tests/testthat/helper_taspprepodin.R
In Chubbychunks/odin_tasp_prep: Impact of PrEP TasP Intervention
# helper files have functions that will be used a lot in the model tests
time <- seq(1986, 2016, length.out = 31)
# dont know what this is
# takes the new varying parameter and it meshes it with the original base parameter set
# f <- function(x, base, v, gen, time) {
# n <- lengths(base[v])
# base[v] <- setNames(split(unlist(x, use.names=FALSE), rep(seq_along(v), n)), v) # base is the original set of pars
# mod <- gen(user=base)
# mod$transform_variables(mod$run(time))
# }
run_model <- function(p, gen, time, output_vars) {
mod <- gen(user = p)
all_results <- mod$transform_variables(mod$run(time))
all_results[output_vars]
}
par_gridplot = function(result, parm) {require(plyr)
fc_df = aperm(result[parm][[1]], c(2, 3, 1))
fc_df_list = alply(fc_df, 3)
fc_df_list = lapply(fc_df_list, function(x) {colnames(x) = rownames(x) = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou")
return(x)})
dat = data.frame(combination = unlist(lapply(fc_df_list, function(x) {
tmp = expand.grid(colnames(x), rownames(x))
return(paste(tmp[,2], tmp[,1], sep = "_"))
})),
value = unlist(lapply(fc_df_list, c)),
year = unlist(sort(rep(time, 81))))
dat$combination = factor(dat$combination, levels =
unlist(lapply(fc_df_list, function(x) {
tmp = expand.grid(colnames(x), rownames(x))
return(paste(tmp[,2], tmp[,1], sep = "_"))
}))[1:81])
return(ggplot(dat, aes(x = year, y = value, color = value)) + geom_line(size = 2) + facet_wrap(~combination) + theme_bw())
}
par_gridplot2 = function(result, parm) {
require(plyr)
fc_df = aperm(result[parm][[1]], c(2, 3, 1))
fc_df_list = alply(fc_df, 3)
fc_df_list_applied = lapply(fc_df_list, function(x) {colnames(x) = rownames(x) = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou")
return(x)})
dat = data.frame(row =
rep(c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"), 1, each = 9),
col =
rep(c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"), 9),
value = unlist(lapply(fc_df_list_applied, c)),
year = unlist(sort(rep(time, 81))))
dat$row = factor(dat$row, levels = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"))
dat$col = factor(dat$col, levels = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"))
# dat$combination = factor(dat$combination, levels =
# unlist(lapply(fc_df_list, function(x) {
# tmp = expand.grid(colnames(x), rownames(x))
# return(paste(tmp[,2], tmp[,1], sep = "_"))
# }))[1:81])
return(ggplot(dat, aes(x = year, y = value, color = value)) + geom_line(size = 2) + facet_grid(row~col) + theme_bw())
}
parameter_names = names(lhs_parameters(1)[[1]])
best_set = list(
prev_init_FSW = 0.0326,
prev_init_rest = 0.0012,
N_init = c(672, 757, 130895, 672, 27124, 100305, 14544, 11145, 0),
fraction_F = 0.515666224,
epsilon_1985 = 0.059346131 * 1.5,
epsilon_1992 = 0.053594832 * 1.5,
epsilon_2002 = 0.026936907 * 1.5,
epsilon_2013 = 0.026936907 * 1.5,
epsilon_2016 = 0.026936907 * 1.5,
# mu = c(0.02597403, 0.02597403, 0.02597403, 0.02597403, 0.02739726, 0.02739726, 0.02597403, 0.02739726, 0.02597403), # women 1/((27 + 50)/2) # men 1/((25 + 48)/2)
# c_comm = c(750, 52, 0, 0, 13.5, 0, 0, 0, 0),
# c_noncomm = c(0.38, 0.38, 0.88, 0.88, 4, 1.065, 0, 0, 0), # partner change rate lowlevel FSW same as pro, others are approximations from various surveys
#
muF = 0.02597403,
muM = 0.02739726,
# PARTNER CHANGE RATE
c_comm_1985 = c(1229.5, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1020 + 1439)/2
c_comm_1993 = c(1229.5, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1020 + 1439)/2
c_comm_1995 = c(1280, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1135 + 1425)/2
