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JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi JMP Community,
The size of my datasets is getting bigger and the basic scripts I created a while back (see below) are not keeping up from a speed point of view.
I would greatly appreciate hearing about ideas on how to optimize this type of script for better speed. For example, I suspect that operations like "Concatenate" are relatively slow, and it might make sense to work with matrices for this type of operation.
Names Default to Here (1);
dt = Current Data Table ();
CList = dt << Get Column Names (string);
dtoutl = New Table ("LSM OUTPUT", "invisible");
dtoutp = New Table ("PVal OUTPUT", "invisible");
For (i = 1, i<= 2000, i++,
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TREATMENT, :VISIT * :TREATMENT, Column (i+2000) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
)
);
fmr = report (fm);
table_list = fmr << xpath ("//OutlineBox [text() = 'VISIT*TREATMENT']//TableBox");
temp_lsm = table_list [1];
temp_pval = table_list [2];
temp_tbl_lsm = temp_lsm << Make into Data Table (invisible (1));
temp_tbl_pval = temp_pval << Make into Data Table (invisible (1));
temp_tbl_lsm << New column ("ID", character, nominal, << set each value (CList [i]));
temp_tbl_pval << New column ("ID", character, nominal, << set each value (CList [i]));
fmr << close window;
dtoutl << Concatenate (Data table (temp_tbl_lsm), "Append to First Table");
Wait (0);
dtoutp << Concatenate (Data table (temp_tbl_pval), "Append to First Table");
Close (temp_tbl_lsm, NoSave);
Close (temp_tbl_pval, NoSave);
);
dtoutl << Bring Window to Front;
dtoutp << Bring Window to Front;
All feedback is welcome.
Best,
TS
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi JMP Community,
I have experimented with matrices as receptacles for aggregating long collections of analysis output tables, and so far I have accelerated my execution time by ~5 folds (see below). The only minor inconvenience is that I have had to rebuild the annotations for the captured output tables because (obviously) matrices do not hold text.
Names Default to Here (1);
st = Today();
dt = Current Data Table ();
dts = dt << Summary( Group( :VISIT, :TRT01A ), Freq( "None" ), Weight( "None" ), invisible );
annot_lsm = {};
met_l = {};
annot_pv = {};
CList = dt << Get Column Names (string);
For (j = 1, j <= N Rows (dts), j++,
insert into (annot_lsm, dts:VISIT [j] || ", " || dts:TRT01A [j]);
);
annot_pv = annot_lsm;
annot_pv = Concat To(annot_pv, {"Estimate","Std Error", "t Ratio", "Prob>|t|", "SS", "Lower 95%", "Upper 95%"});
Close (dts, NoSave);
mtx_lsm = [];
mtx_pval = [];
For (i = 1, i<= 2000, i++, //2000
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TRT01A, :VISIT * :TRT01A, Column (i+2000) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
)
);
fmr = report (fm);
insert into (met_l, CList [i]);
table_list = fmr << xpath ("//OutlineBox [text() = 'VISIT*TRT01A']//TableBox");
temp_lsm = table_list [1];
temp_pval = table_list [2];
temp_mtx_lsm = temp_lsm << Get as Matrix;
temp_mtx_pval = temp_pval << Get as Matrix;
fmr << close window;
mtx_lsm = mtx_lsm |/ temp_mtx_lsm;
mtx_pval = mtx_pval |/ temp_mtx_pval ;
);
dtoutl = as Table (mtx_lsm);
dtoutp = as Table (mtx_pval);
dtoutl << new column ("COMP", Character, Nominal);
dtoutl << new column ("MET_ID", Character, Nominal);
dtoutl:Col1 << set name ("LSMEAN");
dtoutl:Col2 << set name ("STDERR");
dtoutl:Col3 << set name ("UPPER CI");
dtoutl:Col4 << set name ("LOWER CI");
dtoutl << Begin Data Update;
For Each Row( dtoutl, :COMP = annot_lsm[Sequence( 1, N Items (annot_lsm), 1, 1 )] );
For each row( dtoutl, :MET_ID = met_l[Sequence (1,N Items (met_l), 1, 6)] );
dtoutl << End Data Update;
dtoutp << new column ("LABEL", Character, Nominal);
dtoutp << new column ("MET_ID", Character, Nominal);
dtoutp << Begin Data Update;
For Each Row( dtoutp, :LABEL = annot_pv[Sequence( 1, N Items (annot_pv), 1, 1 )] );
For each row( dtoutp, :MET_ID = met_l[Sequence (1,N items (met_l), 1, 13)] );
dtoutp << End Data Update;
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
5X sounds great!
