SimData_Fn: Generate data for simulation experiment
In James-Thorson/spatial_delay-difference: Implements spatial and nonspatial delay-difference model
Usage
1
SimData_Fn(n_t, CV_C, SD_A, SD_E, Scale, Range_X, Range_Y, SpatialSimModel, n_s, Ngrid_sim, M, RecFn, alpha_g, ro, MRPSB, F_equil, S_bioecon, Accel, SD_F, n_samp_per_year, AreaSwept, q_I, mu_R0_total)
Arguments
n_t
CV_C
SD_A
SD_E
Scale
Range_X
Range_Y
SpatialSimModel
n_s
Ngrid_sim
M
RecFn
alpha_g
ro
MRPSB
F_equil
S_bioecon
Accel
SD_F
n_samp_per_year
AreaSwept
q_I
mu_R0_total
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (n_t, CV_C, SD_A, SD_E, Scale, Range_X, Range_Y, SpatialSimModel,
n_s, Ngrid_sim, M, RecFn, alpha_g, ro, MRPSB, F_equil, S_bioecon,
Accel, SD_F, n_samp_per_year, AreaSwept, q_I, mu_R0_total)
{
DomainArea = diff(Range_X) * diff(Range_Y)
w_a = rep(NA, length = 20)
w_a[1] = alpha_g
for (a in 2:length(w_a)) w_a[a] = alpha_g + ro * w_a[a -
1]
w_k = w_a[k]
MRPSB = (1 - exp(-M) - ro * exp(-M) + ro * exp(-2 * M))/(w_k -
(w_k - alpha_g) * exp(-M))
mu_R0_per_area = mu_R0_total/DomainArea
mu_S0_per_area = mu_R0_per_area/MRPSB
if (RecFn == "Ricker") {
log_mu_alpha = log(mu_R0_total)
beta = log(mu_R0/exp(log_mu_alpha)/mu_S0_per_area)/(-mu_S0_per_area)
}
if (RecFn == "BH") {
log_mu_alpha = log(mu_R0_total)
beta = (exp(log_mu_alpha) * mu_S0_per_area/mu_R0_per_area -
1)/mu_S0_per_area
}
if (RecFn == "Constant") {
log_mu_alpha = log(mu_R0_total/DomainArea)
}
Eps_F = rnorm(n_t, mean = 0, sd = SD_F)
Eps_C = rnorm(n_t, mean = 0, sd = CV_c)
Year_Range = c(1, n_t)
F_t = rep(NA, n_t)
Nu = 1
if (SpatialSimModel == "Matern") {
model_A <- RMmatern(nu = Nu, var = SD_A^2, scale = Scale)
model_E <- RMmatern(nu = Nu, var = SD_E^2, scale = Scale)
}
h = seq(-3 * diff(Range_X), 3 * diff(Range_X), length = 10000)
r = abs(h)/Scale
if (SpatialSimModel == "Matern")
C = SD_A^2 * 2^(1 - Nu) * gamma(Nu)^(-1) * (sqrt(2 *
Nu) * r)^Nu * besselK(sqrt(2 * Nu) * r, nu = Nu)
if (SpatialSimModel == "Gaussian")
C = SD_A^2 * exp(-r^2)
if (FALSE) {
par(mfrow = c(1, 2))
plot(model_A, ylim = c(0, 1), xlim = c(-3, 3) * diff(Range_X))
plot(x = h, y = C, ylim = c(0, 1), type = "l")
}
RangeTrue = abs(h[which.min((C - 0.1 * SD_A)^2)])
if (n_samp_per_year < n_s * 2)
error("Increase n_samp_per_year for K-means algorithm")
if (TRUE) {
x_stations = runif(n_samp_per_year, min = Range_X[1],
max = Range_X[2])
y_stations = runif(n_samp_per_year, min = Range_Y[1],
max = Range_Y[2])
loc_samples = cbind(long = x_stations, lat = y_stations)
}
if (FALSE) {
x_stations = seq(Range_X[1], Range_X[2], length = sqrt(n_samp_per_year))
y_stations = seq(Range_Y[1], Range_Y[2], length = sqrt(n_samp_per_year))
loc_samples = expand.grid(long = x_stations, lat = y_stations)
}
K = kmeans(x = loc_samples[, c("long", "lat")], centers = n_s,