c_comm_1998 = c(881, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (757 + 1005)/2
c_comm_2002 = c(598.5, 52, 0, 0, 11.08109, 0, 0, 0, 0), # (498 + 699)/2, (13.387-10.15873)/14 * 4 + 10.15873
c_comm_2005 = c(424, 52, 0, 0, 11.77286, 0, 0, 0, 0), # (366 + 482)/2, (13.387-10.15873)/14 * 7 + 10.15873
c_comm_2008 = c(371.5, 52, 0, 0, 12.46464, 0, 0, 0, 0), # (272 + 471)/2, (13.387-10.15873)/14 * 10 + 10.15873
c_comm_2012 = c(541, 52, 0, 0, 13.387, 0, 0, 0, 0), # (459 + 623)/2
c_comm_2015 = c(400, 52, 0, 0, 17.15294, 0, 0, 0, 0), # (309 + 491)/2
c_comm_2016 = c(400, 52, 0, 0, 17.15294, 0, 0, 0, 0), # (309 + 491)/2
c_noncomm_1985 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0), # (0.4682779 + 0.3886719 + 0.2729358)/3
c_noncomm_1993 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_1995 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_1998 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2002 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2005 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2008 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 2.028986, 0.7878543, 0, 0, 0),
c_noncomm_2012 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 8.086957, 0.7878543, 0, 0, 0),
c_noncomm_2015 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 6.258258, 0.7878543, 0, 0, 0),
c_noncomm_2016 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 6.258258, 0.7878543, 0, 0, 0),
n_comm = matrix(c(0, 0, 0, 0, 1.935, 0, 0, 0, 0, # from client sa per partner
0, 0, 0, 0, 1.935, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
1.935, 1.935, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
nrow = 9, ncol = 9, byrow = T),
n_noncomm = matrix(c(0, 0, 0, 0, 32.7, 0, 0, 0, 0,
0, 0, 0, 0, 32.7, 0, 0, 0, 0, # could replace lowlevel with bargirls parameters
0, 0, 0, 0, 39, 37.875, 0, 0, 0, #(36.75+39)/2
0, 0, 0, 0, 39, 37.875, 0, 0, 0,
32.7, 32.7, 39, 39, 0, 0, 0, 0, 0,
0, 0, 37.875, 37.875, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
nrow = 9, ncol = 9, byrow = T),
#think about transforming to matrix
betaMtoF_comm = 0.00051, # RR circumcision = 0.44
betaFtoM_comm = 0.02442*0.44,
betaMtoF_noncomm = 0.003,
betaFtoM_noncomm = 0.0038*0.44,
infect_acute = 9, # RR for acute phase
infect_AIDS = 2, #7.27, # RR for AIDS phase
infect_ART = 0.9 * 0.523, # infectiousness RR when on ART (efficacy ART assuimed 90% * % undetectable which is 52.3%)
ec = rep_len(0.8, 9), # from kate's paper on nigeria SD couples
eP0 = c(0, rep_len(0, 8)), # assumptions!
eP1a = c(0.9, rep_len(0, 8)),
eP1b = c(0.45, rep_len(0, 8)),
eP1c = c(0, rep_len(0, 8)),
eP1d = c(0, rep_len(0, 8)),
gamma01 = 0.4166667, #years
SC_to_200_349 = 3.4,
gamma04 = 4.45, #years
alpha01 = rep_len(0, 9),
alpha02 = rep_len(0, 9),
alpha03 = rep_len(0.05, 9),
alpha04 = rep_len(0.08, 9),
alpha05 = rep_len(0.27, 9), #1/2.9
alpha11 = rep_len(0, 9),
alpha22 = rep_len(0, 9),
alpha23 = rep_len(0.05, 9),
alpha24 = rep_len(0.08, 9),
alpha25 = rep_len(0.27, 9),
alpha32 = rep_len(0, 9),
alpha33 = rep_len(0.05, 9),
alpha34 = rep_len(0.08, 9),
alpha35 = rep_len(0.27, 9),
alpha42 = rep_len(0, 9),
alpha43 = rep_len(0.05, 9),
alpha44 = rep_len(0.08, 9),
alpha45 = rep_len(0.27, 9),
#PREP
zetaa_t = c(1985, 2013, 2015, 2016),
zetaa_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
zetab_t = c(1985, 2013, 2015, 2016),
zetab_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
zetac_t = c(1985, 2013, 2015, 2016),
zetac_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
psia = rep_len(0.1,9),
psib = rep_len(0.1,9),
#TESTING
