plot.pred.int: Plot objects of class 'pred.int' for predicted interval plots
In PIPS: Predicted Interval Plots

Description Usage Arguments Value Author(s) References See Also Examples

Plot objects of class pred.int for predicted interval plots

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## S3 method for class 'pred.int'
plot(x, conf.int = TRUE, vline = NA, which = NULL, axes = TRUE, 
    pi.col.fun=NULL, ci.col=2, main, xlab, ylab, xlim, ...)

`x`	Object of class `pred.int` containing the PIPs data for plotting
`conf.int`	Print confidence interval for observed data? TRUE (default)/FALSE
`vline`	Vector of x-values for vertical lines to print on the graph. These may represent superiority/inferiority bounds or other x-values of interest
`which`	Only create graphs for some of the comparisons
`axes`	TRUE/FALSE. Print axes on graph? Default is TRUE. Probably you should only suppress axes now if you will add them later (for example if you don't like the default axes)
`pi.col.fun`	An optional one parameter function that takes a number between 0 and 100 and returns a color. This can be used to color the predicted intervals different colors. The input argument is the position of the interval on the vertical axis of the graph. Default coloring is three shades of gray: Percentiles (0-10) and (90-100) are light gray, (10-25) and (75-90) are darker, and (25-75) is darkest
`ci.col`	Color for effect estimate and confidence interval for the observed data. Default is 2 (second color in palette).
`main`	Main title of graph. If blank, a default will be used. Can be either a single title or a vector of titles. If a vector, the first will be used for the first graph, the second for the second graph, etc... If any title contains the string "#BY#", this will be replaced with the name of the comparison (i.e. "B vs A")
`xlab`	Label on xaxis. If blank, a default will be used.
`ylab`	Label on xaxis. If blank, a default will be used.
`xlim`	Limits of xaxis (as vector of length 2). Default is large enough to contain the predicted intervals and confidence interval. Limits narrower than the defaults will be ignored.
`...`	Other options will be passed through to the plot.default function.

No return value. Called for its side effect.

Daniel G. Muenz, Ray Griner rgriner@sdac.harvard.edu, Huichao Chen, Lijuan Deng, Sachiko Miyahara, and Scott R. Evans evans@sdac.harvard.edu, with contributions from Lingling Li, Hajime Uno, and Laura M. Smeaton.

See package documentation for affiliations and contributions.

Evans SR, Li L, Wei LJ, "Data Monitoring in Clinical Trials Using Prediction", Drug Information Journal, 41:733-742, 2007.

Li L, Evans SR, Uno H, Wei LJ. "Predicted Interval Plots: A Graphical Tool for Data Monitoring in Clinical Trials", Statistics in Biopharmaceutical Research, 1:4:348-355, 2009.

PIPS

pred.int

print.pred.int

# Make some fake data
myY<-c(rep(1,times=20),rep(0,times=80),rep(1,times=25),rep(0,times=25))
myGroup<-c(rep('A',100),rep('B',50))

# Run the programs
pips <- pred.int(y=myY, group=myGroup, N=c(200,100), 
                 data.type="binary", iters=100)
print(pips)
plot(pips)

# Run demo(package="PIPS") for more examples.

Sample sizes:
  Observed Simulated Total
A      100       100   200
B       50        50   100

Point estimates and 95% confidence intervals from observed data:
       Point Lower Bound Upper Bound
B vs A   0.3      0.1408      0.4592

Point estimates and 95% predicted intervals from observed+simulated data:
B vs A:
    Point Lower Bound Upper Bound
1   0.200     0.08751      0.3125
2   0.200     0.08751      0.3125
3   0.205     0.09354      0.3165
4   0.230     0.11685      0.3432
...                              
97  0.365     0.25403      0.4760
98  0.370     0.25890      0.4811
99  0.375     0.26460      0.4854
100 0.375     0.26382      0.4862