皆さん、こんにちは。 @sseligman SAS JMPカスタマーサポート（すごい！）では、Fit Y by Xプラットフォームの外でHodges-Lehmann信頼区間を計算する.jslスクリプトを提供しました。
アルファレベルを変更するだけで、対応する片側極限を計算できます。JMPのデフォルトは両面制限です。 さらに、このスクリプトには、これらのノンパラメトリックな制限を計算するという利点があります。ありとなし JMPがデフォルトで採用している、いわゆるSASの「継続性修正」。 たとえば、グループ1とグループ2の2つのグループがあり、既存の仕様にどのような変更を適用する必要があるかを見積もるために、それらをまたぐリスク評価の間隔を計算している場合、これは特に役立ちます。グループ1の場合は、グループ2の場合と同様に保守的です。 もちろん、これは、グループ間の位置の差に統計的に検出可能な差があることを前提としています（そしてその差は実際的に重要です）。
一般に片側区間の場合は、"α/ 2"を "α"に置き換えます。 そのため、片側の場合はConfidence = 1 - αです。 両面の場合、信頼= 1 - （α/ 2）。
最後に、実行した差分の順序（およびそれに続くCIを含む場所の差分の計算）を変更する場合は、スクリプトの下の部分で「Ascending」を「Descending」に変更するだけです。
// Pick up distinct values
sortx = Sort Ascending( x );
ここに完全なスクリプトがあります、そして私は添付ファイルとしてそれも含めています。
// Main routine
MainNonParMCP=Function({},{default local},
// Column dialog to get data values
alpha=0.10;
{alpha,ng, xdistinct, x, y}=GetDataMatrix(alpha);
/* ---------------------------------------------------*/
/* for each pair */
OB1=NonparMCP(ng, xdistinct, x, y, alpha, method=1);
/* for all pairs (Steel-Dwass) */
OB2=NonparMCP(ng, xdistinct, x, y, alpha, method=2);
/* comparisons with the control */
OB3=NonparMCP(ng, xdistinct, x, y, alpha, method=3);
/* ---------------------------------------------------*/
/* Bonferroni for Joint Ranking */
/* for each pair */
OB4=BonferroniJointRanking(ng, xdistinct, x, y, alpha, method=1);
/* for all pairs */
OB5=BonferroniJointRanking(ng, xdistinct, x, y, alpha, method=2);
/* comparisons with the control */
OB6=BonferroniJointRanking(ng, xdistinct, x, y, alpha, method=3);
New Window("Result",
OutlineBox("Steel and Steel-Dwass",
OB1,OB2, OB3
),
OutlineBox("Bonferroni for Joint Ranking",
OB4,OB5, OB6
)
)
);
//
// Invoke column data dialog box, and
// get data values from a data table.
//
GetDataMatrix = Function(
{alpha},{default local},
dt = Current Data Table();
If( Is Empty( dt ),
dt = Open()
);
If( Is Scriptable( dt )==0,
Dialog( "Please open a data table before running this script" );
Throw();
);
// Column Dialog
dlg = Column Dialog(
"Select X and Y Column",
ycol = ColList( "Y, Continuous", DataType( numeric ), MinCol( 1 ), MaxCol( 1 ) ),
xcol = ColList( "X, Nominal", MinCol( 1 ), MaxCol( 1 ) ),
HList("Alpha : ",alpha =EditNumber(alpha))
);
// If canceled
If( Arg( dlg[N Arg( dlg )] ) != 1,
Dialog( "Canceled" );
Throw();
);
// Make Data
xx = Column( dt, dlg["xcol"] ) << GetAsMatrix;
yy = Column( dt, dlg["ycol"] ) << GetAsMatrix;
x = {};
y = [];
// Exclude missing values or excluded values
For( i = 1, i <= N Row( yy ), i++,
If( Excluded( Row State( i ) ) == 0 & Is Missing( xx[i] ) == 0 & Is Missing( yy[i] ) == 0,
Insert Into( x, xx[i] );
y |/= yy[i];
)
