R/biomodel.r
In alfcrisci/rAedesSim: Modeling Aedes Albopictus Mosquito populations by using
#' biomodel
#'
#' @description The biomodel function is the core function that performs simulation.It consists in a S3 object where by using the others objects the simulation is performed.
#' calculating the transitional rate of population by using daily environmental data. With these a a Runge-Kutta
#' @param i_biometeo object rAedesSim object concerning biometeorological parameter of location where mosquito population are simulated.
#' @param i_biocontainer object rAedesSim object concerning trap or habitat considered.
#' @param i_biopopulation object rAedesSim object concerning mosquito population.
#' @param i_bioparameters object rAedesSim object concerning biological parameter of mosquito population.
#' @param state numeric The state vector used in time simulation.The values are number of individuals L1 : Ovideposition rate, L3 : Eggs Mortality rate,
#' L4: Eggs2larvae transition rate, L5 : Pupae2Larvae transition rate, L6 Larvae Mortality,L7 Pupae Mortality
#' L8 Pupae2Adult transition rate, L10 Adult mortality Default: L1=0,L3=0,L4=0,L5=0,L6=0,L7=0,L8=0,L10=0
#' @param time_interval numeric Interval times of simulation.
#' @param stocastic logical performs stocastic elaboration by poisson sampling at the end of simulation for each state.
#' @param n_sampling=10 numeric : number of resampling.
#' @param inibition logical taking into account density larvae inibition.Default is FALSE.
#' @param ID_sim character ID of simulation.Default is FALSE.
#' @param saveparameter logical Save results about paraemters. Default is FALSE.
#' @return biomodel
#' @author Istituto di Biometeorologia Firenze Italy Alfonso crisci \email{a.crisci@@ibimet.cnr.it} ASL LUCCA Marco Selmi \email{marco.selmi@@uslnordovest.toscana.it }
#' @keywords daily simulation modeling
#'
#' @import deSolve
#' @import lubridate
#' @import xts
#'
#' @export
biomodel <- function(i_biometeo,
i_biocontainer,
i_biopopulation,
i_bioparameters,
state=c(L1=0,L2=0,L3=0,L4=0,L5=0,L6=0,L7=0,L8=0),
time_interval=24,
stocastic=TRUE,
n_sampling=10,
inibition=FALSE,
ID_sim="",
saveparameter=FALSE
)
{
#########################################################################################à
# Check arguments of object.
if (class(i_biopopulation)!="biopopulation") { stop("Object i_biopopulation argument must be of class biopopulation" )};
if (class(i_bioparameters)!="bioparameters") { stop("Object i_bioparameters argument must be of class bioparameters" )};
if (class(i_biometeo)!="biometeo") { stop("Object i_biometeo argument must be of class biocontainer" )};
if (class(i_biocontainer)!="biocontainer") { stop("Object i_biometeo don't have a biocontainer object defined" )};
months=as.numeric(format(i_biometeo$dates,"%m"));
#########################################################################################
# Define time vector interval and instance del loop to build outcome.
Time <- c(0:(time_interval-1));
df_outcome_pop=data.frame(index_day=as.vector(1:i_biometeo$ndays),
eggs=NA,
diapausant_eggs=NA,
larvae=NA,
pupae=NA,
adult=NA,
full_eggs=NA
);
df_outcome_pop$eggs[1]=i_biopopulation$eggs;
df_outcome_pop$diapausant_eggs[1]=i_biopopulation$eggs_diap;
df_outcome_pop$larvae[1]=i_biopopulation$larvae;
df_outcome_pop$pupae[1]=i_biopopulation$pupae;
df_outcome_pop$adult[1]=i_biopopulation$adults;
df_outcome_pop$full_eggs[1]=i_biopopulation$eggs+i_biopopulation$eggs_diap;
df_outcome_par=data.frame(index_day=as.vector(1:i_biometeo$ndays),
f_ovo_a = NA,
f_trans_u2l = NA,
f_trans_l2p = NA,
f_trans_p2a = NA,
ma = NA,
mu = NA,
mp = NA,
ml = NA,
d_emergency=NA,
d_induction=NA,
inib_state=NA
);
#########################################################################################
# Main loop.
for ( i_day in 2:i_biometeo$ndays)
{
#########################################################################################
