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    <title>topic Re: Lin's Correspondence coefficient (r) in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627393#M82595</link>
    <description>&lt;P&gt;Try Analyze &amp;gt; Fit Y by X. It should launch the Bivariate platform when X and Y are continuous. Click the red triangle at the top and select Fit Orthogonal &amp;gt; Univariate Variances. You will see a report like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="orthog.PNG" style="width: 409px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/52460i59C12818BC9689FD/image-size/large?v=v2&amp;amp;px=999" role="button" title="orthog.PNG" alt="orthog.PNG" /&gt;&lt;/span&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;See JMP Help for &lt;A href="https://www.jmp.com/support/help/en/17.1/?os=win&amp;amp;source=application#page/jmp/fit-orthogonal-report.shtml#" target="_self"&gt;more information about the report&lt;/A&gt;.&lt;/P&gt;</description>
    <pubDate>Fri, 28 Apr 2023 17:09:10 GMT</pubDate>
    <dc:creator>Mark_Bailey</dc:creator>
    <dc:date>2023-04-28T17:09:10Z</dc:date>
    <item>
      <title>Lin's Correspondence coefficient (r)</title>
      <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627383#M82594</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am trying to recreate an analysis that used Lin's Correspondence coefficient (r). The data are ordinal, not continuous.&amp;nbsp; I cannot find this analysis in JMP, are there any other suggestions.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;More specifically, I have two variables and needing to see how they relate based on three different treatments. Simple regression is not suitable here since X and Y are not related. What is the appropriate analysis to do with this data? See JMP graph below.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks,&lt;/P&gt;&lt;P&gt;Natalie&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="Screenshot 2023-04-28 at 10.50.57 AM.png" style="width: 999px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/52457iB513C6C50AE9B8C5/image-size/large?v=v2&amp;amp;px=999" role="button" title="Screenshot 2023-04-28 at 10.50.57 AM.png" alt="Screenshot 2023-04-28 at 10.50.57 AM.png" /&gt;&lt;/span&gt;&lt;/P&gt;</description>
      <pubDate>Fri, 09 Jun 2023 01:01:35 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627383#M82594</guid>
      <dc:creator>NNKstats</dc:creator>
      <dc:date>2023-06-09T01:01:35Z</dc:date>
    </item>
    <item>
      <title>Re: Lin's Correspondence coefficient (r)</title>
      <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627393#M82595</link>
      <description>&lt;P&gt;Try Analyze &amp;gt; Fit Y by X. It should launch the Bivariate platform when X and Y are continuous. Click the red triangle at the top and select Fit Orthogonal &amp;gt; Univariate Variances. You will see a report like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="orthog.PNG" style="width: 409px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/52460i59C12818BC9689FD/image-size/large?v=v2&amp;amp;px=999" role="button" title="orthog.PNG" alt="orthog.PNG" /&gt;&lt;/span&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;See JMP Help for &lt;A href="https://www.jmp.com/support/help/en/17.1/?os=win&amp;amp;source=application#page/jmp/fit-orthogonal-report.shtml#" target="_self"&gt;more information about the report&lt;/A&gt;.&lt;/P&gt;</description>
      <pubDate>Fri, 28 Apr 2023 17:09:10 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627393#M82595</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2023-04-28T17:09:10Z</dc:date>
    </item>
    <item>
      <title>Re: Lin's Correspondence coefficient (r)</title>
      <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627415#M82597</link>
      <description>&lt;P&gt;A script illustrates how the CCC is calculated and added to the Bivariate report layer. Note that line 3 would be omitted, and the resulting script would be run after the real data table is opened. The X role is for the standard method and the Y role is for the test method.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-jsl"&gt;Names Default To Here( 1 );

dt = Open( "$SAMPLE_DATA/Method Comparison.jmp" );

