Hey Karla, Interpreting dimensions is more an art than a science. I take a column principal component, (the colpcoord slot of ca::mjca output) and then look at which variables have large positive and which variables have large negative values. Then I make a judgment call on as to why the variables are grouped that way. I ignore variables with small values.
You can cluster the rows of data using Row principal coordinates (rowpcoord slot in ca::mjca() output), you can cluster the variable combinations using Column principal coordinates (colpcoord slot in ca::mjca() output).
you need to create playlist for multiple correspondence analysis
How do you know what each dimension represents?
Hey Karla,
Interpreting dimensions is more an art than a science. I take a column principal component, (the colpcoord slot of ca::mjca output) and then look at which variables have large positive and which variables have large negative values. Then I make a judgment call on as to why the variables are grouped that way. I ignore variables with small values.
Thus was very good explanation. My question how do i was the results of MCA to do cluster analysis.
You can cluster the rows of data using Row principal coordinates (rowpcoord slot in ca::mjca() output), you can cluster the variable combinations using Column principal coordinates (colpcoord slot in ca::mjca() output).