisation for inference in comparing the means of one or two samples
rp.tables: Interactive statistical tables
rp.spacetime: A version of rp.plot4d designed for space-time data

Functions to create individual controls:

rp.control: create an rpanel
rp.slider: add a slider to a panel, to graphically control a numeric variable
rp.textentry: adds a box allows text to be entered
rp.button: adds a button to the panel with a nominated function called on pressing
rp.checkbox: adds a checkbox to the panel, to control a logical variable
rp.radiogroup: adds a set of radiobuttons to the panel
rp.listbox: adds a listbox to the panel
rp.combo: adds a combo box to the panel
rp.doublebutton: adds a widget with '+' and '-' buttons, to increment and decrement a variable
rp.menu: adds a menu to the panel
rp.text: adds a text box to the panel
rp.image: adds an image to the panel; the action function is called with coordinates on clicking
rp.line: draws a line connecting the pixel locations x1, y1 to x2, y2 on the specified rp.image
rp.deleteline: removes a line from an rp.image
rp.clearlines: removes all lines from an rp.image
rp.messagebox: displays a message in a pop-up window
rp.tkrplot: allows R graphics to be displayed in a panel
rp.tkrreplot: allows R graphics to be refreshed in a panel.
rp.timer: executes an action function repeatedly until a condition is satisfied
rp.block: blocks use of the R console until a panel is closed
rp.panel: returns a named panel or the most recently created panel
rp.var.put: place an object into the rpanel environment, usually within a panel
rp.var.get: retrieve an object from the rpanel environment, usually from a panel
rp.pos: a demonstration function for layout control
rp.grid: a grid system for layout control
rp.do: executes a nominated user defined callback function
rp.colour.key: a colour key to associate with a plot

Generally speaking these functions have a parameter, name, which is used to later delete or modify a widget.

Author(s)

E. Crawford & A. Bowman

Maintainer: Adrian Bowman <[email protected]>

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.control,rp.button,rp.slider,rp.doublebutton,rp.textentry,rp.checkbox,rp.radiogroup

Examples

## Not run: 
   rp.gulls()

## End(Not run)

Description

These data, reported by Proschan (1963, Technometrics 5, 375-383), refer to the intervals, in service-hours, between failures of the air-conditioning equipment in a Boeing 720 aircraft. (Proschan reports data on 10 different aircraft. The data from only one of the aircraft is used here. Cox and Snell (1981, Applied Statistics: principles and examples, Chapman and Hall, London) discuss the analysis of the data on all 10 aircraft.)

The dataset consists of a single vector of data. They are used in the rp.likelihood example script.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   rp.likelihood("sum(log(dexp(data, theta)))", aircond, 0.005, 0.03)
   rp.likelihood("sum(log(dgamma(data, theta[1], theta[2])))",
        aircond, c(0.3, 0.005), c(3, 0.06))

## End(Not run)

Description

In an interesting early study on changes in 'working capacity' with sex and age, Astrand (1952) recorded measurements of a variety of physiological parameters in subjects who undertook exercise at maximal intensity.

References

Astrand, Per-Olof. Experimental studies of physical working capacity in relation to sex and age. Munksgaard Forlag.

Examples

## Not run: 
plot(Heart_rate ~ Age, data = Astrand)

## End(Not run)

Description

Snedecor & Cochran (1967) report a subsample of measurements from an early study of health and nutrition by Swanson et al. (1955), with a particular focus on cholesterol. Women were recruited from both Iowa and Nebraska and there is interest in whether there is any evidence of a systematic difference between the groups. If there is no evidence of a difference, the groups might reasonably be pooled. An upward trend in cholesterol with age is expected.

The cholesterol dataframe consists of three columns of data:

• Age: age in years;

• Cholesterol: cholesterol in mg / 100 ml;

• Location: The state of residence, Iowa or Nebraska.

References

Snedecor, George W. and Cochran, William G. (1967). Statistical Methods. The Iowa State University Press, USA. Swanson, P., Levertost, R., Gram, M. R., Roberts, H. and Pesek, I. (1955). Blood values of women : cholesterol. Journal of Gerontology 10, 41-47.

Examples

## Not run: 
library(ggplot2)
ggplot(cholesterol, aes(Age, Cholesterol, col = Location)) + geom_point()

## End(Not run)

Description

These data record the water quality, in terms of dissolved oxygen (DO) on a percentage scale, at a number of sampling stations (Station) on the River Clyde. The date (Day, Month, Year) is also available, along with the day of the year (Doy between 1 and 365) and an identified (id) of the survey on which each measurement was made.

The data are used in the rp.plot4d example script.

The data were kindly provided by the Scottish Environment Protection Agency, with the assistance of Dr. Brian Miller.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  with(Clyde, {
    rp.plot4d(cbind(Doy, DO), Station, location.plot = FALSE)
    rp.plot4d(cbind(Station, DO), Doy, location.plot = FALSE)
  })

## End(Not run)

Description

These data record the average annual giving in pounds per church member in the dioceses of the Church of England in the early 1980's. Three potentially relevant covariates are also recorded for each diocese, namely the percentage of the population who are employed, the percentage of the population on the electoral roll of the church and the percentage of the population who usually attend church. Background details are available in Pickering (1985; Applied Economics 17, 619-32).

The data are used in the rp.regression example script.

References

Pickering, J.F. (1985). Giving in the Church of England: an econometric analysis. Applied Economics 17, 619-632.

Examples

## Not run: 
  with(CofE, {
    rp.regression(cbind(Employ, Attend), Giving)
  })

## End(Not run)

Description

These data are from a famous study, initiated by Doll & Hill, which surveyed UK doctors and related their smoking habits to subsequent coronary-related deaths. The data in a form reduced to person-years exposure was reported by Breslow (1985).

References

Breslow, N.E. (1985) Cohort Analysis in Epidemiology. In 'A Celebration of Statistics', A.C. Atkinson and S.E. Fienberg (editors), 109-143. Springer-Verlag.

Doll, R. and Hill, A.B. (1966) Mortality of British doctors in relation to smoking: Observations on coronary thrombosis. National Cancer Institute Monograph, 19, 205-268.

Description

The 'confused flour beetle' Tribolium confusum can infest stored flour and grain. These data on the numbers of beetles killed by exposure to different concentrations of multiple toxins. The data were reported in an early paper by Strand (1930) on how to measure the toxicity of compounds. The trials of some compounds were replicated. Toxin concentration is measured in mg/l.

References

Strand, A.L. (1930). Measuring the toxicity of insect fumigants. Industrial & Engineering Chemistry Analytical Edition, 2, 4-8.

Examples

## Not run: 
hacid <- subset(flour_beetles, Toxin == "Hydrocyanic_acid")
plot(Dead / (Living + Dead) ~ Concentration, data = hacid)

## End(Not run)

Description

These data are part of a large sample collected by Prof. P. Monaghan of the University of Glasgow in a study of the weight changes in herring gulls throughout the year. Some birds were caught in June (coded as month 1) and others in December (month 2). Since weight is dependent on the size of the bird this information is recorded in the form of the head and bill length, hab (in mm), the distance from the back of the head to the tip of the bill.

Note that from version 1.1.6 of the package, month is defined as factors rather than as a simple numerical code.

The data are used in the rp.ancova example script.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   with(gullweight, {
     rp.ancova(hab, weight, month)
   })
   
## End(Not run)

Description

In addition to his pioneering work on the telephone, Alexander Graham Bell was also interested in heredity. He was one of the first people to investigate the relationship between longevity and the age of parents. His book 'The duration of life and conditions associated with longevity: a study of the Hyde genealogy' describes data from the descendants William Hyde, one of the early settlers of Norwich, Conn., who died in 1681. The data were retrieved from the 'Genealogy of the Hyde Family' by Reuben H. Waiworth, LL. D. (1864). The Appendix of the book by Bell refers to tables of detailed data published in Bell's own journal, the Beinn Bhreagh Recorder. These include Table 7: Age at death by father's age when person was born (B. B. Rec., IX, 143-171).

The hyde dataframe consists of three columns of data:

• Father: age of the father at the birth of the child;

• Child: age of the child at death;

• Frequency: the number of cases with this combination of ages in the Hyde genealogy.

Access to these data was kindly facilitated by the Manuscript Division of the US Library of Congress where the Alexander Graham Bell family papers are held.

References

Bell, A.G. (1918). The duration of life and conditions associated with longevity: a study of the Hyde genealogy. Genealogical Record Office.

Alexander Graham Bell family papers, 1834-1974. US Library of Congress. https://lccn.loc.gov/mm76051268.

Waiworth, R. H. (1864). Genealogy of the Hyde Family.