testing_prob_t = c(1985, 2001, 2005, 2006, 2008, 2012, 2013, 2015, 2016),
testing_prob_y = matrix(c(0, 0, 0, 0, 0, 0, 0, 0, 0, # 1985 columns are the risk groups
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2001
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2005
0.081625, 0.142, 0.142, 0.142, 0.0975, 0.0975, 0, 0, 0, # 2006 0.653/8 slope
0.244875, 0.21, 0.21, 0.21, 0.1, 0.1, 0, 0, 0, # 2008 3*0.653/8
0.571375, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2012 7*0.653/8
0.653, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2013
0.68, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2015
0.68, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0), # 2016
nrow = 9, ncol = 9, byrow = T),
#ART
ART_prob_t = c(1985, 2002, 2005, 2016),
ART_prob_y = matrix(c(0, 0, 0, 0, 0, 0, 0, 0, 0, # 1985
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2002
0, 0.1448571, 0.1448571, 0.1448571, 0.1448571, 0.1448571, 0, 0, 0, # 2005 0.676/14 * 3
0.6739, 0.676, 0.676, 0.676, 0.676, 0.676, 0, 0, 0),
nrow = 4, ncol = 9, byrow = T), # 2016 GP: (0.8+0.552)/2
RR_ART_CD4200 = 5.39,
phi2 = c(0.105360516, rep_len(0.025,8)), # former sex workers drop out rate??!
phi3 = c(0.105360516, rep_len(0.025,8)),
phi4 = c(0.105360516, rep_len(0.025,8)),
phi5 = c(0.105360516, rep_len(0.025,8)),
ART_RR = (1.3+3.45)/2,
#CONDOM
fc_y_comm_1985 = matrix(
c(0, 0, 0, 0, 0.145524, 0, 0, 0, 0, # 0.145524 is using John's FSW condom 1989 as prop of 1993, * our measure of 1993
0, 0, 0, 0, 0.145524, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.145524, 0.145524, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1993 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1995 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1998 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2002 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2005 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2008 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2012 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2015 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_1985 = matrix(
c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
# 1998
# (0.33 + 0.2705314)/ 2 # average FSW client
# (0.0326087 + 0.2705314)/ 2 # average client GPF
# (0.0326087 + 0.04989035) / 2 # average gpm gpf
fc_y_noncomm_1998 = matrix(
c(0, 0, 0, 0, 0.3002657, 0, 0, 0, 0,
0, 0, 0, 0, 0.3002657, 0, 0, 0, 0,
0, 0, 0, 0, 0.15157, 0.04124952, 0, 0, 0,
0, 0, 0, 0, 0.15157, 0.04124952, 0, 0, 0,
0.3002657, 0.3002657, 0.15157, 0.15157, 0, 0, 0, 0, 0,
0, 0, 0.04124952, 0.04124952, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
# 2008
# (0.33 + 0.4)/ 2 # average FSW client (both approx)
# ((0.05042017+0.241404781)/2 + 0.4)/ 2 # average client GPF (gpf averaged from 2 estimtes)
# ((0.05042017+0.241404781)/2 + (0.07103825+0.34838295)/2) / 2 # average gpm gpf
fc_y_noncomm_2008 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_2015 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_2016 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_t_comm = c(1985, 1993, 1995, 1998, 2002, 2005, 2008, 2012, 2015, 2016),
fc_t_noncomm = c(1985, 1990, 1998, 2015, 2016),
rate_leave_pro_FSW = 0.2,
FSW_leave_Cotonou_fraction = 0.1,
rate_leave_low_FSW = 0.1,
rate_leave_client = 0.05,
replaceDeaths = 0,
movement = 1
)
Chubbychunks/odin_tasp_prep documentation built on May 6, 2019, 11:49 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# helper files have functions that will be used a lot in the model tests
time <- seq(1986, 2016, length.out = 31)
