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Fitting_BRT_noChl.R
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### Fishery-dependent SDM (L^3) Estimation Model Code
## Function to run BRTs on data from OM, with missing one of the Covariate so only have
# temp and mld, but no chl
## Function returns only the fitted and predicted values
## modified my M.Karp 1/04/21
#############################
# Full Models- WITHOUT Chla #
#############################
#
###Random sampling
#
if("ran" %in% sampling) {
print("Fitting BRT-RAN-nochl")
brt_R_P_nochl <- gbm.step(data=dat_hist_random,
gbm.x = c(25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_R_N_nochl <- gbm.step(data=dat_hist_random[dat_hist_random$abundance>0,],
gbm.x = c(25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presRx <- predict(brt_R_P_nochl, dat_hist, n.trees=brt_R_P_nochl$gbm.call$best.trees, type="response")
abundRx <- exp(predict(brt_R_N_nochl, dat_hist, n.trees=brt_R_N_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Ran_nochl <- presRx * abundRx
presRx <- predict(brt_R_P_nochl, dat_fcast, n.trees=brt_R_P_nochl$gbm.call$best.trees, type="response")
abundRx <- exp(predict(brt_R_N_nochl, dat_fcast, n.trees=brt_R_N_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Ran_nochl <- presRx * abundRx
}
#
### Preferential (target) sampling
#
#Target sampling-0.5
if("tar_0.5" %in% sampling){
print("Fitting BRT-Pref0.5-nochl")
brt_T_P_1_nochl <- gbm.step(data=dat_hist_Tar_1,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_1_nochl <- gbm.step(data=dat_hist_Tar_1[dat_hist_Tar_1$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_1 <- predict(brt_T_P_1_nochl, dat_hist, n.trees=brt_T_P_1_nochl$gbm.call$best.trees, type="response")
abundTx_1 <- exp(predict(brt_T_N_1_nochl, dat_hist, n.trees=brt_T_N_1_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.5_nochl <- presTx_1 * abundTx_1
presTx_1 <- predict(brt_T_P_1_nochl, dat_fcast, n.trees=brt_T_P_1_nochl$gbm.call$best.trees, type="response")
abundTx_1 <- exp(predict(brt_T_N_1_nochl, dat_fcast, n.trees=brt_T_N_1_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.5_nochl <- presTx_1 * abundTx_1
}
#Target sampling-0.6
if("tar_0.6" %in% sampling){
print("Fitting BRT-Pref0.6-nochl")
brt_T_P_2_nochl <- gbm.step(data=dat_hist_Tar_2,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_2_nochl <- gbm.step(data=dat_hist_Tar_2[dat_hist_Tar_2$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_2 <- predict(brt_T_P_2_nochl, dat_hist, n.trees=brt_T_P_2_nochl$gbm.call$best.trees, type="response")
abundTx_2 <- exp(predict(brt_T_N_2_nochl, dat_hist, n.trees=brt_T_N_2_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.6_nochl <- presTx_2 * abundTx_2
presTx_2 <- predict(brt_T_P_2_nochl, dat_fcast, n.trees=brt_T_P_2_nochl$gbm.call$best.trees, type="response")
abundTx_2 <- exp(predict(brt_T_N_2_nochl, dat_fcast, n.trees=brt_T_N_2_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.6_nochl <- presTx_2 * abundTx_2
}
#Target sampling-0.7
if("tar_0.7" %in% sampling){
print("Fitting BRT-Pref0.7-nochl")
