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Matlab vs python online software#
Matlab vs python online code#

That's not a problem when you treat them as numeric, but it is when you try to estimate all coefficients including the interaction in a factorial design. You have no cases for which Var1=1 and Var2=3. I copied your data into an R data frame, set those to factors, and got the following: ftable(anov1 $Var1,anov1$Var2)

Matlab vs python online software#

I suspect that the problem comes from how the software deals with the missing combination of Var1 and Var2 when they are treated as factors. Just trying another Python module: pingouin. Seeing all of the above, which results should I be using? Can I trust anovan? How do I reconcile the differences between anovan and the other methods? None of the methods give the same result as the anovan Matlab function, which is specifically meant to do an N-way ANOVA on an unbalanced design.

The Matlab vs Python versions using categorical variables give similar results (but not identical).

The Matlab vs Python versions not using categorical variables give identical results.

So, to sum everything up, here are the various results I am getting from various different methods: These results mostly mirrored the results from Matlab when I used categorical variables, but were not completely identical. Then, trying as cateogorical: model = ols('Result ~ C(Var1) + C(Var2) + C(Var1):C(Var2)', data=df).fit() The results mirrored the results of when I did fitlm in Matlab without using categorical variables. Model = ols('Result ~ Var1 + Var2 + Var1:Var2', data=df).fit() I tried this in two different ways, first without specifying Var1 and Var2 as categorical, and then the 2nd time I specified them as categorical: import numpy as np So then I switched over to Python, and I tried using the anova_lm function from the statsmodels module. This gave the following results: SumSq DF MeanSq F pValue

Lm = fitlm(t2, 'Result ~ Var1 + Var2 + Var1:Var2')

Matlab vs python online code#

Now, Var1 and Var2 are technically categorical/ordinal variables, so I changed the above code to specify that: t2 = t This gave me the following results: SumSq DF MeanSq F pValue Next, I decided to basically do the same thing, but using the fitlm function in Matlab: lm = fitlm(t, 'Result ~ Var1 + Var2 + Var1:Var2') I ran my code like so: t = readtable('data_file.xlsx') Now, I normally use Matlab for my stats, so I use the anovan function which can handle unbalanced designs ( ).