iter.max = 1000, nstart = 100)
K$centers = K$centers + rnorm(n_s, mean = 0, sd = rep(0.001 *
c(diff(Range_X), diff(Range_Y)), each = n_s))
loc_samples = cbind(loc_samples, Station_j = K$cluster)
loc_stations = cbind(K$centers, Station_j = 1:n_s)
loc_grid = expand.grid(long = seq(Range_X[1], Range_X[2],
length = ceiling(sqrt(Ngrid_sim))), lat = seq(Range_Y[1],
Range_Y[2], length = ceiling(sqrt(Ngrid_sim))))
NN = nn2(query = loc_grid, data = loc_stations[, 1:2], k = 1)
loc_grid = cbind(loc_grid, Station_j = NN$nn.idx)
loc_all = rbind(loc_stations, loc_samples, loc_grid)
Voronoi_samples = calcVoronoi(cbind(X = loc_samples[, "long"],
Y = loc_samples[, "lat"]), xlim = range(Range_X, loc_samples[,
"long"]), ylim = range(Range_Y, loc_samples[, "lat"]))
Area_samples = calcArea(Voronoi_samples)[, 2]
Area_all = c(rep(0, n_s), Area_samples, rep(0, Ngrid_sim))
Omega_s = RFsimulate(model = model_A, x = loc_all[, "long"],
y = loc_all[, "lat"])@data[, 1]
Alpha_s = exp(Omega_s + log_mu_alpha)
Epsilon_tmp_s = Epsilon_s = array(NA, dim = c(n_s + n_samp_per_year +
Ngrid_sim, n_t))
for (t in 1:n_t) {
Epsilon_s[, t] = Epsilon_tmp_s[, t] = RFsimulate(model_E,
x = loc_all[, "long"], y = loc_all[, "lat"])@data[,
1]
}
if (RecFn == "Ricker") {
mu_S_equil_per_area = log((1 - exp(-M - F_equil) - ro *
exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil))/(exp(log_mu_alpha) *
(w_k - (w_k - alpha_g) * exp(-M - F_equil))))/(-beta)
mu_R_equil_per_area = exp(log_mu_alpha) * mu_S_equil *
exp(-beta * mu_S_equil)
}
if (RecFn == "BH") {
mu_S_equil_per_area = ((exp(log_mu_alpha) * (w_k - (w_k -
alpha_g) * exp(-M - F_equil))/(1 - exp(-M - F_equil) -
ro * exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil))) -
1)/beta
mu_R_equil_per_area = exp(log_mu_alpha) * mu_S_equil/(1 +
beta * mu_S_equil)
}
if (RecFn == "Constant") {
mu_S_equil_per_area = (exp(log_mu_alpha) * (w_k - (w_k -
alpha_g) * exp(-M - F_equil))/(1 - exp(-M - F_equil) -
ro * exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil)))
mu_R_equil_per_area = exp(log_mu_alpha)
mu_S0_per_area = (exp(log_mu_alpha) * (w_k - (w_k - alpha_g) *
exp(-M - (0)))/(1 - exp(-M - (0)) - ro * exp(-M -
(0)) + ro * exp(-2 * M - 2 * (0))))
}
mu_N_equil_per_area = mu_R_equil_per_area/(1 - exp(-M - F_equil))
S_equil = mu_S_equil_per_area * exp(Omega_s)
R_equil = mu_R_equil_per_area * exp(Omega_s)
N_equil = mu_N_equil_per_area * exp(Omega_s)
W_equil = S_equil/N_equil
S0 = mu_S0_per_area * exp(Omega_s)
sum_S0 = sum(S0 * Area_all)
if (abs(log(sum_S0/(sum(R_equil * Area_all)/MRPSB))) > 0.5)
stop("Problem with area units")
print(paste("Expected unfished catch rate = ", sum(mu_S_equil_per_area *
AreaSwept)))
C_st = S_st = N_st = R_st = W_st = matrix(NA, nrow = n_s +
n_samp_per_year + Ngrid_sim, ncol = n_t)