brt_T_P_3_nochl <- gbm.step(data=dat_hist_Tar_3,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_3_nochl <- gbm.step(data=dat_hist_Tar_3[dat_hist_Tar_3$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_3 <- predict(brt_T_P_3_nochl, dat_hist, n.trees=brt_T_P_3_nochl$gbm.call$best.trees, type="response")
abundTx_3 <- exp(predict(brt_T_N_3_nochl, dat_hist, n.trees=brt_T_N_3_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.7_nochl <- presTx_3 * abundTx_3
presTx_3 <- predict(brt_T_P_3_nochl, dat_fcast, n.trees=brt_T_P_3_nochl$gbm.call$best.trees, type="response")
abundTx_3 <- exp(predict(brt_T_N_3_nochl, dat_fcast, n.trees=brt_T_N_3_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.7_0.3_nochl <- presTx_3 * abundTx_3
}
#Target sampling-0.8
if("tar_0.8" %in% sampling){
print("Fitting BRT-Pref0.8-nochl")
brt_T_P_4_nochl <- gbm.step(data=dat_hist_Tar_4,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_4_nochl <- gbm.step(data=dat_hist_Tar_4[dat_hist_Tar_4$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_4 <- predict(brt_T_P_4_nochl, dat_hist, n.trees=brt_T_P_4_nochl$gbm.call$best.trees, type="response")
abundTx_4 <- exp(predict(brt_T_N_4_nochl, dat_hist, n.trees=brt_T_N_4_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.8_nochl <- presTx_4 * abundTx_4
presTx_4 <- predict(brt_T_P_4_nochl, dat_fcast, n.trees=brt_T_P_4_nochl$gbm.call$best.trees, type="response")
abundTx_4 <- exp(predict(brt_T_N_4_nochl, dat_fcast, n.trees=brt_T_N_4_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.8_nochl <- presTx_4 * abundTx_4
}
#Target sampling-0.9
if("tar_0.9" %in% sampling){
print("Fitting BRT-Pref0.9-nochl")
brt_T_P_5_nochl <- gbm.step(data=dat_hist_Tar_5,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_5_nochl <- gbm.step(data=dat_hist_Tar_5[dat_hist_Tar_5$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_5 <- predict(brt_T_P_5_nochl, dat_hist, n.trees=brt_T_P_5_nochl$gbm.call$best.trees, type="response")
abundTx_5 <- exp(predict(brt_T_N_5_nochl, dat_hist, n.trees=brt_T_N_5_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.9_nochl <- presTx_5 * abundTx_5
presTx_5 <- predict(brt_T_P_5_nochl, dat_fcast, n.trees=brt_T_P_5_nochl$gbm.call$best.trees, type="response")
abundTx_5 <- exp(predict(brt_T_N_5_nochl, dat_fcast, n.trees=brt_T_N_5_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.9_nochl <- presTx_5 * abundTx_5
}
#
### Distance from Port Sampling
#
#Northern Offshore
if("npo" %in% sampling){
print("Fitting BRT-NPO-nochl")
brt_dist_P_npo_nochl <- gbm.step(data=dat_hist_Dist_npo,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_npo_nochl <- gbm.step(data=dat_hist_Dist_npo[dat_hist_Dist_npo$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_npo <- predict(brt_dist_P_npo_nochl, dat_hist, n.trees=brt_dist_P_npo_nochl$gbm.call$best.trees, type="response")
abundDx_npo <- exp(predict(brt_dist_N_npo_nochl, dat_hist, n.trees=brt_dist_N_npo_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_npo_nochl <- presDx_npo * abundDx_npo
presDx_npo <- predict(brt_dist_P_npo_nochl, dat_fcast, n.trees=brt_dist_P_npo_nochl$gbm.call$best.trees, type="response")
abundDx_npo <- exp(predict(brt_dist_N_npo_nochl, dat_fcast, n.trees=brt_dist_N_npo_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_npo_nochl <- presDx_npo * abundDx_npo
