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Mouse Livers: Derivatives and Functional Linear Models

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Mouse Livers: Derivatives and Functional Linear Models What questions can we ask of the data? What does the real, smooth process look like? – PowerPoint PPT presentation

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Title: Mouse Livers: Derivatives and Functional Linear Models


1
Mouse LiversDerivatives and Functional Linear
Models
2
How does cholesterol get metabolized in the liver?
3
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?

4
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.

5
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.

6
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?

7
What is the relationship between the covariates
and response curves?
Functional Linear Models
  • Functional response Scalar predictors.
  • Regression coefficients are functional.
  • Use basis expansion methods.

X(t) ß0(t) ß1(t)A ß2(t)B ß3(t)AB e(t),
8
  • Receptors affect steady state.
  • B stronger than A.
  • Effects strongest after minute 9.
  • A and B have inhibitory relationship after minute
    9.

9
  • Receptors affect steady state.
  • B stronger than A.
  • Effects strongest after minute 9.
  • A and B have inhibitory relationship after minute
    9.

10
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11
  • Can also do a functional linear model for
    derivative (rate of change)
  • FDA allows us to work with derivatives which
    are closer to the mechanisms of the process

dX/dt ß0(t) ß1(t)A ß2(t)B ß3(t)AB e(t),
12
  • 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?)

13
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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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