Graphical methodsLinear modelsCategorical dataStructural equation modelsUtility macrosMy Books
Univariate displays
Bivariate displays
Multivariate displays
Cluster analysis
Maps
boxplot
density
dotplot
nqplot
splot
symbox
symplot
contour
ellipses
lowess
miplot
resline
sparkline
sunplot
surface
biplot
canplot
coplot
corrgram
cqplot
faces
genscat
hecan
hemat
hemreg
heplot
heplots
mpower
outlier
robcov
scatmat
scatter
stars
gtree
ccmap
map2gen
ANOVA
Regression
Multivariate
Transformations
Power
alleff
effplot
hovplot
meanplot
orpoly
poly
twoway
cpplot
effplot
partial
inflplot
robust
rsqdelta
hecan
hemat
hemreg
heplot
heplots
boxcox
boxglm
boxtid
sprdplot
fpower
mpower
power
rpower
Discrete distributions
Two-way tables
Mosaic displays
Generalized linear models
distplot
goodfit
ordplot
poisplot
rootgram
agree
agreeplot
corresp
ffold
fourfold
power2x2
powerrxc
sieve
sieveplot
missrc
genscat
mosaic
mosaics
mosmat
addvar
catplot
effplot
halfnorm
inflglim
inflogis
logodds
powerlog
robust
caliscmp
calisgfi
csmpower
eqs2ram
ram2dot
str2ram
Data utility
Graphics utility
Macro utility
Color
combine
combos
dummy
expgrid
interact
jitter
lags
map2gen
multisummary
mvnormal
sas2vsta
scale
slice
sort
stat2dat
table
axis
bars
boxanno
boxaxis
equate
gask
gbank
gdispla
genpat
gensym
gkill
gskip
inset
label
labels
lines
panels
points
polygons
pscale
regline
rug
expglm
defined
lastword
vexpand
words
bpycolors
brewerpal
colochrt
colorramp
SAS System for Statistical Graphics
Visualizing Categorical Data
Visual Statistics
pscale Construct annotations for a probability scale pscale

Visualizing Categorical Data: pscale

$Version: 1.0 (4 Dec 1997)
Michael Friendly
York University

The pscale macro ( [download] get pscale.sas)

Construct annotations for a probability scale

The PSCALE macro constructs an annotate data set to draw an unequally- spaced scale of probability values on the vertical axis of a plot (at either the left or right). The probabilities are assumed to correspond to equally-spaced values on a scale corresponding to Normal quantiles (using the probit transformation) or Logistic quantiles (using the logit transformation).

Usage

The PSCALE macro is called with keyword parameters. The arguments may be listed within parentheses in any order, separated by commas. For example: 

  %pscale(out=pscale);
  proc gplot;
     plot logit * X / anno=pscale;

Parameters

ANNO=
Name of annotate data set [Default: ANNO=PSCALE]
OUT=
Synonym for ANNO=
SCALE=
Linear scale: logit or probit [Default: SCALE=LOGIT]
LO=
Low scale value [Default: LO=-3]
HI=
High scale value [Default: HI=3]
PROB=
List of probability values to be displayed on the axis, in the form of a list acceptable in a DO statement. [Default: PROB=%str(.05, .1 to .9 by .1, .95)]
AT=
X-axis percent for the axis. AT=100 plots the axis at the right; AT=0 plots the axis at the left. [Default: AT=100]
TICKLEN=
Length of tick marks [Default: TICKLEN=1.3]
SIZE=
Size of value labels
FONT=
Font for value labels

Example

This example analyses improvement in arthritis in a logistic regression as a function of treatment, sex and age, with a binary outcome, 'better'. It produces plots of predicted log odds, with a scale of probabilities at the right (AT=100). 
%include vcd(pscale);        *-- or include in an autocall library;
%include data(arthrit);

proc logistic data=arthrit;
   format better outcome.;
   model  better = _sex_  _treat_  age;
   output out=results p=predict l=lower u=upper
                      xbeta=logit stdxbeta=selogit / alpha=.33;
proc sort data=results;
   by sex treat age;

%pscale(anno=pscale);
data pscale;
   set pscale;
   sex = 'Female'; output;
   sex = 'Male  '; output;
proc sort;
   by sex;
	
%bars(data=results, var=logit, cvar=treat, class=age, by=sex, barlen=selogit);

proc gplot data=results;
   plot logit   * age = treat / vaxis=axis1 haxis=axis2
                                nolegend anno=bars frame;
   by sex;
   axis1 label=(h=1.3 a=90 f=duplex 'Log Odds Improved (+/- 1 SE)')
         value=(h=1.2) order=(-3 to 3);
   axis2 label=(h=1.3 f=duplex)
         value=(h=1.2) order=(20 to 80 by 10) offset=(2,5);
   symbol1 v=+ h=1.4 i=join l=3 c=black;
   symbol2 v=$ h=1.4 i=join l=1 c=red;

See also

bars Create an annotate data set to draw error bars
catplot Plot observed and predicted logits for logit models
label Create an Annotate dataset to label observations
logodds Plot empirical log-odds for logistic regression
points Create an Annotate dataset to draw points in a plot
[Previous] [Next] [Up] [Contents] [Search]
Previous: powerlog Next: robust Up: Appendix A Top: index.html