}
#Northern Nearshore
if("npn" %in% sampling){
print("Fitting BRT-NPN-nochl")
brt_dist_P_npn_nochl <- gbm.step(data=dat_hist_Dist_npn,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_npn_nochl <- gbm.step(data=dat_hist_Dist_npn[dat_hist_Dist_npn$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_npn <- predict(brt_dist_P_npn_nochl, dat_hist, n.trees=brt_dist_P_npn_nochl$gbm.call$best.trees, type="response")
abundDx_npn <- exp(predict(brt_dist_N_npn_nochl, dat_hist, n.trees=brt_dist_N_npn_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_npn_nochl <- presDx_npn * abundDx_npn
presDx_npn <- predict(brt_dist_P_npn_nochl, dat_fcast, n.trees=brt_dist_P_npn_nochl$gbm.call$best.trees, type="response")
abundDx_npn <- exp(predict(brt_dist_N_npn_nochl, dat_fcast, n.trees=brt_dist_N_npn_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_npn_nochl <- presDx_npn * abundDx_npn
}
#Middle Offshore
if("mpo" %in% sampling){
print("Fitting BRT-MPO-nochl")
brt_dist_P_mpo_nochl <- gbm.step(data=dat_hist_Dist_mpo,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_mpo_nochl <- gbm.step(data=dat_hist_Dist_mpo[dat_hist_Dist_mpo$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_mpo <- predict(brt_dist_P_mpo_nochl, dat_hist, n.trees=brt_dist_P_mpo_nochl$gbm.call$best.trees, type="response")
abundDx_mpo <- exp(predict(brt_dist_N_mpo, dat_hist, n.trees=brt_dist_N_mpo_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_mpo_nochl <- presDx_mpo * abundDx_mpo
presDx_mpo <- predict(brt_dist_P_mpo_nochl, dat_fcast, n.trees=brt_dist_P_mpo_nochl$gbm.call$best.trees, type="response")
abundDx_mpo <- exp(predict(brt_dist_N_mpo_nochl, dat_fcast, n.trees=brt_dist_N_mpo_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_mpo_nochl <- presDx_mpo * abundDx_mpo
}
#Middle nearshore
if("mpn" %in% sampling){
print("Fitting BRT-MPN-nochl")
brt_dist_P_mpn_nochl <- gbm.step(data=dat_hist_Dist_mpn,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_mpn_nochl <- gbm.step(data=dat_hist_Dist_mpn[dat_hist_Dist_mpn$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_mpn <- predict(brt_dist_P_mpn_nochl, dat_hist, n.trees=brt_dist_P_mpn_nochl$gbm.call$best.trees, type="response")
abundDx_mpn <- exp(predict(brt_dist_N_mpn_nochl, dat_hist, n.trees=brt_dist_N_mpn_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_mpn_nochl <- presDx_mpn * abundDx_mpn
presDx_mpn <- predict(brt_dist_P_mpn_nochl, dat_fcast, n.trees=brt_dist_P_mpn_nochl$gbm.call$best.trees, type="response")
abundDx_mpn <- exp(predict(brt_dist_N_mpn_nochl, dat_fcast, n.trees=brt_dist_N_mpn_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_mpn_nochl <- presDx_mpn * abundDx_mpn
}
#Southern Offshore
if("spo" %in% sampling){
print("Fitting BRT-SPO-nochl")
brt_dist_P_spo_nochl <- gbm.step(data=dat_hist_Dist_spo,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_spo_nochl <- gbm.step(data=dat_hist_Dist_spo[dat_hist_Dist_spo$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_spo <- predict(brt_dist_P_spo_nochl, dat_hist, n.trees=brt_dist_P_spo_nochl$gbm.call$best.trees, type="response")
abundDx_spo <- exp(predict(brt_dist_N_spo_nochl, dat_hist, n.trees=brt_dist_N_spo_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_spo_nochl <- presDx_spo * abundDx_spo
