Page 245 Table 11.1 Means and standard deviations for nondepressed and depressed adults in Los Angeles County
data depress; set "c:\cama3\depress"; run; proc sort data = depress out=depress; by cases; run; proc means data = depress mean std; var sex age educat income health beddays acuteill chronill; by cases; run;
CASES=0 The MEANS Procedure Variable Mean Std Dev ---------------------------------------- SEX 1.5860656 0.4935494 AGE 45.2418033 18.1464928 EDUCAT 3.5450820 1.3310228 INCOME 21.6762295 15.9754727 HEALTH 1.7131148 0.7958690 BEDDAYS 0.1721311 0.3782703 ACUTEILL 0.2786885 0.4492755 CHRONILL 0.4836066 0.5007584 ---------------------------------------- CASES=1 Variable Mean Std Dev ---------------------------------------- SEX 1.8000000 0.4040610 AGE 40.3800000 17.4003167 EDUCAT 3.1600000 1.1668902 INCOME 15.2000000 9.8374545 HEALTH 2.0600000 0.9775020 BEDDAYS 0.4200000 0.4985694 ACUTEILL 0.3800000 0.4903144 CHRONILL 0.6200000 0.4903144 ----------------------------------------
Page 248 Figure 11.2 Distribution of income for depressed and nondepressed individuals showing effects of a dividing point at an income of $18440.
We were unable to reproduce this graph.
Page 249 Table 11.2 Classification of individuals as depressed or not depressed on the basis of income alone.
proc discrim data = depress; class cases; var income; run;
<some output omitted>
Number of Observations and Percent Classified into CASES
From
CASES 0 1 Total
0 121 123 244
49.59 50.41 100.00
1 19 31 50
38.00 62.00 100.00
Total 140 154 294
47.62 52.38 100.00
Page 252 Figure 11.5 Classification of individuals as depressed or not depressed on the basis of income and age.
NOTE: The line can be added using an annotated data set.
goptions reset = all;
goptions cells;
axis1 order=(0 to 65 by 5) label=('Income') label=(a=90 r = 0);
axis2 order=(15 to 90 by 5) label=('Age');
symbol1 v=triangle height=1 cells c=blue;
symbol2 v=circle height=1 cells c=red;
proc gplot data=depress ;
plot income*age = cases /vaxis = axis1 haxis = axis2;
run;
quit;
Page 253 Table 11.3 Classification of individuals as depressed or not depressed on the basis of income and age
proc discrim data = depress; class cases; var income age; run;
<some output omitted>
Number of Observations and Percent Classified into CASES
From
CASES 0 1 Total
0 154 90 244
63.11 36.89 100.00
1 20 30 50
40.00 60.00 100.00
Total 174 120 294
59.18 40.82 100.00
Page 257 Table 11.4 Classification function and discriminant coefficients for age and income from BMDP 7M
NOTE: We do not know why the constant is incorrect.
NOTE: We do not know how to get the discriminant functions.
proc discrim data = depress; class cases; var age income; run;
<some output omitted> Linear Discriminant Function for CASES Variable 0 1 Constant -5.17094 -3.65520 AGE 0.16342 0.14249 INCOME 0.13603 0.10242
Page 258 Covariances in the middle of the page
proc corr data = depress cov; var age income; run;
The CORR Procedure
2 Variables: AGE INCOME
Covariance Matrix, DF = 293
AGE INCOME
AGE 327.0831882 -53.0072671
INCOME -53.0072671 233.7878967
<some output omitted>
Page 268 Table 11.5 Partial printout from BMDP 7M for classification into more than two groups, using the depression data with K = 3 groups
NOTE: Before you can do this, you need to add a new variable to the data set and use the variable cesd to recode the new variable, which we called cases3.
NOTE: The signs of the coefficients for canonical variables is arbitrary. The signs of the canonical variables given by SAS is opposite to what is shown in the text.
data depress1; set depress; if cesd = 0 then cases3 = 1; if cesd ge 1 and cesd le 15 then cases3 = 2; if cesd ge 16 then cases3 = 3; run; proc discrim data = depress1 can out=candepress; class cases3; var sex age educat income health beddays; run;
<some output omitted>
The DISCRIM Procedure
Linear Discriminant Function
_ -1 _ -1 _
Constant = -.5 X' COV X Coefficient Vector = COV X
j j j
Linear Discriminant Function for cases3
Variable 1 2 3
Constant -16.52247 -17.44950 -17.71770
SEX 7.07529 7.49617 8.14165
AGE 0.16774 0.13935 0.11698
EDUCAT 2.54993 2.82551 2.68116
INCOME 0.10533 0.09005 0.06537
HEALTH 2.13954 2.75024 3.10425
BEDDAYS -0.97394 -0.80246 0.46685
Raw Canonical Coefficients
Variable Can1 Can2
SEX 0.731029500 0.019771251
AGE -0.031672485 -0.025305150
EDUCAT 0.006170102 0.654808658
INCOME -0.027576962 0.000673965
HEALTH 0.575237409 0.688216467
BEDDAYS 1.136441447 -1.131166668
Page 271 Figure 11.6 Plot of the canonical variables for the depression data set with k = 3 groups
We were unable to reproduce this graph.