for (t in 1:n_t) {
R_st[, t] = R_equil * exp(Epsilon_s[, t])
if (t == 1)
N_st[, t] = N_equil * exp(-M - F_equil) + R_st[,
t]
if (t >= 2)
N_st[, t] = N_st[, t - 1] * exp(-M - F_t[t - 1]) +
R_st[, t]
if (t == 1)
S_st[, t] = exp(-M - F_equil) * (alpha_g * N_equil +
ro * S_equil) + w_k * R_st[, t]
if (t >= 2)
S_st[, t] = exp(-M - F_t[t - 1]) * (alpha_g * N_st[,
t - 1] + ro * S_st[, t - 1]) + w_k * R_st[, t]
W_st[, t] = S_st[, t]/N_st[, t]
if (t == 1)
F_t[t] = F_equil
if (t >= 2)
F_t[t] = F_t[t - 1] * (sum(Area_all * S_st[, t])/sum(Area_all *
S_equil)/S_bioecon)^Accel * exp(Eps_F[t] - SD_F^2/2)
C_st[, t] = F_t[t]/(M + F_t[t]) * (1 - exp(-M - F_t[t])) *
S_st[, t]
}
Y = I_st = rpois(n = n_samp_per_year * n_t, lambda = AreaSwept *
q_I * N_st[n_s + 1:n_samp_per_year, ])
Y_stations = matrix(Y, ncol = n_t, byrow = FALSE)
NAind_stations = array(as.integer(ifelse(is.na(Y_stations),
1, 0)), dim = dim(Y_stations))
Wobs_st = array(rnorm(n = n_samp_per_year * n_t, mean = W_st[n_s +
1:n_samp_per_year, ], sd = W_st[n_s + 1:n_samp_per_year,
] * CV_w), dim = c(n_samp_per_year, n_t))
C_t = colSums(C_st * outer(Area_all, rep(1, ncol(C_st))))
Cobs_t = (C_t + C_t * Eps_C)
S_t = colSums(S_st * outer(Area_all, rep(1, ncol(C_st))))
R_t = colSums(R_st * outer(Area_all, rep(1, ncol(C_st))))
N_t = colSums(N_st * outer(Area_all, rep(1, ncol(C_st))))
DF = data.frame(I_j = as.vector(I_st), W_j = as.vector(Wobs_st),
Station_j = rep(loc_samples[, "Station_j"], n_t), Year_j = as.vector(col(Wobs_st)),
AreaSwept_j = AreaSwept, long_j = rep(loc_samples[, "long"],
n_t), lat_j = rep(loc_samples[, "lat"], n_t))
Return = list(Eps_F = Eps_F, Eps_C = Eps_C, Year_Range = Year_Range,
F_t = F_t, RangeTrue = RangeTrue, loc_samples = loc_samples,
K = K, loc_samples = loc_samples, loc_stations = loc_stations,
loc_grid = loc_grid, NN = NN, loc_grid = loc_grid, loc_all = loc_all,
Area_samples = Area_samples, Area_all = Area_all, Voronoi_samples = Voronoi_samples,
Omega_s = Omega_s, Alpha_s = Alpha_s, Epsilon_s = Epsilon_s,
mu_S_equil_per_area = mu_S_equil_per_area, mu_R_equil_per_area = mu_R_equil_per_area,
mu_S0_per_area = mu_S0_per_area, mu_N_equil_per_area = mu_N_equil_per_area,
S_equil = S_equil, R_equil = R_equil, N_equil = N_equil,
W_equil = W_equil, S0 = S0, sum_S0 = sum_S0, C_st = C_st,
S_st = S_st, N_st = N_st, R_st = R_st, W_st = W_st, I_st = I_st,
Y = Y, Y_stations = Y_stations, NAind_stations = NAind_stations,
Wobs_st = Wobs_st, C_t = C_t, Cobs_t = Cobs_t, S_t = S_t,
R_t = R_t, N_t = N_t, w_a = w_a, w_k = w_k, MRPSB = MRPSB,
mu_R0_per_area = mu_R0_per_area, mu_S0_per_area = mu_S0_per_area,
log_mu_alpha = log_mu_alpha, beta = beta, DomainArea = DomainArea,
DF = DF)
return(Return)
}
James-Thorson/spatial_delay-difference documentation built on May 7, 2019, 10:20 a.m.
