How to plot the estimates (fixed terms) of a linear mixed model

[#1]

Hello,

I have regular JMP (no Pro) and have used a linear mixed model using the REML option having as fixed terms 3 single and 3 crossed terms. All single terms used were nominal with 4, 3 and 2 levels respectively. I want to plot the estimates (fixed terms) as a standardized coefficient with respective associated 95%CI as significance level. However, I am unsure in the output where (or if) I can find this information (i.e. standardized estimate coeff of each fixed-term and associated 95%CI so I can plot them in a graph)? Under parameter estimates I get that information but for each fixed-term level, whereas I want the standardized coefficient per fixed-term and not per level within each term. I hope it makes sense!

Thank you!

[#2]

Hi @Catarina 

did you try the Factor profiling menu under the red triangle as in the picture. it has a few options and from them there are extra options.

otherwise, all the different estimates of the model could be extracted from the effect screening menu as in the picture:

another way is to right click on the parameter estimates column go to Columns >> STD Beta

hope it works.

[#3]

Hi @ron_horne 

Thank you so much for your suggestions. I can see you are using JMP Pro and unfortunately I don't have access to that version. 

I have tried the Factor profiling but I am not sure I can extract standardized coeff from single and crossed fixed factors from there?

 I also don't have access to Effect Screening 

And in my version I don't have access to STD Beta under columns in the Parameter Estimates.

 

Would you have any other suggestions?

[#4]

Hi @Catarina ,

i am a bit surprised to hear these are pro features. i hope someone else in the forum can comment on that.

in any case, you should be able to standardize your variables prior to the analysis and run the regression. then the coefficients would be standardized.

[#5]

Thanks @ron_horne. The model I am using has nominal factors as fixed effects as well as crossed:

plant richness (1, 2, 3, 4);

Mic_dil (D0, D2, D6);

Drought (W, D);

plant richness x mic_dil;

drought x plant richness;

drought x mic_dil.

The model also has random effects.

If they would be continuous I would understand how to standardize them but I am not sure how to in this case? Thank you for your suggestion.

[#6]

Hi @Catarina ,

i have checked and you are right. the options i suggested are not available with mixed models.

you can convert your categorical variables to indicators as in the picture. once you have indicators they can be standardized.

will that work?

do not forget to standardize the dependent variable as well.

[#7]

That will convert each level within each categorical variable into a column. I guess the output would resemble the Parameter Estimates table originally given by JMP where the last level for each term is dropped. However, what I would like to produce as a final result is something similar to this (as an example):

But without the distinction of levels within each fixed-term. I would like to get just the 6 fixed-terms std coeff so I can plot them with 95%CI. 

[#8]

I am confused. How do you standardize nominal or ordinal variables? Also, there is a parameter for each level, so what single estimate do you expect?

[#9]

I was hoping to get a parameter estimate for the fixed effect and not for the each level within each fixed effect. Something similar to a F statistic. But probably that does not make sense?

[#10]

the only way this can make sense is if the effect is binary. then, there is just one coefficient....

I must admit that i have seen over the years people reducing multi categorical effects to binary for simplicity. in many cases without disclosing it properly and as part of bad practice overall.