Examples

## Not run: 
ind  <- rep(1:nrow(Hyde), Hyde$Frequency)
Hyde <- Hyde[ind, 1:2]
hist(Hyde$Child)

## End(Not run)

Description

Loch Leven is situated in lowland Scotland in the Perth and Kinross area. It is the largest shallow lake in Great Britain with an area of 13.3km$^2$, mean depth 3.9m and a maximum depth 25.5m. Regular monitoring is carried out by the *Centre for Ecology & Hydrology* in Edinburgh.

One of the features of interest is the water quality and hence the relationship between *chlorophyll$_a$* (as an indicator of water quality) and *soluble reactive phosphorus* (a nutrient) is very important. The monthly means for chlorophyll$_a$ ('lchla') and soluble reactive phosphorus ('srp') are provided from January 1988 to December 2007, along with the 'year' and 'month' of each measurement. The Loch_Leven dataframe consists of four variables:

• year: the year of the measurement;

• month: the month of the measurement, as an integer (1-12);

• chla: the measurement of chlorophyll$_a$;

• srp: the measurement of soluble reactive phosphorus.

References

Ferguson, C.A., Bowman, A.W., Scott, E.M., Carvalho, L. (2009). Multivariate varying-coefficient models for an ecological system. Environmetrics 20.4, 460-476.

Examples

## Not run: 
with(Loch_Leven, plot(year, log(chla)))

## End(Not run)

Description

These data, reported by Raz(1989, Biometrics 54, 851-71) refer to an experiment which compared the concentrations of leutinizing hormone (LH) in 16 suckled and 16 non-suckled cows. Measurements were made daily from day 1 through to day 4 postpartum, and twice daily from day 5 through to day 10 postpartum. The cows were ovariectomised on day 5 postpartum.

The first column of the dataset defines the group (1 - non-suckled, 2 - suckled) while the remaining columns give the LH values at the successive recording times.

The data are used in the rp.rmplot example script.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   LH <- luthor[,2:16]
   gp     <- factor(luthor[,1])
   times  <- c(1:5,(5+(1:10)/2))
   rp.rmplot(log(LH), fac = gp, timept = times)
   
## End(Not run)

Description

In an investigation of the relationship between mass (kg) and maximal running speed (km/hr) in terrestrial mammals, Garland (1983) collected information from published articles on these two variables for a large number of different species. The measurements are not all recorded to the same level of accuracy since the results have been collated from the work of a number of different scientists.

The mammal_speed dataframe consists of four columns of data:

• Name: the name of the mammal;

• Family: the family of the mammal;

• Mass: the characteristic mass of the mammal in kg;

• Speed: the characteristic maximal running speed of the mammal in km/hr.

The rodent dataset in the rpanel package is a subset of these data.

References

Garland, T. (1983). The relation between maximal running speed and body mass in terrestrial animals. Journal of the Zoological Society of London, 199, 155-170.

Examples

## Not run: 
if (require(ggplot2, quietly = TRUE)) {
  ggplot(mammal_speed, aes(log(Mass), log(Speed), col = Family)) + geom_point()
}

## End(Not run)

Description

These data record the survival times (in units of 10 hours) of animals in a 3 x 4 factorial experiment. Four animals were allocated to each combination of three poisons and four treatments, using a randomisation procedure.

The data are used in the rp.anova example script.

The data were reported in the paper by Box and Cox (1964) referenced below.

Note that from version 1.1.6 of the package, the poisons are treatments are defined as factors rather than as simple codes.

References

Box, GEP and Cox, DR (1964), An analysis of transformations. Journal of the Royal Statistical Society Series B - Statistical Methodology, 26, 211-252.

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  with(poisons, {
    rp.anova(1/stime, treatment, poison)
  })

## End(Not run)

Description

These data record the water temperature at a sampling station on the River Clyde, together with an indicator of whether (1) or not (0) the concentration of dissolved oxygen fell below the threshold of 5 percent.

The data are used in the rp.logistic example script.

The data were kindly provided by the Scottish Environment Protection Agency, with the assistance of Dr. Brian Miller.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  rp.logistic(river$Temperature, river$Low)

## End(Not run)

Description

In an investigation of the relationship between mass (kg) and speed (km/hr) in mammals, Garland (1983) collected information from published articles on these two variables for a large number of different species. These measurements are given below for a variety of four-footed rodents. (The common names of the species are taken from Corbet & Hill (1986).) Notice that the measurements are not all recorded to the same level of accuracy since the results have been collated from the work of a number of different scientists.

References

Bowman, A.W. & Robinson, D.R. (1990). Introduction to Regression and Analysis of Variance: a computer illustrated text. Bristol: Adam Hilger.

Garland, T. (1983). The relation between maximal running speed and body mass in terrestrial animals. Journal of the Zoological Society of London, 199, 155-170.

Corbet, G.B. & Hill, J.E. (1986). A World List of Mammalian Species. 2nd edition. London: British Museum, Natural History.

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   with(rodent, {
     rp.regression(log(Mass), log(Speed))
   })

## End(Not run)

Description

This function plots a response variable against a covariate, with different groups of data identified by colour and symbol. It also creates a panel which controls the model which is fitted to the data and displayed on the plot.

The function is superseded by rp.lm which is strongly recommended as it has a more flexible interface and improved plotting, using ggplot.

Usage

rp.ancova(x, y, group, panel = TRUE, panel.plot = TRUE, model = NA,
            xlab, ylab, glab, hscale = NA, vscale = hscale, style = 'ggplot')

Arguments

Details

The rp.lm function provides an alternative means of creating the display, based on model formulae.

The 'ggplot' style is strongly recommended as in this case the rp.lm function is called. The older style of display has very limited functionality.

Static plots, for printing or other purposes can be created by setting the panel argument to FALSE and specifying the model of interest.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   with(gullweight, {
     rp.ancova(hab, weight, month)
   })

## End(Not run)

Description

This function plots response data, separated by one or two factors. It also creates a panel which controls the models which can be fitted to the data and displayed on the plot. A comparison model can also be selected and the results of an F-test are displayed graphically.

The function is superseded by rp.lm which is strongly recommended as it has a more flexible interface and improved plotting, using ggplot. The rp.anova function is likely to be dropped in a future release of the rpanel package.

Usage

rp.anova(y, x, z, model = NA, model0 = NA, ylab = NA, xlab = NA, zlab = NA, title = NULL,
         lines = TRUE, panel = TRUE, panel.plot = TRUE, hscale = 1.3,
         vscale = hscale / 1.3)

Arguments

Details

The data are displayed as points superimposed on a density strip created by the ggplot2 package. Selected models are displayed through the fitted values for each group. When a valid comparison model is selected, its fitted values are displayed along with a shaded regions expressing the contribution of the differences between the two sets of fitted values to the F-statistic. The F-test is displayed in graphical form with a density strip to represent the F-distribution and a point to indicate the observed value of the F-statistic.

Static plots, for printing or other purposes can be created by setting the panel argument to FALSE and specifying the models of interest.

Value

When the function is called in interactive mode, the tcltk panel object is returned, otherwise the ggplot object containing the requested plot is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   with(poisons, {
     rp.anova(1/stime, treatment, poison)
   })

## End(Not run)

Description

This function prevents the R console from accepting further input waits until a panel is closed. The function has two uses. The first is to keep R active when an R script is run in batch mode. This prevents the R session from terminating until the panel has been closed. The second use is to block the user from further use of the command prompt. There may be circumstances in which it is helpful to do this.

Usage

rp.block(panel)

Arguments

Details

rp.block should usually be the very last function executed in a script, to prevent termination until the panel has been closed.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package (https://www.maths.gla.ac.uk/~adrian/rpanel/)

See Also

rp.control

Examples

## Not run: 
# This function will be called on pressing the button "Simulate".
boxp.sim <- function(panel) {
  boxplot(rnorm(50))
  panel
}
# Create an rpanel and add the button "Simulate" to it.
panel <- rp.control()
rp.button(panel, action = boxp.sim, title = "Simulate")
rp.block(panel)

## End(Not run)

Description

This function produces a scatterplot of two variables, with the values of third and fourth variables represented by size and colour of the plotted points. In addition, the scatterplot is animated over a fifth variable, such as time.

Usage

rp.bubbleplot(x, y, year, size, col, col.palette = topo.colors(20),
                     interpolate = FALSE, fill.in = FALSE, labels = rownames(x),
                     hscale = 1, vscale = hscale)

Arguments

Details

This plot mimics the plots made famous by Hans Rosling through the Gapminder project (see https://www.gapminder.org). The aim of this function is to make this type of plot available directly from within R. The controls provide a slider or button for animation, plus a list of country names for individual identification.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   rp.bubbleplot(log(gdp), log(co2.emissions), 1960:2007, size = population, 
      col = life.expectancy, interpolate = TRUE)

## End(Not run)

Description

This function adds a button to the panel. A nominated function is called when the button is pressed.