# dont know what this is
# takes the new varying parameter and it meshes it with the original base parameter set
# f <- function(x, base, v, gen, time) {
# n <- lengths(base[v])
# base[v] <- setNames(split(unlist(x, use.names=FALSE), rep(seq_along(v), n)), v) # base is the original set of pars
# mod <- gen(user=base)
# mod$transform_variables(mod$run(time))
# }
run_model <- function(p, gen, time, output_vars) {
mod <- gen(user = p)
all_results <- mod$transform_variables(mod$run(time))
all_results[output_vars]
}
par_gridplot = function(result, parm) {require(plyr)
fc_df = aperm(result[parm][[1]], c(2, 3, 1))
fc_df_list = alply(fc_df, 3)
fc_df_list = lapply(fc_df_list, function(x) {colnames(x) = rownames(x) = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou")
return(x)})
dat = data.frame(combination = unlist(lapply(fc_df_list, function(x) {
tmp = expand.grid(colnames(x), rownames(x))
return(paste(tmp[,2], tmp[,1], sep = "_"))
})),
value = unlist(lapply(fc_df_list, c)),
year = unlist(sort(rep(time, 81))))
dat$combination = factor(dat$combination, levels =
unlist(lapply(fc_df_list, function(x) {
tmp = expand.grid(colnames(x), rownames(x))
return(paste(tmp[,2], tmp[,1], sep = "_"))
}))[1:81])
return(ggplot(dat, aes(x = year, y = value, color = value)) + geom_line(size = 2) + facet_wrap(~combination) + theme_bw())
}
par_gridplot2 = function(result, parm) {
require(plyr)
fc_df = aperm(result[parm][[1]], c(2, 3, 1))
fc_df_list = alply(fc_df, 3)
fc_df_list_applied = lapply(fc_df_list, function(x) {colnames(x) = rownames(x) = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou")
return(x)})
dat = data.frame(row =
rep(c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"), 1, each = 9),
col =
rep(c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"), 9),
value = unlist(lapply(fc_df_list_applied, c)),
year = unlist(sort(rep(time, 81))))
dat$row = factor(dat$row, levels = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"))
dat$col = factor(dat$col, levels = c("Pro FSW", "Low-level FSW", "GPF", "Former FSW in Cotonou", "Clients",
"GPM", "Virgin female", "Virgin male", "Former FSW outside Cotonou"))
# dat$combination = factor(dat$combination, levels =
# unlist(lapply(fc_df_list, function(x) {
# tmp = expand.grid(colnames(x), rownames(x))
# return(paste(tmp[,2], tmp[,1], sep = "_"))
# }))[1:81])
return(ggplot(dat, aes(x = year, y = value, color = value)) + geom_line(size = 2) + facet_grid(row~col) + theme_bw())
}
parameter_names = names(lhs_parameters(1)[[1]])
best_set = list(
prev_init_FSW = 0.0326,
prev_init_rest = 0.0012,
N_init = c(672, 757, 130895, 672, 27124, 100305, 14544, 11145, 0),
fraction_F = 0.515666224,
epsilon_1985 = 0.059346131 * 1.5,
epsilon_1992 = 0.053594832 * 1.5,
epsilon_2002 = 0.026936907 * 1.5,
epsilon_2013 = 0.026936907 * 1.5,
epsilon_2016 = 0.026936907 * 1.5,
# mu = c(0.02597403, 0.02597403, 0.02597403, 0.02597403, 0.02739726, 0.02739726, 0.02597403, 0.02739726, 0.02597403), # women 1/((27 + 50)/2) # men 1/((25 + 48)/2)
# c_comm = c(750, 52, 0, 0, 13.5, 0, 0, 0, 0),
# c_noncomm = c(0.38, 0.38, 0.88, 0.88, 4, 1.065, 0, 0, 0), # partner change rate lowlevel FSW same as pro, others are approximations from various surveys
#
muF = 0.02597403,
muM = 0.02739726,
# PARTNER CHANGE RATE
c_comm_1985 = c(1229.5, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1020 + 1439)/2
c_comm_1993 = c(1229.5, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1020 + 1439)/2
c_comm_1995 = c(1280, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (1135 + 1425)/2
c_comm_1998 = c(881, 52, 0, 0, 10.15873, 0, 0, 0, 0), # (757 + 1005)/2
c_comm_2002 = c(598.5, 52, 0, 0, 11.08109, 0, 0, 0, 0), # (498 + 699)/2, (13.387-10.15873)/14 * 4 + 10.15873
