interpreting gam outputs
play

Interpreting GAM outputs Noam Ross Senior Research Scientist, - PowerPoint PPT Presentation

Feb 11, 2023 •315 likes •706 views

DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Interpreting GAM outputs Noam Ross Senior Research Scientist, EcoHealth Alliance DataCamp Nonlinear Modeling in R with GAMs GAM Summaries mod_hwy <- gam(hw.mpg ~

  1. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Interpreting GAM outputs Noam Ross Senior Research Scientist, EcoHealth Alliance

  2. DataCamp Nonlinear Modeling in R with GAMs GAM Summaries mod_hwy <- gam(hw.mpg ~ s(weight) + s(r s(price) + s(comp.ratio s(width) + fuel + cylind data = mpg, method = "RE summary(mod_hwy)

  3. DataCamp Nonlinear Modeling in R with GAMs GAM Summaries (2)

  4. DataCamp Nonlinear Modeling in R with GAMs GAM Summaries (3) summary(mod_hwy) Family: gaussian Link function: identity Formula: hw.mpg ~ s(weight) + s(rpm) + s(price) + s(comp.ratio) + s(width) + fuel

  5. DataCamp Nonlinear Modeling in R with GAMs GAM Summaries (4) summary(mod_hwy) Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 23.873 3.531 6.760 1.89e-10 *** fuelgas 7.571 3.922 1.931 0.0551 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.

  6. DataCamp Nonlinear Modeling in R with GAMs GAM Summaries (5) summary(mod_hwy) Approximate significance of smooth terms: edf Ref.df F p-value s(weight) 6.254 7.439 20.909 < 2e-16 *** s(rpm) 7.499 8.285 8.534 2.07e-09 *** s(price) 2.681 3.421 1.678 0.155 s(comp.ratio) 1.000 1.001 18.923 2.22e-05 *** s(width) 1.001 1.001 0.357 0.551 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.

  7. DataCamp Nonlinear Modeling in R with GAMs Effective Degrees of Freedom Approximate significance of smooth terms: edf Ref.df F p-value s(weight) 6.254 7.439 20.909 < 2e-16 *** <-- s(rpm) 7.499 8.285 8.534 2.07e-09 *** s(price) 2.681 3.421 1.678 0.155 s(comp.ratio) 1.000 1.001 18.923 2.22e-05 *** <-- s(width) 1.001 1.001 0.357 0.551

  8. DataCamp Nonlinear Modeling in R with GAMs Significance of Smooth Terms Approximate significance of smooth terms: edf Ref.df F p-value s(weight) 6.254 7.439 20.909 < 2e-16 *** s(rpm) 7.499 8.285 8.534 2.07e-09 *** s(price) 2.681 3.421 1.678 0.155 s(comp.ratio) 1.000 1.001 18.923 2.22e-05 *** s(width) 1.001 1.001 0.357 0.551 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.

  9. DataCamp Nonlinear Modeling in R with GAMs Significance of Smooth Terms (2) Approximate significance of smooth terms: edf Ref.df F p-value s(weight) 6.254 7.439 20.909 < 2e-16 *** <-- s(rpm) 7.499 8.285 8.534 2.07e-09 *** s(price) 2.681 3.421 1.678 0.155 <-- s(comp.ratio) 1.000 1.001 18.923 2.22e-05 *** s(width) 1.001 1.001 0.357 0.551

  10. DataCamp Nonlinear Modeling in R with GAMs Significance and Effective Degress of Freedom Approximate significance of smooth terms: edf Ref.df F p-value s(weight) 6.254 7.439 20.909 < 2e-16 *** s(rpm) 7.499 8.285 8.534 2.07e-09 *** s(price) 2.681 3.421 1.678 0.155 <-- s(comp.ratio) 1.000 1.001 18.923 2.22e-05 *** <-- s(width) 1.001 1.001 0.357 0.551 <--

  11. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Let's practice!