Usage

rp.button(panel, action = I, title=deparse(substitute(action)), repeatdelay=0,
  repeatinterval=0, quitbutton=FALSE, pos=NULL, foreground=NULL, 
  background=NULL, font=NULL, parentname=deparse(substitute(panel)), 
  name=paste("button", .nc(), sep=""), ...)

Arguments

Details

Setting repeatinterval and repeatdelay to positive values has the effect of call the function repeatedly when the button is held down.

The function action should take one argument, which should be the panel. See rp.grid for details of the grid layout system.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

Note

The arguments id and parent have been discontinued in version 1.1.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.doublebutton,rp.control

Examples

## Not run: 
   # This function will be called on pressing the button "Simulate".
   boxp.sim <- function(panel) {
     boxplot(rnorm(50))
     panel
     }
   # Create an rpanel and add the button "Simulate" to it.
   panel <- rp.control()
   rp.button(panel, action = boxp.sim, title = "Simulate")

## End(Not run)

Description

This function creates a panel with a menu which launches a variety of rpanel illustrations. The function provides a template which can be amended by users to create tailored sets of illustrations.

Usage

rp.cartoons(hscale = 1)

Arguments

Value

Nothing.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   rp.cartoons()

## End(Not run)

Description

Adds one or more checkboxes to the panel, to control logical variables.

Usage

rp.checkbox(panel, variable, action=I, labels=NULL, names=NULL, title=NULL, 
  initval=rep(FALSE, length(labels)), pos=NULL, doaction=FALSE, foreground=NULL,
  background=NULL, font=NULL, parentname=deparse(substitute(panel)),
  name=paste("checkbox", .nc(), sep=""), ...)

Arguments

Details

The function action should take one argument, which should be the panel to which the checkbox is attached. See rp.grid for details of the grid layout system.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.radiogroup,rp.control

Examples

## Not run: 
   plot.hist <- function(panel) {
	 with(panel, {
		xlim <- range(c(x, mean(x) + c(-3, 3) * sd(x)))
	   if (panel$cbox[3])
	      clr <- "lightblue" else clr <- NULL
	   hist(x, freq = FALSE, col = clr, xlim = xlim)
	   if (panel$cbox[1]) {
	      xgrid <- seq(xlim[1], xlim[2], length = 50)
	      dgrid <- dnorm(xgrid, mean(x), sd(x))
	      lines(xgrid, dgrid, col = "red", lwd = 3)
	      }
	   if (panel$cbox[2])
	      box()
	   })
	 panel
	 }
   x <- rnorm(50)
   panel <- rp.control(x = x)
   rp.checkbox(panel, cbox, plot.hist, 
      labels = c("normal density", "box", "shading"), title = "Options")
   rp.do(panel, plot.hist)

## End(Not run)

Description

This function shows simulated confidence intervals for the mean of a normal distribution. It also creates a panel which controls the mean and standard deviation of the population and the size of the simulated sample.

Usage

rp.ci(mu = 0, sigma = 1, sample.sizes = c(30, 50, 100, 200, 500),
         confidence = 0.95, panel = TRUE,
         panel.plot = TRUE, hscale = NA, vscale = hscale)

Arguments

Details

A button is provided to sample repeatedly from the current settings. Confidence intervals which cover the population mean are coloured blue while those which miss are coloured red. Repeated simulations illustrate the property of confidence intervals to capture the true value with probability determined by the confidence level (which here is set to 0.95).

Value

The tcltk panel object is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  rp.ci()

## End(Not run)

Description

This function removes line(s) from an rpanel image widget: rp.clearlines removes all the lines from an image while rp.deleteline deletes only a given line.

Usage

rp.clearlines(panel, imagename)

Arguments

Value

If the parameter panel is the panelname string the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Note

In version 1.1 "id" has been renamed "name" to be consistent with the rest of rpanel.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.image,rp.line

Examples

## Not run: 
   panel <- rp.control()
   image.file <- file.path(system.file(package = "rpanel"), "images", "gulllmks.gif")
   panel <- rp.image(panel, image.file, imagename="gulls.image")
   rp.line(panel, imagename=gulls.image, 10, 10, 100, 100, color = "green")
   rp.line(panel, imagename=gulls.image, 100, 100, 100, 10, color = "blue")
   rp.clearlines(panel, imagename=gulls.image)

## End(Not run)

Description

The effects of the coefficients of a linear or generalised linear model are displayed in graphical form. The effects are made comparable by viewing the change in the mean value of the response as each predictor variable changes across its range.

Usage

rp.coefficients(model, style = 'density', ci = TRUE, point.estimate = !ci,
                 se.scale = FALSE, marks, cols, ngrid)

Arguments

Details

The covariate range information is added to the labels by the function.

When coefficient labels are lengthy, or there are many coefficients in the model, some overlapping may occur. Options to deal with this are shown in the examples below.

Value

A ggplot object is returned. This allows further annotation of the display.

Examples

## Not run: 
   model <- lm(Giving ~ Employ + Elect + Attend + 1, data = CofE)
   rp.coefficients(model)
   
   # Change colours
   
   # Options for labelling
   plt <- rp.coefficients(model)
   plt + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90))
   plt + ggplot2::scale_x_discrete(labels = scales::label_wrap(5))
   plt + ggplot2::coord_flip()
   
## End(Not run)

Description

A colour key is created using the specified colours (cols) and an axis defined by the specified breaks (brks). This is usually an additional component of a panel which allows the colours on the main plot to be interpreted. The function is used in that way in the function rp.plot4d.

Usage

rp.colour.key(cols, brks, par.mar = c(5, 0, 4, 3) + 0.1,
natural = TRUE, cex = 1, margin = FALSE)

Arguments

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  key.plot <- function(panel) {
  	rp.colour.key(topo.colors(12), 0:12)
  	panel
  }
  panel <- rp.control()
  rp.tkrplot(panel, key, key.plot, hscale = 0.15)

## End(Not run)

Description

Returns a named vector specifying the colours to be used in some rpanel functions. Where colours for particular purposes are not specified default values are returned.

Usage

rp.colours(cols)

Arguments

Details

Some rpanel functions have a cols argument which can be used to specify the colours used when creating some aspects of the resulting visual display. The rp.colours function is used internally to set these colours, altered where appropriate by the elements of cols. Users need not employ this function, except to view the default colours.

Value

A named vector of the colours.

Examples

## Not run: 
# View the defauklt colours.
rp.colours()

# Alter particular colours
model <- lm(Giving ~ Attend + Employ, data = CofE)
rp.coefficients(model)
rp.coefficients(model, cols = c('estimate' = 'darkgreen'))

## End(Not run)

Description

This function adds a ‘combobox’ to the panel. When an item is pressed, a variable is set and an action function is called.

Usage

rp.combo(panel, variable, prompt=NULL, vals, initval=vals[1], pos=NULL, action=I,
  foreground=NULL, background=NULL, font=NULL, editable=FALSE,
  parentname=deparse(substitute(panel)), name=paste("combo", .nc(), sep=""), ...)

Arguments

Details

The function action should take one argument, which should be the panel to which the combobox is attached.

See rp.grid for details of the grid layout system.

This function makes use of the BWidget extension to the Tcl/Tk system. If Bwidget has not been installed on your system, download it from https://sourceforge.net/projects/tcllib/files/BWidget/ and expand the compressed file into a folder. On a Windows machine, this folder should then be copied into the folder containing the Tcl libraries that were installed as part of R. This may be in a location such as C:\Program Files\R\R-4.0.2\Tcl\lib (with an obvious change to the version number of R being used). On a Mac, the downloaded folder should be copied into the folder where the main Tcl package is located (note: not inside the Tcl folder but at the same level as the Tcl folder). This may be in a location such as /usr/local/lib.

Note that rp.listbox provides an alternative to rp.combo if the latter is unavailable.

Value

If the parameter panel is the panelname string the same string is returned. If the panel object is used the altered panel is assigned to both the calling level and panel's environment level.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

Note

Parameters parent and ... have been discontinued in version 1.1. Note that the argument previously named var has been renamed variable to avoid reserved word issues.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.checkbox, rp.control

Examples

## Not run: 
   callback <- function(panel) {
      print(panel$option)
      panel
   }
   panel <- rp.control()
   rp.combo(panel, option, "Pick an option:", 
            c("Option1","Option2","Other options"), action=callback)

## End(Not run)

Description

A two-way contingency table is displayed graphically and the uncertainties associated with a chi-squared test may be visualised.

Usage

rp.contingency(x, uncertainty = 'none', proportion.scale = 'fixed', cols)

Arguments

Details

The table is displayed in a form which stratifies by columns. If it more appropriate to stratify by the rows of x then the function may be applied to t(x).

The uncertainties associated with each cell are based on the standard errors associated with standardised residuals. See the help file for chisq.test for details.

Missing data are not allowed.

