Hi,
I'm still trying to figure out how to run a regression analysis using functional data. I found the information in using qdec, however, the example uses a between subject design (with age/gender) and looks at thickness measures.
I tried creating a qdec.table.dat, but I am unsure how to do it with a within subject design, as when I ended up with it reading the file as me having 16 subjects instead of 8.
What I have is a discrete variable of load (high and low) and a continuous variable of set size (1,3,6), but I want to correlate the data with the performance at each set size for each load as well as load.
This is what my qdec table looked like:
fsid dist k1 k3 k6 subj1 low 1 2.9 4.0 subj1 high 0.8 1.3 2.0 subj2 low 0.9 2.9 5.7 subj2 high 0.9 1.9 1.4 ... subj8 low 0.8 2.4 2.5 subj8 high 0.8 1.4 2.7
Can anyone tell me how to appropriately structure this to do what I want?
Thank you!
Katie
It looks like you have 1 discrete variable with two levels and three covariates. Three is too many for qdec, so you'll have to set up your own FSGD. See the wiki for how to set up an FSGD. There are examples there for many different designs.
doug
Katie Bettencourt wrote:
Hi,
I'm still trying to figure out how to run a regression analysis using functional data. I found the information in using qdec, however, the example uses a between subject design (with age/gender) and looks at thickness measures.
I tried creating a qdec.table.dat, but I am unsure how to do it with a within subject design, as when I ended up with it reading the file as me having 16 subjects instead of 8.
What I have is a discrete variable of load (high and low) and a continuous variable of set size (1,3,6), but I want to correlate the data with the performance at each set size for each load as well as load.
This is what my qdec table looked like:
fsid dist k1 k3 k6 subj1 low 1 2.9 4.0 subj1 high 0.8 1.3 2.0 subj2 low 0.9 2.9 5.7 subj2 high 0.9 1.9 1.4 ... subj8 low 0.8 2.4 2.5 subj8 high 0.8 1.4 2.7
Can anyone tell me how to appropriately structure this to do what I want?
Thank you!
Katie
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