Statistical Graphics

2月 122018
 

Did you know that SAS can combine or "merge" a symbol and a line pattern into a single legend item, as shown below? This kind of legend is useful when you are overlaying a group of curves onto a scatter plot. It enables the reader to quickly associate values of a categorical variable with colors, line patterns, and marker symbols in a plot.

Merged legend that combines symbols and line patterns in SAS

Legend items for marker/curve overlays

When you use PROC SGPLOT and the GROUP= option to create a graph, the SGPLOT procedure automatically displays the group attributes (such a symbol, color, and line pattern) in a legend. If you overlay multiple plot types (such as a series plot on a scatter plot) the default behavior is to create a legend for the first plot statement. You can use the KEYLEGEND statement to control which plots contribute to the legend. In the following example, the KEYLEGEND statement creates a legend that shows the attributes for the scatter plot (the marker shapes) and also the series plot (line patterns):

data ScatCurve;    /* example data: scatter plot and curves */
call streaminit(1);
do Group = 1 to 2;
   do x = 0 to 5 by 0.2;
      Pred = Group + (1/Group)*x - (0.2*x)**2;
      y = Pred + rand("Normal",0,0.5);
      output;
   end;
end;
run;
 
ods graphics / antialias=on subpixel=on;
title "Legend Not Merged";
proc sgplot data=ScatCurve;
   scatter x=x y=y / group=Group name="obs" markerattrs=(symbol=CircleFilled);
   series x=x y=Pred / group=Group name="curves"; 
   keylegend "obs" "curves" / title="Group"; /* separate items for markers and lines */
run;
Legend that shows symbols and line patterns separately

The legend contains all the relevant information about symbols, colors, and line patterns, but it is longer than it needs to be. When you display curves and markers for the same groups, you can obtain a more compact representation by merging the symbols and line patterns into a single legend, as shown in the next sections.

The MERGEDLEGEND statement in GTL

If you are comfortable with the Graph Template Language (GTL) in SAS, then you can use the MERGEDLEGEND statement to create a merged legend. The following statements create a graph template that overlays a scatter plot and a series plot and creates a merged legend in which each item contains both a symbol and a line pattern:

proc template;
  define statgraph ScatCurveLegend;
  dynamic _X _Y _Curve _Group _Title;       /* dynamic variables */
  begingraph;
  entrytitle _Title;  
   layout overlay; 
     scatterplot x=_X y=_Y / group=_Group name="obs" markerattrs=(symbol=CircleFilled);
     seriesplot x=_X y=_Curve / group=_Group name="curves";
     /* specify exactly two names for the MERGEDLEGEND statement */
     mergedlegend "obs" "curves" / border=true title=_Group;
   endlayout;
  endgraph;
  end;
run;
 
proc sgrender data=ScatCurve template=ScatCurveLegend;
   dynamic _Title="Overlay Curves, Merged Legend"
           _X="x" _Y="y" _Curve="Pred" _Group="Group";
run;
Merged legend that combines symbols and line patterns in SAS

The graph is the same as before, but the legend is different. The MERGEDLEGEND statement is used in many graphs that are created by SAS regression procedures. As you can see, the legend shows the symbol and line pattern for each group.

The LEGENDITEM statement in PROC SGPLOT

Unfortunately, the MERGEDLEGEND statement is not supported by PROC SGPLOT. However, SAS 9.4M5 supports the LEGENDITEM statement in PROC SGPLOT. The LEGENDITEM statement enables you to construct a custom legend and gives you complete control over every item in a legend. The following example constructs a legend that uses the symbols, line patterns, and group values that are present in the graph. Notice that you have to specify these attributes manually, as shown in the following example:

title "Merged Legend by Using LEGENDITEM in SGPLOT";
proc sgplot data=ScatCurve;
   scatter x=x y=y / group=Group markeratters=(symbol=CircleFilled);
   series x=x y=Pred / group=Group; 
   legenditem type=markerline name="I1" /
              label="1" lineattrs=GraphData1 markerattrs=GraphData1(symbol=CircleFilled);
   legenditem type=markerline name="I2" /
              label="2" lineattrs=GraphData2 markerattrs=GraphData2(symbol=CircleFilled);
   keylegend "I1" "I2" / title="Group"; 
run;

The graph and legend are identical to the previous graph and are not shown.

The advantage of the LEGENDITEM statement is that you can layout the legend however you choose; the legend is not tied to the attributes in any previous graph component. However, this is also a disadvantage. If you change the marker attributes in the SCATTER statement, the legend will not reflect that change until you manually modify each LEGENDITEM statement. Although there is no denying the power of the LEGENDITEM statement, the MERGEDLEGEND statement in the GTL always faithfully and automatically reflects the attributes in the SCATTERPLOT and SERIESPLOT statements.

In summary, the SG procedures in SAS automatically create a legend. When you overlay multiple plots, you can use the KEYLEGEND statement to control which plots contribute to the legend. However, it is also possible to merge the symbols and line patterns into a single compact legend. In the GTL, you can use the MERGEDLEGEND statement. In SAS 9.4M5, PROC SGPLOT supports the LEGENDITEM statement to customize the items that appear in a legend.

The post Merged legends: Overlay a symbol and line in a legend item appeared first on The DO Loop.

1月 312018
 

This article shows how to construct a "stacked band plot" in SAS, as shown to the right. (Click to enlarge.) You are probably familiar with a stacked bar chart in which the cumulative amount of some quantity is displayed by stacking the contributions of several groups. A canonical example is a bar chart of revenue for a company in which the revenues for several regions (North America, Europe, Asia, and so forth) are stacked to show the total revenue. If you plot the revenue for several years, you get a stacked bar chart that shows how the total revenue changes over time, as well as the changes in the regional revenues. A stacked band plot is similar, but it is used when the horizontal variable is continuous rather than discrete.

Create a stacked bar chart in SAS

A few years ago Sanjay and I each wrote about ways to create a stacked bar chart in SAS by using the SGPLOT procedure in SAS. Recently I created a stacked bar chart that spanned about 20 years of data. When I looked at it, I realized that as the number of categories (years) grows, the bars become a nuisance. The graph becomes cluttered with vertical bars that are not necessary to show the underlying trends in the data. At that point, it is better to switch to a continuous scale and use a stacked band plot.

This section shows how to create a stacked bar chart by using PROC SGPLOT. The next section shows how to create a stacked band chart.

For data, I decided to use the amount of carbon dioxide (CO2, in million metric tons) that is produced by several sources from 1973–2016. These data were plotted as a stacked bar chart by my friend Robert Allison. The data are originally in "wide form," with five variables that contain the data. For a stacked bar chart, you should convert the data to "long form," as follows. You can download the complete SAS program that creates the graphs in this article.