There might also be an invisible option for the platform that might help.
(Try the JSL profiler, the next suggestion may be way off-base. There is no point making something faster that uses < 1% of the time...)
If it still isn't enough, the last two statements in the loop may be the next place to look. I think your JSL is reallocating the arrays 2000 times at progressively larger sizes. If you can pre-allocate the arrays (using the J(nr,nc,initvalue) function) and store into them with something like mtx_lsm[a::b,0] = temp_mtx_lsm[0,0] you'll save some time.
Edit: this is potentially an N^2 problem because it not only reallocates, it copies the data too; going from a run of 1000 to a run of 2000 will copy 4X (not 2X) the amount of data.
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
These might give some further (most likely fairly small) speed increases:
You can most likely add invisible to fit model
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TRT01A, :VISIT * :TRT01A, Column (i-1933) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
),
invisible
);
Also if you are creating columns with formulas and then immediately removing them, you could use << Set Each value instead of Formula and Delete Formula
dtoutl << New Column("METAB_ID", Character, Nominal, << Set Each Value(Substitute(:MET_ID, "LOG10 FC ", "")));
You might be able to replace For Each Row with << Set Each Value also (not sure about this one, also not sure if this will have speed increase):
Column(dtoutp, "LABEL") << Set Each Value(annot_pv[Sequence( 1, N Items (annot_pv), 1, 1 )]);
You can also try to keep datatables private (at least invisible) until you need them. You can make private tables visible with << New Data View (didn't know this before today).
Close datatables / reports / windows immediately after you have no need for them.
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi JMP Community,
I have experimented with matrices as receptacles for aggregating long collections of analysis output tables, and so far I have accelerated my execution time by ~5 folds (see below). The only minor inconvenience is that I have had to rebuild the annotations for the captured output tables because (obviously) matrices do not hold text.
Names Default to Here (1);
st = Today();
dt = Current Data Table ();
dts = dt << Summary( Group( :VISIT, :TRT01A ), Freq( "None" ), Weight( "None" ), invisible );
annot_lsm = {};
met_l = {};
annot_pv = {};
CList = dt << Get Column Names (string);
For (j = 1, j <= N Rows (dts), j++,
insert into (annot_lsm, dts:VISIT [j] || ", " || dts:TRT01A [j]);
);
annot_pv = annot_lsm;
annot_pv = Concat To(annot_pv, {"Estimate","Std Error", "t Ratio", "Prob>|t|", "SS", "Lower 95%", "Upper 95%"});
Close (dts, NoSave);
mtx_lsm = [];
mtx_pval = [];
For (i = 1, i<= 2000, i++, //2000
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TRT01A, :VISIT * :TRT01A, Column (i+2000) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
)
);
fmr = report (fm);
insert into (met_l, CList [i]);
table_list = fmr << xpath ("//OutlineBox [text() = 'VISIT*TRT01A']//TableBox");
temp_lsm = table_list [1];
temp_pval = table_list [2];
temp_mtx_lsm = temp_lsm << Get as Matrix;
