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----
-title: "Simulated Data Example"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Simulated Data Example}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-```{r setup, include = FALSE} 
-library(growthbreaks)
-library(MASS)
-library(dplyr)
-library(ggplot2)
-library(sf)
-library(rnaturalearth)
-```
-
-
-
-# Simulated length-at-age data
-A spatial process gives rise to heterogeneity in fish length-at-age with a predominant north-south cline, as shown in the figure below:
-
-```{r, echo = FALSE}
-## based on code by G.D. Adams
-## Specify data size
-set.seed(731)
-G= 50 ## number of years
-nsamples = 5000
-group <- sample(1:G, nsamples, replace = TRUE) # Group for individual X
-N <- nsamples # Total number of samples
-
-
-## Mu VBGM hyperparameters 
-mu.Linf = 50
-mu.k = 0.3 
-mut.t0 = -0.5
-mu.parms <- c(mu.Linf, mu.k, mut.t0)
-sigma = 0.1*mu.Linf # Observation error
-
-
-## Group level random effects
-sigma.group = c(0.3, 0.05, 0.2)
-rho = 0.3 # Correlation between group level parameters
-cor.group.mat = matrix(rho, 3, 3)
-diag(cor.group.mat) <- 1
-cov.group.mat <- diag(sigma.group) %*% cor.group.mat %*% diag(sigma.group) # Get covariance
-
-
-## Simulate parameters for groups----
-# - Empty matrix and vectors to fill with parameters and data, respectively
-group.param.mat <- group.re.mat <- matrix(NA,G,3,byrow = T)
-
-# - Random effects
-colnames(group.re.mat) <- c("log.Linf.group.re", "log.k.group.re", "t0.group.re")
-
-# - On VBGF scale
-colnames(group.param.mat) <- c("Linf.group", "k.group", "t0.group")
-
-
-# - Simulate group level parameters
-for(i in 1:G){
-  # - Sim from mvnorm
-  group.re.mat[i,] <- mvrnorm(1, rep(0,3), cov.group.mat) 
-  
-  # - Convert to parameter space
-  group.param.mat[i,1:2] <- mu.parms[1:2] * exp(group.re.mat[i,1:2]) # Log to natural scale
-  group.param.mat[i,3] <- mu.parms[3] + group.re.mat[i,3]
-}
-
-group.param.mat <- group.param.mat %>%
-  data.frame() %>%
-  arrange(Linf.group)
-
-
-## Simulate length-at-age data ----
-ages = seq(from=1,to=20, by = 1)
-age = c()
-length = c()
-for(j in 1:N) {
-  age[j] = sample(ages, 1) # Sample random age from age range
-  length[j] = (group.param.mat[group[j],1] * (1 - exp(-group.param.mat[group[j],2]*(age[j]-group.param.mat[group[j],3])))) + rnorm(1,0,sigma) # Add normally distributed random error
-}
-
-
-# Assign data to data frame and fill spatial info
-dat <- data.frame(age = age, length = length, year = as.numeric(group))
-dat <- dat[which(dat$length > 0),] # Make sure all lengths are positive
-dat <- dat %>% arrange(year, age, length)
-
-dat$long <- runif(nrow(dat),-180, -135)
-
-sample_value <- function(group) {
-  weights <- seq(50, 68, length.out = 50)
-  sample(seq(50, 68, length.out = 50), 1, prob = weights^(2*group))
-} 
-dat$lat <- sapply(as.numeric(dat$year), sample_value) 
-# Plot the data
-cols <- c("#86BBD8","#2F4858", "#F6AE2D", "#F26419", "#E86A92", "#57A773") 
-p1 <- ggplot(dat, aes(x = age, y = length, colour = group)) +
-  geom_point(size = 2) + 
-  # scale_colour_manual(values=cols) +
-  theme_minimal()+
-  theme(legend.position = 'none')+
-  labs(title = 'length at age observations')
-
-p2 <- ggplot(dat, aes(x = long, y = lat, colour = group, size= length)) +
-  geom_point(alpha = 0.5) + 
-  # scale_colour_manual(values=cols) +
-  theme_minimal()+
-  labs(title = 'spatial length-at-age') 
-
-# Create a dataframe with latitude and longitude columns
-df <- dat %>%
-      mutate(meanl = mean(length), .by = c('age')) %>%
-      mutate(resid = length-meanl) 
-
-# Convert the dataframe to an sf object
-sf_df <- st_as_sf(df, coords = c("long", "lat"), crs = 4326)
-
-# Obtain a map of the US
-us <- ne_countries(scale = "medium", returnclass = "sf") %>%
-  filter(admin == "United States of America"  | admin == 'Canada')
-
-# Perform spatial operation to remove points in the dataframe that overlap with the US polygon
-sf_df_clipped <- sf_df[!st_within(sf_df, st_union(us), sparse = FALSE), ]
-
-## a few add'l steps to save this into data/ 
-simulated_data <- sf_df_clipped %>% 
-  tidyr::extract(geometry, c('long', 'lat'), '\\((.*), (.*)\\)', convert = TRUE)  %>% 
-  select(year, age, length, lat, long)
-
-usethis::use_data(simulated_data,overwrite = TRUE)
-
-# dat<- simulated_data
-```
- 
-```{r, echo = FALSE}
-# Plot the US polygon and the points outside of it using ggplot2
-p1 <- ggplot( ) +
-  geom_point(aes(x = age, y = length, color = group), size = 2)+
-  scale_color_gradient2(low = "blue", mid = "grey90", high = "red", midpoint = 3) +
-  guides(color = 'none')+
-  theme_minimal() 
-
-p2 <- ggplot() +
-  geom_sf(data = us, fill = NA, color = 'black') +
-  geom_sf(data = sf_df_clipped, aes( color =resid, size = length), alpha = 0.9) +
-  scale_y_continuous(limits = c(50,71)) +
-  scale_x_continuous(limits = c(-185,-130))+
-  guides(size = 'none')+
-  theme_minimal() +
-  scale_color_gradient2(low = "blue", mid = "grey90", high = "red", midpoint = 0) +
-  labs(color = 'length residual')
-
-p1
-p2
-```
-