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chin_tune/scripts/sim_variables.R

+####
+####----1. ABOUT------------------------------------------------------------####
+####
+####   Evaluating alternative methods for modeling trap         
+####   efficiencies of outmigrating juvenile salmonids          
+####
+####   Authors:  Walden et al.                               
+####
+####   Content: R-Code for sourcing declared variables.
+####
+####   This is the file for Supplement X of the manuscript.
+####
+
+####----2. "True" parameter values for simulated data-----------------------####
+
+## Covariates
+Qmin <- 500             # discharge
+Qmax <- 7000            # discharge
+degCmin <- 6            # temperature
+degCmax <- 21           # temperature
+effortmin <- 720        # effort
+effortmax <- 3840       # effort
+precipitation <- 0      # precipitation (yes/no) (nuisance)
+nyear <- 10             # groups (random effects)
+
+## Set parameter values
+Beta0_maxpt1 <- -3.2    # Trap efficiency max 0.10
+Beta0_maxpt25 <- -2.1   # Trap efficiency max 0.25
+Beta1 <- -0.3       # discharge
+Beta2 <- 0.2        # temperature
+Beta3 <- 0.2        # effort
+Beta4 <- 0.0        # precipitation yes/no
+Beta5 <- -0.4       # effort^2
+Beta6 <- 0.4        # discharge x temperature interaction
+raneffSD <- 0.1      # year effect standard deviation
+phiTrue <- 0.0002
+
+####----3. Simulation variables---------------------------------------------####
+
+# vector of sample sizes to test
+nset <- c(50,100,150,200) 
+# vector of trap efficiencies to test
+theseefficiencies <- c("Max efficiency 0.1","Max efficiency 0.25")
+# Number of simulations to complete
+nsim <- 1000              
+# size of test dataset
+ntest <- 100
+
+####----4. Model parameters, hyperparameters, or variables------------------####
+
+## For boosted regression modeling
+mutuneiter <- 2000
+phituneiter <- 2000
+nu_hp=0.1
+fmu_null <- as.formula(y ~ 1)
+fmu <- as.formula(y ~ 1 + 
+                    bols(discharge, intercept=FALSE) + 
+                    bols(temperature, intercept=FALSE) + 
+                    bols(effort, intercept=FALSE) + 
+                    bols(precip, intercept=FALSE) + 
+                    bbs(effort, knots=2, boundary.knots=NULL, degree=2, 
+                        df=1, center=TRUE) + 
+                    bols(temperature, intercept=FALSE, df=1) %X% 
+                    bols(discharge, intercept=FALSE, df=1) + 
+                    brandom(year)
+)
+fphi <- as.formula(y ~ 1)
+nboot <- 100
+kfoldB <- 25
+
+## For random forest modeling
+seed.forest <- 234672817
+rftrees <- 2000
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