25009 - Macros to plot statistics generated by PROC PLS

Sample 25009: Macros to plot statistics generated by PROC PLS

Macros to plot statistics generated by PROC PLS

Contents:

Purpose / Requirements / Usage / Missing Values / See Also / References

PURPOSE:

Given an OUTMODEL= and OUT= data set from PROC PLS, the macros specified below retrieve and/or plot the following PLS-related quantities:

Residuals (%RES_PLOT, %NOR_PLOT)
Scores for predictors and responses (%PLOT_SCR, %PLOTXSCR)
Loadings for predictors and responses (%GETXLOAD, %GETYLOAD, %PLTYLOAD, %PLT_Y_LV)
Weights for predictors (%GET_WTS, %PLOT_WT, %PLTWTFRQ, %PLTXYWTS)
PLS regression coefficients (%GET_BPLS, %PLT_BPLS)
Variable importance for the projection (%GET_VIP, %PLOT_VIP)
Distance from each observation to the model in predcitors and responses (%GET_DMOD)
Contribution of each variable to an X-score for a given observation (%CONT_SCR)
Contribution of each variable to a pair of X-scores for a given pair of observations (%CONT2SCR)

REQUIREMENTS:

Base SAS^®, SAS/STAT^®, SAS/IML^®, and SAS/GRAPH^® are required. Data requirements for the individual macros are given in the macro definitions.

USAGE:

Follow the instructions in the Downloads tab of this sample to save the macro definitions. Replace the text within quotes in the following statement with the location of the macro definitions file on your system. In your SAS program or in the SAS editor window, specify this statement to define the macros and make them available for use:

   %inc "<location of your file containing the PLS macros>" / nosource;

Following this statement, you may call any if the macros. See the Results tab for an example.

See the individual macro definitions for syntax details on each macro.

MISSING VALUES:

In general, input data sets used in the macros should not contain missing values in variables used within the macros. This is especially true for macros which produce plots or which do matrix calculations using SAS/IML.

SEE ALSO:

Examples Using the PLS Procedure

REFERENCES:

Lindberg, W., Persson, J-A., and Wold, S. (1983), "Partial Least-Squares Method for Spectrofluorimetric Analysis of Mixtures of Humic Acid and Ligninsulfonate," Analytical Chemistry 55, 643-648.

McAvoy, T. J., Wang, N. S., Naidu, S., Bhat, N., Gunter, J., and Simmons, M. (1989), "Interpreting Biosensor Data via Backpropagation," International Joint Conference On Neural Networks, 1, 227-233.

Shao, J. (1993), "Linear Model Selection by Cross-Validation," JASA, 88, 486-494.

Tobias, R. (1995), "An Introduction to Partial Least Squares Regression," Proceedings of the Twentieth Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc., 1250-1257.

Ufkes, J. G. R., Visser, B. J., Heuver, G., and Van Der Meer, C. (1978), "Structure-Activity Relationships of Bradykinin-Potentiating Peptides," European Journal of Pharmacology, 50, 119.

Ufkes, J. G. R., Visser, B. J., Heuver, G., Wynne, H. J., and Van Der Meer, C. (1982), "Further Studies on the Structure-Activity Relationships of Bradykinin-Potentiating Peptides," European Journal of Pharmacology, 79, 155.

Umetrics, Inc. (1995), Multivariate Analysis (3-day course), Winchester, MA.

Wold, S. (1994), "PLS for Multivariate Linear Modeling," QSAR: Chemometric Methods for Molecular Design. Methods and Principles in Medicinal Chemistry. (Ed. H. van de Waterbeemd), Weinheim, Germany: Verlag- Chemie.

These sample files and code examples are provided by SAS Institute Inc. "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Recipients acknowledge and agree that SAS Institute shall not be liable for any damages whatsoever arising out of their use of this material. In addition, SAS Institute will provide no support for the materials contained herein.

EXAMPLE: Spectrometric Calibration

Data from Lindberg, et al. (1983), supplied by Umetrics (1995).

