I am trying to develop mixed logit model for my data, my data is in the format shown in the image. I am getting errors while running the model like data format, alt name cannot be same as columns etc. in packages like xlogit in python and mlogit, apollo in R
My question is does one need to convert their data into the format shown even if RP data is available and only one specific choice out of few is for each individual.
If the data size is a bit large how does one convert this data to the formal using code.
Also, what happens in the case all my variable are categorical and there is no varing variables across each individual
Apparently you have your data in Excel, and the format is fine.
However, if you want to read that in, you should have your variable names on the first line and use the R package 'openxlsx' to read them in (function read.xlsx).
@Nikolaj Sir I am able to load the data in R using read CSV, later i convert it into factors and I set the reference levels as well. The description part of data is working fine. The images I have shared are representative of how my data looks.
The problem I am concerned about is while converting the wide format data to long format, some errors arise. What I wanted ask is if there are some guidelines for conversion. Also, what does one do when all the variables are categorical and only one choice response is there for each individual.
Good afternoon,
Do you try to convert your data file under csv format. This format is easy to download in R studio and IDE for Python script. I think you have to remove the line between your dataset and the top of sheet (begin directly to the line 1)
Sorry, I did not notice previous answer about csv file. for change the format table, i think you can use reshape2. Please, see this tutorial: http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/
It's difficult to provide a precise answer without more information about your specific data and the packages you are using. However, I can provide some general guidance on working with mixed logit models.
First, it's important to make sure that your data is in the correct format for the package you are using. For example, some packages may require the data to be in a long format with each observation on a separate row, while others may require it to be in a wide format with each individual on a separate row.
If you have RP (Revealed Preference) data and only one specific choice out of a few is available for each individual, you can still use mixed logit models. However, you may need to adjust your data format to make it compatible with the package you are using.
If your data size is large, you can use code to convert your data to the required format. This can be done using functions or loops in Python or R, depending on the package you are using. For example, in Python, you can use the pandas library to reshape your data into the desired format.
If all your variables are categorical and there is no varying variable across each individual, you can still use mixed logit models, but you may need to include random effects or heterogeneity parameters to capture any unobserved differences between individuals.
In general, working with mixed logit models can be complex, and it's important to carefully review the documentation and examples provided by the packages you are using to ensure that your data is in the correct format and that you are using the correct functions and parameters. Additionally, it can be helpful to consult with experts in the field or seek assistance from online forums or communities for guidance on working with these models.
I hope you find this useful.
With best regards
Ajaz Mir
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