Without getting into the code (I’m away from my laptop right now), I would guess you have a numeric variable for the groups.  I thought I had addressed that in my last update, but maybe not.  Change the group to a character column and try again.  Make sure you have the most recent version ... View more

Thank you!  I had JMP Profiler Core checked, but not JMP Profiler GUI.  Once I had both checked, the menu appeared for me as well. ... View more

Thank you Sheila.  Consolidating all the books into 1 document is probably my least favorite new feature.  I'm so glad to have the Books menu back! ... View more

I think JMP Tech Support may be the fastest solution path for you. It might take some time for another Mac user connecting JMP to R to see this. ... View more

You have to realize those efficiencies are computed based on the required model terms, and the 2-factor interactions in Design 2 would not be included in that evaluation.  The prediction variance is obviously going to be much lower for for the second design because you are using 48 runs to basically just model main effects.  In the first design, it has to estimate main effects and second order interactions.   You are not getting apples-to-apples comparisons as is in terms of average prediction variance and optimality criteria efficiencies.  You could do that by using Compare Designs and specifying the model that is used to compare both of them.  It's probably worthwhile to look at the design comparison for both models (all main effects, and then all main effects + 2FI).  You may not be able to do the second one if any interactions are completely confounded in Design 2.  At the very least, it is a good idea to compare the designs for the main effects model because if Design 2 doesn't offer any major advantages over Design 1 in that scenario, then the choice is easy.  Design 1 does better with many active interactions, and performs comparably if there are no active interactions.   I actually don't think those efficiencies are that low for each of those designs, nor is it metric I usually pay attention to.  Those are relative to hypothetical designs with characteristics that are unachievable at the given design size.  For example, you cannot get a completely orthogonal design for 2-level factors except where the run size is a power of 2, but that's the only way to get 100% D-efficiency.  Let's say you can get 100% D-efficiency at 32 runs, but choose 48.  At 48, you will not have 100% efficiency because you cannot get a completely orthogonal design, but you can still potentially have much higher power for all model terms compared to the 32 runs.  If it doesn't cost me much to do the 16 additional runs, I'm obviously going to prefer that design even if it's not completely orthogonal.  I think efficiencies are much more useful in comparing 2 designs in a ratio rather than as a standalone metric.  In fact, I'd be very interested in efficiency ratios for Design 1 vs. Design 2 for the main effects model. ... View more

It's not really something specific to optimal DOEs, so you can totally analyze a blocked classical design with random blocks even if it defaults to fixed. There's not really any downside there. That being said, if you are doing a DSD, I highly recommend using Fit Definitive Screening for the analysis, where I don't think you'll be able to coerce it into treating Block as random. ... View more

Fixed blocks and random blocks are treated differently in the design optimization, and you should use the type of block that corresponds to how you will fit your model. They consume degrees of freedom differently, so there will be an impact in how points are allocated in your design space depending on the type of blocks you selected.  Notice the difference in minimum and recommended design sizes between the 2 approaches.  Fixed blocks generally require more runs.   There are some reasons you might treat a block as fixed, but I find that random blocks make more sense in most contexts that I've seen, particularly if your objective is to account for the variation caused for the blocks and not needing make any inference about effects.  For example, if I include multiple lots of raw material in my design, I don't care that Lot A increases the average response by 1.5 and Lot B by -0.6 because once Lots A and B are consumed, they are gone forever and we'll never have another Lot A or Lot B.  I only want the lots I use in the design to by representative of the typical variation in raw material lots I will encounter in the future.   ... View more