temp_mtx_pval = temp_pval << Get as Matrix;
fmr << close window;
mtx_lsm = mtx_lsm |/ temp_mtx_lsm;
mtx_pval = mtx_pval |/ temp_mtx_pval ;
);
dtoutl = as Table (mtx_lsm);
dtoutp = as Table (mtx_pval);
dtoutl << new column ("COMP", Character, Nominal);
dtoutl << new column ("MET_ID", Character, Nominal);
dtoutl:Col1 << set name ("LSMEAN");
dtoutl:Col2 << set name ("STDERR");
dtoutl:Col3 << set name ("UPPER CI");
dtoutl:Col4 << set name ("LOWER CI");
dtoutl << Begin Data Update;
For Each Row( dtoutl, :COMP = annot_lsm[Sequence( 1, N Items (annot_lsm), 1, 1 )] );
For each row( dtoutl, :MET_ID = met_l[Sequence (1,N Items (met_l), 1, 6)] );
dtoutl << End Data Update;
dtoutp << new column ("LABEL", Character, Nominal);
dtoutp << new column ("MET_ID", Character, Nominal);
dtoutp << Begin Data Update;
For Each Row( dtoutp, :LABEL = annot_pv[Sequence( 1, N Items (annot_pv), 1, 1 )] );
For each row( dtoutp, :MET_ID = met_l[Sequence (1,N items (met_l), 1, 13)] );
dtoutp << End Data Update;
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
5X sounds great!
There might also be an invisible option for the platform that might help.
(Try the JSL profiler, the next suggestion may be way off-base. There is no point making something faster that uses < 1% of the time...)
If it still isn't enough, the last two statements in the loop may be the next place to look. I think your JSL is reallocating the arrays 2000 times at progressively larger sizes. If you can pre-allocate the arrays (using the J(nr,nc,initvalue) function) and store into them with something like mtx_lsm[a::b,0] = temp_mtx_lsm[0,0] you'll save some time.
Edit: this is potentially an N^2 problem because it not only reallocates, it copies the data too; going from a run of 1000 to a run of 2000 will copy 4X (not 2X) the amount of data.
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi @Craige_Hales ,
Thanks for the suggestion. Thanks to the method you recommended, I have improved the speed by another 33%.
There are still some tweaks that I need to perfect but I started at a total run time of about 450 seconds, down to 91 seconds with my initial changes, and down to 61 seconds now.
Best
TS
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Cool! I think the community would like to see what you did to get there, or maybe summarize how important different changes were.
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi JMP Community,
Find below the annotated version of the most improved script to run and capture the output of a large number of Fit Model analyses.
The two most essential steps to improve the run time are:
- Usage of matrices instead of Data Tables to aggregate the analysis output
- Replacement instead of insertion of the analysis output data into master matrices created ahead of the main loop.
Names Default to Here (1);
st = Today();
dt = Current Data Table ();
lc_path = dt << GetPath;
// START: CAPTURING OUTPUT ANNOTATION TO REBUILD ANNOTATION //
dts = dt << Summary( Group( :VISIT, :TRT01A ), Freq( "None" ), Weight( "None" ), invisible );
annot_lsm = {};
met_l = {};
annot_pv = {};
CList = dt << Get Column Names (string);
For (j = 1, j <= N Rows (dts), j++,
insert into (annot_lsm, dts:VISIT [j] || ", " || dts:TRT01A [j]);