Examples

## Not run: 
   x <- matrix(c(19, 41, 32, 28), ncol = 2,
            dimnames = list(c("non-smoker", "smoker"),
                            c("cases", "controls")))
   rp.contingency(x)
   rp.contingency(x, style = "aligned")
   rp.contingency(x, uncertainty = TRUE)
   rp.contingency(x, uncertainty = TRUE) + ggplot2::coord_flip()

## End(Not run)

Description

The function rp.control creates a panel window into which rpanel widgets can be placed. It can also set up variables within the rpanel object. The function rp.control.dispose disposes of an rpanel.

Usage

rp.control(title = "", size = c(100, 100), panelname, background, ...)
rp.control.dispose(panel)

Arguments

Details

Objects passed into rp.control are then available to be used by action functions.

Value

The list object which defines the panel.

Note

Previous arguments realname and aschar have been discontinued in version 1.1.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.button, rp.checkbox, rp.combo, rp.doublebutton, rp.grid, rp.image, rp.listbox, rp.menu, rp.radiogroup, rp.slider, rp.text, rp.textentry, rp.tkrplot, rp.widget.dispose

Examples

## Not run: 
   hist.or.boxp <- function(panel) {
     if (panel$plot.type == "histogram")
       hist(panel$x)
     else
       boxplot(panel$x)
     panel
   }
   panel <- rp.control(x=rnorm(50), panelname="panel")
   rp.radiogroup(panel, plot.type, c("histogram", "boxplot"),
                 title="Plot type", action = hist.or.boxp)  

   # Try also
   # panel <- rp.control()
   # rp.control.dispose(panel)

## End(Not run)

Description

Sometimes an action function makes changes to the panel list object. When the action function is completed, the panel environment is updated. However, if there are other calls to action functions within the original action function, then the panel environment needs to be updated before these calls. This function achieves that.

Usage

rp.control.put(panelname, panel)

Arguments

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.control

Examples

## Not run: 
  action1 <- function(panel) {
    panel$x <- rnorm(1)
    rp.control.put(panel$panelname, panel)
    rp.do(panel, action2)
    panel
  }
  action2 <- function(panel) {
  	print(panel$x)
  	panel
  }
  panel <- rp.control(x = 0)
  rp.button(panel, action1, "new x")

## End(Not run)

Description

Links to specific remote datasets are provided. These can be read from the source locations or downloaded to a local directory for convenience.

Usage

rp.datalink(name, action = "retrieve filename")

Arguments

Details

Calling the function with no arguments, as rp.datalink(), will print the dataset names, the file extensions and the remote link.

When action = "retrieve filename" but the file has not been downloaded to a local directory, the file is downloaded to a temporary file and the name of this temporary file is returned.

The specification of the local directory can be removed by setting the name argument to NULL.

The file associated with 'name' can be a zip file, in which case the unz function can be used to locate the specific file of interest. The Examples below illustrate this. Some functions to read data require a specific path to the file of interest, rather than a connection. In this case, the unzip function can be used to unzip the files to a temporary (using tempdir) or other specified directory first.

Value

If the function is called with no arguments, the dataset names, the file extensions and the remote link re returned in a dataframe. If action = "set local directory", the name of the local directory is returned. When action = "download", the names of the local files created are returned. When action = "retrieve filename", the location of the specified dataset is returned.

Examples

## Not run: 
  rp.datalink()
  rp.datalink("~/Desktop/temp", "set local directory")
  rp.datalink("global_temperature")
  
  # Reading from a file within a zip file
  zfile   <- rp.datalink("children_services")
  dirname <- "Data and Code CSRR Strand 3 - August 2023 Update"
  path <- unz(zfile, paste(dirname, "Example_Indicator_CSR.csv", sep = "/"))
  if (!requireNamespace("readr", quietly = TRUE)) 
    indicator <- read_csv(path, skip = 6, col_names = c("LA", 2011:2021))
  
## End(Not run)

Description

This removes a previously drawn line which was given an id in rp.line.

Usage

rp.deleteline(panel, imagename, id)

Arguments

Value

If the argument panel is the panelname string the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Note

In version 1.1, the former argument image has been renamed name to be consistent with the rest of rpanel.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   panel <- rp.control()
   image.file <- file.path(system.file(package = "rpanel"), "images", "gulllmks.gif")
   panel <- rp.image(panel, image.file, imagename="gulls.image")
   rp.line(panel, imagename=gulls.image, 10, 10, 100, 100, color = "green", id="first")
   rp.line(panel, imagename=gulls.image, 100, 100, 100, 10, color = "blue", id="second")
   rp.deleteline(panel, imagename=gulls.image, id="first")

## End(Not run)

Description

Runs a user-written action function, passing a panel to it as a parameter. This can be used to put the rpanel into its initial state. For example, it is useful when using radiobuttons as these do not automatically call the action function when the controls are first created.

Usage

rp.do(panel, action, x = NA, y = NA)

Arguments

Value

If the argument panel is the panelname string the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.radiogroup

Examples

## Not run: 
   data.plotfn <- function(panel) {
     if (panel$plot.type == "histogram") 
       hist(panel$x)
     else 
       if (panel$plot.type == "boxplot")
         boxplot(panel$x)
       else 
         plot(density(panel$x))
     panel
   }
   panel <- rp.control(x = rnorm(50))
   rp.radiogroup(panel, plot.type, 
          c("histogram", "boxplot", "density estimate"), 
          action = data.plotfn, title = "Plot type", initval="histogram")     
   rp.do(panel, data.plotfn)

## End(Not run)

Description

Adds a control with '+' and '-' buttons, to increment and decrement a variable.

Usage

rp.doublebutton(panel, variable, step, title=deparse(substitute(variable)),
                action=I, initval=NULL, range=c(NA, NA), log=FALSE,
                showvalue=FALSE, showvaluewidth=4, repeatinterval=100,
                repeatdelay=100, pos=NULL, foreground=NULL, 
                background=NULL, font=NULL, parentname=deparse(substitute(panel)),
                name=paste("doublebutton", .nc(), sep=""), ...)

Arguments

Details

action should be a function of one argument, which should be the panel. The panel can then be manipulated, and data stored in the panel may be used/modified, then the (optionally modified) panel must be returned.

See rp.grid for details of the grid layout system.

Value

If the argument panel is the panelname string, the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

Note that setting log=TRUE and showvalue=TRUE is not allowed.

Note

The former arguments parent and ... have been discontinued in version 1.1. Note also that the argument var has been renamed variable to avoid reserved word issues.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.radiogroup,rp.control

Examples

## Not run: 
   density.draw <- function(panel) {
     plot(density(panel$x, bw = panel$h))
     panel
     }
   panel <- rp.control(x = rnorm(50))
   rp.doublebutton(panel, var = h, step = 0.05, 
     title = "Density estimate", action = density.draw,
     range = c(0.1, 5), initval=1)

## End(Not run)

Description

Plot the results, in terms of F-tests, of dropping all individual terms from a linear model. This is essentially a graphical display of the results from the function drop1. The hierarchy of the model formula is respected so that when interaction terms are present main effect terms are not considered.

Usage

rp.drop1(model, subset.terms, p.reference = c(0.05, 0.01), cols)

Arguments

Details

The rp.colours function is used internally to set these colours, altered where appropriate by the elements of cols. Users need not employ this function, except to view the default colours.

The ggplot2 package is required by this function. If the ggforce is available, this is used to scale the heights of facet rows when there are multiple terms with different degrees of freedom.

Value

A ggplot object containing the plot.

Examples

## Not run: 
model <- lm(Giving ~ Attend + Employ, data = CofE)
rp.drop1(model)

## End(Not run)

Description

This function gives access to a sampling scenario which is based on the mapping of radioactivity and the calculation of a radionuclide inventory within a water body. (A ‘firth’ is a Scottish term for a long, narrow indentation of the sea coast at the mouth of a river.) Interest lies in nuclides which, on release into a water body, attach (absorb) to sediment in a manner which depends on the sediment particle size. Cobalt-60 and caesium-137 are examples of nuclides which exhibit this behaviour. In this sampling scenario, the map of sediment type is used to define regions of different particle size from which the sediment samples will be collected by grabs from a boat. The presence of strata therefore has to be considered, as the different types of material on the sea bed may affect the mean values of the measurements taken.

The function displays a map and gives graphical control over a variety of sampling strategies. Once the user has drawn a sample, some simple predictions over the whole firth can be produced. The geoR package is used to construct these predictions.

Usage

rp.firth(hscale = NA, col.palette = rev(heat.colors(40)), col.se = "blue", file = NA,
          parameters = NA)

Arguments

Details

The use of the function is discussed in detail in the paper by Bowman et al. (2008) referenced below.

Once the data have been sampled, a data file may be saved for further analysis external to the rp.firth function, using the file argument. A convenient way of saving to the current working directory, for example to a file named firth.dmp, is to set the file argument to file.path(getwd(), "firth.dmp"). The load function can then be applied to the saved file to create an object called mururoa.data, which is a three-column matrix with the x and y locations in columns 1 and 2 and the observed values in column 3.