More examples can be found in Examples Using the PLS Procedure

The following example demonstrates how to use a few of the PLS macros to do a basic analysis of a PLS model. To begin with, a PLS model with two factors is fit using PROC PLS. Using the resulting OUT= data set, the %PLOT_SCR macro is called to plot the X- and Y-scores, and the %PLOTXSCR macro is used to plot X-scores for the first and second PLS factors against each other. These plots are used for diagnostic purposes, especially to look for outliers. Plots of Y-scores versus X-scores should have points clustered at random along a line, while the plots of X-scores against each other should look like a random scatter cloud.

Once you examine the score plots and determine that the model fit is adequate, a look at the X-weights will reveal which X-variables are important in each PLS factor. Using the OUTMODEL= data set, the %GET_WTS macro is used to create a SAS data set containing just the X-weights, and the %PLTWTFRQ macro is called to plot the X-weights versus the frequency for each PLS factor, since the X-variables are frequencies in this example.

You can use other macros to analyze other facets of the PLS model. For more detailed examples, including the continuation of this one, see Examples Using the PLS Procedure.

data mswkal;
  input n OBSNAM $ v1-v27 LS HA DT @@;
  datalines;
1  EM1   2766  2610  3306  3630  3600  3438  3213  3051  2907
   2844  2796  2787  2760  2754  2670  2520  2310  2100  1917  1755
   1602   1467   1353   1260   1167   1101   1017   3.011    0    0
2  EM2    1492   1419   1369   1158   958   887   905   929   920
   887   800   710   617   535   451   368   296   241   190   157
   128   106    89    70    65    56    50    0   0.4005    0
3  EM3   2450  2379  2400  2055  1689  1355  1109  908  750
   673   644   640   630   618   571   512   440   368   305   247
   196   156   120    98    80    61    50    0    0   90.63
4  EM4   2751  2883  3492  3570  3282  2937  2634  2370  2187
   2070  2007  1974  1950  1890  1824  1680  1527  1350  1206  1080
   984   888   810   732   669   630   582   1.482   0.158   40
5  EM5   2652  2691  3225  3285  3033  2784  2520  2340  2235
   2148  2094  2049  2007  1917  1800  1650  1464  1299  1140  1020
   909   810   726   657   594   549   507   1.116   0.4104   30.45
6  EM6   3993  4722  6147  6720  6531  5970  5382  4842  4470
   4200  4077  4008  3948  3864  3663  3390  3090  2787  2481  2241
   2028  1830  1680  1533  1440  1314  1227  3.397  0.3032  50.82
7  EM7   4032  4350  5430  5763  5490  4974  4452  3990  3690
   3474  3357  3300  3213  3147  3000  2772  2490  2220  1980  1779
   1599  1440  1320  1200  1119  1032  957  2.428  0.2981  70.59
8  EM8   4530  5190  6910  7580  7510  6930  6150  5490  4990
   4670  4490  4370  4300  4210  4000  3770  3420  3060  2760  2490
   2230  2060  1860  1700  1590  1490  1380  4.024  0.1153  89.39
9  EM9   4077  4410  5460  5857  5607  5097  4605  4170  3864
   3708  3588  3537  3480  3330  3192  2910  2610  2325  2064  1830
   1638  1476  1350  1236  1122  1044  963  2.275  0.504  81.75
10 EM10  3450  3432  3969  4020  3678  3237  2814  2487  2205
   2061  2001  1965  1947  1890  1776  1635  1452  1278  1128  981
   867   753   663   600   552   507   468   0.9588   0.145   101.1
11 EM11  4989  5301  6807  7425  7155  6525  5784  5166  4695
   4380  4197  4131  4077  3972  3777  3531  3168  2835  2517  2244
   2004  1809  1620  1470  1359  1266  1167  3.19  0.253  120
12 EM12  5340  5790  7590  8390  8310  7670  6890  6190  5700
   5380  5200  5110  5040  4900  4700  4390  3970  3540  3170  2810
   2490  2240  2060  1870  1700  1590  1470  4.132  0.5691  117.7
13 EM13  3162  3477  4365  4650  4470  4107  3717  3432  3228
   3093  3009  2964  2916  2838  2694  2490  2253  2013  1788  1599
   1431  1305  1194  1077  990  927  855  2.16  0.436  27.59
14 EM14  4380  4695  6018  6510  6342  5760  5151  4596  4200
   3948  3807  3720  3672  3567  3438  3171  2880  2571  2280  2046
   1857  1680  1548  1413  1314  1200  1119  3.094  0.2471  61.71
15 EM15  4587  4200  5040  5289  4965  4449  3939  3507  3174
   2970  2850  2814  2748  2670  2529  2328  2088  1851  1641  1431
   1284  1134  1020  918  840  756  714  1.604  0.2856  108.8
16 EM16  4017  4725  6090  6570  6354  5895  5346  4911  4611
   4422  4314  4287  4224  4110  3915  3600  3240  2913  2598  2325
   2088  1917  1734  1587  1452  1356  1257  3.162  0.7012  60
17 EM17  3933  4518  5637  6006  5721  5187  4641  4149  3789
   3579  3447  3381  3327  3234  3078  2832  2571  2274  2040  1818
   1629   1470   1350   1245   1134   1050   987   2.5   0.3   80
18 EM25  2904  2997  3255  3150  2922  2778  2700  2646  2571
   2487  2370  2250  2127  2052  1713  1419  1200  984  795  648
   525    426    351    291    240    204    162    30    40    150
;