);
// Pick up distinct values
sortx = Sort Descending( x );
/*change "Descending" to "Ascending" in the above line of code to change the order of the groups*/
xdistinct = {};
Insert Into( xdistinct, sortx[1] );
For( i = 2, i <= N Items( sortx ), i++,
If( sortx[i] != sortx[i - 1],
Insert Into( xdistinct, sortx[i] )
)
);
ng = N Items( xdistinct );
If(IsMissing(dlg["alpha"])!=0,
alpha=dlg["alpha"]
);
Eval List( {alpha, ng, xdistinct, x, y} );
);
// Wilcoxon tests and Hodges-Lehman confidence interval for two samples
Wilcoxon_HL=Function({y1,y2,q},{default local},
n1=NRow(y1); /* Sample Size of Group 1 */
n2=NRow(y2); /* Sample Size of Group 2 */
n=n1+n2;
r=RankingTie(y1|/y2);
T1=Sum(r[1::n1]);
T2=n*(n+1)/2-T1;
diff=T2/n2-T1/n1;
stderr=Stddev(r)*Sqrt(1/n1+1/n2);
z=diff/stderr;
If(diff>0,
diffc=max(0,(T2-0.5)/n2-(T1+0.5)/n1),
diffc=min(0,(T2+0.5)/n2-(T1-0.5)/n1)
);
zc=diffc/stderr;
/* Hodges-Lehman: Point estimation and Confidence Interval */
dvec=J(n1*n2,1,.);
k=0;
For(i=1,i<=n1,i++,
For(j=1,j<=n2,j++,
k++;
dvec[k]=y2[j]-y1[i]
)
);
dvec=Sort Ascending(dvec);
/* Point Estimate */
If(Mod(n1*n2,2)==1,
HLEst= dvec[(n1*n2+1)/2] ,
HLEst= (dvec[(n1*n2)/2]+dvec[(n1*n2)/2+1])/2
);
/* Confidence interval without continuity correction */
C = Floor( n1*n2/2 - q*Sqrt(n1*n2*(n1+n2+1)/12));
If(C<1,
L=.;
H=.;
,
L=dvec[C];
H=dvec[n1*n2+1-C]
);
/* Confidence interval without continuity correction */
Cc = Floor( n1*n2/2 -0.5 - q*Sqrt(n1*n2*(n1+n2+1)/12));
If(Cc<1,
Lc=.;
Hc=.;
,
Lc=dvec[Cc];
Hc=dvec[n1*n2+1-Cc]
);
EvalList({diff,stderr,z,zc,HLEst,L,H,Lc,Hc});
);
//Nonparametric comparison
// Method==1 ... For each pair, without continuity correction
// Method==2 ... For all pairs, nonparametric Tukey based on pairwise ranking
// "Steel-Dwass test"
// Mehtod==3 ... Comparison with the control, nonparametric Dunnett based on pairwise ranking
// "Steel test"
NonparMCP=Function({ng, xdistinct, x, y, alpha, method},{default local},
Result=[];
g1Label={};
g2Label={};
n=J(NItems(xdistinct),1,0);
For(i=1,i<=NItems(x),i++,
For(j=1,j<=ng,j++,
If(x[i]==xdistinct[j],
n[j]++;
Break()
);
)
);
/* In order to perform, Steel test (nonparametric Dunnett),
we need to get sample size for each group*/
If(method==3,
ncont=n[1];
nother=Matrix(n[2::NRow(n)]);
rho=(1+ncont:/nother)^(-0.5);
);
/* Calculate Crit. value */
If(
method==1,
q=NormalQuantile(1-alpha/2),
method==2,
q=Tukey HSD Quantile(1-alpha,ng,.),
method==3,
q=DunnettQuantile(1-alpha,Nrow(nother),df=.,rho);
);
/* Loop Start */
For(i=1,i<=ng-1,i++,
If(method==3&i>=2,Break());
For(j=i+1,j<=ng,j++,
gval1=xdistinct[i];
gval2=xdistinct[j];
n1=n[i];
n2=n[j];
y1=[];
y2=[];
For(k=1,k<=NRow(y),k++,