Value

Nothing in returned.

References

Bowman, A.W., Crawford, E., Alexander, G. Gibson and Bowman, R.W. (2007). rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Bowman, A.W., Gibson, I., Scott, E.M. and Crawford, E. (2008). Interactive Teaching Tools for Spatial Sampling. Journal of Statistical Software, 36, 13, 1–17.

See Also

rp.mururoa, rp.geosim

Examples

## Not run: 
  rp.firth()

## End(Not run)

Description

This function allows Gaussian random fields to be simulated and visualised, using graphical controls for a variety of parameter settings.

Usage

rp.geosim(max.Range = 0.5, max.pSill = 1, max.Nugget = 1, max.Kappa = 10, 
          max.aniso.ratio = 3, min.ngrid = 10, max.ngrid = 25,
          hscale = NA, vscale = hscale, smgrid = 40, ngrid = 15,
          Range = 0.1, pSill = 0.5, Nugget = 0, kappa = 4,
          aniso.angle = 0, aniso.ratio = 1,
          col.palette = terrain.colors(40), panel = TRUE)

Arguments

Details

The aim of the tool is to allow the generation of repeated simulated fields without the distraction of re-executing code explicitly. This can help to gain an intuitive understanding of the nature of spatial data. In particular, interactive control of parameters can help greatly in understanding the meaning and effects of parameter values. Nugget effects can be added and sampled points displayed. Two-dimensional contour plots are produced. Three-dimensional plots are also produced if the rgl package is available.

The use of the function is discussed in the paper paper by Bowman et al. (2008) referenced below.

The geoR and fields packages are used to generate the data.

Note that the Matern covariance function is parameterised in the form described by Handcock & Wallis (1994) which separates the effects of the shape and range parameters.

Value

The tcltk panel object is returned.

References

Adler, D. (2005). rgl: 3D Visualization Device System (OpenGL). https://cran.r-project.org.

Bowman, A.W., Crawford, E., Alexander, G. Gibson and Bowman, R.W. (2007). rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Bowman, A.W., Gibson, I., Scott, E.M. and Crawford, E. (2008). Interactive Teaching Tools for Spatial Sampling. Journal of Statistical Software, 36, 13, 1–17.

Diggle, P.J. and Ribiero, P.J. (2008). Model-based Geostatistics. Springer, New York.

Handcock, M.S. and Wallis, J.R. (1994). An approach to statistical spatial-temporal modeling of meteorological fields. Journal of the American Statistical Association, 89, 368-378.

See Also

rp.firth, rp.mururoa

Examples

## Not run: 
   rp.geosim()

## End(Not run)

Description

A subsidiary grid is defined at a specified location within an rpanel.

Usage

rp.grid(panel, name=paste("grid", .nc(), sep=""), pos=NULL, background=NULL, 
  parentname=deparse(substitute(panel)), ...)

Arguments

Details

The role of this function is to specify a subsidiary grid at a particular row and column position of the parent grid. Nesting of grids within grids is permitted. See the help information on "grid" mode in rp.pos for a description of the settings of the pos argument.

Note

The former argument parent has been discontinued in version 1.1, while the argument bg has been renamed background for consistency with the other functions.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
panel <- rp.control()
rp.grid(panel, pos=list(row=0, column=0, sticky="news"),
        background="red", name="g0")
rp.grid(panel, pos=list(row=1, column=1, sticky="news", width=100, height=100),
        background="navy", name="g1")
rp.grid(panel, pos=list(row=2, column=2, sticky="news", width=150, height=200),
        background="green", name="g2")
rp.button(panel, function(panel) { panel }, "press A",
        pos=list(row=1, column=1, sticky=""), parentname="g1")
rp.button(panel, function(panel) { panel }, "press B",
        pos=list(row=2, column=2, sticky="news"), parentname="g1")
rp.button(panel, function(panel) { panel }, "press C",
        pos=list("left",width=50, height=150), parentname="g2")
rp.grid(panel, pos=list(row=0, column=0, sticky="", width=10, height=10),
        background="yellow", parentname="g0")

## End(Not run)

Description

The function launches a panel which contains an image of a herring gull. With this bird, sex cannot easily be identified by visual inspection. The user is invited to identify length measurements, defined by pairs of landmarks, which will enable males and females to be identified.

Usage

rp.gulls(df.name = "", panel.plot = TRUE)

Arguments

Details

The panel contains an image with landmarks indicated by yellow dots. When the user clicks two landmarks, a length measurement is indicated by a coloured line. The ‘Collect data’ button can be clicked to request that this measurement is collected, on a database of birds whose sex is known. If the measurement is a valid and useful one, it is added to the named dataframe, which is immediately saved in the file df.name and is therefore available for inspection and analysis simply by loading this file. If the measurement is invalid or not useful, an appropriate message is given in a pop-up window.

Note that in versions of rpanel earlier than 1.1-1 the dataframe containing the collected data was previously forced into the global environment for immediate access. This has been replaced by the use of a user-nominated file.

This function does not have hscale and vscale arguments because the size of the window is determined by the resolution of the image of the gull.

Value

the name of the panel created.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  rp.gulls()

## End(Not run)

Description

An image is placed inside a panel. When the image is clicked the action function is called with the x and y coordinates of the clicked position.

Usage

rp.image(panel, filename, imagename, action=NA, mousedrag=NA, mouseup=NA, pos=NULL,
  parentname=deparse(substitute(panel)), ...)

Arguments

Details

The function action should take three arguments, the panel and the coordinates x and y where the image was clicked. At present only GIF images are supported.

See rp.grid for details of the grid layout system.

Value

If the argument panel is the panelname string, the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

Note

The former arguments parent and ... have been discontinued in version 1.1. Note also that the argument id has been renamed name to be consistent with the rest of rpanel.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   gulls.click <- function(panel, x, y) {
     print(c(x, y))
     panel
     }
   panel <- rp.control()
   image.file <- file.path(system.file(package = "rpanel"), "images", "gulllmks.gif")
   rp.image(panel, image.file, gulls.image, action = gulls.click)

## End(Not run)

Description

This function plots a likelihood surface for a model with one or two parameters. It also creates a panel which allows the maximum likelihood estimate, a confidence region and other objects of interest to be added to the plot. For two-parameter models the rgl package is required.

Usage

rp.likelihood(loglik.fn, data, theta.low, theta.high, form = "log-likelihood", 
                  hscale = NA, vscale = hscale)

Arguments

Details

The interactive controls allow a variety of aspects of the plots to be altered. This is intended to allow students and lecturers to explore likelihood surfaces in a manner which promotes an intuitive understanding of the concepts involved.

In the case of one parameter, the vertical axes of the (log-)likelihood plot can be clicked and grabbed to alter the plotting region interactively. This can be useful, in particular, in identifying the maximum likelihood estimator graphically.

Value

Nothing is returned.

References

rpanel: Statistical cartoons in R. MSOR Connections, 7, 3-7.

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   rp.likelihood("sum(log(dexp(data, theta)))", aircond, 0.005, 0.03)
   rp.likelihood("sum(log(dgamma(data, theta[1], theta[2])))",
        aircond, c(0.3, 0.005), c(3, 0.06))

## End(Not run)

Description

This draws a line connecting the pixel locations x1, y1 to x2, y2 on the specified image. The colour and width of the line can be controlled.

Usage

rp.line(panel, imagename, x1, y1, x2, y2, color = "black", width = 2, id = 'rpline')

Arguments

Details

The function action should take one argument, which should be the panel to which the line is attached.

Value

If the argument panel is the panelname string, the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Note

In version 1.0, the former argument image has been renamed name to be consistent with the rest of rpanel.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.tkrplot,rp.image

Examples

## Not run: 
  click.capture <- function(panel,x,y) {
    if (is.null(panel$x)) { 
      panel$x <- as.numeric(x)
      panel$y <- as.numeric(y) 
    } 
    else { 
      rp.line(panel, imagename=gulls.image, panel$x, panel$y,
              as.numeric(x), as.numeric(y), width=3, id = "current")
      panel$x <- as.numeric(x)
      panel$y <- as.numeric(y)
    }
    panel
  }
  gulls.panel <- rp.control()
  image.file <- file.path(system.file(package = "rpanel"), "images", "gulllmks.gif")
  rp.image(gulls.panel, image.file, imagename="gulls.image", action = click.capture)

## End(Not run)

Description

This function adds a listbox to the panel. When an item is pressed, a variable is set and an action function is called.

Usage

rp.listbox(panel, variable, vals, labels = vals,
           rows=length(labels), initval=vals[1], pos=NULL, 
           title=deparse(substitute(variable)), action=I, foreground=NULL,
           background=NULL, font=NULL, parentname=deparse(substitute(panel)),
           sleep = 0.01, name=paste("listbox", .nc(), sep=""), ...)