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Usage
1 | SimData_Fn(n_t, CV_C, SD_A, SD_E, Scale, Range_X, Range_Y, SpatialSimModel, n_s, Ngrid_sim, M, RecFn, alpha_g, ro, MRPSB, F_equil, S_bioecon, Accel, SD_F, n_samp_per_year, AreaSwept, q_I, mu_R0_total)
|
Arguments
n_t |
|
CV_C |
|
SD_A |
|
SD_E |
|
Scale |
|
Range_X |
|
Range_Y |
|
SpatialSimModel |
|
n_s |
|
Ngrid_sim |
|
M |
|
RecFn |
|
alpha_g |
|
ro |
|
MRPSB |
|
F_equil |
|
S_bioecon |
|
Accel |
|
SD_F |
|
n_samp_per_year |
|
AreaSwept |
|
q_I |
|
mu_R0_total |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (n_t, CV_C, SD_A, SD_E, Scale, Range_X, Range_Y, SpatialSimModel,
n_s, Ngrid_sim, M, RecFn, alpha_g, ro, MRPSB, F_equil, S_bioecon,
Accel, SD_F, n_samp_per_year, AreaSwept, q_I, mu_R0_total)
{
DomainArea = diff(Range_X) * diff(Range_Y)
w_a = rep(NA, length = 20)
w_a[1] = alpha_g
for (a in 2:length(w_a)) w_a[a] = alpha_g + ro * w_a[a -
1]
w_k = w_a[k]
MRPSB = (1 - exp(-M) - ro * exp(-M) + ro * exp(-2 * M))/(w_k -
(w_k - alpha_g) * exp(-M))
mu_R0_per_area = mu_R0_total/DomainArea
mu_S0_per_area = mu_R0_per_area/MRPSB
if (RecFn == "Ricker") {
log_mu_alpha = log(mu_R0_total)
beta = log(mu_R0/exp(log_mu_alpha)/mu_S0_per_area)/(-mu_S0_per_area)
}
if (RecFn == "BH") {
log_mu_alpha = log(mu_R0_total)
beta = (exp(log_mu_alpha) * mu_S0_per_area/mu_R0_per_area -
1)/mu_S0_per_area
}
if (RecFn == "Constant") {
log_mu_alpha = log(mu_R0_total/DomainArea)
}
Eps_F = rnorm(n_t, mean = 0, sd = SD_F)
Eps_C = rnorm(n_t, mean = 0, sd = CV_c)
Year_Range = c(1, n_t)
F_t = rep(NA, n_t)
Nu = 1
if (SpatialSimModel == "Matern") {
model_A <- RMmatern(nu = Nu, var = SD_A^2, scale = Scale)
model_E <- RMmatern(nu = Nu, var = SD_E^2, scale = Scale)
}
h = seq(-3 * diff(Range_X), 3 * diff(Range_X), length = 10000)
r = abs(h)/Scale
if (SpatialSimModel == "Matern")
C = SD_A^2 * 2^(1 - Nu) * gamma(Nu)^(-1) * (sqrt(2 *
Nu) * r)^Nu * besselK(sqrt(2 * Nu) * r, nu = Nu)
if (SpatialSimModel == "Gaussian")
C = SD_A^2 * exp(-r^2)
if (FALSE) {
par(mfrow = c(1, 2))
plot(model_A, ylim = c(0, 1), xlim = c(-3, 3) * diff(Range_X))
plot(x = h, y = C, ylim = c(0, 1), type = "l")
}
RangeTrue = abs(h[which.min((C - 0.1 * SD_A)^2)])
if (n_samp_per_year < n_s * 2)
error("Increase n_samp_per_year for K-means algorithm")
if (TRUE) {
x_stations = runif(n_samp_per_year, min = Range_X[1],
max = Range_X[2])
y_stations = runif(n_samp_per_year, min = Range_Y[1],
max = Range_Y[2])
loc_samples = cbind(long = x_stations, lat = y_stations)
}
if (FALSE) {
x_stations = seq(Range_X[1], Range_X[2], length = sqrt(n_samp_per_year))
y_stations = seq(Range_Y[1], Range_Y[2], length = sqrt(n_samp_per_year))
loc_samples = expand.grid(long = x_stations, lat = y_stations)
}
K = kmeans(x = loc_samples[, c("long", "lat")], centers = n_s,
