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scenario_bias.R
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scenario_bias.R
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#### Code to run scenarios - BIAS
# Lambda default = -2
# prob = 0.2
# env = 1.2
# run all multiple times and save output
# all run as functions
# load packages needed to run in parallel
library(foreach)
library(doParallel)
# choose number of times to run
n_runs <- 500
# create a randomly generated string of seeds
# seed must be integer
seed <- sample(round(1:100000000),n_runs,replace=F)
#seed <- NULL
# set up the scenario with parameters that need to be changed
# set parameters
#source("setParams.R") # this is now redundant
source("run_scenario.R")
# change those that need changing
## BIAS
probs <- list(0.2,0.16,0.12,0.08,0.04,0.02,0.004) # 7 scenarios
# structured model
mapply(FUN = run_scenario,
probs = probs[1],
parameter = probs[1],
MoreArgs = list(
model_type="structured",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting
# unstructured model
mapply(FUN = run_scenario,
probs = probs,
parameter = probs,
MoreArgs = list(
model_type="unstructured",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting
# unstructuredcov model
mapply(FUN = run_scenario,
probs = probs,
parameter = probs,
MoreArgs = list(
model_type = "unstructuredcov",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting
# joint model
mapply(FUN = run_scenario,
probs = probs,
parameter = probs,
MoreArgs = list(
model_type="joint",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting
# jointcov model
mapply(FUN = run_scenario,
probs = probs,
parameter = probs,
MoreArgs = list(
model_type="jointcov",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting
# joint2 model
mapply(FUN = run_scenario,
probs = probs,
parameter = probs,
MoreArgs = list(
model_type="jointtwo",
plotting=FALSE,
summary_results=FALSE,
seed = seed,
plot = FALSE,
n_runs = n_runs,
scenario_name = "Bias_",
dim = c(300,300),
env.beta = 2,
plotdat = TRUE,
sigma2x = 0.5,
kappa = 0.05,
strata = 25,
rows = 5,
cols = 5,
qsize = 1,
rho = 0.99,
lambda = -2,
nsamp = 150,
resolution = c(10,10))) # to use the function you must put in all parameters it is expecting