Arguments

Details

The function action should take one argument, which should be the panel to which the listbox is attached.

See rp.grid for details of the grid layout system.

Value

If the argument panel is the panelname string, the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

Note

The former arguments parent and ... have been discontinued in version 1.1. Note also that the argument var has been renamed variable to avoid reserved word issues.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.checkbox,rp.control

Examples

## Not run: 
   data.plotfn <- function(panel) {
     if (panel$plot.type == "histogram")
       hist(panel$x)
     else
       if (panel$plot.type == "boxplot")
         boxplot(panel$x)
       else
         plot(density(panel$x))
     panel
   }
   panel <- rp.control(x = rnorm(50))
   rp.listbox(panel, plot.type,
       c("histogram", "boxplot", "density estimate"),
       action = data.plotfn, title = "Plot type") 

## End(Not run)

Description

This function is primarily intended to provide graphical representations of linear models with no more than two explanatory variables. These can be covariates or factors or a mixture of the two. When there are more than two explanatory variables the model is passed on to the rp.coefficients or rp.drop1 functions.

Usage

rp.lm(x, ylab, xlab, zlab, panel = TRUE, panel.plot = TRUE,
      style = 'ggplot', plot.nodes = FALSE,
      uncertainty.display = 'density', inference = 'coefficients',
      ci = TRUE, cols, display.model, comparison.model,
      residuals.showing, linewidth = 1,
      hscale = 1, vscale = hscale, ...)

Arguments

Details

The display of the data is adapted to the form of the explanatory variables. When panel = TRUE a lattice of potential models is displayed in graphical form. When a node of the lattice is clicked the corresponding fitted model is displayed. When a connection between adjacent models is clicked, a comparison of the two adjacent models is displayed, either as model coefficients (display = 'coefficients') or model terms (display = 'coefficients').

When the model defined by x contains two explanatory variables, where at least one is a factor, the models which can be fitted include the interaction term, even where this is not explicitly included in x.

When there are exactly two covariates in x, the function will not consider an interaction between these, even when x includes this.

If display.model is not specified, its default setting is NULL when panel = TRUE and the model specified by x when panel = FALSE.

Missing data are removed before the linear models are fitted.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  y <- rnorm(50, mean = 10, sd = 0.5)
  rp.normal(y)
  
## End(Not run)

Description

The function rp.logistic plots a binary or binomial response variable against a single covariates and creates a panel which controls the position of a logistic curve and allows a logistic regression to be fitted to the data and displayed on the plot.

Usage

rp.logistic(x, y, xlab = NA, ylab = NA, panel.plot = TRUE, panel = TRUE,
               hscale = NA, vscale = hscale, alpha = 0, beta = 0, 
               display = c("jitter" = FALSE, "regression line" = FALSE,
               "fitted model" = FALSE))

Arguments

Details

The control panel allows a logistic regression line to be drawn on the plot and the intercept and slope of the linear predictor altered interactively. The fitted logistic regression can also be displayed.

If y is a vector of responses with two values, these are treated as a factor which is then converted to the (0,1) scale by as.numeric.

The values of the response variable can be ‘jittered’.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.regression

Examples

## Not run: 
  rp.logistic(river$Temperature, river$Low)

## End(Not run)

Description

This function adds a menu to the top of the panel window. When a menu item is selected, a variable is set and an action function is called.

Usage

rp.menu(panel, variable, labels, initval = NULL, action = I,
        foreground = NULL, background = NULL, font = NULL,
        name = paste("menu", .nc(), sep = ""))

Arguments

Details

The function action should take one argument, which should be the panel to which the listbox is attached.

The list for a menu consisting of "File" and "Edit" only would be defined as list(list("File"), list("Edit")).

The list for a menu consisting of "File" with subitem "Quit", and "Edit" with subitems "Copy", "Cut" and "Paste", would be defined as list(list("File", "Quit"), list("Edit", "Copy", "Cut", "Paste")).

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

The action function must be defined before rp.menu is called.

Note

The former argument parent was discontinued in version 1.1 and the name of the variables whose value is set by the menu was renamed as variable from var in version 2.0 as the latter is a reserved word.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.checkbox,rp.control

Examples

## Not run: 
   a <- rp.control()
   # The action function has to come first so that it already exists for rp.menu, 
   # as it creates the callback functions on the fly it requires action to already 
   # be defined.
   domenu <- function(panel) {
      rp.messagebox(panel$menuchoice, title = "You chose")
      panel
      }
   rp.menu(a, menuchoice, labels=list(list("File","Quit"),
              list("Edit","Copy","Cut","Paste")), action=domenu)

## End(Not run)

Description

This function displays a message in a pop-up window.

Usage

rp.messagebox(..., title="rpanel Message")

Arguments

Details

The pop-up window remains displayed and no other action can be taken, until the 'ok' button is pressed.

Value

None.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.control

Examples

## Not run: 
   rp.messagebox("Click OK to continue.", title = "Test message")

## End(Not run)

Description

This function is based on a real sampling study on the effects of nuclear experiments conducted between 1966 and 1996 in the South Pacific, at the atolls of Mururoa and Fangataufa, (Report by International Advisory Committee, IAEA, 1998). As part of the assessment of subsequent radiological conditions, both terrestrial and aquatic samples were collected and assayed for activities due to strontium-90, caesium-137, plutonium and tritium. The sampling scenario in the function is based on water sampling by boat in the Mururoa atoll. A graphical control panel allows users to select sampling points. Once the user has drawn a sample, some simple predictions over the whole atoll can be produced.

Usage

rp.mururoa(hscale = NA, col.palette = rev(heat.colors(40)), col.se = "blue", file = NA,
    parameters = NA)

Arguments

Details

The panel controls allow the user to experiment with random and systematic sampling, with further control of the alignment and patterns of points in the systematic case. The number of points can also be selected. When a sample is taken, simulated data are generated. Some further controls allow predicted surfaces and standard errors to be displayed, using different types of trend functions. The geoR package is used to construct these predictions. The true simulated surface can also be displayed, to indicate the success of the predictions.

Once the data have been sampled, a data file may be saved for further analysis external to the rp.mururoa function, using the file argument. A convenient way of saving to the current working directory, for example to a file named mururoa.dmp, is to set the file argument to file.path(getwd(), "mururoa.dmp"). The load function can then be applied to the saved file to create an object called mururoa.data, which is a three-column matrix with the x and y locations in columns 1 and 2 and the observed values in column 3.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Bowman, A.W., Gibson, I., Scott, E.M. and Crawford, E. (2008). Interactive Teaching Tools for Spatial Sampling. Journal of Statistical Software, 36, 13, 1–17.

See Also

rp.firth, rp.geosim

Examples

## Not run: 
  rp.mururoa()

## End(Not run)

Description

This function plots a histogram of a sample of data and creates a panel which controls the mean and standard deviation of the normal distribution which is fitted to the data and displayed on the plot.

Usage

rp.normal(y, ylab = deparse(substitute(y)),
            panel.plot = TRUE, hscale = NA, vscale = hscale)

Arguments

Details

The interactive controls allow a normal density curve to be added to the histogram, with doublebuttons used to control the values of the normal mean and standard deviation. The fitted normal density based on the sample mean and standard deviation can also be displayed.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  y <- rnorm(50, mean = 10, sd = 0.5)
  rp.normal(y)
  
## End(Not run)

Description

A tabbed notebook, the location of which is defined by pos, is created within an rpanel. Further widgets, grids or even notebooks can then be placed within the notebook.

Usage

rp.notebook(panel, tabs, tabnames=tabs, width = 600, height = 400, pos = NULL, 
  foreground = NULL, background = "lightgray", font = NULL, 
  parentname = deparse(substitute(panel)),
  name = paste("notebook", .nc(), sep = ""), ...)
rp.notebook.raise(panel, parentname, label)

Arguments

Details

The role of this function is to specify a notebook. Nesting of notebooks is permitted. rp.notebook.raise is used to bring the contents of a particular tab to the foreground.

These functions make use of the BWidget extension to the Tcl/Tk system. If Bwidget has not been installed on your system, download it from https://sourceforge.net/projects/tcllib/files/BWidget/ and expand the compressed file into a folder. On a Windows machine, this folder should then be copied into the folder containing the Tcl libraries that were installed as part of R. This may be in a location such as C:\Program Files\R\R-4.0.2\Tcl\lib (with an obvious change to the version number of R being used). On a Mac, the downloaded folder should be copied into the folder where the main Tcl package is located (note: not inside the Tcl folder but at the same level as the Tcl folder). This may be in a location such as /usr/local/lib.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
panel <- rp.control(title="Notebook example with two notebooks")
rp.notebook(panel, c("File", "Edit"), width=600, height=400,
            pos=list(row=0, column=0), background="lightgray",
            font="Arial", name="n1")
rp.notebook.raise(panel, "n1", "Edit")
rp.button(panel, function(panel){
	                rp.messagebox("Button pressed!"); panel },
	                "Test this", parentname="Edit")
rp.messagebox("A second tabbed notebook can be added to the same window.")
rp.notebook(panel, c("A tab 1", "A tab 2"), width=200, height=200,
            pos=list(row=1, column=1), background="Navy", foreground="White")
rp.messagebox("A tabbed notebook can be placed inside a tabbed notebook.")
rp.notebook(panel, c("Tab within tab", "Another tab"),
            width=200, height=100, parentname="File", name="n3")
rp.notebook.raise(panel, "n1", "File")
rp.notebook.raise(panel, "n3", "Another tab")

## End(Not run)

Description

Returns a named (by passing the name as a string parameter) panel.

