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set.seed(42)

# Num individuals
par( mfrow=c(1,4) )
# Num markers
M = 500
# Minor allele frequency
maf = 0.5

LABEL = TRUE

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# Num individuals
N = 2e3
# Num markers
M = 1e3
# Affected cutoff
aff_thresh = 70
# Minor allele frequency
maf = 0.5

# simulate different parameter settings 

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# reproducibility:
set.seed(42)

# number of causal variants:
M = 5e3
# number of individuals
N = 10e3
# heritability
hsq = 0.25
# sample individuals with admixture

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get_correlated_trait = function(input,r) {
  r * (input) + sqrt(1-r^2) * rnorm(length(input),0,1)
}
get_noisy_trait = function(input,noise) {
  sqrt(1-noise) * (input) + sqrt(noise) * rnorm(length(input),0,1)
}

simulate_non_genetic = function(N,AM,ENV,err) {
  set.seed(42)

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N = 50000

get_correlated_trait = function(input,r) {
  r * scale(input) + sqrt(1-r^2) * rnorm(length(input),0,1)
}
get_noisy_trait = function(input,noise) {
  sqrt(1-noise) * scale(input) + sqrt(noise) * rnorm(length(input),0,1)
}

# good parameters from a 2-dof grid search

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library("RColorBrewer")

# function for generating twis
make_all_twins = function(N,rge,rgc,h2g,h2c,h2e,h2gxe=0,h2gxc=0,pop_strat=0,env_strat=0){
  # --- generate MZs
  # generate MZ twins
  gv_MZ1 = scale(rnorm(N))
  gv_MZ2 = scale(gv_MZ1)
  # generate rGE
  ev_MZ1 = scale(rge * gv_MZ1 + sqrt(1 - rge^2) * scale(rnorm(N)))

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library("RColorBrewer")
clr = brewer.pal(3,"Set1")

# unrelated samples
N = 50000
# heritability parameter
h2g = 0.4
# shared environment parameter
h2c = 0.4
# environment parameter

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library("survival")
library("cmprsk")
library("RColorBrewer")

set.seed(66)

# Standard function for simulating survival with a Weibull baseline hazard
# https://stats.stackexchange.com/questions/135124/how-to-create-a-toy-survival-time-to-event-data-with-right-censoring

# baseline hazard: Weibull

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library("survival")
library("cmprsk")
library("reshape2")
library("ggplot2")
library("RColorBrewer")

# Standard function for simulating survival with a Weibull baseline hazard
# https://stats.stackexchange.com/questions/135124/how-to-create-a-toy-survival-time-to-event-data-with-right-censoring

# baseline hazard: Weibull

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library("NMF")
library("ggplot2")
library("reshape2")

N_cells = 500
N_genes = 100

N_cells_program = 20
N_genes_program = 20