  12. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Visualizing GAMs Noam Ross Senior Research Scientist, EcoHealth Alliance

  13. DataCamp Nonlinear Modeling in R with GAMs The Plot Command plot(gam_model) ?plot.gam

  14. DataCamp Nonlinear Modeling in R with GAMs

  15. DataCamp Nonlinear Modeling in R with GAMs Selecting partial effects plot(gam_model, select = c(2, 3)) plot(gam_model, pages = 1) plot(gam_model, pages = 1, all.terms = TRUE)

  16. DataCamp Nonlinear Modeling in R with GAMs Showing data on the plots plot(gam_model, rug = TRUE)

  17. DataCamp Nonlinear Modeling in R with GAMs Showing data on the plots (2) plot(gam_model, residuals = TRUE)

  18. DataCamp Nonlinear Modeling in R with GAMs Showing data on the plots (3) plot(gam_model, rug = TRUE, residuals = TRUE, pch = 1, cex = 1)

  19. DataCamp Nonlinear Modeling in R with GAMs Showing Standard Errors plot(gam_model, se = TRUE)

  20. DataCamp Nonlinear Modeling in R with GAMs Showing Standard Errors (2) plot(gam_model, shade = TRUE)

  21. DataCamp Nonlinear Modeling in R with GAMs Showing Standard Errors plot(gam_model, shade = TRUE, shade.col = "lightblue")

  22. DataCamp Nonlinear Modeling in R with GAMs Transforming Standard Errors plot(gam_model, seWithMean = TRUE)

  23. DataCamp Nonlinear Modeling in R with GAMs Transforming Standard Errors (2) plot(gam_model, seWithMean = TRUE, shift = coef(gam_model)[1])

  24. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Now lets make some plots!

  25. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Model checking with gam.check() Noam Ross Senior Research Scientist, EcoHealth Alliance

  26. DataCamp Nonlinear Modeling in R with GAMs Pitfall One: Inadequate Basis Number mod <- gam(y ~ s(x1, k = 4) + s(x2, k = 4), data = check_data, method = "REML")

  27. DataCamp Nonlinear Modeling in R with GAMs Running gam.check gam.check(mod) Method: REML Optimizer: outer newton full convergence after 9 iterations. Gradient range [-0.0001467222,0.00171085] (score 784.6012 & scale 2.868607). Hessian positive definite, eigenvalue range [0.00014,198.5] Model rank = 7 / 7 Basis dimension (k) checking results. Low p-value (k-index<1) may indicate that k is too low, especially if edf is close to k'. k' edf k-index p-value s(x1) 3.00 1.00 0.35 <2e-16 *** s(x2) 3.00 2.88 1.00 0.52 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1

  28. DataCamp Nonlinear Modeling in R with GAMs Running gam.check (2) mod <- gam(y ~ s(x1, k = 12) + s(x2, k = 4), data = dat, method = "REML") gam.check(mod) ... k' edf k-index p-value s(x1) 11.00 10.85 1.05 0.830 s(x2) 3.00 2.98 0.89 0.015 * ...

  29. DataCamp Nonlinear Modeling in R with GAMs Running gam.check (3) mod <- gam(y ~ s(x1, k = 12) + s(x2, k = 12), data = dat, method = "REML") gam.check(mod) ... k' edf k-index p-value s(x1) 11.00 10.86 1.08 0.94 s(x2) 11.00 7.78 0.94 0.12 ...

  30. DataCamp Nonlinear Modeling in R with GAMs

  31. DataCamp Nonlinear Modeling in R with GAMs

  32. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Let's check some models

  33. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Checking concurvity Noam Ross Senior Research Scientist, EcoHealth Alliance

  34. DataCamp Nonlinear Modeling in R with GAMs

  35. DataCamp Nonlinear Modeling in R with GAMs Concurvity

  36. DataCamp Nonlinear Modeling in R with GAMs The concurvity() function concurvity(m1, full = TRUE) para s(X1) s(X2) worst 0 0.84 0.84 observed 0 0.22 0.57 estimate 0 0.28 0.60