dialog = New Window( "Launch Bivariate",
	&amp;lt;&amp;lt;Modal, 
	// require the user to select two variables before clicking OK
	&amp;lt;&amp;lt;On Validate(
		Show( xvar, yvar );
		If( Is Missing( xvar ) | Is Missing( yvar ), 
		// if xvar or yvar are missing do nothing when OK is clicked
			0
		,
			1
		);
	),
	Text Box( " Select two numeric columns. " ),
	H List Box(
		Text Box( " X, Factor " ),
		x = Col List Box(
			dt, // data table reference
			all, // display all columns from the data table
			// get the name of the selected column before the window closes
			xvar = (x &amp;lt;&amp;lt; Get Selected)[1];
			Show( xvar );
		),
		Text Box( "Y, Response" ),
		y = Col List Box(
			dt,
			all,
			yvar = (y &amp;lt;&amp;lt; Get Selected)[1];
			Show( yvar );
		)
	)
);
If( dialog["Button"] == 1, // if the user clicks OK...
	xcol = Column( dt, xvar ); // get the columns
	ycol = Column( dt, yvar );
);

obj = Bivariate(
	Y( ycol ),
	X( xcol ),
	Density Ellipse( 0.95, {Line Color( {66, 112, 221} )} ),
	Fit Line( {Line Color( {212, 73, 88} )} ),
	Fit Orthogonal( Univariate Variances, {Line Color( {61, 174, 70} )} )
);

rpt = obj &amp;lt;&amp;lt; Report;

// Pearson correlation r three ways
regrC = rpt["Bivariate Normal Ellipse P=0.950"][Number Col Box( "Correlation" )][1];
regrR = Sqrt( rpt["Summary of Fit"][Number Col Box( 1 )][1] );
ob = (rpt &amp;lt;&amp;lt; XPath( "//OutlineBox[contains( text(), 'Orthogonal Fit Ratio')]" ))[1];
regrO = ob[Number Col Box( "Correlation" )][1];

// Lin's concordance correlation coefficient
muX = Col Mean( :Standard );
muY = Col Mean( :Method 1 );
sigmaX = Col Std Dev( :Standard );
sigmaY = Col Std Dev( :Method 1 );
ccc = regrC * (2 / ((((muY - muX) ^ 2) / (sigmaY * sigmaX)) + (sigmaY / sigmaX) + (sigmaX / sigmaY)));

Show( regrC, regrR, regrO, ccc );

ob[TableBox(1)] &amp;lt;&amp;lt; Append( Number Col Box( "Lin's CCC", { ccc } ) );&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;See &lt;A href="https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/PASS/Lins_Concordance_Correlation_Coefficient.pdf" target="_self"&gt;NCSS source&lt;/A&gt;.&lt;/P&gt;</description>
      <pubDate>Fri, 28 Apr 2023 18:24:55 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/627415#M82597</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2023-04-28T18:24:55Z</dc:date>
    </item>
    <item>
      <title>Re: Lin's Correspondence coefficient (r)</title>
      <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/628288#M82661</link>
      <description>&lt;P&gt;Thank you!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Is the first solution Lin's correspondence analysis or is it called something else?&amp;nbsp; If I use the first solution, the univariate analysis, I want to be able to describe what I did in the methods correctly. Do you have any examples of the use of this analysis?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks again!&lt;/P&gt;</description>
      <pubDate>Wed, 03 May 2023 18:26:02 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/628288#M82661</guid>
      <dc:creator>NNKstats</dc:creator>
      <dc:date>2023-05-03T18:26:02Z</dc:date>
    </item>
    <item>
      <title>Re: Lin's Correspondence coefficient (r)</title>
      <link>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/628300#M82662</link>
      <description>&lt;P&gt;No. I confirmed that it is Pearson's correlation coefficient. I am not sure which analysis you refer to in your last reply. Can you repeat the question with specific references to the analysis about which you want to know?&lt;/P&gt;</description>
      <pubDate>Wed, 03 May 2023 18:59:46 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Lin-s-Correspondence-coefficient-r/m-p/628300#M82662</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2023-05-03T18:59:46Z</dc:date>
    </item>
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