/* 1. Original data is in "wide form": CO2 emission for five sources for each year. */
data CO2;
informat Transportation Commercial Residential Industrial Electric COMMA5.;
input Year Transportation Commercial Residential Industrial Electric;
datalines;
2016	1,879	235	308	928	1,821
2015	1,845	240	321	942	1,913
2014	1,821	233	347	955	2,050
2013	1,803	223	333	952	2,050
   ... more lines ...   
;
 
/* 2. Convert from wide to long format */
proc sort data=CO2 out=Wide;
   by Year;                     /* sort X categories */
run;
 
proc transpose data=Wide 
   out=Long(rename=(Col1=Value))/* name of new column that contains values */
   name=Source;                 /* name of new column that contains categories */
   by Year;                     /* original X var */
   var Transportation Commercial Residential Industrial Electric; /* original Y vars */
run;

For data in the long form, you can use the VBAR statement and the GROUPDISPLAY=STACK option in PROC SGPLOT to create a stacked bar chart, as follows:

/* 3. create a stacked bar chart */
ods graphics / subpixel=on;
title "U.S. Carbon Dioxide Emissions from Energy Consumption";
title2 "Stacked Bar Chart";
proc sgplot data=Long;
   vbar Year / response=Value group=Source groupdisplay=stack grouporder=data;
   xaxis type=linear thresholdmin=0;   /* use TYPE=LINEAR so not every bar is labeled */ 
   yaxis grid values=(0 to 6000 by 1000);
   label Source = "Source of CO2" Value = "CO2 (mmt)";
run;

The graph is not terrible, but it can be improved. The vertical lines are a distraction. With 43 years of data, the horizontal axis begins to lose its discrete nature. All those bars and the gaps between bars are cluttering the display. The next section shows how to create two new variables and use those variables to create a stacked band plot.

Create a stacked band plot

To create a stacked bar chart with PROC SGPLOT, you must create two new variables. The cumValue variable will contain the cumulative amount of CO2 per year as you accumulate the amount for each group (Transportation, Commercial, Residential, and so forth). This will serve as the upper boundary of each band. The Previous variable will contain the lower boundary of each band. You can use the LAG function in Base SAS to easily obtain the previous cumulative value: Previous = lag(cumValue). When you create these variables, you need to initialize the value for the first group (Transportation). Use the BY YEAR statement and the FIRST.YEAR indicator variable to detect the beginning of each new value of the Year variable, as follows:

/* 4. Accumulate the contributions of each group in the 'cumValue' variable.
      Add the lag(cumValue) variable and call it 'Previous'. 
      Create a band plot for each group with LOWER=Previous and UPPER=cumValue.
*/
data Energy;   
   set Long;
   by Year;
   if first.Year then cumValue=0;   /* for each year, initialize cumulative amount to 0 */
   cumValue + Value;
   Previous = lag(cumValue);
   if first.Year then Previous=0;   /* for each year, initialize baseline value */
   label Source = "Source of CO2" cumValue = "CO2 (mmt)";
run;
 
title2 "Stacked Band Plot";
proc sgplot data=Energy;
   band x=Year lower=Previous upper=cumValue / group=Source;
   xaxis display=(nolabel) thresholdmin=0; 
   yaxis grid;
   keylegend / position=right sortorder=reverseauto; /* SAS 9.4M5: reverse legend order */
run;

The graph appears at the top of this article. The vertical lines are gone. The height of a band shows the amount of CO2 that is produced by the corresponding source. The top of the highest band (Electric) shows the cumulative amount of CO2 that is produced by all sources. This kind of display makes it easy to see the cumulative total. If you want to see the trends for each individual source, a time series plot would be a better choice.

The graph uses a new feature of SAS 9.4M5. The KEYLEGEND statement now supports the SORTORDER=REVERSEAUTO option, which reverses the order of the legend elements. This option makes the legend match the bottom-to-top progression of the stacked bar chart. I also used the POSTITION= option to move the legend to the right side so that the legend is next to the color bands.

Label the stacked bands directly

If you have very thin bands, a legend is probably the best way to associate colors with groups. However, for these data the bands are wide enough that you might want to display the name of each group on the bands. I tried several label positions (left, center, and right) and decided to display the group name near the left side of the graph. Since many people scan a graph from left to right, this causes the reader to see the labels early in the scanning process.

The following DATA step computes the positions for the labels. I use 1979 as the horizontal position and use the midpoint of the band as the vertical position. After you compute the positions for the labels, you can concatenate the data and the labels and use the TEXT statement to overlay the labels on the band plot, as follows:

data Labels;
   set Energy;
   where Year = 1979;  /* position labels at 1979 */
   Label = Source;
   XPos = Year;
   YPos = (cumValue + Previous) / 2;
   keep Label XPos YPos;
run;
 
data EnergyLabels;
   set Energy Labels;
run;
 
title2 "Stacked Band Plot";
proc sgplot data=EnergyLabels noautolegend;
band x=Year lower=Previous upper=cumValue / group=Source;
refline 1000 to 6000 by 1000 / axis=y lineattrs=GraphGridLines transparency=0.75;
text x=XPos y=Ypos text=Label;
xaxis display=(nolabel) values=(1973, 1980 to 2010 by 10, 2016)
      offsetmin=0 offsetmax=0;
yaxis grid values=(0 to 6000 by 1000) label="CO2 (mmt)"; 
run;

Summary

In summary, you can use PROC SGPLOT to create a stacked band plot in SAS. A stacked band plot is similar to a stacked bar chart but presumes that the positions of the bars represent a continuous variable on a linear scale. For this example, the bars represented years. You can let PROC SGPLOT automatically place the legend along the bottom, but if you have SAS 9.4M5 you might want to move the legend to the right and use the SORTORDER=REVERSEAUTO option to reverse the legend order. Alternatively, you can display labels on the bands directly if there is sufficient room.

The post Create a stacked band plot in SAS appeared first on The DO Loop.

1月 172018
 

Money magazine (Jan/Feb 2018) contains an article about how much it costs to give birth in the US. The costs, which are based on insurance data, include prenatal care and hospital delivery but exclude infant care. The data are compiled for each state (including Washington, DC) and by type of delivery (vaginal versus cesarean section). The data includes the average and median costs for each state.

The online version of the article contains a map and a table of average costs, colored by the quintiles of the costs. Because I think that median costs are more relevant, I decided to create a visualization of the distribution of the median costs. Additionally, I want to visualize the incremental cost of a C-section over a vaginal delivery. According to the CDC, about 32% of deliveries are C-sections in the US. Cesarean delivery is major surgery and often requires an additional two days of hospital recovery in addition to operating-room charges.

With a little sleuthing, I was able to locate the data and download it into a SAS data set. You can download the data and SAS program that creates the graphs in this article.

Cost Distribution and incremental cost of a cesarean delivery

Median cost of vaginal and cesarean delivery by US state (2016-2017)

The adjacent bar chart (click to enlarge) shows the distribution of the median costs of childbirth in the US. Since the median cost of a cesarian delivery is always more than the median cost of a vaginal delivery, I overlaid the two graphs. The states are ordered by the median cost of a vaginal delivery. The data shows that the states of Alabama, Rhode Island, Nebraska, Louisiana, and Utah are the least expensive states for vaginal delivery. The median cost is about $5000 in those states. The most expensive states include Alaska, New Jersey, New York, Wisconsin, and Massachusetts. The median cost is more than $8000 for those states, with Alaska topping out at $14,500.

If you are more interested in the cost of a cesarean delivery, I created a similar graph sorted by the cost of a C-section. No matter how you sort it, the graph indicates that a C-section costs about $2500 to $3500 more than a vaginal delivery. In Washington, DC, the incremental cost is about $1100, which is relatively low. In Vermont and Alaska, the incremental cost is more than $4000, which is relatively high.

The Money magazine map of the data does not reveal any unexpected regional trends. Costs are high in Alaska and New England. Costs are low in some southern states.

Comparing delivery types by using a scatter plot

For a more sophisticated audience, you can use a scatter plot to plot the costs for vaginal and cesarean delivery in each state. A plot of the median costs is shown below. A regression line to these data has a slope of 1.2, which indicates that, on average, the median cost of a C-section is about 20% more than for a vaginal delivery. This visualization also enables you to see that Alaska is an extreme outlier for both types of delivery.