iter.max = 1000, nstart = 100)
K$centers = K$centers + rnorm(n_s, mean = 0, sd = rep(0.001 *
c(diff(Range_X), diff(Range_Y)), each = n_s))
loc_samples = cbind(loc_samples, Station_j = K$cluster)
loc_stations = cbind(K$centers, Station_j = 1:n_s)
loc_grid = expand.grid(long = seq(Range_X[1], Range_X[2],
length = ceiling(sqrt(Ngrid_sim))), lat = seq(Range_Y[1],
Range_Y[2], length = ceiling(sqrt(Ngrid_sim))))
NN = nn2(query = loc_grid, data = loc_stations[, 1:2], k = 1)
loc_grid = cbind(loc_grid, Station_j = NN$nn.idx)
loc_all = rbind(loc_stations, loc_samples, loc_grid)
Voronoi_samples = calcVoronoi(cbind(X = loc_samples[, "long"],
Y = loc_samples[, "lat"]), xlim = range(Range_X, loc_samples[,
"long"]), ylim = range(Range_Y, loc_samples[, "lat"]))
Area_samples = calcArea(Voronoi_samples)[, 2]
Area_all = c(rep(0, n_s), Area_samples, rep(0, Ngrid_sim))
Omega_s = RFsimulate(model = model_A, x = loc_all[, "long"],
y = loc_all[, "lat"])@data[, 1]
Alpha_s = exp(Omega_s + log_mu_alpha)
Epsilon_tmp_s = Epsilon_s = array(NA, dim = c(n_s + n_samp_per_year +
Ngrid_sim, n_t))
for (t in 1:n_t) {
Epsilon_s[, t] = Epsilon_tmp_s[, t] = RFsimulate(model_E,
x = loc_all[, "long"], y = loc_all[, "lat"])@data[,
1]
}
if (RecFn == "Ricker") {
mu_S_equil_per_area = log((1 - exp(-M - F_equil) - ro *
exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil))/(exp(log_mu_alpha) *
(w_k - (w_k - alpha_g) * exp(-M - F_equil))))/(-beta)
mu_R_equil_per_area = exp(log_mu_alpha) * mu_S_equil *
exp(-beta * mu_S_equil)
}
if (RecFn == "BH") {
mu_S_equil_per_area = ((exp(log_mu_alpha) * (w_k - (w_k -
alpha_g) * exp(-M - F_equil))/(1 - exp(-M - F_equil) -
ro * exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil))) -
1)/beta
mu_R_equil_per_area = exp(log_mu_alpha) * mu_S_equil/(1 +
beta * mu_S_equil)
}
if (RecFn == "Constant") {
mu_S_equil_per_area = (exp(log_mu_alpha) * (w_k - (w_k -
alpha_g) * exp(-M - F_equil))/(1 - exp(-M - F_equil) -
ro * exp(-M - F_equil) + ro * exp(-2 * M - 2 * F_equil)))
mu_R_equil_per_area = exp(log_mu_alpha)
mu_S0_per_area = (exp(log_mu_alpha) * (w_k - (w_k - alpha_g) *
exp(-M - (0)))/(1 - exp(-M - (0)) - ro * exp(-M -
(0)) + ro * exp(-2 * M - 2 * (0))))
}
mu_N_equil_per_area = mu_R_equil_per_area/(1 - exp(-M - F_equil))
S_equil = mu_S_equil_per_area * exp(Omega_s)
R_equil = mu_R_equil_per_area * exp(Omega_s)
N_equil = mu_N_equil_per_area * exp(Omega_s)
W_equil = S_equil/N_equil
S0 = mu_S0_per_area * exp(Omega_s)
sum_S0 = sum(S0 * Area_all)
if (abs(log(sum_S0/(sum(R_equil * Area_all)/MRPSB))) > 0.5)
stop("Problem with area units")
print(paste("Expected unfished catch rate = ", sum(mu_S_equil_per_area *
AreaSwept)))
C_st = S_st = N_st = R_st = W_st = matrix(NA, nrow = n_s +
n_samp_per_year + Ngrid_sim, ncol = n_t)
