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#Simulate from an RW2 (random walk order 2) model.
# Useful for generating predictions from INLA RW2 models. 
# When forecast, RW2 models predict continuation of trend, with 
# deviations from the trend controlled by the sd parameter.
#
# See: https://inla.r-inla-download.org/r-inla.org/doc/latent/rw2.pdf
# Can intialize the RW2 at a given starting point, this needs
# needs to be two consecutive points, given we are working with
# second order differences. 

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#Functions to simulate predictions and CIs from a GAM
# CJ Brown 
# 2023-03-24
# 
# based on code in ?gam.predict and  Wood, S.N. (2017) Generalized Additive Models: An
# Introduction with R (2nd edition). Chapman and
# Hall/CRC.

library(lazyeval)

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# Template for doing empirical Bayes prediction from a GAM with mgcv
# CJ Brown 2021-08-23
#
# See Wood 2017 and ?predict.gam for more info

#First of all its often easiest to create your prediction dataframe with
# visreg, e.g. this makes a df across all values of NEOLI_Total
# and "has_fee": 
xfit <-  visreg(tour_mod, xvar = "NEOLI_Total", partial = FALSE, 
                plot = FALSE, by = "has_fee")

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#Demonstration of splines in R
#Can be used to add nonlinearity to linear models (e.g. GLMs)

library(splines)
x <- rnorm(10000)
n_df <- 5
ns_design_mat <- ns(x, n_df) #n_df degrees of freedom
plot(x, ns_design_mat[,1], ylim = c(-1, 1))
points(x, ns_design_mat[,2], col = "red")
points(x, ns_design_mat[,3], col = "blue")

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#Apply scale to just numeric variables

#keep all variables
dat2 <- dat %>%
    mutate(across(where(is.numeric), scale))

#keep just numerical variables, drop the others from the data.frame
dat2 <- dat %>%
    transmute(across(where(is.numeric), scale))

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#Function that converts dataframe of cluster identities
# to distance matrix representation of clusters (where rows/cols are samples/IDs and
# 0 indicates that pair are not in same cluster, 1 indicates they are in same cluster
as_dist_mat <- function(dtemp){
  # dtemp is dataframe with two columns:
 #ID is a unique cell ID
 #Cluster is a variable giving cluster a cell belongs to 
 #It should be arranged so we are certain
  #cells are in ascending order

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#Parameterize gamma distribution with mode and SD

#Choose summary stats for the gamma
xmode <- 1 #mode of distribution
xsd <- 0.5 # SD 

#Calculate gamma parameters
beta <- (xmode + sqrt(xmode^2 + 4*xsd^2 ) ) / ( 2 * xsd^2 )
alpha <- 1 + xmode * beta

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# Why you should plot data before doing statistical tests 
# CJ Brown 2020-11-06
#More at www.conservationhackers.org

library(ggplot2)

#
# Make some data 
#
n <- 50 #Sample size per group

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# How to ggplot2 efficiently
# CJ Brown 2020-10-27


library(ggplot2)

#
# Make some data 
#
n <- 20 #Sample size per group

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#Takes as an input a stars raster layer
#CJ Brown 2020-05-18

st_as_raster <- function(rstars){
  rext <- st_bbox(rstars)
  raster(t(rstars[[1]]), xmn = rext[1], xmx = rext[3],
                   ymn = rext[2], ymx=rext[4],
                   crs = st_crs(rstars)$proj4string)
}