presDx_spo <- predict(brt_dist_P_spo_nochl, dat_fcast, n.trees=brt_dist_P_spo_nochl$gbm.call$best.trees, type="response")
abundDx_spo <- exp(predict(brt_dist_N_spo_nochl, dat_fcast, n.trees=brt_dist_N_spo_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_spo_nochl <- presDx_spo * abundDx_spo
}
#Southern Nearshore
if("spn" %in% sampling){
print("Fitting BRT-SPN-nochl")
brt_dist_P_spn_nochl <- gbm.step(data=dat_hist_Dist_spn,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_spn_nochl <- gbm.step(data=dat_hist_Dist_spn[dat_hist_Dist_spn$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_spn <- predict(brt_dist_P_spn_nochl, dat_hist, n.trees=brt_dist_P_spn_nochl$gbm.call$best.trees, type="response")
abundDx_spn <- exp(predict(brt_dist_N_spn_nochl, dat_hist, n.trees=brt_dist_N_spn_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_spn_nochl <- presDx_spn * abundDx_spn
presDx_spn <- predict(brt_dist_P_spn_nochl, dat_fcast, n.trees=brt_dist_P_spn_nochl$gbm.call$best.trees, type="response")
abundDx_spn <- exp(predict(brt_dist_N_spn_nochl, dat_fcast, n.trees=brt_dist_N_spn_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_spn_nochl <- presDx_spn * abundDx_spn
}
#All Offshore
if("allo" %in% sampling){
print("Fitting BRT-ALLO-nochl")
brt_dist_P_allo_nochl <- gbm.step(data=dat_hist_Dist_allo,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_allo_nochl <- gbm.step(data=dat_hist_Dist_allo[dat_hist_Dist_allo$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_allo <- predict(brt_dist_P_allo_nochl, dat_hist, n.trees=brt_dist_P_allo_nochl$gbm.call$best.trees, type="response")
abundDx_allo <- exp(predict(brt_dist_N_allo_nochl, dat_hist, n.trees=brt_dist_N_allo_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_allo_nochl <- presDx_allo * abundDx_allo
presDx_allo <- predict(brt_dist_P_allo_nochl, dat_fcast, n.trees=brt_dist_P_allo_nochl$gbm.call$best.trees, type="response")
abundDx_allo <- exp(predict(brt_dist_N_allo_nochl, dat_fcast, n.trees=brt_dist_N_allo_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_allo_nochl <- presDx_allo * abundDx_allo
}
#All Nearshore
if("alln" %in% sampling){
print("Fitting BRT-ALLN-nochl")
brt_dist_P_alln_nochl <- gbm.step(data=dat_hist_Dist_alln,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_alln_nochl <- gbm.step(data=dat_hist_Dist_alln[dat_hist_Dist_alln$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_alln <- predict(brt_dist_P_alln_nochl, dat_hist, n.trees=brt_dist_P_alln_nochl$gbm.call$best.trees, type="response")
abundDx_alln <- exp(predict(brt_dist_N_alln_nochl, dat_hist, n.trees=brt_dist_N_alln_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_alln_nochl <- presDx_alln * abundDx_alln
presDx_alln <- predict(brt_dist_P_alln_nochl, dat_fcast, n.trees=brt_dist_P_alln_nochl$gbm.call$best.trees, type="response")
abundDx_alln <- exp(predict(brt_dist_N_alln_nochl, dat_fcast, n.trees=brt_dist_N_alln_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_alln_nochl <- presDx_alln * abundDx_alln
}
#
## Bycatch + Opt Target Sampling
#
if("BY" %in% sampling){
print("Fitting BRT-BY-nochl")
