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Beginner Question

Artaxerxes90

Hi community,

this is my first post ever in a forum and also the first time I'm using DoE for experiments. I have watched the DoE Video from JMP and have a question or rather look for confirmation:

I have an experiment with two continuous factors (e.g. range of pHs) and one categorical factor (e.g. type of acid). I want to use RSM but apparently, it won't work when a categorical factor is included. Hence, I have decided to go for a "Custom Design" and included all the factors. Now the system is saying "Categorical factors cannot appear in polynomial model terms. Only the quadratic terms for continuous factors are being added to the model.".

Would a more senior DoE user say this is a correct approach? And is there anything I need to keep in mind? Alternatively, I was thinking of doing it like all the others and assume the categorical factor (e.g. type of acid) as a continuous factor. But this just doesn't make sense in my point of view. Thank you for any help.

Accepted answers

Mark_Bailey

I would not recommend the CCD when you have a custom design that is optimally tailored to your experiment.

Mark_Bailey

Yes, exactly. Try to achieve the level given by the design but when it deviates a lot, like 12 instead of 14, then just update the level for the analysis.

All comments

Mark_Bailey

The much older RSM designs only accommodated continuous factors. The modern Custom Design can handle any kind of factor, so you can have continuous and categorical factors in the same experiment. You can also include terms for interaction effects between continuous and categorical factors, and non-linear effects of continuous factors.

The warning is just letting you know that Acid*Acid will not appear in the list of model terms.

P_Bartell

To add just a bit to @Mark_Bailey 's sound guidance...strictly speaking you can create a what I'll call pseudo RSM (think like a full factorial plus central composite as an illustrative example) design with categorical and continuous factors...but, and here's the BIG BUT, the design will be very inflexible and in all likelihood have a much larger number of runs, with a predefined model underlying the design...I won't go into how you could create the design because @Mark_Bailey 's advice is way more efficient and rational. Use the JMP Custom Design platform to create your design.

statman

First, welcome to the community. I have some thoughts on the questions you pose, but to be honest, you have not provided enough information regarding the actual situation for me to provide most appropriate advice. For example, you don't describe the response variable.

My thoughts:

1. RSM is meant to "map" the design space so you may select optimum conditions for the selected response variable. It should be done after you have thorough knowledge regarding which, of the few, factors have a significant effect on the response and more importantly, you understand the effect of noise (factors you are not willing to manage or control). You should already have a first order model...

2. Creating a continuous regression model with a categorical factor is a bit non-sensical. You could create a model for each category, but including the term in the model makes no sense. You don't have an inference between the categories nor could there be any non-linear terms. (it's either this acid or that acid type, there is no continuum)

3. You could hypothetically convert acid type into a continuous variable if you can quantify the differences in the types (e.g., amount of a certain chemical compound), but my guess is your types are hydrochloric vs. sulfuric or nitric or something like that.

Are you sure you need the non-linear terms? Have you predicted what the model should be (based on science, not statistics)? What are your hypotheses? Typically we build models starting with first order and augmenting this through iteration, rather than a one-shot experiment. We experiment to understand, not to pick a winner.

Experimentation is extremely powerful, so have at it.

Artaxerxes90

Hi Mark and all others who replied,

thank you very much for explaining. I still have one question. Recently, I read a paper (S. Sadhukhan, U. Sarkar /Energy Conversion and Management 118 (2016) 450–458) where the aim was to increase the glycerol purity to a maximum, and they did following (which confused me a bit):

They used a CCD with three continuous factors:

1.) Type of Acid

2.) pH

3.) Amount of adsorbent

What is confusing me is exactly what @statman said: factor 1) Type of acid is not a continuous factor. Actually, it should be a categorical variable. Hence, it is actually a pseudo RSM as @P_Bartell said, since they converted an actually categorical factor into a continuous factor, correct? (Q1)

Is this approach valid from a scientific point of view? (Q2) I have seen at least 4-5 other authors doing exactly the same.

According to my understanding, you cannot do this, as there is no continuity between the type of acids (it is a category! e.g. sulphric, phosphoric, acetic etc...), so no curvature. I'm doing something similar and would like to know: would a correct approach be defining three different variables which are continuous e.g.

1) pH - Saponification - conti

2) pH - Acidification - conti

3) Salting out (=ratio of solvent:aqueous layer) - conti

and use a fourth categorical factor as "type of base"? (Q3) If yes, this would mean that the model does not take into account the relationship between factor 4 and factors 1-3, correct? (Q4) But this would also be not logical since the influence of the type of base is highly relevant for factors 1-3 as well. Your replies are very much appreciated.

SamD

Hi Everyone, I am trying to find a word "PASS" in column Test. then I want to be able to write another word "FAIL" in a cell just above the PASS in same column

Thanks for your help

Sam

txnelson

dt=current data table();
theRows=dt<<get rows where(:text=="PASS");

:test[theRows[1] -1 ] = "FAIL";

Mark_Bailey

A1: They are using the older RSM design and analysis, so they had to treat the type of acid as a continuous factor. You do not need to do the same thing. You are correct that it is a categorical factor and should be modeled as such.

A2: It is absolutely valid.

A3: Yes, your definition of the factors makes sense.

A4: You can model interactions between X4 and the first three factors. Just cross the factors in the model. For example, X1*X4 would estimate and test the interaction effect between them.

SamD

Hi Jim, thanks for your help, the code works only on one row, I have few hundred similar rows that need to be updated

Thanks

Sam

Artaxerxes90

Dear Mark,

many thanks for your replay. Last question: Why does the Run 9 have a saponification of 8.99. Is there any logical explanation to this? Thank you in advance!!