  37. DataCamp Nonlinear Modeling in R with GAMs Pairwise concurvities concurvity(model, full = FALSE) $worst para s(X1) s(X2) para 1 0.00 0.00 s(X1) 0 1.00 0.84 s(X2) 0 0.84 1.00 $observed | $estimate para s(X1) s(X2) | para s(X1) s(X2) para 1 0.00 0.00 | para 1 0.00 0.0 s(X1) 0 1.00 0.57 | s(X1) 0 1.00 0.6 s(X2) 0 0.22 1.00 | s(X2) 0 0.28 1.0

  38. DataCamp Nonlinear Modeling in R with GAMs NONLINEAR MODELING IN R WITH GAMS Let's practice!

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Designing Interactive Graphics for Validating and Interpreting Storm Track Model Outputs Aidan

Designing Interactive Graphics for Validating and Interpreting Storm Track Model Outputs Aidan

Designing Interactive Graphics for Validating and Interpreting Storm Track Model Outputs Aidan Slingsby 1 , Jane Strachan 2 , Jason Dykes 1 , Jo Wood 1 and Pier-Luigi Vidale 2 1 giCentre, City University London; 2 National Centre for Atmospheric

490 views • 11 slides
Toward a Toward a Overview Sociology of Sociology of Introduction Interpreting

Toward a Toward a Overview Sociology of Sociology of Introduction Interpreting

6/9/2010 Toward a Toward a Overview Sociology of Sociology of Introduction Interpreting Interpreting Traditional foci in Sign Language Interpreting Research What a sociology of interpreting would look like. Jeremy L. Brunson

82 views • 5 slides
INTERPRETING THE PACEMAKER ECG - GANZ 2017 1 SUPERIOR VENA CAVAL SYNDROME EXAMINATION OF THE

INTERPRETING THE PACEMAKER ECG - GANZ 2017 1 SUPERIOR VENA CAVAL SYNDROME EXAMINATION OF THE

INTERPRETING THE ECG IN PATIENTS WITH PACEMAKERS BEFORE INTERPRETING THE ECG: Nora Goldschlager, M.D. MACP, FACC, FAHA, FHRS. Cardiology San Francisco General Hospital UCSF Disclosures: None 1 2 QUESTIONS FOR THE PATIENT

252 views • 14 slides
indicators Julia Slay, NEF Introductions around the room The first distinction: outputs

indicators Julia Slay, NEF Introductions around the room The first distinction: outputs

Outputs, outcomes and indicators Julia Slay, NEF Introductions around the room The first distinction: outputs Outputs Outputs are a quantitative summary of an activity. For example, the activity is we provide training and the output

560 views • 20 slides
Interpreting with Functions Comparing Different Representations of Functions Glossary Return to

Interpreting with Functions Comparing Different Representations of Functions Glossary Return to

Slide 1 / 122 Slide 2 / 122 8th Grade Modeling Relationships 2015-11-30 www.njctl.org Slide 3 / 122 Slide 4 / 122 Table of Contents Click on the topic to go to that section Interpreting with Functions Analyzing a Graph Interpreting with

638 views • 21 slides
Interpreting Symptoms A E V I of Cognitive Load in Speech Input Andr Berthold and Anthony

Interpreting Symptoms A E V I of Cognitive Load in Speech Input Andr Berthold and Anthony

1 Introduction 2 E R S V I T I N A U S S S I A S R N Interpreting Symptoms A

291 views • 12 slides
PRESENTATION #5 OUTPUTS 1.2 &amp; 3.2: NORTHERN CAPE HUB, R&amp;D AND SUPPORT TO CULTIVATION GEF

PRESENTATION #5 OUTPUTS 1.2 &amp; 3.2: NORTHERN CAPE HUB, R&amp;D AND SUPPORT TO CULTIVATION GEF

PRESENTATION #5 OUTPUTS 1.2 &amp; 3.2: NORTHERN CAPE HUB, R&amp;D AND SUPPORT TO CULTIVATION GEF Full-Sized Project (FSP): Development of value chains for products derived from genetic resources OUTPUTS 1.2 &amp; 3.2: Bioprospecting R R&amp;D

