About di_michelson

di_michelson · ‎09-05-2019

Note: This was originally released as a script. The Gauge Study Designer creates an experimental design worksheet with randomization of columns within other columns. Gauge studies are special designed experiments that support mixed models and require incomplete randomization. Random factors can not be randomized (days, repeats), but fixed factors should be randomized (machines, locations). Click the Add Factor button to add new factors. Name the factors and specify the number of levels for each factor. Check the Randomize checkbox if you want the levels of that factor randomized within the factor above it. Use the Remove Last Factor button to correct mistakes. Click the Create Worksheet button to display the worksheet. A run order column is included so you can get back to this order if you would like to sort the table into standard order. An empty response column is also included.

di_michelson · ‎09-12-2018

I don't think there is a way to get just the three-step ahead prediction in a column from the time series platform. I also checked with the developer. I think it's possible to do with a script that excludes rows, fits the model, saves the predictions, writes them to the table, then loops by unexcluding one row at a time.

di_michelson · ‎09-11-2018

Hi Mattia, I think you are looking for the three-step ahead forecast, is that correct? I think you can apply the Lag() function to the prediction formula and it will do what you want. There is probably an easier way, but try this. Save your prediction formula. Then right-click at the top of the prediction formula column and select New Formula Column > Row > Lag. This will give you the predictions going the wrong way. Right-click at the top of the lagged column and select Formula. In the second argument, enter –1. (You will probably have to enter a 1, then click the +/- button to make it negative one.) Then click OK. This is the one-step ahead forecast. Note that row 2 of the prediction formula is row 1 of the lagged prediction formula. For the three-step ahead forecast, you would lag by –3. Please let me know if any of this is not clear or if I have answered a different question than you were asking! --Di

di_michelson · ‎05-30-2017

Hi Jason, You won't learn much about the measurement system unless you make multiple measurements on the same unit. Design and analysis of a measurement system study is a well-researched topic, similar to design of experiments on process systems. Look at some JMP on-demand webinars: https://www.jmp.com/en_us/events/ondemand/mastering-jmp/evaluating-and-monitoring-your-process-using-msa-and-spc.html, or take a training class: https://support.sas.com/edu/schedules.html?ctry=us&crs=JMSA (there is a LiveWeb class scheduled for next week!). I hope this helps. It's great that you are asking this question and looking for more analytical answers. --Di

di_michelson · ‎02-24-2017

I'm glad the explanation made sense, and I look forward to seeing you in the course. Yes, the loadings are found from the eigenvalue decomposition of the R–U (R minus U) matrix. The maximum likelihood procedure with SMC starts with those Rsquares on the diagonal of that matrix, then it iterates to maximize the likelihood function. In your example, the iteration did not converge. The principal factoring method starts with the full R matrix, with ones on the diagonal. It is important in factor analysis to choose the manifest variables carefully. You need to factors to be interpretable, so you want to choose manifest variables that will support interpretation of the factors. There is not a mathematical rule to do this, only subject matter knowledge. However, you can always use the Multivariate platform to find the correlation matrix. Perfectly correlated variables will have a correlation of 1, and one of the pair can be removed.

di_michelson · ‎02-24-2017

Factor analysis tries to fit the model Y = XB + E where you only know Y. X, B, and E are all unknown. Therefore, many key conditions have to be imposed on the parameters. Factor analysis involves the decomposition of the R–U matrix, where R is the correlation matrix of the manifest variables (Y) and U is the correlation matrix of the unique factors (E). Principal factor analysis does eigenvalue decomposition of the R–U matrix. It is not PCA! Maximum likelihood is an iterative method that maximizes the likelihood function given the factors. SMC uses the Rsquare of each variable with the rest as an estimate of the diagonal element of R–U. PC uses 1 on the diagonal of R–U. This point is key: since each of these methods are valid mathematically, they are all correct. The researcher needs to use subject matter knowledge in addition to the graphs and statistics from the analysis to determine the factors and their interpretability. The factor analysis model assumes that there are unmeasureable factors causing the manifest variables, and the model which gives the best interpretion is the most useful model, the winner. Please let me know if I can provide more detail to help your understanding of factor analysis. Small advertisement: for more information, JMP offers a one-day course in unsupervised learning, which covers PCA, factor analysis, and clustering. It will be taught in two half-days via Live Web in June. See jmp.com/training for more info.

di_michelson · ‎01-04-2017

Short answer: no. Long answer: The ANOVA you are fitting is probably a random effects ANOVA, with the Part effect being random. Maybe you have other random terms as well, such as time effects, operator effects, materials, gauges, setups, etc. Maybe one of those effects should be the nominal value of the part. Then you can model the variance as a function of the nominal value (and other terms) and you don't have to assume equal variances. If the nominal values are really far apart, consider doing a separate analysis for each range. For example, if you have parts at 10, 1000, and 10000, you might perform three separate analyses. In JMP, if all your effects are random, you can use the EMP or Variability platforms to get those variance components. If some effects are fixed and others are random, use Analyze > Fit Model to specify the mixed model and get the variance components. I hope this helps!

di_michelson · ‎01-04-2017

It's a good question! You are correct that technically, in the measurement standard documents, linearity is defined as the absence of variability due to bias over the measurement range. In the JMP course notes for the MSA course, it says "Some authors include the absence of variability due to reproducibility over the measurement range." In my experience, companies that notice this problem call it linearity, and other companies don't notice the problem, so don't even ask the question. I have not heard of another term for it, in MSA particularly.

di_michelson · ‎12-05-2014

I have just uploaded a script to the File Exchange that will randomize factors within other factors. I developed the script for use in the JMSA (JMP Software: Measurement System Analysis) class for version 11 that will be ready in early 2014. Feedback is appreciated! Gauge Study Design

di_michelson · ‎12-05-2014

Editors note: This script is now available as a JMP Add-in: Gauge Study Designer This Gauge Study Design script creates an experimental design worksheet with randomization of columns within other columns. Gauge studies are special designed experiments that support mixed models and require incomplete randomization. Random factors can not be randomized (days, repeats), but fixed factors should be randomized (machines, locations). After running the script, click the Add Factor button to add new factors. Name the factors and specify the number of levels for each factor. Check the Randomize? checkbox if you want the levels of that factor randomized within the factor above it. Use the Remove Last Factor button to correct mistakes. Click the Create Worksheet button to display the worksheet. A run order column is included so you can get back to this order if you would like to sort the table into standard order. An empty response column is also included. This script will be used extensively in the upcoming JMSA11 course. Please give any feedback to the author.

Date Registered	‎09-10-2014 02:02 PM
Date Last Visited	2 hours ago
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Gauge Study Designer

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Re: 3-periods ahead forecasting with ARIMA/Transfer Model function

Re: Test 1000 units with two different test equipments, how to compare the equip...

Re: Factor Analysis - Which combination to use?

Re: Factor Analysis - Which combination to use?

Re: Gauge variation, linearity and bias

Gauge Study Designer

Gauge Study Designer

Re: 3-periods ahead forecasting with ARIMA/Transfer Model function

Re: 3-periods ahead forecasting with ARIMA/Transfer Model function

Re: Test 1000 units with two different test equipments, how to compare the equip...

Re: Factor Analysis - Which combination to use?

Re: Factor Analysis - Which combination to use?

Re: Gauge variation, linearity and bias

Re: Gauge variation, linearity and bias

Re: Measurement R&R Data Sheet

Gauge Study Design