That's a whole course of study contained in that question, but I'll try to summarize as best I can.  Custom Design is built on a completely different paradigm of "optimal" or "algorithmic" designs.  Full factorial designs are a classical type of DOE.  They enforce all possible combinations of factor levels into the design.  This gives the design some very desirable properties, such as orthogonal effects (no correlation between model terms, so coefficients are estimated with maximal precision for given number of runs).  You can also estimate all main effects and higher order terms up to n-way interactions for n factors.  However, you'll find that these designs quickly explode in size.  A full factorial with 8 factors, each with 2 levels, has 2^8 = 256 runs.  In most cases that is way too many runs.   In classical designs, you would look to fractional factorials to reduce the number of runs.  These designs strategically confound the higher order model terms in order to reduce the design size significantly.  In many cases, experimenters are only interested in main effects and 2 factor interactions.  So losing the ability to estimate an 8, 7, 6, 5, 4-way interactions is of little consequence.  For example, I could do a fractional factorial with 8 factors in only 64 runs (quite a bit lower than 254!), and still have uncorrelated main effects and 2-factor interactions, and can maybe even estimate higher orders than that depending on the level of fractionization.  These designs, amongst many other classical designs, are all derived from theory.   Optimal designs are generated from computer algorithms where you basically describe what you want as inputs into the algorithm, and it returns a design with the most optimal set of design points to meet your experimental objectives.  It does so through a process of rearranging design points (aka your run conditions) inside the design space until some optimization criteria is optimized (maximized for some criteria or minimized for others).  There are 2 really prominent optimality criteria, D- and I-optimality.  D-optimality emphasizes precise estimates of model coefficients (makes it a pretty good all-around choice, but excels in screening situations where people would commonly choose fractional factorial designs).  I-optimality emphasizes low prediction variance, and is more associated with response surface designs.  The basic idea is that while classical designs are very rigid and uncompromising, optimal designs can tailor an experiment to your needs.  I can do a decent design for the 8-factor situation with something like 48 runs and still be able to estimate all the model terms I care about (main effects and 2-factor interactions).  I pay for this convenience by allowing some partial correlations between the model terms, but that is often of little consequence.   You will find other optimality criteria in Custom Design.  A-optimality is a newer addition, and I think they are very compelling designs. ... View more

For the one-sample case, you specify the values of these variables: n = 15; //total sample size conf = 0.95; //confidence mu = 505; //hypothesized mean muu = 510; //upper equivalence limit mul = 490; //lower equivalence limit sigma = 4; //estimated standard deviation For the two-sample case, you specify the values of these variables:   N = 36; //total sample size W = 1; //weighting for sample size (e.g. use 2 for 1 of the groups to have 2x as many samples as the other conf = 0.95; //confidence level mudiff_l = -2; //lower equivalence limit on mean difference mudiff_u = 2; //upper equivalence limit on mean difference mudiff = 0; //estimated mean difference sigma = 2; //estimated standard deviation To find a sample size, you would just need to implement a loop to increase N until the desired power is achieved.   I understand your concern about needing to submit this to an authority for review.  I work in a regulated business myself, and we have a validation protocol for the JMP add-in with the calculators we built based upon this script. The SAS documentation for PROC POWER shows the formulas for the power calculation for 1 and 2 sample TOST.  You could reference that document to show the calculations are accurate, reproduce it in another software (e.g. R, I do have a script for that if you would like), or confirm power estimates through simulation.  If you have SAS in anywhere in your organization, you can just get a proc power printout and be done. ... View more

We developed an app that does TOST sample size calculations.  I can't send that out, but I can share the script I used to replicate power calculations for TOST in SAS using Owen's Q.  It has the code for 1 and 2-sided scenarios with an example for each.  Note, this will not handle small signal-to-noise scenarios well.  In my experience with this so far, it only works until nu (sample size parameter) gets to be around 200-250 because Gamma( nu/2 ) runs into a numerical overflow issue and returns a missing value. //Owen's Q OwensQ = Function( {t, delta, a, b, nu}, c = Sqrt( 2 * Pi() ) / (Gamma( nu / 2 ) * Power( 2, (nu - 2) / 2 )); integrand = Expr( Normal Distribution( ((t * x / Sqrt( nu )) - delta) ) * Power( x, nu - 1 ) * Normal Density( x ) ); c * Integrate( integrand, x, a, b, <<Starting Value( Mean( a, b ) ) ); ); /******** 1-Sample TOST Power ****************/ One_TOST_power = expr( nu = n - 1; t1 = -t Quantile( conf, nu ); t2 = t Quantile( conf, nu ); delta1 = (mu - muu) / (sigma / Sqrt( n )); delta2 = (mu - mul) / (sigma / Sqrt( n )); b = (Sqrt( nu ) * (muu - mul)) / ((2 * sigma / Sqrt( n )) * t Quantile( conf, nu )); //Power: OwensQ( t1, delta1, 0, b, nu ) - OwensQ( t2, delta2, 0, b, nu ); ); //1-sample case: n = 15; conf = 0.95; mu = 505; muu = 510; mul = 490; sigma = 4; One_TOST_power(); //should be 0.9983947 (second part is effectively 0) /******** 2-Sample TOST Power ****************/ Two_TOST_power = expr( nu = N - 2; w1 = 1/(W+1); w2 = W/(W+1); t1 = -t Quantile( conf, nu ); t2 = t Quantile( conf, nu ); //For Unequal Sample Sizes //Non-centrality parameters: delta1 = (mudiff - mudiff_u) / (sigma / (Sqrt( N ) * Sqrt( w1 * w2 ))); delta2 = (mudiff - mudiff_l) / (sigma / (Sqrt( N ) * Sqrt( w1 * w2 ))); //Upper integration limit: b = (Sqrt( nu ) * (mudiff_u - mudiff_l)) / ((2 * sigma / (Sqrt( N ) * Sqrt( w1 * w2 ))) * t Quantile( conf, nu )); Power = OwensQ( t1, delta1, 0, b, nu ) - OwensQ( t2, delta2, 0, b, nu ); ); //2-sample equal sizes: N = 36; W = 1; conf = 0.95; mudiff_l = -2; mudiff_u = 2; mudiff = 0; sigma = 2; Two_TOST_power(); //Should be just over 0.8 //2-sample unequal sizes: N = 42; W = 2; //unequal sample sizes: conf = 0.95; mudiff_l = -2; mudiff_u = 2; mudiff = 0; sigma = 2; Two_TOST_power(); //Should be just over 0.8   ... View more