475 views • 14 slides
Experiences from the Research Outputs Team, UQ Library Helen Morgan, Fei Yu, Sarah Evans, Andrew

Experiences from the Research Outputs Team, UQ Library Helen Morgan, Fei Yu, Sarah Evans, Andrew

Experiences from the Research Outputs Team, UQ Library Helen Morgan, Fei Yu, Sarah Evans, Andrew Heath &amp; Sharon Bunce The Research Outputs Team Knowledge Sharing and Training Global drivers behind the formation of the Research Outputs

751 views • 51 slides
2011 Census Outputs and dissemination Ian White: Head of Census Legislation, Parliamentary

2011 Census Outputs and dissemination Ian White: Head of Census Legislation, Parliamentary

2011 Census Outputs and dissemination Ian White: Head of Census Legislation, Parliamentary Support, and International Liaison PAMS 28 March 2012 2011 Census outputs and dissemination Confidence in the results Census outputs

299 views • 28 slides
ARDUINO SERIES BASIC DIGITAL INPUTS AND OUTPUTS 1. THE BASICS: INPUTS, OUTPUTS, SOURCES AND

ARDUINO SERIES BASIC DIGITAL INPUTS AND OUTPUTS 1. THE BASICS: INPUTS, OUTPUTS, SOURCES AND

ARDUINO SERIES BASIC DIGITAL INPUTS AND OUTPUTS 1. THE BASICS: INPUTS, OUTPUTS, SOURCES AND SINKS 2. CONNECT A SWITCH TO AN ARDUINO NORMALLY LOW OR HIGH 3. CONNECT AN LED TO AN ARDUINO INVERTED LOGIC OR REGULAR These slides are the

385 views • 5 slides
Communities of Practice the missing link? Danielle DHayer Associate Professor Interpreting

Communities of Practice the missing link? Danielle DHayer Associate Professor Interpreting

Communities of Practice the missing link? Danielle DHayer Associate Professor Interpreting Studies London Metropolitan University londonmet.ac.uk Exciting times for interpreting studies! Research Resources Technology Quality Pedagogy

157 views • 13 slides
UMBC A B M A L T F O U M B C I M Y O R T 1 (12/11/06) I E S R C E O V

UMBC A B M A L T F O U M B C I M Y O R T 1 (12/11/06) I E S R C E O V

VLSI Design Verification and Test BIST II CMPE 646 Space Compaction Multiple Outputs We need to treat the general case of a k-output circuit. m k Test Set L LFSR CUT There are several possibilities: Multiplex the k outputs of the CUT.

319 views • 12 slides
improve outcomes for vulnerable people in Scotland? Outputs and Presentation pack Intentions

improve outcomes for vulnerable people in Scotland? Outputs and Presentation pack Intentions

How can data analytics improve outcomes for vulnerable people in Scotland? Outputs and Presentation pack Intentions and outputs of the event. We considered how the world is changing in terms of digital progress, and whether we can afford

339 views • 31 slides
Outputs update September 2011 Agenda Consultation Release plans : First release and

Outputs update September 2011 Agenda Consultation Release plans : First release and

Outputs update September 2011 Agenda Consultation Release plans : First release and proposed running order for outputs Approach to dissemination Data feeds work Census analysis Address Engagement Field Online Data

428 views • 29 slides
Regular Relation (of strings) Relation: like a function, but multiple outputs ok Regular:

Regular Relation (of strings) Relation: like a function, but multiple outputs ok Regular:

a little pre-talk review Regular Relation (of strings) Relation: like a function, but multiple outputs ok Regular: finite-state a: Transducer: automaton w/ outputs b:b b ? a ? a:a a:c aaaaa ? ?:c

863 views • 53 slides
Interpreting for the Standardized Test Presented by Alaina Webb and Sammie Sheppard for Region

Interpreting for the Standardized Test Presented by Alaina Webb and Sammie Sheppard for Region