brt_B_P_nochl <- gbm.step(data=dat_hist_BY,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_B_N_nochl <- gbm.step(data=dat_hist_BY[dat_hist_BY$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presBx <- predict(brt_B_P_nochl, dat_hist, n.trees=brt_B_P_nochl$gbm.call$best.trees, type="response")
abundBx <- exp(predict(brt_B_N_nochl, dat_hist, n.trees=brt_B_N_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_BY_nochl <- presBx * abundBx
presBx <- predict(brt_B_P_nochl, dat_fcast, n.trees=brt_B_P_nochl$gbm.call$best.trees, type="response")
abundBx <- exp(predict(brt_B_N_nochl, dat_fcast, n.trees=brt_B_N_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_BY_nochl<- presBx * abundBx
}
#
###Closed Areas + Opt Target Species
#
# Small Closed Area
if("CA_sm" %in% sampling){
print("Fitting BRT-CASM-nochl")
brt_CA_P_sm_nochl <- gbm.step(data=dat_hist_CA_sm,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_sm_nochl <- gbm.step(data=dat_hist_CA_sm[dat_hist_CA_sm$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_sm <- predict(brt_CA_P_sm_nochl, dat_hist, n.trees=brt_CA_P_sm_nochl$gbm.call$best.trees, type="response")
abundCAx_sm <- exp(predict(brt_CA_N_sm_nochl, dat_hist, n.trees=brt_CA_N_sm_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_sm_nochl <- presCAx_sm * abundCAx_sm
presCAx_sm <- predict(brt_CA_P_sm_nochl, dat_fcast, n.trees=brt_CA_P_sm_nochl$gbm.call$best.trees, type="response")
abundCAx_sm <- exp(predict(brt_CA_N_sm_nochl, dat_fcast, n.trees=brt_CA_N_sm_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_sm_nochl<- presCAx_sm * abundCAx_sm
}
# Medium Closed Area
if("CA_med" %in% sampling){
print("Fitting BRT-CAMED-nochl")
brt_CA_P_med_nochl <- gbm.step(data=dat_hist_CA_med,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_med_nochl <- gbm.step(data=dat_hist_CA_med[dat_hist_CA_med$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_med <- predict(brt_CA_P_med_nochl, dat_hist, n.trees=brt_CA_P_med_nochl$gbm.call$best.trees, type="response")
abundCAx_med <- exp(predict(brt_CA_N_med_nochl, dat_hist, n.trees=brt_CA_N_med_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_med_nochl <- presCAx_med * abundCAx_med
presCAx_med <- predict(brt_CA_P_med_nochl, dat_fcast, n.trees=brt_CA_P_med_nochl$gbm.call$best.trees, type="response")
abundCAx_med <- exp(predict(brt_CA_N_med_nochl, dat_fcast, n.trees=brt_CA_N_med_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_med_nochl<- presCAx_med * abundCAx_med
}
# Large Closed Area
if("CA_lar" %in% sampling){
print("Fitting BRT-CALAR-nochl")
brt_CA_P_lar_nochl <- gbm.step(data=dat_hist_CA_lar,
gbm.x = c(25,27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_lar_nochl <- gbm.step(data=dat_hist_CA_lar[dat_hist_CA_lar$abundance>0,],
gbm.x = c(25,27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_lar <- predict(brt_CA_P_lar_nochl, dat_hist, n.trees=brt_CA_P_lar_nochl$gbm.call$best.trees, type="response")
abundCAx_lar <- exp(predict(brt_CA_N_lar_nochl, dat_hist, n.trees=brt_CA_N_lar_nochl$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_lar_nochl <- presCAx_lar * abundCAx_lar
presCAx_lar <- predict(brt_CA_P_lar_nochl, dat_fcast, n.trees=brt_CA_P_lar_nochl$gbm.call$best.trees, type="response")