Can you elaborate on under which conditions you would want to automatically click "OK"?  In JSL, you can programatically click a button using something like "OK_button << click;"   If, for example, you want to programatically click OK once all required inputs are populated, you could write a function or expression like this: check_inputs = Expr( If( N Items( y_inputs << get items ) > 0 & N Items( x_inputs << get items ) > 0 & N Items( parts_inputs << get items ) > 0, OK_button << click ) ); Then, within  the script for each of the input buttons, you could add check_inputs() at the end.   ... View more

This should do it for you, but the script assumes that the status columns correspond to the column just preceding them.  You could do this by looking at the name of the status column and finding a column with the same name minus " Status", but I figured that is unnecessarily complicated if the assumption is true.   dt = Current Data Table(); col_names = dt << Get Column Names(string); //Assuming the corresponding value column for each status column is always the preceding one //Loop backwards through the columns for(i = N Col(dt), i>= 1, i--, //If column name has word "status" in it if(Contains(Lowercase(col_names[i]), "status") > 0, //Look for instances of "{down}" down_rows = dt << Get Rows Where( dt:(As Name(col_names[i])) == "{down}" ); //Assuming preceding column is the value column, replace {down} rows with missing Column(dt, i-1)[down_rows] = .; dt << Delete Columns(i); ); ); //Create table summary to consolidate time stamp rows with same value //Build a string that we can parse for the summary operation remaining_cols = dt << Get Column Names("string"); summary_str = "dt_clean = dt << Summary( Group( :TimeStamp ), "; //Loop through all columns except TimeStamp to add "Sum( <current col> )" to summary string for(i = 2, i <= N Col(dt), i++, summary_str = summary_str||"Sum( :Name(\!""||remaining_cols[i]||"\!")), "; ); //Attach last part of script summary_str = summary_str||"Freq( \!"None\!"), Weight(\!"None\!"), statistics column name format( \!"column\!" ), Link to original data table( 0 ), output table name( \!"CleanedData\!" ));"; //Parse and evaluate the string to create the summary table dt_new eval(parse(summary_str)); //Delete N Rows column dt_clean << Delete Columns(:N Rows); ... View more

Interesting idea, but I can imagine things would get really interesting if the whole prior experiment is 1 random block, and then you want to fully randomize that factor in the new runs. ... View more

I have had the same issue for years and the solutions in the linked note have never solved the issue for me. I believe what initially triggered this was having more than 1 version of JMP installed and then uninstalling the one that was the default for the jmp file types. I have mostly just learned to work around that issue, but I would love it if someone could provide a more effective solution than what is in that note. ... View more

I'm struggling a little bit to wrap my head around what your process looks like, but if you are referring to the dependence from the 3 tubes in process step 3 all coming from the same tube in process steps 1 and 2, then the dependence you are concerned about is already handled by the whole plot random block effect.  The conditional residuals should look independent. ... View more