Median cost of vaginal and cesarean delivery by US state (2016-2017)

The Money article about these data points out two facts that cannot be seen in the data. First, it says that women "who have no insurance... are usually charged a higher amount than the negotiated rate." Second, US women "pay more to have a baby than residents of any other country. The highest prices in the U.S. were more than double those of the second-most expensive country, Switzerland" (emphasis added)." For a comparison of different countries, see Parents magazine (Jan 2017).

What interesting facts do you notice about these data? Leave a comment.

The post How much does it cost to give birth in the US? appeared first on The DO Loop.

1月 102018
 

Last week I wrote about the 10 most popular articles from The DO Loop in 2017. My most popular articles tend to be about elementary statistics or SAS programming tips. Less popular are the articles about advanced statistical and programming techniques. However, these technical articles fill an important niche. Not everyone needs to know how to interpret a diffogram that visually compares the differences between means between groups, but those who do often send me a note of thanks, which motivates me to research and write similar articles.

Statistical programmers might want to review the following technical articles from 2017. This ain't summertime, and the reading ain't easy, but I think these articles are worth the effort. I've broken them into three categories. Enjoy!

Statistical Concepts

Visualization of regression that uses a weight variable in SAS

Statistical Data Analysis and Visualization

Visualize multivariate regression model by slicing the continuous variables. Graph created by using the EFFECTPLOT SLICEFIT statement in SAS.

Advanced SAS Programming Techniques

If you are searching for a way to enhance your SAS knowledge in this New Year, I think these 10 articles from The DO Loop are worth a second look. Was there an article from 2017 that you found particularly useful? Leave a comment.

The post 10 posts from 2017 that deserve a second look appeared first on The DO Loop.

1月 082018
 
Label multiple regression lines in a graph in SAS by using PROC SGPLOT

A SAS programmer asked how to label multiple regression lines that are overlaid on a single scatter plot. Specifically, he asked to label the curves that are produced by using the REG statement with the GROUP= option in PROC SGPLOT. He wanted the labels to be the slope and intercept of a linear regression line, as shown to the right. (Click to enlarge.)

Initially I thought that you could use the CURVELABEL option on the REG statement to generate labels, as follows:

proc sgplot data=sashelp.iris noautolegend;
   reg x=SepalLength y=PetalLength / group=Species CURVELABEL; /* does NOT work */
run;

However, the SAS log displays the following warning:

WARNING: CURVELABEL not supported for fit plots when a group variable is
         used.  The option will be ignored.

Fortunately, I thought of two other ways to create a graph that has a regression line for each group level, each with its own label. For linear regression, you can use the LINEPARM statement, as shown in the article "Add a diagonal line to a scatter plot." For general (possibly nonlinear) regression curves, you can find the location of the end of the curve and use the TEXT statement in PROC SGPLOT to add a label at that location.

Label the regression line for each group: The LINEPARM statement

Let's use Fisher's Iris data set for our example data. The Iris data contains 50 observations for each of three species of flowers: iris Setosa, iris Versicolor, and iris Virginica. The programmer wants to label the regression line for each species by using the slope and intercept of the line. The first step is to create a SAS data set that contains the intercept and slope for each curve. You can use the OUTEST= option in PROC REG to write the parameter estimates (intercept and slope) to a SAS data set. You can then use the CATX function in the DATA step to construct the labels, as follows:

proc sort data=sashelp.iris out=iris;
   by Species;
run;
 
/* compute parameter estimates */
proc reg data=iris outest=PE noprint;
   by Species;
   model PetalLength = SepalLength;
run;
 
/* construct labels from the parameter estimates */
data Labels;
   length Label $30;
   set PE(rename=(SepalLength=Slope));                   /* independent variable */
   Label = catx(" ", put(Intercept, BestD5.), '+',       /* separate by blank */
                     put(Slope, BestD5.), '* SepalLength');
   keep Label Species Intercept Slope;
run;
 
proc print noobs; run;

The LABELS data set contains a label for the regression line in each group. You can use other labels if you prefer. The following DATA step combines the labels with the original data. The SCATTER statement in PROC SGPLOT displays the data. The LINEPARM statement draws the lines and adds labels to the end of each line.

data Plot;
   set iris Labels;
run;
 
title "Regression Lines Labeled with Slope and Intercept";
proc sgplot data=Plot;
  scatter x=SepalLength y=PetalLength / group=Species;
  lineparm x=0 y=Intercept slope=Slope / group=Label curvelabel 
               curvelabelloc=outside clip;
run;
Label multiple regression lines in a graph in SAS by using PROC SGPLOT

Success! The regression line for each group is labeled by the formula for the line. For more information about displaying the formula for a regression line, see the SAS/STAT example "Adding Equations and Special Characters to Fit Plots."

Label the regression line for each group: The TEXT statement

The preceding method uses the LINEPARM statement, so it only works for lines. However, the user actually wanted to use the REG statement. With a little work, you can label curves that are produced by the REG statement or other curve-fitting statements. The idea is to obtain the data coordinates for the end of the curve, which will become the location of the label.

You might be thinking, if the curve is produced by a regression statement in PROC SGPLOT, how can we get the data coordinates out of the plot and into a data set? The answer is simple: You can use the ODS OUTPUT statement to write a data set that contains the data in any ODS graph. You can apply this trick to any ODS graph, including graphs created by SGPLOT. The following call to PROC SGPLOT uses an ODS OUTPUT statement to create a SAS data set that contains the data in the regression plot:

/* use ODS OUTPUT to find data coordinates of end of lines */
proc sgplot data=iris;
   ods output sgplot=RegPlot;    /* name of ODS table is 'sgplot' */
   reg x=SepalLength y=PetalLength / group=Species;
run;
 
proc contents short varnum; run; /* find names used by graph */

The variable names that are automatically manufactured by SAS procedures can be long and unwieldy, as shown by the call to PROC CONTENTS. I usually rename the long names to simpler names such as X, Y, GROUP, and so on. You should look at the structure of the REGPLOT data set so that the next DATA step makes sense. The DATA step saves only the last coordinates along the curve for each group (species).

data Coords;
set RegPlot(
    rename=(REGRESSION_SEPALLENGTH_PETAL___X = x
            REGRESSION_SEPALLENGTH_PETAL___Y = y
            REGRESSION_SEPALLENGTH_PETAL__GP = Group)
    where=(x ^=.));
by Group;
if last.Group;
keep x y Group;
run;
 
proc print noobs; run;

The COORDS data contains the location (in data coordinates) of the end of each regression line. You can overlay labels at these coordinates to label the curves. From the preceding section, the labels are in the LABELS data set, so you can merge the two data sets, as follows:

/* combine the positions and labels with original data */
data A;
merge Labels Coords(rename=(Group=Species));
by Species;
run;
 
data Plot;
set iris A;
/* optional: pad label with blanks on the left (if length is long enough) */
Label = "   " || Label;  
run;
 
proc sgplot data=Plot;
  reg x=SepalLength y=PetalLength / group=Species;
  text x=x y=y text=Label / position=right;
run;

The graph is shown at the top of this article.

