Gavin Simpson gavinsimpson

## predict-bam-bug-with-terms-argument.R
# Weird behaviour of predict.bam()

# packages
library("mgcv")

# simulate some data
set.seed(42)
df <- gamSim(1, n = 10000)

# fit a bam with and without discrete = TRUE and a gam

## smooth_re2.R
#' Create a random effect basis with integers rather than factors
#' @import mgcv
#' @export
smooth.construct.re2.smooth.spec <- function (object, data, knots) {
  if (!is.null(object$id))
    stop("random effects don't work with ids.")
  if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor)   ## <-- All I did was this (and below)
  form <- as.formula(paste("~", paste(object$term, collapse = ":"),
                           "-1"))
  object$X <- model.matrix(form, data)

## b-spline-extrapolation.R
## Can we do better extrapolation than the TPRS example used by Gabriel
## Riutort-Mayol et al in their recent paper on low rank approximations to
## Gaussian Processes? https://arxiv.org/abs/2004.11408

## Packages
library('ggplot2')
library('tibble')
library('tidyr')
library('dplyr')
library('mgcv')

## animated-basis-functions.R
## plots and animations of how basis functions are used to make
## splines and how these are fitted to data

library('ggplot2')
library('tibble')
library('tidyr')
library('dplyr')
library('mgcv')
library('mvnfast')
library('purrr')

## animated-basis-functions.R
## Procuces an animation showing how weighted basis functions combine to produce a sline
##  - this version is for a cubic regression spline basis

## packages required
library('ggplot2')
library('tibble')
library('tidyr')
library('dplyr')
library('mgcv')
library('mvnfast')

## maddy-peat.csv

          
            Sample
            upperDepth
            lowerDepth
            ageBP
            ageError
            calUpper
            calLower

            
              SRR-4556
              20
              22
              355
              35
              509
              307

            
              SRR-4557
              26
              28
              465
              35
              542
              480

            
              SRR-4558
              32
              34
              635
              35
              671
              545

            
              SRR-4559
              38
              40
              740
              35
              732
              666

            
              SRR-4560
              44
              46
              865
              35
              916
              691

            
              SRR-4561
              50
              52.5
              870
              35
              918
              692

            
              SRR-4562
              56
              58
              985
              35
              967
              795

            
              SRR-4563
              100
              108
              1270
              35
              1284
              1097

            
              SRR-4564
              200
              207
              2575
              35
              2761
              2558

## darlingtonia.csv
## leafHeight is in cm.; visited, 1 == TRUE, 0 == FALSE"
"leafHeight","visited"
67,1
84,1
49,1
70,1
77,1
77,1
66,1
65,1

## data-plus-effects-original-scale.png

      
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                / data-plus-effects-original-scale.png
            
            
              Last active
              February 3, 2021 18:57
            
              
                Analyse transect data from Shultz et al (2016) PeerJ
              
          
      View data-plus-effects-original-scale.png
  
    
## geochimica-metals.csv
## Subset of data used in Rose et al (2012) Geochimica et Cosmochimica Acta 82, 113–135 doi: http://doi.org/10.1016/j.gca.2010.12.026
SiteCode,Date,SoilType,Region,Hg
CHNA,2000,thin,NW,3.84339855275794
CHNA,1990,thin,NW,5.42461797827638
CHNA,1980,thin,NW,8.81972984714937
CHNA,1970,thin,NW,11.4174572948292
CHNA,1960,thin,NW,16.513540420367
CHNA,1950,thin,NW,16.5120469510654
CHNA,1940,thin,NW,11.1888397621818
CHNA,1930,thin,NW,11.6222216338607

## compare-glmmTMB-with-mgcv.R
## Compare Brooks et al glmmTMB paper with mgcv

## Packages
library("glmmTMB")
library("mgcv")
library("ggplot2")
theme_set(theme_bw())
library("ggstance")

## Salamander
	# Weird behaviour of predict.bam()

	# packages
	library("mgcv")

	# simulate some data
	set.seed(42)
	df <- gamSim(1, n = 10000)

	# fit a bam with and without discrete = TRUE and a gam
	#' Create a random effect basis with integers rather than factors
	#' @import mgcv
	#' @export
	smooth.construct.re2.smooth.spec <- function (object, data, knots) {
	if (!is.null(object$id))
	stop("random effects don't work with ids.")
	if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor) ## <-- All I did was this (and below)
	form <- as.formula(paste("~", paste(object$term, collapse = ":"),
	"-1"))
	object$X <- model.matrix(form, data)
	## Can we do better extrapolation than the TPRS example used by Gabriel
	## Riutort-Mayol et al in their recent paper on low rank approximations to
	## Gaussian Processes? https://arxiv.org/abs/2004.11408

	## Packages
	library('ggplot2')
	library('tibble')
	library('tidyr')
	library('dplyr')
	library('mgcv')
	## plots and animations of how basis functions are used to make
	## splines and how these are fitted to data

	library('ggplot2')
	library('tibble')
	library('tidyr')
	library('dplyr')
	library('mgcv')
	library('mvnfast')
	library('purrr')
	## Procuces an animation showing how weighted basis functions combine to produce a sline
	## - this version is for a cubic regression spline basis

	## packages required
	library('ggplot2')
	library('tibble')
	library('tidyr')
	library('dplyr')
	library('mgcv')
	library('mvnfast')
Sample	upperDepth	lowerDepth	ageBP	ageError	calUpper	calLower
SRR-4556	20	22	355	35	509	307
SRR-4557	26	28	465	35	542	480
SRR-4558	32	34	635	35	671	545
SRR-4559	38	40	740	35	732	666
SRR-4560	44	46	865	35	916	691
SRR-4561	50	52.5	870	35	918	692
SRR-4562	56	58	985	35	967	795
SRR-4563	100	108	1270	35	1284	1097
SRR-4564	200	207	2575	35	2761	2558
	## leafHeight is in cm.; visited, 1 == TRUE, 0 == FALSE"
	"leafHeight","visited"
	67,1
	84,1
	49,1
	70,1
	77,1
	77,1
	66,1
	65,1
	## Subset of data used in Rose et al (2012) Geochimica et Cosmochimica Acta 82, 113–135 doi: http://doi.org/10.1016/j.gca.2010.12.026
	SiteCode,Date,SoilType,Region,Hg
	CHNA,2000,thin,NW,3.84339855275794
	CHNA,1990,thin,NW,5.42461797827638
	CHNA,1980,thin,NW,8.81972984714937
	CHNA,1970,thin,NW,11.4174572948292
	CHNA,1960,thin,NW,16.513540420367
	CHNA,1950,thin,NW,16.5120469510654
	CHNA,1940,thin,NW,11.1888397621818
	CHNA,1930,thin,NW,11.6222216338607
	## Compare Brooks et al glmmTMB paper with mgcv

	## Packages
	library("glmmTMB")
	library("mgcv")
	library("ggplot2")
	theme_set(theme_bw())
	library("ggstance")

	## Salamander