Hello Everyone, I wanted to provide some resolution to this thread courtesy of @sseligman at SAS JMP customer support (awesome!), who provided a .jsl script which computes the Hodges-Lehmann Confidence Intervals outside of the Fit Y by X platform.
By simply modifying the alpha level, one can compute the corresponding 1-sided limits vs. JMPs default 2-sided limits. In addition, this script offers the benefit of calculating these non-parametric limits both with and without a so-called "continuity-correction" which JMP employs by default, and SAS does not. This could be particularly useful for you if, for example, you have two groups, Group 1 and Group 2, and you are computing intervals for risk-assessment accross them for the purpose of estimating what change you would need to apply to an existing specification for Group 1 such that it is remains equally conservative for Group 2. Of course this prespupposes that there is a statistically detectable difference in the location difference accross Groups (and that that difference is of practical importance).
For 1-sided intervals in general, replace "α/2" with "α". So, for 1-sided case Confidence = 1 - α. For 2-sided case, Confidence = 1 - (α/2).
Finally, if you wish to change the order of the difference taken (and the subsequent calculations on the location difference including the CIs) you simply need to change "Ascending" to "Descending" in the below portion of the script:
// Pick up distinct values
sortx = Sort Ascending( x );
Here is the complete script, and I am also including it as an attachment.
// 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( 7, "PValue" )),
NumberColBox("Z with cc", Matrix(Result[0,7])),
NumberColBox("p with cc", Matrix(Result[0,8]),<<Set Format( 7, "PValue" )),
NumberColBox("Hodges-Lehmann",Matrix(Result[0,9])),
NumberColBox(Char(100*(1-alpha))||"% Lower",Matrix(Result[0,10])),
NumberColBox(Char(100*(1-alpha))||"% Upper",Matrix(Result[0,11])),
NumberColBox(Char(100*(1-alpha))||"% Lower with cc",Matrix(Result[0,12])),
NumberColBox(Char(100*(1-alpha))||"% Upper with cc",Matrix(Result[0,13])),
)
);
OB
);
BonferroniJointRanking=Function({ng, xdistinct, x, y, alpha, method},{default local},
Result=[];
g1Label={};
g2Label={};
ry=Ranking Tie(y);
STotal=Stddev(ry);
n=J(NItems(xdistinct),1,0);
T=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]++;
T[j]+=ry[i];
Break()
);
)
);
/* Calculate Crit. value */
/* 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];
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( 7, "PValue" )),
NumberColBox("Z with cc", Matrix(Result[0,7])),
NumberColBox("p with cc", Matrix(Result[0,8]),<<Set Format( 7, "PValue" )),
)
);
OB
);
// Invoke Main Function
MainNonParMCP();
as the median of all paired differences between observations in the two samples, which can be written as
is the largest integer less than or equal to 
, which is computed as
is the 
th percentile of the standard normal distribution.
but is at least