Label multiple regression curves

If you study the previous section, you will see that the code does not rely on the linearity of the regression model. The same code works for polynomial regression and nonparametric regression curves such as are created by the LOESS and PBSPLINE statements in PROC SGPLOT. The following graph shows a PBSPLINE fit to the IRIS data. Because the penalized B-spline curve is nonparametric, there is no equation to display as a label. Instead, I use the Species name as a label and suppress the legend at the bottom of the graph. You can download the SAS program that creates this and all the graphs in this article.

Label multiple regression curves in a graph in SAS by using PROC SGPLOT

The post Label multiple regression lines in SAS appeared first on The DO Loop.

12月 202017
 

I previously showed an easy way to visualize a regression model that has several continuous explanatory variables: use the SLICEFIT option in the EFFECTPLOT statement in SAS to create a sliced fit plot. The EFFECTPLOT statement is directly supported by the syntax of the GENMOD, LOGISTIC, and ORTHOREG procedures in SAS/STAT. If you are using another SAS regression procedure, you can still visualize multivariate regression models:

  • If a procedure supports the STORE statement, you can save the model to an item store and then use the EFFECTPLOT statement in PROC PLM to create a sliced fit plot.
  • If a procedure does not support the STORE statement, you can manually create the "slice" of observations and score the model on the slice.

Use PROC PLM to score regression models

Most parametric regression procedures in SAS (GLM, GLIMMIX, MIXED, ...) support the STORE statement, which enables you to save a representation of the model in a SAS item store. The following program creates sample data for 500 patients in a medical study. The call to PROC GLM fits a linear regression model that predicts the level of cholesterol from five explanatory variables. The STORE statement saves the model to an item store named 'GLMModel'. The call to PROC PLM creates a sliced fit plot that shows the predicted values versus the systolic blood pressure for males and females in the study. The explanatory variables that are not shown in the plot are set to reference values by using the AT option in the EFFECTPLOT statement:

data Heart;    /* create example data */
set sashelp.heart(obs=500);
where cholesterol < 400;
run;
 
proc glm data=Heart;
   class Sex Smoking_Status BP_Status;
   model Cholesterol = Sex      Smoking_Status BP_Status  /* class vars  */
                       Systolic Weight;                   /* contin vars */
   store GLMModel;                    /* save the model to an item store */
run;
 
proc plm restore=GLMModel;                       /* load the saved model */
   effectplot slicefit / at(Smoking_Status='Non-smoker' BP_Status='Normal'
                            Weight=150);   /* create the sliced fit plot */
run;
Visualize multivariate regression models: Sliced fit plot by using PROC PLM in SAS

The graph shows a sliced fit plot. The footnote states that the lines obtained by slicing through two response surfaces that correspond to (Smoking_Status, BP_Status) = ('Non-smoker', 'Normal') at the value Weight = 150. As shown in the previous article, you can specify multiple values within the AT option to obtain a panel of sliced fit plots.

Create a sliced fit plot manually by using the SCORE statement

The nonparametric regression procedures in SAS (ADAPTIVEREG, GAMPL, LOESS, ...) do not support the STORE statement. Nevertheless, you can create a sliced fit plot using a traditional scoring technique: use the DATA step to create observations in the plane of the slice and score the model on those observations.

There are two ways to score regression models in SAS. The easiest way is to use PROC SCORE, the SCORE statement, or the CODE statement. The following DATA step creates the same "slice" through the space of explanatory variables as was created by using the EFFECTPLOT statement in the previous example. The SCORE statement in the ADAPTIVEREG procedure then fits the model and scores it on the slice. (Technical note: By default, PROC ADAPTIVEREG uses variable selection techniques. For easier comparison with the model from PROC GLM, I used the KEEP= option on the MODEL statement to force the procedure to keep all variables in the model.)

/* create the scoring observations that define the slice */
data Score;
length Sex $6 Smoking_Status $17 BP_Status $7; /* same as for data */
Cholesterol = .;             /* set response variable to missing   */
Smoking_Status='Non-smoker'; /* set reference levels ("slices")    */
BP_Status='Normal';          /*     for class vars                 */
Weight=150;                  /*     and continuous covariates      */
do Sex = "Female", "Male";       /* primary class var */
   do Systolic = 98 to 272 by 2; /* evenly spaced points for X variable */
      output;
   end;
end;
run;
 
proc adaptivereg data=Heart;
   class Sex Smoking_Status BP_Status;
   model Cholesterol = Sex      Smoking_Status BP_Status
                       Systolic Weight / nomiss 
   /* for comparison with other models, FORCE all variables to be selected */
                       keep=(Sex Smoking_Status BP_Status Systolic Weight);
   score data=Score out=ScoreOut Pred;   /* score the model on the slice */
run;
 
proc sgplot data=ScoreOut;             
   series x=Systolic y=Pred / group=Sex;  /* create sliced fit plot */
   xaxis grid; yaxis grid;
run;

The output, which is not shown, is very similar to the graph in the previous section.

Create a sliced fit plot manually by using the missing value trick

If your regression procedure does not support a SCORE statement, an alternative way to score a model is to use "the missing value trick," which requires appending the scoring data set to the end of the original data. I like to add an indicator variable to make it easier to know which observations are data and which are for scoring. The following statements concatenate the original data and the observations in the slice. It then calls the GAMPL procedure to fit a generalized additive model (GAM) by using penalized likelihood (PL) estimation.

/* missing value trick: append score data to original data */
data All;
set Heart         /* data to fit the model */
    Score(in=s);  /* grid of values on which to score model */
ScoreData=s;      /* SCoreData=0 for orig data; =1 for scoring observations */
run;
 
proc gampl data=All;
   class Sex Smoking_Status BP_Status;
   model Cholesterol = Param(Sex Smoking_Status BP_Status)
                       Spline(Systolic Weight);
   output out=GamOut pred;
   id ScoreData Sex Systolic; /* include these vars in output data set */
run;
 
proc sgplot data=GamOut(where=(ScoreData=1)); /* plot only the scoring obs */
   series x=Systolic y=Pred / group=Sex;  /* create sliced fit plot */
   xaxis grid; yaxis grid;
run;
Visualize multivariate regression models: create sliced fit plot in SAS by using the missing value trick

The GAMPL procedure does not automatically include all input variables in the output data set; the ID statement specifies the variables that you want to output. The OUTPUT statement produces predicted values for all observations in the ALL data set, but the call to PROC SGPLOT creates the sliced plot by using only the observations for which ScoreData = 1. The output shows the nonparametric regression model from PROC GAMPL.

You can also use the ALL data set to overlay the original data and the sliced fit plot. The details are left as an exercise for the reader.

Summary

The EFFECTPLOT statement provides an easy way to create a sliced fit plot. You can use the EFFECTPLOT statement directly in some regression procedures (such as LOGISTIC and GENMOD) or by using the STORE statement to save the model and PROC PLM to display the graph. For procedures that do not support the STORE statement, you can use the DATA step to create "the slice" (as a scoring data set) and use traditional scoring techniques to evaluate the model on the slice.

The post How to create a sliced fit plot in SAS appeared first on The DO Loop.

11月 292017
 
Heat map of missing values among among 8 variables

Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example, by using multiple imputation), you need to understand which variables contain the missing values and to examine the patterns of missing values. I have previously written about how to use SAS to do the following:

An example of a visualization of a missing value pattern is shown to the right. The heat map shows the missing data for eight variables and 5209 observations in the Sashelp.Heart data set. It is essentially the heat map of a binary matrix where 1 indicates nonmissing values (white) and 0 indicates missing values (black).