Interpreting for the STAAR Test The Dos and Donts of Interpreting for the Standardized Test Presented by Alaina Webb and Sammie Sheppard for Region XI ESC March, 2015 TEP pre-test What is our job as an interpreter? RID Code of

881 views • 73 slides
Scheduling Algorithm and Analysis EDF (Module 28) Yann-Hang Lee Arizona State University

Scheduling Algorithm and Analysis EDF (Module 28) Yann-Hang Lee Arizona State University

Scheduling Algorithm and Analysis EDF (Module 28) Yann-Hang Lee Arizona State University yhlee@asu.edu (480) 727-7507 Summer 2014 Real-time Systems Lab, Computer Science and Engineering, ASU Priority-Driven Scheduling of Periodic Tasks

517 views • 9 slides
The Impacts of Neighborhoods on Economic Opportunity New Evidence and Policy Lessons Raj Chetty

The Impacts of Neighborhoods on Economic Opportunity New Evidence and Policy Lessons Raj Chetty

The Impacts of Neighborhoods on Economic Opportunity New Evidence and Policy Lessons Raj Chetty Harvard University Photo Credit: Florida Atlantic University The American Dream? Probability that a child born to parents in the bottom fifth

353 views • 33 slides
developments Summary Presentation In this seminar, based on my daily experience, it will focus

developments Summary Presentation In this seminar, based on my daily experience, it will focus

Albanias energy sector: towards a new era of large and sustainable developments Summary Presentation In this seminar, based on my daily experience, it will focus on an assessment of the most significant opportunities for foreign

645 views • 35 slides
E l e c t r i c i t y G e n e r a t i o n C a p a c i t y D e v e l o p m e n t i n A l b a n

E l e c t r i c i t y G e n e r a t i o n C a p a c i t y D e v e l o p m e n t i n A l b a n

L e g a l A s s e s s m e n t o n t h e E l e c t r i c i t y G e n e r a t i o n C a p a c i t y D e v e l o p m e n t i n A l b a n i a Adv. Lorenc Gordani, PhD Law Adviser in Energy Policy, Regulation &amp; Infrastructure T h e M a

525 views • 14 slides
using Shapers Yue Tang 1 , Yuming Jiang 2 , Xu Jiang 1 , Nan Guan 1 1 The Hong Kong Polytechnic

using Shapers Yue Tang 1 , Yuming Jiang 2 , Xu Jiang 1 , Nan Guan 1 1 The Hong Kong Polytechnic

Improving Multiprocessor Real-Time Systems with Bursty Inputs under Global EDF using Shapers Yue Tang 1 , Yuming Jiang 2 , Xu Jiang 1 , Nan Guan 1 1 The Hong Kong Polytechnic University 2 Norwegian University of Science and Technology Outline

635 views • 29 slides
Real Real- -Time Systems Time Systems Example: scheduling using EDF Example: scheduling using

Real Real- -Time Systems Time Systems Example: scheduling using EDF Example: scheduling using

EDA222/DIT160 Real-Time Systems, Chalmers/GU, 2008/2009 Lecture #14 Updated 2009-03-01 Real Real- -Time Systems Time Systems Example: scheduling using EDF Example: scheduling using EDF Problem: Assume a system with tasks according to the

593 views • 5 slides
Model checking perhaps the most important part of applied statistical modelling Simon Wood

Model checking perhaps the most important part of applied statistical modelling Simon Wood

Model checking perhaps the most important part of applied statistical modelling Simon Wood Model checking Checking validation! As with detection function, checking is important Want to know the model conforms to assumptions What

524 views • 26 slides
On the Computational Complexity of Periodic Scheduling PhD defense Thomas Rothvo Real-time

On the Computational Complexity of Periodic Scheduling PhD defense Thomas Rothvo Real-time

On the Computational Complexity of Periodic Scheduling PhD defense Thomas Rothvo Real-time Scheduling Given: (synchronous) tasks 1 , . . . , n with i = ( c ( i ) , d ( i ) , p ( i )) Real-time Scheduling Given: (synchronous)

947 views • 65 slides

More recommend