Usage

rp.panel(panelname)

Arguments

Value

If panelname is set, the panel of that name is returned. If it is not set, the most recently created panel is returned.

Warning

Note: returning of the most recent panel may fail when running R on a Windows machine in DOS. A warning is contained within the function.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.control

Examples

## Not run: 
   # create a panel - will be created in .rpenv as "newpanel"
   rp.control(panelname = "newpanel")
   # creates the panel, but does not return a handle to it - created as ".rpanel2"
   rp.control()
   # pick up the first panel
   panel2 <- rp.panel("newpanel")

## End(Not run)

Description

This function produces a scatterplot of three variables, using the rgl package for three-dimensional display.

Usage

rp.plot3d(x, y, z, xlab  = NA, ylab = NA, zlab = NA, 
                   axes = TRUE, new.window = TRUE, type = "p", size = 3, col = "red",
                   cex = 1, xlim = NA, ylim = NA, zlim = NA, plot = TRUE, ...)

Arguments

Details

The plot is produced by appropriate calls to the rgl package. This allows interactive control of the viewing position. Other objects may subsequently be added to the plot by using rgl functions and data which are centred and scaled by the returned values indicated below.

Value

A scaling function is returned to allow further objects to be added to the plot. The function accepts x, y, z vector arguments and returns a list with x, y, z components defining the co-ordinates for plotting. An illustration is given in the example below.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.regression

Examples

## Not run: 
  x <- rnorm(50)
  y <- rnorm(50)
  z <- rnorm(50)
  scaling <- rp.plot3d(x, y, z, xlim = c(-3, 3))
  # In addition you may add a line to the plot with these two lines;
  #  a <- scaling(c(-3,3), c(0,0), c(0,0))
  #  rgl::lines3d(a$x, a$y, a$z, col = "green", size = 2) 

## End(Not run)

Description

This function plots two covariates coloured by a response variable and animates this by a third covariate. In particular, it is useful for plotting spatiotemporal data.

Usage

rp.plot4d(x, z, y, model, group, subset,
             col.palette, col.breaks, col.labels,
             hscale = 1, vscale = hscale, panel = TRUE,
             x1lab, x2lab, zlab, ylab, cex = 1,
             display = "image", Display = NULL,
             background.plot = NULL, foreground.plot = NULL, 
             z.window = "normal", z.window.pars = c(min(z), sd(z)/5),
             coords = rep(NA, 2), radius = 0.05,
             col.circle = "black", lwd.circle = 1,
             location.plot = TRUE, retain.location.plot = FALSE,
             group.level, group.name, colour.key = TRUE,
             z.key = TRUE, new.window = TRUE,
             eqscplot = FALSE, location.plot.type = "histogram")
 
   rp.spacetime(space, time, y, model, group, subset, col.palette,
                col.breaks, col.labels, hscale = 1, vscale = hscale,
                panel = TRUE, x1lab, x2lab, zlab, ylab, cex = 1,
                display = "image", Display = NULL,
                background.plot = NULL, foreground.plot = NULL,
                time.window = "normal",
                time.window.pars = c(min(time), sd(time)/5),
                coords = rep(NA, 2), radius = 0.05,
                col.circle = "black", lwd.circle = 1,
                location.plot = TRUE, retain.location.plot = FALSE,
                group.level, group.name, colour.key = TRUE,
                z.key = TRUE, new.window = TRUE,
                eqscplot = TRUE, location.plot.type = "histogram")

Arguments

Details

The colour black should be avoided when using a normal window shape for z. This is because hsv shading is used to indicate increasing distance from the current z location and black has an hsv representation with s component 0, which cannot therefore be reduced further.

Value

The tcltk panel object is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
# The quakes data

with(quakes, {
  rp.plot4d(cbind(long, lat), depth)
  rp.plot4d(cbind(long, lat), depth, mag)
})

# SO2 over Europe

with(SO2, {
  location <- cbind(longitude, latitude)

  if (require(mgcv) & require(maps)) {
     location1 <- location[,1]
     location2 <- location[,2]
     model <- gam(logSO2 ~ s(location1, location2, year))
     loc1  <- seq(min(location1), max(location1), length = 30)
     loc2  <- seq(min(location2), max(location2), length = 30)
     yr    <- seq(min(year), max(year), length = 30)
     newdata <- expand.grid(loc1, loc2, yr)
     names(newdata) <- c("location1", "location2", "year")
     model <- predict(model, newdata)
     model <- list(x = cbind(loc1, loc2), z = yr,
                   y = array(model, dim = rep(30, 3)))
     mapxy <- map('world', plot = FALSE,
                  xlim = range(longitude), ylim = range(latitude))
     rp.plot4d(location, year, logSO2, model,
                 col.palette = rev(heat.colors(20)),
                 foreground.plot = function() map(mapxy, add = TRUE))
  }
  else
    rp.plot4d(location, year, logSO2, col.palette = rev(heat.colors(20)))
})

# Dissolved Oxygen in the River Clyde

with(Clyde, {

  rp.plot4d(cbind(Doy, DO), Station, location.plot = FALSE)
  rp.plot4d(cbind(Station, DO), Doy, location.plot = FALSE)
  rp.plot4d(cbind(Station, Doy), Year, DO)

  # Highlight the data before and after a sewage treatment plant update in 1985
  ind     <- Year >= 80 & Year <= 89 & !(Year == 85)
  year    <- Year[ind] + Doy[ind] / 365
  station <- Station[ind]
  doy     <- Doy[ind]
  do      <- DO[ind]
  group   <- factor(c("after 1985", "before 1985")[1 + 
                  as.numeric(year < 85)])
  rp.plot4d(cbind(doy, do), station, group,
       col.palette = c("red", "green"), location.plot = FALSE)
})


## End(Not run)

Description

This function provides demonstrations of the use of the pos argument in functions to create controls.

Usage

rp.pos(layout="default")

Arguments

Details

The various functions to create controls accept a parameter called pos which can be used to specify the layout of the controls. It has various modes of operation and the mode is determined from the type of information provided in the pos argument. The different modes are outlined below.

default

If pos is not specified, controls are arranged in a column with the most recent added to the bottom. Each control is aligned to the left hand side.

pack

if pos is set to "left", "right", "top" or "bottom", then the control is set to the left, right, top or bottom edge of the panel. If there is already a control in that position, the new control is placed beside that control, closer to the centre. (This uses Tk's "pack" layout manager.)

place

If pos is set to a vector of four integer values, these are interpreted as (x, y, width, height) where all dimensions are in pixels. x and y define the co-ordinates in from the left hand side and down from the top respectively. When using this mode of laying out objects, it usually helps to define the size of the panel in rp.control. (This uses Tk's "place" layout manager.)

grid

This mode provides greater flexibility in layout. The following arguments can be passed to pos in any of the function calls to create controls. Alternatively, pos can be passed a list which has these named components.

column

An integer which specifies the column number. Columns count from 0. This is a mandatory field for grids.

row

An integer which specifies the row number. Rows count from 0. This is a mandatory field for grids.

grid

A string which gives the name of the grid the control has to be placed in. This field is optional. If omitted the default grid belonging to the panel is used.

columnspan

An integer which specifies how many columns the control should span. Columns are counted to the right from the start column specified by column. This field is optional. If omitted one column is assumed.

rowspan

An integer which specifies how many rows the control should span. Rows are counted down from the start row specified by row. This field is optional. If omitted one row is assumed.

width

An integer which specifies the width of the control. For controls with writing (buttons, listboxes etc) this is in characters and for images this is in pixels. This field is optional. If omitted the control is sized horizontally to fill the cell the control is placed within.

height

An integer which specifies the height of the control. For controls with writing (buttons, listboxes etc) this is in characters and for images this is in pixels. This field is optional. If omitted the control is sized vertically to fill the cell the control is placed within.

sticky

An string which specifies how the control expands to fill the cell. This is a string with any combination of 'n', 'e', 'w', 's', representing north/east/west/south expansions. An empty string assignment (”) will centre the control. If the argument is not assigned a value then the control is 'w' (west) aligned by default.

background

Specifies the background colour of the grid. If left blank this defaults to the operating system's standard background colour.