In this article, I present a bar chart that helps to visualize the "Missing Data Patterns" table from PROC MI in SAS/STAT software. I also show two SAS tricks:

The pattern of missing data

The MI procedure can perform multiple imputation of missing data, but it also can be used as a diagnostic tool to group observations according to the pattern of missing data. You can use the NIMPUTE=0 option to display the pattern of missing value. By default, the "Missing Data Patterns" table is very wide because it includes the group means for each variable. However, you can use the DISPLAYPATTERN=NOMEANS option (SAS9.4M5) to suppress the group means, as follows:

%let Vars = AgeAtStart Height Weight Diastolic Systolic MRW Smoking Cholesterol;
%let numVars = %sysfunc(countw(&Vars));    /* &numVars = 8 for this example */
 
proc mi data=Sashelp.Heart nimpute=0 displaypattern=nomeans;   /* SAS 9.4M5 option */
var &Vars;
ods output MissPattern=Pattern;        /* output missing value pattern to data set */
run;

In the table, an X indicates that a variable does not contain any missing values, whereas a dot (.) indicates that a variable contains missing values. The table shows that Group 1 consists of about 5000 observations that are complete. Groups 2, 3, and 6 contains observations for which exactly one variable contains missing values. Groups 4 and 5 contain observations for which two variables are jointly missing. Group 7 contains observations for which three variables are missing. You can see that the size of the groups vary widely. Some patterns of missing value appear only a few times, whereas others appear much more often.

By inspecting the table, you can determine which combinations of variables are missing for each group. However, imagine a dataset that has 20 variables. The "Missing Data Patterns" table for 20 variables would be very wide, and it would be cumbersome to determine which combination of variables are jointly missing. The next section condenses the table into a smaller format and then creates a graph to summarize the pattern of missing values.

Shorten the labels for the missing data patterns

The information in the "Missing Data Patterns" table can be condensed. I saw the following idea in Chapter 10 of Gerhard Svolba's Data Quality for Analytics Using SAS, who credits the idea to a display that appears in JMP software. (Svolba does not use PROC MI, but defines a macro that you can download from the book's website.)

The table is wide because there is a column for every variable in the analysis. But the information in those columns is binary: for the i_th group does the j_th variable have a missing value? If the analysis involves k variables, you can replace the k columns by a single binary string that has k digits. The j_th digit in the string indicates whether the j_th variable has a missing value.

In the preceding analysis, the ODS OUTPUT statement wrote the "Missing Data Patterns" table to a data set. The following SAS DATA step reads the data and constructs a binary string of length k from the k character variables in the data. The binary string is formed by using the CATT function to concatenate the 'X' and '.' values in the table. The TRANSLATE function then converts those characters to '0' and '1' characters. The DATA step also computes the NumMiss variable, which counts the number of variables that have missing values for each row:

data Miss;
set Pattern;
array vars[*] _CHARACTER_;    /* or use &Vars */
length Pattern $&numVars.;    /* length = number of variables in array */
Pattern = translate(catt(of vars[*]), '01', 'X.');     /* Ex: 00100100 */
NumMiss = countc(pattern, '1');
run;
 
proc print data=Miss noobs;
   var Group Pattern NumMiss Freq;
run;

The table shows that the eight columns for the analysis variables have been replaced by a single column that displays an eight-character binary string. For Group 1, the string is all zeros, which indicates that no variable has missing values. For Group 2, the binary string contains all zeros except for a 1 in the last position. This means that Group 1 is the set of observations for which the last variable contains a missing value. Similarly, Group 7 is the set of observations for which the second, third, and sixth variables contain a missing value. Notice that this table does not provide the names of the variables. If you cannot remember the name of the second, third, and sixth variables, you need to look them up in the VARS macro variable.

Create a bar chart that has a logarithmic scale

The condensed version of the "Missing Data Patterns" table is suitable to graph as a bar chart. However, for these data the size of the groups vary widely. Consequently, you might want to plot the frequencies of the groups on a logarithmic scale. (Or you might not! There is considerable debate about whether you should display a bar chart that has a logarithmic axis. For a discussion and alternatives, see Sanjay's article "Graphs with log axis." My opinion is that a log axis is fine to use for a technical audience such as statisticians.)

By default, you cannot use a logarithmic scale on a bar chart because the baseline for the vertical axis (the frequency or count axis) starts at 0 and the LOG function is not defined at 0. If you have count data and no category has zero counts, then you can set the baseline of the graph to 1. The bars then indicate the log-frequency of the counts. The following call to PROC SGPLOT creates the bar chart and a marginal table:

title "Pattern of Missing Values";
proc sgplot data=Miss;
   hbar Pattern /response=Freq datalabel
                       baseline=1;  /* set BASELINE=1 for log scale */
   yaxistable NumMiss / valuejustify=left label="Num Miss"
                       valueattrs=GraphValueText labelattrs=GraphLabelText; /* use same attributes as axis */
   yaxis labelposition=top; 
   xaxis grid type=log logbase=10 label="Frequency (log10 scale)";
run;

This graph displays the counts of the number of observations in each pattern group. The labels on the Y axis indicate which variables have missing data. The values in the right margin indicate how many variables have missing data. If you are opposed to drawing bar charts on a logarithmic axis, you can use one of Sanjay's alternative visualizations.

In summary, you can create a bar chart that visualizes the number of observations that have a similar pattern of missing values. The summarization comes from PROC MI in SAS, which has a new DISPLAYPATTERN=NOMEANS option. A short DATA step can create a binary string for each group. That string can be used to indicate the missing data pattern in each group. If the groups vary widely in size, you can use a logarithmic axis to display the data. To create a bar chart on a logarithmic scale in SAS, set BASELINE=1 in the HBAR statement and use TYPE=LOG on the XAXIS statement in PROC SGPLOT.

The post Visualize patterns of missing values appeared first on The DO Loop.

11月 292017
 
Heat map of missing values among among 8 variables

Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example, by using multiple imputation), you need to understand which variables contain the missing values and to examine the patterns of missing values. I have previously written about how to use SAS to do the following:

An example of a visualization of a missing value pattern is shown to the right. The heat map shows the missing data for eight variables and 5209 observations in the Sashelp.Heart data set. It is essentially the heat map of a binary matrix where 1 indicates nonmissing values (white) and 0 indicates missing values (black).

In this article, I present a bar chart that helps to visualize the "Missing Data Patterns" table from PROC MI in SAS/STAT software. I also show two SAS tricks:

The pattern of missing data

The MI procedure can perform multiple imputation of missing data, but it also can be used as a diagnostic tool to group observations according to the pattern of missing data. You can use the NIMPUTE=0 option to display the pattern of missing value. By default, the "Missing Data Patterns" table is very wide because it includes the group means for each variable. However, you can use the DISPLAYPATTERN=NOMEANS option (SAS9.4M5) to suppress the group means, as follows:

%let Vars = AgeAtStart Height Weight Diastolic Systolic MRW Smoking Cholesterol;
%let numVars = %sysfunc(countw(&Vars));    /* &numVars = 8 for this example */
 
proc mi data=Sashelp.Heart nimpute=0 displaypattern=nomeans;   /* SAS 9.4M5 option */
var &Vars;
ods output MissPattern=Pattern;        /* output missing value pattern to data set */
run;

In the table, an X indicates that a variable does not contain any missing values, whereas a dot (.) indicates that a variable contains missing values. The table shows that Group 1 consists of about 5000 observations that are complete. Groups 2, 3, and 6 contains observations for which exactly one variable contains missing values. Groups 4 and 5 contain observations for which two variables are jointly missing. Group 7 contains observations for which three variables are missing. You can see that the size of the groups vary widely. Some patterns of missing value appear only a few times, whereas others appear much more often.