abundCAx_lar <- exp(predict(brt_CA_N_lar_nochl, dat_fcast, n.trees=brt_CA_N_lar_nochl$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_lar_nochl<- presCAx_lar * abundCAx_lar
}
##PLOTS
gbm.plot(brt_R_P_nochl, write.title=F, main="brt_R_P_nochl", plot.layout = c(2,2))
gbm.plot(brt_R_N_nochl, write.title=F, main="brt_R_N_nochl", plot.layout = c(2,2))
gbm.plot(brt_T_P_1_nochl, write.title=F, main="brt_T_P_1", plot.layout = c(2,2))
gbm.plot(brt_T_N_1_nochl, write.title=F, main="brt_T_N_1", plot.layout = c(2,2))
gbm.plot(brt_T_P_2_nochl, write.title=F, main="brt_T_P_2", plot.layout = c(2,2))
gbm.plot(brt_T_N_2_nochl, write.title=F, main="brt_T_N_2", plot.layout = c(2,2))
gbm.plot(brt_T_P_3_nochl, write.title=F, main="brt_T_P_3", plot.layout = c(2,2))
gbm.plot(brt_T_N_3_nochl, write.title=F, main="brt_T_N_3", plot.layout = c(2,2))
gbm.plot(brt_T_P_4_nochl, write.title=F, main="brt_T_P_4", plot.layout = c(2,2))
gbm.plot(brt_T_N_4_nochl, write.title=F, main="brt_T_N_4", plot.layout = c(2,2))
gbm.plot(brt_T_P_5_nochl, write.title=F, main="brt_T_P_5", plot.layout = c(2,2))
gbm.plot(brt_T_N_5_nochl, write.title=F, main="brt_T_N_5", plot.layout = c(2,2))
gbm.plot(brt_dist_P_npo_nochl, write.title=F, main="brt_dist_P_npo", plot.layout = c(2,2))
gbm.plot(brt_dist_N_npo_nochl, write.title=F, main="brt_dist_N_npo", plot.layout = c(2,2))
gbm.plot(brt_dist_P_mpo_nochl, write.title=F, main="brt_dist_P_mpo", plot.layout = c(2,2))
gbm.plot(brt_dist_N_mpo_nochl, write.title=F, main="brt_dist_N_mpo", plot.layout = c(2,2))
gbm.plot(brt_dist_P_spo_nochl, write.title=F, main="brt_dist_P_spo", plot.layout = c(2,2))
gbm.plot(brt_dist_N_spo_nochl, write.title=F, main="brt_dist_N_spo", plot.layout = c(2,2))
gbm.plot(brt_dist_P_allo_nochl, write.title=F, main="brt_dist_P_allo", plot.layout = c(2,2))
gbm.plot(brt_dist_N_allo_nochl, write.title=F, main="brt_dist_N_allo", plot.layout = c(2,2))
gbm.plot(brt_dist_P_npn_nochl, write.title=F, main="brt_dist_P_npn", plot.layout = c(2,2))
gbm.plot(brt_dist_N_npn_nochl, write.title=F, main="brt_dist_N_npn", plot.layout = c(2,2))
gbm.plot(brt_dist_P_mpn_nochl, write.title=F, main="brt_dist_P_mpn", plot.layout = c(2,2))
gbm.plot(brt_dist_N_mpn_nochl, write.title=F, main="brt_dist_N_mpn", plot.layout = c(2,2))
gbm.plot(brt_dist_P_spn_nochl, write.title=F, main="brt_dist_P_spn", plot.layout = c(2,2))
gbm.plot(brt_dist_N_spn_nochl, write.title=F, main="brt_dist_N_spn", plot.layout = c(2,2))
gbm.plot(brt_dist_P_alln_nochl, write.title=F, main="brt_dist_P_alln", plot.layout = c(2,2))
gbm.plot(brt_dist_N_alln_nochl, write.title=F, main="brt_dist_N_alln", plot.layout = c(2,2))
gbm.plot(brt_CA_P_sm_nochl, write.title=F, main="brt_CA_P_sm", plot.layout = c(2,2))
gbm.plot(brt_CA_N_sm_nochl, write.title=F, main="brt_CA_N_sm", plot.layout = c(2,2))
gbm.plot(brt_CA_P_med_nochl, write.title=F, main="brt_CA_P_med", plot.layout = c(2,2))
gbm.plot(brt_CA_N_med_nochl, write.title=F, main="brt_CA_N_med", plot.layout = c(2,2))
gbm.plot(brt_CA_P_lar_nochl, write.title=F, main="brt_CA_P_lar", plot.layout = c(2,2))
gbm.plot(brt_CA_N_lar_nochl, write.title=F, main="brt_CA_N_lar", plot.layout = c(2,2))