SAS forest森图综合

Code大致由四部分组成

First Part: 把 SIDEBAR 区域分成两行四列，分别输入entry。这里默认 order = rowmajor，即先按水平方向填充entry。

　　　　siderbar区域啥意思，可参考SAS lattice_2中的图。

Second Part:  用SCATTERPLOT 把数据集的数据画出来。其实一个SCATTERPLOT也可以画出所有数据，之所以有两个，是因为第二个SCATTERPLOR需要控制背景色(暗灰色)。

Third Part: 用SCATTERPLOT画出点和95%区间。

Fourth Part: 用SCATTERPLOT画出数据集中数据。xaxis = x2就是用到了副坐标轴，把label显示在了上面，否则就会显示在下面。

 

数据集都是提前整理好的，非常费时间。这些例子都是在sasblog中找的零散例子，分享给大家。

 

 

proc template; 
define statgraph Forest; 
dynamic _bandcolor _headercolor _subgroupcolor; 
begingraph; 
layout lattice / columns=4 columnweights=(0.23 0.07 0.4 0.3);

*第一部分；
            sidebar / align=top; 
                layout lattice / rows=2 columns=4 columnweights=(0.18 0.17 0.350.3) backgroundcolor=_headercolor opaque=true; 
                    entry textattrs=(size=8 weight=bold) halign=left "Subgroup"; 
                    entry textattrs=(size=8 weight=bold) " No.of Patients (%)"; 
                    entry textattrs=(size=8 weight=bold) "Hazard Ratio"; 
                    entry halign=center textattrs=(size=8 weight=bold) "4-Yr CumulativeEvent Rate" ; 
                    entry " "; 
                    entry " ";     
                    entry ""; 
                    entry halign=center textattrs=(size=6) "Medical Therapy"; 
                endlayout; 
            endsidebar;
*第二部分；
            layout overlay / walldisplay=none xaxisopts=(display=none) yaxisopts=(reverse=true display=none 
                    tickvalueattrs=(weight=bold)); 
                referenceline y=ref / lineattrs=(thickness=15 color=_bandcolor); 
                scatterplot y=obsid x=zero / markercharacter=subgroup markercharacterattrs=(family='Lucida Console' size=6); 
                scatterplot y=obsid x=zero / markercharacter=heading markercharacterattrs=(family='Lucida Console' size=6 color=_subgroupcolor weight=bold); 
            endlayout;

            layout overlay / xaxisopts=(display=none) yaxisopts=(reverse=true display=none) walldisplay=none; 
                referenceline y=ref / lineattrs=(thickness=15 color=_bandcolor); 
                scatterplot y=obsid x=zero /markercharacter=countpct markercharacterattrs=graphvaluetext; 
            endlayout;
*第三部分；
            layout overlay /xaxisopts=(label=' <---PCIBetter---- ----Medical TherapyBetter--->' linearopts=(tickvaluepriority=true tickvaluelist=(0.0 0.5 1.0 1.5 2.0 2.5))) 
                        yaxisopts=(reverse=true display=none) walldisplay=none; 
                referenceline y=ref / lineattrs=(thickness=15 color=_bandcolor); 
                scatterplot y=obsid x=mean / xerrorlower=low xerrorupper=high markerattrs=(symbol=squarefilled); 
                referenceline x=1; 
            endlayout;
*第四部分；
            layout overlay / x2axisopts=(display=(tickvalues) offsetmin=0.25 offsetmax=0.25) yaxisopts=(reverse=true display=none) 
                            walldisplay=none; 
                referenceline y=ref / lineattrs=(thickness=15 color=_bandcolor); 
                scatterplot y=obsid x=pci_lbl / markercharacter=PCIGroup xaxis=x2 markercharacterattrs=graphvaluetext; 
                scatterplot y=obsid x=grp_lbl / markercharacter=group xaxis=x2 markercharacterattrs=graphvaluetext; 
                scatterplot y=obsid x=pval_lbl / markercharacter=pvalue xaxis=x2 markercharacterattrs=graphvaluetext; 
            endlayout; 
endlayout; 
entryfootnote halign=left textattrs=(size=7) 'The p-value is from the test statistic for testing the interactionbetween the ' 'treatment and any subgroup variable'; 
endgraph; 
end;
run;

data forest_subgroup;
label PCIGroup='PCI Group' Group='Therapy Group';
input Id Subgroup $3-27 Count Percent Mean Low High PCIGroup Group PValue;
datalines;
1 Overall 2166 100 1.3 0.9 1.5 17.2 15.6 .
1 Age . . . . . . . 0.05
2 <= 65 Yr 1534 71 1.5 1.05 1.9 17.0 13.2 .
2 > 65 Yr 632 29 0.8 0.6 1.25 17.8 21.3 .
1 Sex . . . . . . . 0.13
2 Male 1690 78 1.5 1.05 1.9 16.8 13.5 .
2 Female 476 22 0.8 0.6 1.3 18.3 22.9 .
1 Race or ethnic group . . . . . . . 0.52
2 Nonwhite 428 20 1.05 0.6 1.8 18.8 17.8 .
2 White 1738 80 1.2 0.85 1.6 16.7 15.0 .
1 From MI to Randomization . . . . . . . 0.81
2 <= 7 days 963 44 1.2 0.8 1.5 18.9 18.6 .
2 > 7 days 1203 56 1.15 0.75 1.5 15.9 12.9 .
1 Diabetes . . . . . . . 0.41
2 Yes 446 21 1.4 0.9 2.0 29.3 23.3 .
2 No 720 79 1.1 0.8 1.5 14.4 13.5 .
;
run;

/*--Set indent weight, add insets and horizontal bands--*/
data forest_subgroup_2;
set forest_subgroup end=last;
label countPct='No. of Patients (%)';
length tValue $20 tPos $8;
drop Count Percent val;

yVal=_n_;
if count ne . then CountPct=put(count, 4.0) || "(" || put(percent, 3.0) || ")";
indentWt=ifn(id EQ 1, 0, 1); /* indent sub headings */
val=mod(_N_-1, 6);
if val in (1:3) then ref=yVal;
output;

if last then do;
call missing (Id, subGroup, mean, low, high, pcigroup, group, countPct, indentWt, ref);
yVal+1;
xl=0.9; tValue='<--PCI Better'; tPos='left'; output;
xl=1.1; tValue='Therapy Better-->'; tPos='right'; output;
end;
run;

 

标签：none,weight,scatterplot,obsid,forest,森图,SAS,entry,size
来源： https://www.cnblogs.com/Python-T/p/15375713.html