By inspecting the table, you can determine which combinations of variables are missing for each group. However, imagine a dataset that has 20 variables. The "Missing Data Patterns" table for 20 variables would be very wide, and it would be cumbersome to determine which combination of variables are jointly missing. The next section condenses the table into a smaller format and then creates a graph to summarize the pattern of missing values.

Shorten the labels for the missing data patterns

The information in the "Missing Data Patterns" table can be condensed. I saw the following idea in Chapter 10 of Gerhard Svolba's Data Quality for Analytics Using SAS, who credits the idea to a display that appears in JMP software. (Svolba does not use PROC MI, but defines a macro that you can download from the book's website.)

The table is wide because there is a column for every variable in the analysis. But the information in those columns is binary: for the i_th group does the j_th variable have a missing value? If the analysis involves k variables, you can replace the k columns by a single binary string that has k digits. The j_th digit in the string indicates whether the j_th variable has a missing value.

In the preceding analysis, the ODS OUTPUT statement wrote the "Missing Data Patterns" table to a data set. The following SAS DATA step reads the data and constructs a binary string of length k from the k character variables in the data. The binary string is formed by using the CATT function to concatenate the 'X' and '.' values in the table. The TRANSLATE function then converts those characters to '0' and '1' characters. The DATA step also computes the NumMiss variable, which counts the number of variables that have missing values for each row:

data Miss;
set Pattern;
array vars[*] _CHARACTER_;    /* or use &Vars */
length Pattern $&numVars.;    /* length = number of variables in array */
Pattern = translate(catt(of vars[*]), '01', 'X.');     /* Ex: 00100100 */
NumMiss = countc(pattern, '1');
run;
 
proc print data=Miss noobs;
   var Group Pattern NumMiss Freq;
run;

The table shows that the eight columns for the analysis variables have been replaced by a single column that displays an eight-character binary string. For Group 1, the string is all zeros, which indicates that no variable has missing values. For Group 2, the binary string contains all zeros except for a 1 in the last position. This means that Group 1 is the set of observations for which the last variable contains a missing value. Similarly, Group 7 is the set of observations for which the second, third, and sixth variables contain a missing value. Notice that this table does not provide the names of the variables. If you cannot remember the name of the second, third, and sixth variables, you need to look them up in the VARS macro variable.

Create a bar chart that has a logarithmic scale

The condensed version of the "Missing Data Patterns" table is suitable to graph as a bar chart. However, for these data the size of the groups vary widely. Consequently, you might want to plot the frequencies of the groups on a logarithmic scale. (Or you might not! There is considerable debate about whether you should display a bar chart that has a logarithmic axis. For a discussion and alternatives, see Sanjay's article "Graphs with log axis." My opinion is that a log axis is fine to use for a technical audience such as statisticians.)

By default, you cannot use a logarithmic scale on a bar chart because the baseline for the vertical axis (the frequency or count axis) starts at 0 and the LOG function is not defined at 0. If you have count data and no category has zero counts, then you can set the baseline of the graph to 1. The bars then indicate the log-frequency of the counts. The following call to PROC SGPLOT creates the bar chart and a marginal table:

title "Pattern of Missing Values";
proc sgplot data=Miss;
   hbar Pattern /response=Freq datalabel
                       baseline=1;  /* set BASELINE=1 for log scale */
   yaxistable NumMiss / valuejustify=left label="Num Miss"
                       valueattrs=GraphValueText labelattrs=GraphLabelText; /* use same attributes as axis */
   yaxis labelposition=top; 
   xaxis grid type=log logbase=10 label="Frequency (log10 scale)";
run;

This graph displays the counts of the number of observations in each pattern group. The labels on the Y axis indicate which variables have missing data. The values in the right margin indicate how many variables have missing data. If you are opposed to drawing bar charts on a logarithmic axis, you can use one of Sanjay's alternative visualizations.

In summary, you can create a bar chart that visualizes the number of observations that have a similar pattern of missing values. The summarization comes from PROC MI in SAS, which has a new DISPLAYPATTERN=NOMEANS option. A short DATA step can create a binary string for each group. That string can be used to indicate the missing data pattern in each group. If the groups vary widely in size, you can use a logarithmic axis to display the data. To create a bar chart on a logarithmic scale in SAS, set BASELINE=1 in the HBAR statement and use TYPE=LOG on the XAXIS statement in PROC SGPLOT.

The post Visualize patterns of missing values appeared first on The DO Loop.

10月 302017
 

This article demonstrates a SAS programming technique that I call Kuhfeld's template modification technique. The technique enables you to dynamically modify an ODS template and immediately call the modified template to produce a new graph or table. By following the five steps in this article, you can implement the technique in less than 20 lines of SAS code.

Kuhfeld's Template Modification Technique (TMT)

The template modification technique (TMT) is one of several advanced techniques that Warren Kuhfeld created and promoted in a series of articles that culminated with Kuhfeld (2016). (Slides are also available.) The technique that I call the TMT is a DATA _NULL_ program that modifies a SAS-supplied template, uses CALL EXECUTE to compiles it "on the fly," and immediately calls the modified template to render a new graph (pp. 12ff in the paper; p. 14 of his slides).

When I attended Warren's 2016 presentation, I felt a bit overwhelmed by the volume of information. It was difficult for me to see the forest because of all the interconnected "trees." Graph customization is easier to understand if you restrict your attention only to GTL templates. You can make simple modifications to a template (for example, changing the value of an option) by using the following five steps:

  1. Prepend a private item store to the ODS search path. The private item store will store the modified template.
  2. Write the existing template to a temporary text file.
  3. Run a DATA step that reads the existing template one line at a time. Check each line to see if it contains the option that you want to change. If so, modify the line to change the template's behavior.
  4. Compile the modified template into the private item store.
  5. Call PROC SGRENDER to render the graph by using the new template.

This article is intentionally brief. For more information see the chapter "ODS Graphics Template Modification" in the SAS/STAT documentation.

Most of Kuhfeld's examples use the TMT to modify ODS templates that are distributed with SAS. These templates can be complex and often contain conditional logic. In an effort to produce a simpler example, the next section creates a simple template that uses a heat map to display a response variable on a uniform grid.

Example: A template that specifies a color ramp

Programmers who use the GTL to create graphs have probably encountered dynamic variables and run-time macro variables. You can use these to specify certain options at run time, rather than hard-coding information into a template. However, the documentation states that "dynamic variables ... cannot resolve to statement or option keywords." In particular, the GTL does not support changing the color ramp at run time: whatever color ramp is hard-coded in the template is the color ramp that is used. (Note: If you have SAS 9.4M3, the HEATMAPPARM statement in PROC SGPLOT supports specifying a color ramp at run time.)

However, you can use Kuhfeld's TMT to modify the color ramp at run time. The following GTL defines a template named "HEATMAP." The template uses the HEATMAPPARM statement and hard-codes the COLORMODEL= option to be the "ThreeColorRamp" in the current ODS style. The template is adapted from the article "Creating a basic heat map in SAS."

