ANOFA (version 0.1.3)

count: anofaPlot.

Description

The function anofaPlot() performs a plot of frequencies for designs with up to 4 factors according to the ANOFA framework. See lc23b;textualANOFA for more. The plot is realized using the suberb library; see cgh21;textualANOFA. The functions count(), init.count() and CI.count() are internal functions.

Usage

anofaPlot(w, formula, confidenceLevel = .95, showPlotOnly = TRUE, plotStyle = "line", 
    errorbarParams  = list( width =0.5, linewidth=0.75 ), ...)
count(n)
init.count(df)
CI.count(n, gamma =0.95)

Value

a ggplot2 object of the given frequencies.

Arguments

n

the count for which a confidence interval is required

w

An ANOFA object obtained with anofa();

formula

(optional) Use formula to plot just specific terms of the omnibus test. For example, if your analysis stored in w has factors A, B and C, then anofaPlot(w, ~ A * B) will only plot the factors A and B.

confidenceLevel

Provide the confidence level for the confidence intervals. (default is 0.95, i.e., 95%).

plotStyle

(optional; default "line") How to plot the frequencies. See superb for other layouts (e.g., "line")

showPlotOnly

(optional, default True) shows only the plot or else shows the numbers needed to make the plot yourself.

errorbarParams

(optional; default list( width =0.5, linewidth=0.75 ) ) A list of attributes used to plot the error bars. See superb for more.

...

Other directives sent to superb(), typically 'plotStyle', 'errorbarParams', etc.

df

a data frame for initialization of the CI function

gamma

the confidence level

Details

The plot shows the frequencies (the count of cases) on the vertical axis as a function of the factors (the first on the horizontal axis, the second if any in a legend; and if a third or even a fourth factors are present, as distinct rows and columns). It also shows 95% confidence intervals of the frequency, adjusted for between-cells comparisons. The confidence intervals are based on the Clopper and Pearson method cp34ANOFA using the Leemis and Trivedi analytic formula lt96ANOFA. This "stand-alone" confidence interval is then adjusted for between-cell comparisons using the superb framework cgh21ANOFA.

See the vignette DataFormatsForFrequencies for more on data format and how to write their formula. See the vignette ConfidenceInterval for details on the adjustment and its purpose.

References

Examples

# 
# The Landis et al. (2013) example has two factors, program of treatment and provider of services.
LandisBarrettGalvin2013

# This examine the omnibus analysis, that is, a 5 (provider) x 3 (program):
w <- anofa(obsfreq ~ provider * program, LandisBarrettGalvin2013) 

# Once processed into w, we can ask for a standard plot
anofaPlot(w)

# We place the factor `program` on the x-axis:
anofaPlot(w,  factorOrder = c("program","provider"))

# The above example can also be obtained with a formula:
anofaPlot(w, ~ program * provider)

# Change the style for a plot with bars instead of lines
anofaPlot(w, plotStyle = "bar")

# Changing the error bar style
anofaPlot(w, plotStyle = "bar", errorbarParams = list( width =0.1, linewidth=0.1 ) )

# An example with 4 factors:
if (FALSE) {
dta <- data.frame(Detergent)
dta

w <- anofa( Freq ~ Temperature * M_User * Preference * Water_softness, dta )
anofaPlot(w)
anofaPlot(w, factorOrder = c("M_User","Preference","Water_softness","Temperature")) 


# Illustrating the main effect of Temperature (not interacting with other factors)
# and the interaction Preference * Previously used M brand
# (Left and right panels of Figure 4 of the main article)
anofaPlot(w, ~ Temperature)
anofaPlot(w, ~ Preference * M_User)

# All these plots are ggplot2 so they can be followed with additional directives, e.g.
library(ggplot2)
anofaPlot(w, ~ Temperature) + ylim(200,800) + theme_classic()
anofaPlot(w, ~ Preference * M_User) + ylim(100,400) + theme_classic()
}
# etc. Any ggplot2 directive can be added to customize the plot to your liking.
# See the vignette `Example2`.