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Mouse Livers:

Derivatives and Functional Linear

Models

How does cholesterol get

metabolized in the liver?

What questions can we ask of the

data?

 What does the

real, “smooth”

process look like?

 Do shapes differ

among groups?

 Do rates of

change differ

among groups?

What do the flow curves look like

as functional objects?

 Took the derivative

of the smoothed

curves.

 Still retain curve-to-

curve variability, but

now much

smoother.

How can I graphically explore the data?

Phase-Plane Plots  Have:

 flow curves x(t).

 rate of change of flow

curves Dx(t).

 Plot Dx(t) vs x(t). No

longer an explicit

function of time!

 Overlay time points

on the curve for

interpretation.

 Gives information

about how function is

linked with its

derivative.

What do we see in these

phase-plane plots?

 Difference in

curves between

receptors and no

receptors

 Cusps or „change-

points‟ when there

are receptors

 Minute 9 for

Receptor A; Minute

15 for Receptor B

 Minute 9 for Both

Receptors:

Interactive Effect?

What is the relationship between

the covariates and response

curves?

Functional Linear Models



 Functional response; Scalar predictors.

 Regression coefficients are functional.



X(t) = β0(t) + β1(t)A + β2(t)B + β3(t)A*B + ε(t),



 Use basis expansion methods.

 Receptors affect

steady state.

 B stronger than

A.

 Effects

strongest after

minute 9.

 A and B have

inhibitory

relationship

after minute 9.

 Receptors affect

steady state.

 B stronger than

A.

 Effects

strongest after

minute 9.

 A and B have

inhibitory

relationship

after minute 9.

Can also do a functional linear model for

derivative (rate of change):





dX/dt = β0(t) + β1(t)A + β2(t)B + β3(t)A*B + ε(t),





FDA allows us to work with derivatives –

which are closer to the mechanisms of the

process

 A “kicks in”

earlier than

does B.

 A kicks in at

minute 9, B

at minute 15.

 When

together, see

push only at

minute 9

(from A?)

What have we learned?

 Creating a functional object

 Smoothing with basis expansions to reduce

noise

 Examining derivatives graphically

 Phase-plane plots

 Building functional linear models

 Functional regression coefficients

 Derivatives helpful here, too



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