/* Construct example template: a basic heat map with continuous color ramp */
proc template; 
define statgraph HEATMAP;
dynamic _X _Y _Z;           /* dynamic variables */
 begingraph;
 layout overlay;
    heatmapparm x=_X y=_Y colorresponse=_Z /  /* specify variables at run time */
       name="heatmap" primary=true xbinaxis=false ybinaxis=false
       colormodel = THREECOLORRAMP;           /* <== hard-coded color ramp */
    continuouslegend "heatmap";
  endlayout;
endgraph;
end;
run;

When the PROC TEMPLATE step runs, the HEATMAP template is stored in a location that is determined by your ODS search path. You can run ods path show; to display the search path. You can discover where the HEATMAP template is stored by running proc template; list HEATMAP; run;.

The following DATA step defines (x,y) data on a uniform grid and defines z to be a cubic function of x and y. The heat map shows an approximate contour plot of the cubic function:

/* Construct example data: A cubic function z = f(x,y) on regular grid */
do x = -1 to 1 by 0.05;
   do y = -1 to 1 by 0.05;
      z = x**3 - y**2 - x + 0.5;
      output;
   end;
end;
run;
 
/* visualize the data using the built-in color ramp */
proc sgrender data=Cubic template=Heatmap; 
   dynamic _X='X' _Y='Y' _Z='Z';
run;
Heat map rendered with original template

The remainder of this article shows how to use Kuhfeld's TMT to create a new template which is identical to the HEATMAP template except that it uses a different color ramp. Kuhfeld's technique copies the existing template line by line but replaces the text "THREECOLORRAMP" with text that defines a new custom color ramp.

In the following program, capital letters are used for the name of the existing template, the name of the new template, and the value of the new color ramp. Most of the uncapitalized terms are "boilerplate" for the TMT, although each application will require unique logic in the DATA _NULL_ step.

Step 1: Prepend a private item store to the ODS search path

PROC TEMPLATE writes templates to the first (writable) item store on the ODS search path. PROC SGRENDER looks through the ODS search path until it locates a template with a given name. The first step is to prepend a private item store to the search path, as shown by the following statement:

/* 1. Modify the search path for ODS. Prepend an item store in WORK */
ods path (prepend) work.modtemp(update);

The ODS PATH statement ensures that the modified template will be stored in WORK.MODTEMP. You can provide any name you want. The name WORK.TEMPLAT is a convention that is used in the SAS documentation. Because you might already have WORK.TEMPLAT on your ODS path, I used a different name. (Note: Any item stores in WORK are deleted when SAS exits.) The SAS/STAT documentation provides more information about the template search path.

Step 2: Write the existing template to a temporary text file

You need to write the source code for the HEATMAP template to a plain text file so that it can be read by the SAS DATA step. You can use the FILENAME statement to point to a hard-coded location (such as filename t "C:/temp/tmplt.txt";), but I prefer to use the SAS keyword TEMP, which is more portable. The TEMP keyword tells SAS to create a temporary file name in a location that is independent of the operating system and the file system:

/* 2. Write body of existing template to text file */
filename _tmplt TEMP;             /* create temporary file and name */
proc template;
   source HEATMAP / file=_tmplt;  /* write template source to text file */
run;

If you omit the FILE= option, the template source is written to the SAS log. The text file contains only the body of the HEATMAP template.

Steps 3 and 4: Create a new template from the old template

The next step reads and modifies the existing template line by line. There are many SAS character functions that enable you to discover whether some keyword exists in a line of text. Examples include the INDEX and FIND family of functions, the PRXCHANGE function (which uses regular expressions), and the TRANWRD function.

The goal of the current example is to replace the word "THREECOLORRAMP" by another string. The TRANWRD function replaces the string if it is found; the line is unchanged if the word is not found. Therefore it is safe to call the TRANWRD function on every line in the existing template.

You can use the CALL EXECUTE function to place each (possibly modified) line of the template onto a queue, which will be executed when the DATA step completes. However, the text file does not start with a PROC TEMPLATE statement nor end with a RUN statement. You must add those statements to the execution queue in order to compile the template, as follows:

/* one possible way to specify a new color ramp */
%let NEWCOLORRAMP = (blue cyan yellow red);
 
/* 3. Read old template; make modifications.
   4. Use CALL EXECUTE to compile (modified) template with a new name. */
data _null_;                       /* Modify graph template to replace default color ramp */
   infile _tmplt end=eof;          /* read from text file; last line sets EOF flag to true */
   input;                          /* read one line at a time */
   if _n_ = 1 then 
       call execute('proc template;');      /* add 'PROC TEMPLATE;' before */
 
   if findw(_infile_, 'define statgraph') then
      _infile_ = "define statgraph NEWHEATMAP;";  /* optional: assign new template name */
   /* use TRANWRD to make substitutions here */
   _infile_ = tranwrd(_infile_, 'THREECOLORRAMP', "&NEWCOLORRAMP"); /* replace COLORMODEL= option */
 
   call execute(_infile_);                  /* push line onto queue; compile after DATA step */
   if eof then call execute('run;');        /* add 'RUN;' at end */
run;

The DATA step copies the existing template source, but modifies the template name and the COLORMODEL= option. The CALL EXECUTE statements push each (potentially modified) statement onto a queue that is executed when the DATA step exits. The result is that PROC TEMPLATE compiles a new template and stores it in the WORK.MODTEMP item store. Recall that the TRANWRD function and other string-manipulation functions are case sensitive, so be sure to use the exact capitalization that exists in the template. In other words, this technique requires that you know details of the template that you wish to modify!

If you are using this technique to modify one of the built-in SAS templates that are used by SAS procedure, do not modify the template name.

Inspect the modified template

Before you attempt to use the new template, you might want to confirm that it is correct. The following call to PROC TEMPLATE shows the location and creation date for the new template. The source for the template is displayed in the log so that you can verify that the substitutions were made correctly.

proc template;
   list NEWHEATMAP / stats=created; /* location of the modified template */
   source NEWHEATMAP;       /* confirm that substitutions were performed */
run;

Step 5: Render the graph by using the new template

The final step is to call PROC SGRENDER to use the newly created template to create the new graph:

/* 5. Render graph by using new template */
proc sgrender data=Cubic template=NEWHEATMAP; 
   dynamic _X='X' _Y='Y' _Z='Z';
run;
Render graph with modified template

The output demonstrates that the original color ramp ("ThreeColorRamp") has been replaced by a four-color ramp that uses the colors blue, cyan, yellow, and red.

When is this necessary?

You might wonder if the result is worth all this complexity and abstraction. Couldn't I have merely copied the template into a program editor and changed the color ramp? Yes, you can use a program editor to modify a template for your personal use. However, this programming technique enables you to write macros, tasks, and SAS/IML functions that other people can use. For example, you could encapsulate the program in this article into a %CreateHeatmap macro that accepts a color ramp and data set as an argument. Or in SAS/IML you can write modules such as the HEATMAPCONT and HEATMAPDISC subroutines, which enable SAS/IML programmers to specify any color ramp to visualize a matrix.

If you work in a regulated industry and are modifying templates that SAS distributes, this technique provides reproducibility. A reviewer or auditor can run a program to reproduce a graph.

Summary

Kuhfeld's template modification technique (TMT) enables you to write a SAS program that modifies an ODS template. The TMT combines many SAS programming techniques and can be difficult to comprehend when you first see it. This article breaks down the technique into five simpler steps. You can use the TMT to edit ODS templates that are distributed by SAS and used in SAS procedures, or (as shown in this article) to change options at run time for a template that you wrote yourself. You can download the SAS program for this article.

Happy Halloween to all my readers. I hope you'll agree that Kuhfeld's TRICKS are a real TREAT!