c_comm_2005 = c(424, 52, 0, 0, 11.77286, 0, 0, 0, 0), # (366 + 482)/2, (13.387-10.15873)/14 * 7 + 10.15873
c_comm_2008 = c(371.5, 52, 0, 0, 12.46464, 0, 0, 0, 0), # (272 + 471)/2, (13.387-10.15873)/14 * 10 + 10.15873
c_comm_2012 = c(541, 52, 0, 0, 13.387, 0, 0, 0, 0), # (459 + 623)/2
c_comm_2015 = c(400, 52, 0, 0, 17.15294, 0, 0, 0, 0), # (309 + 491)/2
c_comm_2016 = c(400, 52, 0, 0, 17.15294, 0, 0, 0, 0), # (309 + 491)/2
c_noncomm_1985 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0), # (0.4682779 + 0.3886719 + 0.2729358)/3
c_noncomm_1993 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_1995 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_1998 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2002 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2005 = c(0.3766285, 0.3766285, 0.9610526, 0.9610526, 2.028986, 1.337444, 0, 0, 0),
c_noncomm_2008 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 2.028986, 0.7878543, 0, 0, 0),
c_noncomm_2012 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 8.086957, 0.7878543, 0, 0, 0),
c_noncomm_2015 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 6.258258, 0.7878543, 0, 0, 0),
c_noncomm_2016 = c(0.3766285, 0.3766285, 0.7943578, 0.7943578, 6.258258, 0.7878543, 0, 0, 0),
n_comm = matrix(c(0, 0, 0, 0, 1.935, 0, 0, 0, 0, # from client sa per partner
0, 0, 0, 0, 1.935, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
1.935, 1.935, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
nrow = 9, ncol = 9, byrow = T),
n_noncomm = matrix(c(0, 0, 0, 0, 32.7, 0, 0, 0, 0,
0, 0, 0, 0, 32.7, 0, 0, 0, 0, # could replace lowlevel with bargirls parameters
0, 0, 0, 0, 39, 37.875, 0, 0, 0, #(36.75+39)/2
0, 0, 0, 0, 39, 37.875, 0, 0, 0,
32.7, 32.7, 39, 39, 0, 0, 0, 0, 0,
0, 0, 37.875, 37.875, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
nrow = 9, ncol = 9, byrow = T),
#think about transforming to matrix
betaMtoF_comm = 0.00051, # RR circumcision = 0.44
betaFtoM_comm = 0.02442*0.44,
betaMtoF_noncomm = 0.003,
betaFtoM_noncomm = 0.0038*0.44,
infect_acute = 9, # RR for acute phase
infect_AIDS = 2, #7.27, # RR for AIDS phase
infect_ART = 0.9 * 0.523, # infectiousness RR when on ART (efficacy ART assuimed 90% * % undetectable which is 52.3%)
ec = rep_len(0.8, 9), # from kate's paper on nigeria SD couples
eP0 = c(0, rep_len(0, 8)), # assumptions!
eP1a = c(0.9, rep_len(0, 8)),
eP1b = c(0.45, rep_len(0, 8)),
eP1c = c(0, rep_len(0, 8)),
eP1d = c(0, rep_len(0, 8)),
gamma01 = 0.4166667, #years
SC_to_200_349 = 3.4,
gamma04 = 4.45, #years
alpha01 = rep_len(0, 9),
alpha02 = rep_len(0, 9),
alpha03 = rep_len(0.05, 9),
alpha04 = rep_len(0.08, 9),
alpha05 = rep_len(0.27, 9), #1/2.9
alpha11 = rep_len(0, 9),
alpha22 = rep_len(0, 9),
alpha23 = rep_len(0.05, 9),
alpha24 = rep_len(0.08, 9),
alpha25 = rep_len(0.27, 9),
alpha32 = rep_len(0, 9),
alpha33 = rep_len(0.05, 9),
alpha34 = rep_len(0.08, 9),
alpha35 = rep_len(0.27, 9),
alpha42 = rep_len(0, 9),
alpha43 = rep_len(0.05, 9),
alpha44 = rep_len(0.08, 9),
alpha45 = rep_len(0.27, 9),
#PREP
zetaa_t = c(1985, 2013, 2015, 2016),
zetaa_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
zetab_t = c(1985, 2013, 2015, 2016),
zetab_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
zetac_t = c(1985, 2013, 2015, 2016),
zetac_y = matrix(c(rep(0, 9), 0.0075, rep(0, 9-1), rep(0, 9), rep(0, 9)), ncol = 9, byrow = T),
psia = rep_len(0.1,9),
psib = rep_len(0.1,9),
#TESTING
testing_prob_t = c(1985, 2001, 2005, 2006, 2008, 2012, 2013, 2015, 2016),
testing_prob_y = matrix(c(0, 0, 0, 0, 0, 0, 0, 0, 0, # 1985 columns are the risk groups
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2001