If(
x[k]==gval1, y1|/=y[k],
x[k]==gval2, y2|/=y[k]
)
);
// Caclulate z-value and CI */
{diff,stderr,z,zc,HLEst,L,H,Lc,Hc}=Wilcoxon_HL(y1,y2,q);
// Calculate p-values from z
If(method==1,
p=2*NormalDistribution(-Abs(z));
pc=2*NormalDistribution(-Abs(zc));
,
method==2,
p=Tukey HSD P value(Abs(z),ng,.);
pc=Tukey HSD P Value(Abs(zc),ng,.);
,
method==3,
p=Dunnett P Value(Abs(z),ng-1,.,rho);
pc=Dunnett P Value(Abs(zc),ng-1,.,rho);
);
Insert Into(g1Label,Char(gval1));
Insert Into(g2Label,Char(gval2));
Result|/= (n1||n2||diff||stderr||z||p||zc||pc||HLEst||L||H||Lc||Hc);
)
);
If(
method==1, titleOB="Nonparametric Comparison for Each Pair",
method==2, titleOB="Nonparametric Comparison for All Pairs (Steel-Dwass)",
method==3, titleOB="Nonparametric Comparison for Each Pair (Steel)"
);
OB=OutlineBox(titleOB,
TableBox(
NumberColBox(Char(100*alpha)||"% Critical value",Matrix(q))
),
TableBox(
StringColBox("Group2",g2Label),
StringColBox("v.s. Group1",g1Label),
NumberColBox("n2", Matrix(Result[0,2])),
NumberColBox("n1", Matrix(Result[0,1])),
NumberColBox("Diff in Mean Rank", Matrix(Result[0,3])),
NumberColBox("Std Err", Matrix(Result[0,4])),
NumberColBox("Z", Matrix(Result[0,5])),
NumberColBox("p", Matrix(Result[0,6]),<<SET format="">=2,Break());
For(j=i+1,j<=ng,j++,
gval1=xdistinct[i];
gval2=xdistinct[j];
n1=n[i];
n2=n[j];
stderr=STotal*Sqrt(1/n1+1/n2);
diff=T[j]/n2-T[i]/n1;
z=diff/stderr;
If(diff>0,
diffc=Max(0,(T[j]-0.5)/n2-(T[i]+0.5)/n1),
diffc=Min(0,(T[j]+0.5)/n2-(T[i]-0.5)/n1)
);
zc=diffc/stderr;
If(method==1,
p=2*NormalDistribution(-Abs(z));
pc=2*NormalDistribution(-Abs(zc));
,
method==2,
p=Min(1,2*ng*(ng-1)/2*NormalDistribution(-Abs(z)));
pc=Min(1,2*ng*(ng-1)/2*NormalDistribution(-Abs(zc)));
,
method==3,
p=Min(1,2*(ng-1)*NormalDistribution(-Abs(z)));
pc=Min(1,2*(ng-1)*NormalDistribution(-Abs(zc)));
);
Insert Into(g1Label,Char(gval1));
Insert Into(g2Label,Char(gval2));
Result|/= (n1||n2||diff||stderr||z||p||zc||pc);
)
);
If(
method==1, titleOB="Nonparametric Comparison for Each Pair (without adjustment for Joint Ranking)",
method==2, titleOB="Nonparametric Comparison for All Pairs (Bonferonni for Joint Ranking)",
method==3, titleOB="Nonparametric Comparison for Each Pair (Bonferonni for Joint Ranking)"
);
OB=OutlineBox(titleOB,
TableBox(
StringColBox("Group2",g2Label),
StringColBox("v.s. Group1",g1Label),
NumberColBox("n2", Matrix(Result[0,2])),
NumberColBox("n1", Matrix(Result[0,1])),
NumberColBox("Diff in Mean Rank", Matrix(Result[0,3])),
NumberColBox("Std Err", Matrix(Result[0,4])),
NumberColBox("Z", Matrix(Result[0,5])),
NumberColBox("p", Matrix(Result[0,6]),<<SET format=""></SET></SET>