data mswkal_a; set mswkal;
   if n <= 16;
   run;

/* Define the PLS macros */
%inc "<location of your file containing the PLS macros>" / nosource;

%global xvars yvars xscrname yscrname num_x num_y lv;

%let xvars=v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14
           v15 v16 v17 v18 v19 v20 v21 v22 v23 v24 v25 v26 v27;
%let yvars=ls ha dt;
%let xscrname=xscr;  *** Names for scores in output data set  ***;
%let yscrname=yscr;  
%let num_y=3;        *** Numbers of variables in the data set ***;
%let num_x=27;
%let lv=2;           *** Number of PLS components in model    ***;

proc pls data=mswkal_a method=pls outmodel=est1 lv=2;
   model ls ha dt = v1-v27;
   output out=outpls xscore=xscr yscore=yscr;
   run;

 /**********************************************************
 /  Plot Y-scores versus X-scores for each PLS factor      /
 /  (both the X- and Y-scores are contained in data set    /
 /  OUTPLS)                                                /
 **********************************************************/

%plot_scr(outpls);

 /**********************************************************
 /  Plot X-scores for succesive PLS factors against each   /
 /  other                                                  /
 **********************************************************/

%plotxscr(outpls);

 /**********************************************************
 /  Get the X-weights from the OUTMODEL data set EST1 and  /
 /  put them into data set XWTS                            /
 **********************************************************/

%get_wts(est1,dsxwts=xwts);

 /**********************************************************
 /  Plot the X-weights vs. the frequencies                 /
 **********************************************************/

%pltwtfrq(xwts,plotyvar=w,plotxvar=n,max_lv=&lv,label=Weight);

RESULTS:

The above example produces the following plots. The results are further discussed in Examples for the PLS Procedure.

Using the OUTMODEL= and OUT= data sets from PROC PLS, these macros collect and plot residuals, scores, loadings, weights, coefficients, and other statistics.

Type:	Sample
Topic:	SAS Reference ==> Procedures ==> PLS

Date Modified:	2017-06-20 16:31:32
Date Created:	2005-01-13 15:03:30

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Product Family	Product	Host	Starting	Ending
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		Microsoft Windows Server 2003 Datacenter 64-bit Edition
		Microsoft Windows Server 2003 Enterprise 64-bit Edition
		Microsoft Windows XP 64-bit Edition
		Microsoft® Windows® for x64
		OS/2
		Microsoft Windows 8 Enterprise 32-bit
		Microsoft Windows 8 Enterprise x64
		Microsoft Windows 8 Pro 32-bit
		Microsoft Windows 8 Pro x64
		Microsoft Windows 8.1 Enterprise 32-bit
		Microsoft Windows 8.1 Enterprise x64
		Microsoft Windows 8.1 Pro
		Microsoft Windows 8.1 Pro 32-bit
		Microsoft Windows 95/98
		Microsoft Windows 2000 Advanced Server
		Microsoft Windows 2000 Datacenter Server
		Microsoft Windows 2000 Server
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Sample 25009: Macros to plot statistics generated by PROC PLS

Macros to plot statistics generated by PROC PLS

Operating System and Release Information