);
annot_pv = annot_lsm;
annot_pv = Concat To(annot_pv, {"Estimate","Std Error", "t Ratio", "Prob>|t|", "SS", "Lower 95%", "Upper 95%"});
Close (dts, NoSave);
// END: CAPTURING OUTPUT ANNOTATION TO REBUILD ANNOTATION //
// START: CREATING EMPTY NASTER MATRICES TO HOLD TTHE OUTPUT OF THE FIT MODEL//
nro_l = 1933*6;
nco_l = 4;
nro_p = 1933 * 13;
nco_p = 2;
mtx_lsm = J(nro_l, nco_l);
mtx_pval = J(nro_p, nco_p);
// END: CREATING EMPTY MASTER MATRICES TO HOLD TTHE OUTPUT OF THE FIT MODEL//
//START MAIN LOOP//
For (i = 3921, i<= 5853, i++, //5853
//START LINEAR MODEL WITH CUSTGOMIZED CONTRASTS //
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TRT01A, :VISIT * :TRT01A, Column (i-1933) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
)
);
//END LINEAR MODEL WITH CUSTGOMIZED CONTRASTS //
//START CAPTURE ANALYSIS OUTPUTS FOR EACH RUN//
fmr = report (fm);
insert into (met_l, CList [i]); //COLLATING THE NAME OF THE COLUMN FOR EACH RUN//
table_list = fmr << xpath ("//OutlineBox [text() = 'VISIT*TRT01A']//TableBox");
temp_lsm = table_list [1]; //POINTER TO LSMEAN + STDERR TABLE//
temp_pval = table_list [2]; // POINTER TO CONTRAST TABLE //
temp_mtx_lsm = temp_lsm << Get as Matrix; // EXTRACT LSMEAN + STDERR OUTPUT AS A MATRIX //
temp_mtx_pval = temp_pval << Get as Matrix; // EXTRACT CONTRAST OUTPUT AS A MATRIX //
locr1_l = ((i-3921)*6) + 1; //GENERATE THE TOP ROW BLOCK FOR INSERTION OF LSMEAN + STDERR IN TO THE MASTER MATRIX //
locr2_l = ((i-3921)*6) + 6; //GENERATE THE BOTTOM ROW BLOCK FOR INSERTION OF LSMEAN + STDERR IN TO THE MASTER MATRIX //
mtx_lsm [locr1_l::locr2_l, 0] = temp_mtx_lsm [0,0]; //REPLACE PLACEHOLDER VALUES IN MASTER MATRIX WITH LOCAL LSMEAN + STDERR OUTPUT DATA FROM EACH RUN //
locr1_p = ((i-3921)*13) + 1; //GENERATE THE TOP ROW BLOCK FOR INSERTION OF CONTRAST IN TO THE MASTER MATRIX //
locr2_p = ((i-3921)*13) + 13; //GENERATE THE BOTTOM ROW BLOCK FOR INSERTION OF CONTRAST IN TO THE MASTER MATRIX //
mtx_pval [locr1_p::locr2_p, 0] = temp_mtx_pval [0,0]; //REPLACE PLACEHOLDER VALUES IN MASTER MATRIX WITH LOCAL CONTRAST OUTPUT DATA FROM EACH RUN //
fmr << close window;
);
// START CONVERT MASTER MATRICES INTO TABLES//
dtoutl = as Table (mtx_lsm);
dtoutp = as Table (mtx_pval);
// END CONVERT MASTER MATRICES INTO TABLES//
//START REBUILD ANNOTATIONS AND REFORMAT FOR LSMEAN + STDERR TABLE//
dtoutl << new column ("COMP", Character, Nominal);
dtoutl << new column ("MET_ID", Character, Nominal);
dtoutl:Col1 << set name ("LSMEAN");
dtoutl:Col2 << set name ("STDERR");
dtoutl:Col3 << set name ("UPPER CI");
dtoutl:Col4 << set name ("LOWER CI");
dtoutl << Begin Data Update;
For Each Row( dtoutl, :COMP = annot_lsm[Sequence( 1, N Items (annot_lsm), 1, 1 )] );
For each row( dtoutl, :MET_ID = met_l[Sequence (1,N Items (met_l), 1, 6)] );
dtoutl << End Data Update;
dtoutl << New Column ("METAB_ID", Character, Nominal, Formula (Substitute (:MET_ID, "LOG10 FC ", ""))) ;
dtoutl << New Column ("VISIT", Character, Nominal, Formula (Word (1,:COMP, ",")));
dtoutl << New Column ("ARM", Character, Ordinal, Formula (Word (-1, :COMP, ",")));
dtoutl:METAB_ID << Delete Formula;
dtoutl:ARM << Delete Formula;
dtoutl:VISIT << Delete Formula;