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2005
0.081625, 0.142, 0.142, 0.142, 0.0975, 0.0975, 0, 0, 0, # 2006 0.653/8 slope
0.244875, 0.21, 0.21, 0.21, 0.1, 0.1, 0, 0, 0, # 2008 3*0.653/8
0.571375, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2012 7*0.653/8
0.653, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2013
0.68, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0, # 2015
0.68, 0.331, 0.331, 0.331, 0.0582, 0.0582, 0, 0, 0), # 2016
nrow = 9, ncol = 9, byrow = T),
#ART
ART_prob_t = c(1985, 2002, 2005, 2016),
ART_prob_y = matrix(c(0, 0, 0, 0, 0, 0, 0, 0, 0, # 1985
0, 0, 0, 0, 0, 0, 0, 0, 0, # 2002
0, 0.1448571, 0.1448571, 0.1448571, 0.1448571, 0.1448571, 0, 0, 0, # 2005 0.676/14 * 3
0.6739, 0.676, 0.676, 0.676, 0.676, 0.676, 0, 0, 0),
nrow = 4, ncol = 9, byrow = T), # 2016 GP: (0.8+0.552)/2
RR_ART_CD4200 = 5.39,
phi2 = c(0.105360516, rep_len(0.025,8)), # former sex workers drop out rate??!
phi3 = c(0.105360516, rep_len(0.025,8)),
phi4 = c(0.105360516, rep_len(0.025,8)),
phi5 = c(0.105360516, rep_len(0.025,8)),
ART_RR = (1.3+3.45)/2,
#CONDOM
fc_y_comm_1985 = matrix(
c(0, 0, 0, 0, 0.145524, 0, 0, 0, 0, # 0.145524 is using John's FSW condom 1989 as prop of 1993, * our measure of 1993
0, 0, 0, 0, 0.145524, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.145524, 0.145524, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1993 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1995 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_1998 = matrix(
c(0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0.536, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.536, 0.536, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2002 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2005 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2008 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2012 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_comm_2015 = matrix(
c(0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0.8, 0.8, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_1985 = matrix(
c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
# 1998
# (0.33 + 0.2705314)/ 2 # average FSW client
# (0.0326087 + 0.2705314)/ 2 # average client GPF
# (0.0326087 + 0.04989035) / 2 # average gpm gpf
fc_y_noncomm_1998 = matrix(
c(0, 0, 0, 0, 0.3002657, 0, 0, 0, 0,
0, 0, 0, 0, 0.3002657, 0, 0, 0, 0,
0, 0, 0, 0, 0.15157, 0.04124952, 0, 0, 0,
0, 0, 0, 0, 0.15157, 0.04124952, 0, 0, 0,
0.3002657, 0.3002657, 0.15157, 0.15157, 0, 0, 0, 0, 0,
0, 0, 0.04124952, 0.04124952, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
# 2008
# (0.33 + 0.4)/ 2 # average FSW client (both approx)
# ((0.05042017+0.241404781)/2 + 0.4)/ 2 # average client GPF (gpf averaged from 2 estimtes)
# ((0.05042017+0.241404781)/2 + (0.07103825+0.34838295)/2) / 2 # average gpm gpf
fc_y_noncomm_2008 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_2015 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_y_noncomm_2016 = matrix(
c(0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.365, 0, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0, 0, 0, 0, 0.2729562, 0.1778115, 0, 0, 0,
0.365, 0.365, 0.2729562, 0.2729562, 0, 0, 0, 0, 0,
0, 0, 0.1778115, 0.1778115, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9),
fc_t_comm = c(1985, 1993, 1995, 1998, 2002, 2005, 2008, 2012, 2015, 2016),
fc_t_noncomm = c(1985, 1990, 1998, 2015, 2016),
rate_leave_pro_FSW = 0.2,
FSW_leave_Cotonou_fraction = 0.1,
rate_leave_low_FSW = 0.1,
rate_leave_client = 0.05,
replaceDeaths = 0,
movement = 1
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.