(This uses Tk's "grid" layout manager.)

The "grid" mode of layout should not be mixed with the other modes.

Description

This function creates a panel which allows the sample size, population means and common standard deviation to be set. The corresponding power curve for a two-sample t-test is displayed in the graphics window.

Usage

rp.power(panel = TRUE, panel.plot = TRUE,
         populations.showing = FALSE,
         ngrid = seq(10, 300), mu1 = 0, mu2 = 1,
         sigma = 1, n = 20, xgrid = seq(- 4, 5, length = 100),
         popdens.lim = 0.7, hscale = 1, vscale = hscale)

Arguments

Details

The sample size refer to the total sample size, assuming two groups of equal size. A checkbox allows plots of the population distributions also to be displayed.

Value

The tcltk panel object is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
  rp.power()

## End(Not run)

Description

This function adds a set of radiobuttons to the panel. When a radiobutton is pressed, a variable is set and an action function is called.

Usage

rp.radiogroup(panel, variable, vals, labels=NULL, initval=vals[1], pos=NULL, 
  title=deparse(substitute(variable)), 
  action=I, foreground=NULL, background=NULL, font=NULL, 
  parentname=deparse(substitute(panel)), name=paste("radiogroup", .nc(), sep=""), ...)

Arguments

Details

The function action should take one argument, which should be the panel to which the radiobuttons are attached.

See rp.grid for details of the grid layout system.

Value

If the argument panel is the panelname string, the same string is returned. If the panel object is used, the altered panel is assigned to both the calling level and panel's environment level.

Warning

The action function should return the panel. Without this assignment any widgets added or alterations made to panel parameters within the action function will be lost.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.checkbox, rp.control

Examples

## Not run: 
   data.plotfn <- function(panel) {
     if (panel$plot.type == "histogram") 
       hist(panel$x)
     else 
       if (panel$plot.type == "boxplot")
         boxplot(panel$x)
       else 
         plot(density(panel$x))
     panel
     }
   panel <- rp.control(x = rnorm(50))
   rp.radiogroup(panel, plot.type, 
       c("histogram", "boxplot", "density estimate"), 
       action = data.plotfn, title = "Plot type")     

## End(Not run)

Description

When there are one or two covariates, specified through the x and y arguments, the function rp.regression creates a panel which controls the model which is fitted to the data and displayed on the plot. In the case of two covariates, a three-dimensional display is created. If a formula or a fitted linear model is passed through the x argument, then a graphical display of the regression effects is created, irrespective of the number of covariates. The function rp.regression2 is retained simply for compatibility with earlier releases of the package.

Usage

rp.regression(x, y, ylab = NA, x1lab = NA, x2lab = NA, xlab = NA,
                model = "None", line.showing = TRUE,
                residuals.showing = FALSE, size = 3,
                panel = TRUE, panel.plot = TRUE, hscale = NA,
                vscale = hscale, yrange, ci = TRUE,
                point.estimate = !ci, labels, subset, ngrid = 200, col)
  rp.regression2(y, x1, x2, ylab  = NA, x1lab = NA, x2lab = NA, 
                 panel = TRUE, model = "None", residuals.showing = FALSE,
                 size = 3, col = "red", ...)

Arguments

The following arguments refer to the case of one or two covariates, specified explicitly:

The following arguments refer to the case where the tcltk package is used:

The following argument refers to the case where a regression model is specified through a formula or a call to the lm function:

Details

In the case of one covariate, the control panel allows a line to be drawn on the plot and its intercept and slope altered interactively. The residuals and the least squares fitted line can be displayed. When the fitted line is displayed, the effects of moving individual points can be viewed by clicking and dragging.

In the case of two covariates, the plot is constructed with the aid of the rgl package for three-dimensional display, through the rpanel function rp.plot3d. This display can be rotated and linear models involving one, two or none of the covariates can be displayed. Residuals can also be superimposed. Static plots, for printing or other purposes can be created by setting the panel argument to FALSE and specifying model and residuals.showing as required.

If x is a formula or a fitted linear model, then a static plot of the regression effects is created. Each coefficient is scaled by the length of the range of corresponding covariate values, in order to display the regression effects in a manner which allows these to be compared. Density strips are used to indicate the uncertainty involved. The uncertainty strips have a common maximum intensity, as this makes it easier to assess the 'extremity' of values of interest for distributions which are very diffuse.

Value

When x is a formula or a fitted linear model, the plot is returned as a ggplot object. This allows details of the plot to be modified through further ggplot functions. In other cases, the panel object is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

See Also

rp.plot3d

Examples

## Not run: 
  with(CofE, {
    rp.regression(Employ, Giving)
    rp.regression(cbind(Employ, Attend), Giving)
    rp.regression(Giving ~ Employ + Elect + Attend)
    rp.regression(cbind(Employ, Attend), Giving, model = "Employ and Attend", panel = FALSE)
  })

## End(Not run)

Description

This function creates a panel which controls the display of data which have a repeated measurement structure across time. Groups, means and standard errors can be displayed. Individual profiles can also be inspected.

Usage

rp.rmplot(y, id = NA, timept = NA, fac = NA, type = "all", 
                          xlab = NA, ylab = NA, xlabels = NA, add = FALSE,
                          lwd = 1, col = NA, lty = NA, panel = TRUE, 
                          panel.plot = TRUE, hscale = NA, vscale = hscale, ...)

Arguments

Details

This function is designed principally for repeated measurements over time, with common time points for each profile. A set of radiobuttons allows all the individual profiles to be plotted, or summaries in the form of means and two standard errors. A checkbox allows the data to be split into groups identified by the variable fac. When there are only two groups, a band can be displayed to indicate time points at which the distance between the observed means is more than two standard errors of the differences between the means.

Value

Nothing is returned.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   LH    <- luthor[,2:16]
   gp    <- factor(luthor[,1])
   times <- c(1:5,(5+(1:10)/2))
   rp.rmplot(log(LH), fac = gp, timept = times)

## End(Not run)

Description

Graphical exploration of variation in samples and sample means. The primary use of the function is in interactive mode, using a variety of controls for the display. Normal or binomial distributions can be used.

Usage

rp.sample(n, mu, sigma, distribution  = 'normal', shape = 0,
          panel = TRUE, nbins = 20, nbins.mean = 20,
          display, display.sample, display.mean, nsim = 50,
          show.out.of.range = TRUE, ggplot = TRUE,
          hscale = NA, vscale = hscale, pause = 0.01)

Arguments

Details

When display is set to density or violin, density estimates are constructed using a bandwidth which is optimal for a normal distribution. For small samples this provides a stable and conservative estimate which is not unduly influenced by features which may well simply be due sampling variation. As the sample size increases, the estimate will still converge to the true density function.

When the size of the sample is less than 10, a histogram or density estimate is not a very effective display. This also causes issues of scaling the vertical axis. So in this case individual points are displayed instead.

The visual effect of the animation is assisted by holding the axes constant. This means that there may occasionally be observations outside the displayed horizontal range, or a histogram height which exceeds the displayed vertical range. This is denoted by a + symbol at the top of the relevant histogram bars. This issue can often be tackled by reducing the number of histogram bins.

When display is set to 'density' or 'violin', individual points are plotted, with a random vertical position. This is suppressed when the number of points exceeds 5000.

The display.sample and display.mean arguments control the details of what is displayed initially and, more usefully, when the function operates in non-interactive mode. Each argument is a logical vector with named values. display.sample has the default setting c(data = TRUE, population = FALSE, mean = FALSE, 'st.dev. scale' = FALSE) while the default for display.mean is c('sample mean' = FALSE, 'accumulate' = FALSE, 'se scale' = FALSE, 't-statistic' = FALSE, 'zoom' = FALSE, 'distribution' = FALSE). Any elements of these arguments which are not explicitly identified are set to the default values. In the case of binomial data the 'st.dev. scale' setting is disabled as it is not a helpful addition to the plot.

The principal use of the function is in interactive mode, when panel is set to TRUE. If panel is set to FALSE then interactive mode is switched off. In this case, if the ggplot2 package is available and the ggplot argument is set to TRUE, the function returns plots of a sample of data and of accumulated means as components plotdata and plotmean of the returned object. The number of accumulated means is set by the nsim argument.

If the ggplot2 package is not available, standard graphics are used with simpler display options, along the lines of the function provided in version 1.1-5 of the package.

Value

When the function operates in interactive mode, with panel set to TRUE, nothing is returned. When panel is set to FALSE, plots of a sample of data and of accumulated means are provided as components sample and mean of the returned object.

References

rpanel: Simple interactive controls for R functions using the tcltk package. Journal of Statistical Software, 17, issue 9.

Examples

## Not run: 
   rp.sample()

## End(Not run)

Description

This returns the current screen resolution as a list with two components; width and height.

Usage

rp.screenresolution()

Arguments

None