The post A SAS programming technique to modify ODS templates appeared first on The DO Loop.

10月 182017
 

In a previous article, I discussed the lines plot for multiple comparisons of means. Another graph that is frequently used for multiple comparisons is the diffogram, which indicates whether the pairwise differences between means of groups are statistically significant. This article discusses how to interpret a diffogram. Two related plots are also discussed.

How to create a diffogram in SAS

The diffogram (also called a mean-mean scatter diagram) is automatically created when you use the PDIFF=ALL option on the LSMEANS statement in several SAS/STAT regression procedures such as PROC GLM and PROC GLMMIX, assuming that you have enabled ODS graphics. The documentation for PROC GLM contains an example that uses data about the chemical composition of shards of pottery from four archaeological sites in Great Britain. Researchers want to determine which sites have shards that are chemically similar. The data are contained in the pottery data set. The following call to PROC GLM performs an ANOVA of the calcium oxide in the pottery at the sites. The PDIFF=ALL option requests an analysis of all pairwise comparisons between the LS-means of calcium oxide for the different sites. The ADJUST=TUKEY option is one way to adjust the confidence intervals for multiple comparisons.

ods graphics on;
proc glm data=pottery plots(only)=( diffplot(center)          /* diffogram */
                                    meanplot(cl ascending) ); /* plot of means and CIs */
   label Ca = "Calcium Oxide (%)";
   class Site;
   model Ca = Site;
   lsmeans Site / pdiff=all cl adjust=tukey;  /* all pairwise comparisons of means w/ adjusted CL */
   ods output LSMeanDiffCL=MeanDiff;          /* optional: save mean differences and CIs */
quit;

Two graphs are requested: the diffogram (or "diffplot") and a "mean plot" that shows the group means and 95% confidence intervals. The ODS OUTPUT statement creates a data set from a table that contains the mean differences between pairs of groups, along with 95% confidence intervals for the differences. You can use that information to construct a plot of the mean differences, as shown later in this article.

How to interpret a diffogram

Diffogram in SAS showing multiple comparisons of means

The diffogram, which is shown to the right (click to enlarge), is my favorite graph for multiple comparisons of means. Every diffogram displays a diagonal reference line that has unit slope. Horizontal and vertical reference lines are placed along the axes at the location of the means of the groups. For these data, there are four vertical and four horizontal reference lines. At the intersection of most reference lines there is a small colored line segment. These segments indicate which of the 4(4-1)/2 = 6 pairwise comparisons of means are significant.

Let's start at the top of the diffogram. The mean for the Caldecot site is about 0.3, as shown by a horizontal reference line near Y=0.3. On the reference line for Caldecot are three line segments that are centered at the mean values for the other groups, which are (from left to right) IslandThorns, AshleyRails, and Llanederyn. The first two line segments (in blue) do not intersect the dashed diagonal reference line, which indicates that the means for the (IslandThorns, Caldecot) and (AshleyRails, Caldecot) pairs are significantly different. The third line segment (in red) intersects the diagonal reference line, which indicates that the (Llanederyn, Caldecot) comparison is not significant.

Similarly, the mean for the Llanederyn site is about 0.2, as shown by a horizontal reference line near Y=0.2. On the reference line for Llanederyn are two line segments, which represent the (IslandThorns, Llanederyn) and (AshleyRails, Llanederyn) comparisons. Neither line segment intersects the diagonal reference line, which indicates that the means for those pairs are significantly different.

Lastly, the mean for the AshleyRails site is about 0.05, as shown by a horizontal reference line near Y=0.05. The line segment on that reference line represents the (IslandThorns, AshleyRails) comparison. It intersects the diagonal reference line, which indicates that those means are not significantly different.

The colors for the significant/insignificant pairs depends on the ODS style, but it is easy to remember the simple rule: if a line segment intersects the diagonal reference line, then the corresponding group means are not significantly different. A mnemonic is "Intersect? Insignificant!"

The mean plot with confidence interval: Use wisely!

Mean plot and 95% confidence intervals, created with SAS

The previous section shows how to use the diffogram to visually determine which pairs of means are significantly different. This section reminds you that you should not try to use the mean plot (shown at the right) for making those inferences.

I have seen presentations in which the speaker erroneously claims that "the means of these groups are significantly different because their 95% confidence intervals do not overlap." That is not a correct inference. In general, the overlap (or lack thereof) between two (1 – α)100% confidence intervals does not give sufficient information about whether the difference between the means is significant at the α level. In particular, you can construct examples where

  • Two confidence intervals overlap, but the difference of means is significant. (See Figure 2 in High (2014).)
  • Two confidence intervals do not overlap, but the difference of means is not significant.

The reason is twofold. First, the confidence intervals in the plot are constructed by using the sample sizes and standard deviations for each group, whereas tests for the difference between the means are constructed by using pooled standard deviations and sample sizes. Second, if you are making multiple comparisons, you need to adjust the widths of the intervals to accommodate the multiple (simultaneous) inferences.

"But Rick," you might say, "in the mean plot for these data, the Llanederyn and Caldecot confidence intervals overlap. The intervals for IslandThorns and AshleyRails also overlap. And these are exactly the two pairs that are not significantly different, as shown by the diffogram!" Yes, that is true for these data, but it is not true in general. Use the diffogram, not the means plot, to visualize multiple comparisons of means.

Plot the pairwise difference of means and confidence intervals

In addition to the diffogram, you can visualize comparisons of means by plotting the confidence intervals for the pairwise mean differences. Those intervals that contain 0 represent insignificant differences. Recall that the call to PROC GLM included an ODS output statement that created a data set (MeanDiff) that contains the mean differences. The following DATA step constructs labels for each pair and computes whether each pairwise difference is significant:

data Intervals;
length Pair $28.;
length S $16.;
set MeanDiff;
/* The next line is data dependent. For class variable 'C', concatenate C and _C variables */
Pair = catx(' - ', Site, _Site);           
Significant = (0 < LowerCL | UpperCL < 0); /* is 0 in interior of interval? */
S = ifc(Significant, "Significant", "Not significant");
run;
 
title "Pairwise Difference of LSMeans (Tukey Adjustment)";
title2 "95% Confidence Intervals of Mean Difference";
footnote J=L "Pairs Whose Intervals Contain 0 Are Not Significantly Different";
proc sgplot data=Intervals;
   scatter y=Pair x=Difference / group=S name="CIs"
           xerrorlower=LowerCL xerrorupper=UpperCL;
   refline 0 / axis=x;
   yaxis reverse colorbands=odd display=(nolabel) 
                 offsetmin=0.06 offsetmax=0.06;
   keylegend "CIs" / sortorder=ascending;
run;
Plot of mean differences and confidence intervals, created with SAS

The resulting graph is shown to the right. For each pair of groups, the graph shows an estimate for the difference of means and the Tukey-adjusted 95% confidence intervals for the difference. Intervals that contain 0 indicate that the difference of means is not significant. Intervals that do not contain 0 indicate significant differences.

Although the diffogram and the difference-of-means plot provide the same information, I prefer the diffogram because it shows the values for the means rather than for the mean differences. Furthermore, the height of the difference-of-means plot needs to be increased as more groups are added (there are k(k-1)/2 rows for k groups), whereas the diffogram can accommodate a moderate number of groups without being rescaled. On the other hand, it can be difficult to read the reference lines in the diffogram when there are many groups, especially if some of the groups have similar means.

For more about multiple comparisons tests and how to visualize the differences between means, see the following references:

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