for (t in 1:n_t) {
R_st[, t] = R_equil * exp(Epsilon_s[, t])
if (t == 1)
N_st[, t] = N_equil * exp(-M - F_equil) + R_st[,
t]
if (t >= 2)
N_st[, t] = N_st[, t - 1] * exp(-M - F_t[t - 1]) +
R_st[, t]
if (t == 1)
S_st[, t] = exp(-M - F_equil) * (alpha_g * N_equil +
ro * S_equil) + w_k * R_st[, t]
if (t >= 2)
S_st[, t] = exp(-M - F_t[t - 1]) * (alpha_g * N_st[,
t - 1] + ro * S_st[, t - 1]) + w_k * R_st[, t]
W_st[, t] = S_st[, t]/N_st[, t]
if (t == 1)
F_t[t] = F_equil
if (t >= 2)
F_t[t] = F_t[t - 1] * (sum(Area_all * S_st[, t])/sum(Area_all *
S_equil)/S_bioecon)^Accel * exp(Eps_F[t] - SD_F^2/2)
C_st[, t] = F_t[t]/(M + F_t[t]) * (1 - exp(-M - F_t[t])) *
S_st[, t]
}
Y = I_st = rpois(n = n_samp_per_year * n_t, lambda = AreaSwept *
q_I * N_st[n_s + 1:n_samp_per_year, ])
Y_stations = matrix(Y, ncol = n_t, byrow = FALSE)
NAind_stations = array(as.integer(ifelse(is.na(Y_stations),
1, 0)), dim = dim(Y_stations))
Wobs_st = array(rnorm(n = n_samp_per_year * n_t, mean = W_st[n_s +
1:n_samp_per_year, ], sd = W_st[n_s + 1:n_samp_per_year,
] * CV_w), dim = c(n_samp_per_year, n_t))
C_t = colSums(C_st * outer(Area_all, rep(1, ncol(C_st))))
Cobs_t = (C_t + C_t * Eps_C)
S_t = colSums(S_st * outer(Area_all, rep(1, ncol(C_st))))
R_t = colSums(R_st * outer(Area_all, rep(1, ncol(C_st))))
N_t = colSums(N_st * outer(Area_all, rep(1, ncol(C_st))))
DF = data.frame(I_j = as.vector(I_st), W_j = as.vector(Wobs_st),
Station_j = rep(loc_samples[, "Station_j"], n_t), Year_j = as.vector(col(Wobs_st)),
AreaSwept_j = AreaSwept, long_j = rep(loc_samples[, "long"],
n_t), lat_j = rep(loc_samples[, "lat"], n_t))
Return = list(Eps_F = Eps_F, Eps_C = Eps_C, Year_Range = Year_Range,
F_t = F_t, RangeTrue = RangeTrue, loc_samples = loc_samples,
K = K, loc_samples = loc_samples, loc_stations = loc_stations,
loc_grid = loc_grid, NN = NN, loc_grid = loc_grid, loc_all = loc_all,
Area_samples = Area_samples, Area_all = Area_all, Voronoi_samples = Voronoi_samples,
Omega_s = Omega_s, Alpha_s = Alpha_s, Epsilon_s = Epsilon_s,
mu_S_equil_per_area = mu_S_equil_per_area, mu_R_equil_per_area = mu_R_equil_per_area,
mu_S0_per_area = mu_S0_per_area, mu_N_equil_per_area = mu_N_equil_per_area,
S_equil = S_equil, R_equil = R_equil, N_equil = N_equil,
W_equil = W_equil, S0 = S0, sum_S0 = sum_S0, C_st = C_st,
S_st = S_st, N_st = N_st, R_st = R_st, W_st = W_st, I_st = I_st,
Y = Y, Y_stations = Y_stations, NAind_stations = NAind_stations,
Wobs_st = Wobs_st, C_t = C_t, Cobs_t = Cobs_t, S_t = S_t,
R_t = R_t, N_t = N_t, w_a = w_a, w_k = w_k, MRPSB = MRPSB,
mu_R0_per_area = mu_R0_per_area, mu_S0_per_area = mu_S0_per_area,
log_mu_alpha = log_mu_alpha, beta = beta, DomainArea = DomainArea,
DF = DF)
return(Return)
}
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