//END REBUILD ANNOTATIONS AND REFORMAT FOR LSMEAN + STDERR TABLE//
//START REBUILD ANNOTATIONS AND REFORMAT FOR CONTRAST TABLE//
dtoutp << new column ("LABEL", Character, Nominal);
dtoutp << new column ("MET_ID", Character, Nominal);
dtoutp << Begin Data Update;
For Each Row( dtoutp, :LABEL = annot_pv[Sequence( 1, N Items (annot_pv), 1, 1 )] );
For each row( dtoutp, :MET_ID = met_l[Sequence (1,N items (met_l), 1, 13)] );
dtoutp << End Data Update;
CNAME1 = Column (dtoutp, 3) [3];
CNAME2 = Column (dtoutp, 3) [5];
dtoutp << Select Where (:LABEL == "Prob>|t|");
dtoutp_s = dtoutp << subset (Selected Rows, Output Table Name ("P VAL"));
Close (dtoutp, NoSave);
dtoutp_s << New Column ("METAB_ID", Character, Nominal, Formula (Substitute (:MET_ID, "LOG10 FC ", "")));
dtoutp_s:Col1 << Set Name (CNAME1);
dtoutp_s:Col2 << Set Name (CNAME2);
dtoutp_s_s = dtoutp_s << Stack (Columns (as name(CNAME1), as name (CNAME2)), Output Table Name ("STACK P VAL"));
Close (dtoutp_s, NoSave);
dtoutp_s_s << New Column ("VISIT", Character, Nominal, Formula (Word (1,:Label, ",")));
dtoutp_s_s << New Column ("xARM", Character, Ordinal, Formula (Word (-1, :Label, ",")));
dtoutp_s_s:Data << Set name ("NOM P VAL");
dtoutl << Update(
With( dtoutp_s_s ),
Match Columns( :METAB_ID = :METAB_ID, :VISIT = :VISIT ),
Add Columns from Update Table( :NOM P VAL ),
Replace Columns in Main Table( None )
);
Close (dtoutp_s_s, NoSave);
//END REBUILD ANNOTATIONS AND REFORMAT FOR CONTRAST TABLE//
dtoutl << Save (substitute (lc_path, dt << get name||".jmp", "LINMOD_OUT_")||char(today())); //
et = Today ();
show (Date Difference (st, et, "second"));
Best,
TS
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
These might give some further (most likely fairly small) speed increases:
You can most likely add invisible to fit model
fm = dt << Fit Model(
Y( Column (i) ),
Effects( :VISIT, :TRT01A, :VISIT * :TRT01A, Column (i-1933) ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
Column (i) << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 ), {:VISIT * :TRT01A <<
{LSMeans Contrast( [0 0 1 -1 0 0, 0 0 0 0 1 -1] )}}}
),
invisible
);
Also if you are creating columns with formulas and then immediately removing them, you could use << Set Each value instead of Formula and Delete Formula
dtoutl << New Column("METAB_ID", Character, Nominal, << Set Each Value(Substitute(:MET_ID, "LOG10 FC ", "")));
You might be able to replace For Each Row with << Set Each Value also (not sure about this one, also not sure if this will have speed increase):
Column(dtoutp, "LABEL") << Set Each Value(annot_pv[Sequence( 1, N Items (annot_pv), 1, 1 )]);
You can also try to keep datatables private (at least invisible) until you need them. You can make private tables visible with << New Data View (didn't know this before today).
Close datatables / reports / windows immediately after you have no need for them.
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Re: JMP > JSL > Script Acceleration for Multiple Call to Fit Model (N > 2000)?
Hi jthi,
Making the Fit Model invisible shaved another 8 seconds (from 61 seconds to 53 seconds), and replacing the "For Each Row" with direct assignments trims another 5 seconds (53 seconds to 48 seconds).
Brilliant!
Best,
TS