Great video. Thanks so much. My question is why "predict comp1" only? What if comp2 and comp3 have eigen values greater than 1, do we also run the "predict comp2" and "predict comp2" command.
Thanks! This is actually simpler than I anticipated it to be. Great work! I will try it out. However, I was wondering, do you not think that we are losing a nuance in the data by converting it into a binary variable? For example, maybe there's some important information between "having an item (1) and not having that item (0), which is most likely lost when categorising into a binary variable?
Great video. Thanks so much. My question is why "predict comp1" only? What if comp2 and comp3 have eigen values greater than 1, do we also run the "predict comp2" and "predict comp2" command.
Thanks! This is actually simpler than I anticipated it to be. Great work! I will try it out. However, I was wondering, do you not think that we are losing a nuance in the data by converting it into a binary variable? For example, maybe there's some important information between "having an item (1) and not having that item (0), which is most likely lost when categorising into a binary variable?
After comp1, is the procedure the same for all other components?
@@aklimakhatun1530
Yes, if you need 2nd or 3rd, then type
predict comp2 comp3
There is no link in the description
Sorry, Now the link has been given.
Thank you.
@@biostatbd Many thanks.
please i have this message : estat kmo
correlation matrix is singular