Handbook_of_statistical_analysis_using_SAS

First, a new variable is created to hold the standardized depression scores. Then the standardization is done separately for each subject.

goptions reset=symbol;

symbol1 i=join v=none l=1 r=27; symbol2 i=join v=none l=2 r=34; proc gplot data=pndep2;

plot depz*time=idno /nolegend skipmiss;

The two baseline measures are treated as if they were made at the same time.

proc sort data=pndep2; by idno;

run;

proc reg data=pndep2 outest=regout(keep=idno time) noprint;

model dep=time; by idno;

run;

A separate regression is run for each subject and the slope estimate saved. This is renamed as it is merged into the pndep data set so that the variable time is not overwritten.

data pndep;

merge pndep regout (rename=(time=slope)); by idno;

run;

proc ttest data=pndep; class group;

var slope; run;

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proc glm data=pndep; class group;

model slope=mnbase group /solution; run;

Chapter 11

proc reg data=alzheim outest=regout(keep=idno intercept visit) noprint ;

model score=visit; by idno;

run;

data regout;

merge regout(rename=(visit=slope)) alzheim; by idno;

if first.idno; run;

proc gplot data=regout;

plot intercept*slope=group; symbol1 v='L';

symbol2 v='P'; run;

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data pndep(keep=idno group x1-x8)

pndep2(keep=idno group time dep mnbase); /* 11.3 */ infile 'n:\handbook2\datasets\channi.dat';

input group x1-x8; idno=_n_; mnbase=mean(x1,x2);

if x1=-9 or x2=-9 then mnbase=max(x1,x2); array xarr {8} x1-x8;

do i=1 to 8;

if xarr{i}=-9 then xarr{i}=.; time=i;

dep=xarr{i}; output pndep2;

end;

output pndep; run;

The data step is rerun to include the mean of the baseline measures in pndep2, the data set with one observation per measurement. A where statement is then used with the proc step to exclude the baseline observations from the analysis.

proc mixed data=pndep2 method=ml covtest; class group idno;

model dep=mnbase time group /s; random int /sub=idno;

where time>2;

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run;

proc ttest data=alzheim; class group;

var mnscore maxscore; where visit=1;

run;

Chapter 12

Like proc reg, proc phreg has no facility for specifying categorical predictors or interaction terms on the model statement. Additional variables must be created to represent these terms. The following steps censor times over 450 days and create suitable variables for the exercises.

model time*status(0)=prison dose clinic / rl;

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;

proc phreg data=heroin3;

model time*status(0)=prison clinic dose1 dose2 / rl; baseline covariates=covvals out=baseout survival=bs /

method=ch nomean; run;

data baseout; set baseout;

dosegrp=3-dose1*2-dose2; run;

proc gplot data=baseout; plot bs*time=dosegrp;

symbol1 v=none i=stepjs l=1; symbol2 v=none i=stepjs l=3; symbol3 v=none i=stepjs l=33 w=20;

run;

By default, the baseline statement produces survival function estimates at the mean values of the covariates. To obtain estimates at specific values of the covariates, a data set is created where the covariates have these values, and it is named in the covariates= option of the baseline statement. The covariate values data set must have a corresponding variable for each predictor in the phreg model.

The survival estimates are plotted as step functions using the step interpolation in the symbol statements. The j suffix specifies that the steps are to be joined and the s suffix sorts the data by the x-axis variable.

model time*status(0)=prison clinic dose1 dose2 clindose1 clindose2/ rl;

test clindose1=0, clindose2=0; run;

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proc phreg data=heroin3;

model time*status(0)=prison clinic dose clindose / rl; run;

proc phreg data=heroin3; /* 12.4 */ model time*status(0)=prison clinic dose / rl;

output out=phout xbeta=lp resmart=resm resdev=resd; run;

goptions reset=symbol; proc gplot data=phout;

plot (resm resd)*lp / vref=-2,2 lvref=2; symbol1 v=circle;

run;

Chapter 13

proc factor data=decathlon method=principal priors=smc mineigen=1 rotate=oblimin; /* 13.3 */

var run100--run1500; where score>6000;

run;

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proc factor data=decathlon method=ml min=1 rotate=obvari max;

var run100--run1500; where score>6000;

run;

Chapter 14

proc modeclus data=usair2 out=modeout method=1 std

r=1 to 3 by .25 test; /* 14.1 */ var temperature--rainydays;

id city; run;

proc print data=modeout; where _R_=2;

run;

The proc statement specifies a range of values for the kernel radius based on the value suggested as a “reasonable first guess” in the documentation. The test option is included for illustration, although it should not be relied on with the small numbers in this example.

proc stdize data=usair2 method=range out=usair3; /* 14.3 */ var temperature--rainydays;

run;

/* then repeat the analyses without the std option */

Chapter 15

15.2The first example uses a nearest neighbour method with k = 4 and the second a kernel method with a value of the smoothing parameter derived from a formula due to Epanechnikov (Epanechnikov [1969]).

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proc discrim data=skulls method=npar k=4 crossvalidate;

/* 15.2 */

class type;

var length--facewidth; run;

proc discrim data=skulls method=npar kernel=normal r=.7 crossvalidate;

class type;

var length--facewidth; run;

Chapter 16

16.1 Use the scatterplot matrix macro introduced in Chapter 4.

16.2The cp and rp options (row profile and column profile), together with the row and column masses, provide the proportions used in the calculations.

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