# Define necessary i_bioparameters in the loop. Here eventual time serial modifications.
# Eggs - Larval - Pupae - Adult are the stage considered in differential system
alfa_lar = i_bioparameters$alfa_l; # Density dependent competition parameter for larvae
alfa_ad = i_bioparameters$alfa_a; # Density dependent competition parameter for adults
par_egn = i_bioparameters$egn; # Mean eggs for individual female cycle
par_ef = i_bioparameters$ef; # Pupal emergency factor
inib_state = flag_inib(N_larvae=i_biopopulation$larvae,
vol_water=i_biocontainer$pooled_volume_current,
critical_density=i_bioparameters$l_density); # inibition state
inib_state=ifelse(inibition==FALSE,0,inib_state)
f_ovo_a = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Ovideposition");
f_trans_u2l = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Eggs2larvae");
f_trans_l2p = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Larvae2Pupae");
f_trans_p2a = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Pupae2Adult");
urel=ifelse(is.na(i_biometeo$rhum[i_day]),60,i_biometeo$rhum[i_day]);
ma = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Adult")
mu = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Eggs")
mp = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Pupae")
ml = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Larval")
##############################################################################################################################
# Define parameter for daily simulation
Parameter <- c(f_ovo_a=f_ovo_a,
f_trans_u2l=f_trans_u2l,
f_trans_l2p=f_trans_l2p,
f_trans_p2a=f_trans_p2a,
ma=ma,
mu=mu,
mp=mp,
ml=ml,
alfa_l=alfa_lar,
alfa_a=alfa_ad,
ef=par_ef,
egn=par_egn,
N_ini_a=i_biopopulation$adults,
N_ini_p=i_biopopulation$pupae,
N_ini_l=i_biopopulation$larvae,
N_ini_u=i_biopopulation$eggs,
sexratio=i_bioparameters$sex_ratio,
deltatime=time_interval
);
#########################################################################################################################################
# Model calling C interface library
aedesmodelday <- suppressWarnings(ode(func = "derivs",
y = state,
parms = Parameter,
times=Time,
initfunc = "initmod",
dllname = "rAedesSim",
method = rkMethod("ode45"),
nout=1
))
##########################################################################################################################################
# Take sum of daily derivative
aedesmodelday=as.list(aedesmodelday[nrow(aedesmodelday),][2:9])
if (stocastic == TRUE)
{
for ( j in 2:(length(aedesmodelday)-1)) {
aedesmodelday[[j]]=ifelse(aedesmodelday[j]<=0,0,round(mean(rpois(n_sampling,aedesmodelday[[j]]))));
}
}
#########################################################################################################################################
if (inib_state == 1) { par_egn=ifelse(inib_state == 1,0,par_egn)}
dadults=as.numeric(aedesmodelday$L7 - aedesmodelday$L8);
dlarvae=as.numeric(aedesmodelday$L3-aedesmodelday$L5-aedesmodelday$L4);
dpupae=as.numeric(aedesmodelday$L4-aedesmodelday$L6-aedesmodelday$L7);
deggs_pop=as.numeric(par_egn*aedesmodelday$L1-aedesmodelday$L2)+i_biopopulation$eggs_diap*i_biometeo$d_emergency[i_day]
neggs_diap_pop=i_biopopulation$adults*i_bioparameters$sex_ratio*par_egn*f_ovo_a*i_biometeo$d_induction[i_day];
#########################################################################################################################################
# update population
i_biopopulation$larvae=i_biopopulation$larvae+dlarvae;
i_biopopulation$pupae=i_biopopulation$pupae+dpupae;
i_biopopulation$adults=i_biopopulation$adults+dadults;
i_biopopulation$eggs=i_biopopulation$eggs+deggs_pop;
i_biopopulation$eggs_diap=ifelse(i_biometeo$d_emergency[i_day]>0,
i_biopopulation$eggs_diap*(1-i_biometeo$d_emergency[i_day]),
i_biopopulation$eggs_diap+neggs_diap_pop);
i_biopopulation$eggs_diap=ifelse(as.numeric(format(i_biometeo$dates[i_day],"%j"))==i_biometeo$datemax,0,i_biopopulation$eggs_diap)
#########################################################################################################################################
# to avoid complete extiction
i_biopopulation$eggs_diap =ifelse(i_biopopulation$eggs_diap<0,0,i_biopopulation$eggs_diap)
i_biopopulation$eggs=ifelse(i_biopopulation$eggs<0,0.1,i_biopopulation$eggs);
i_biopopulation$eggs=ifelse(i_biopopulation$eggs<2 &as.numeric(format(i_biometeo$dates[i_day],"%j")) >274,i_biopopulation$larvae*1.15,i_biopopulation$eggs)
i_biopopulation$larvae=ifelse(i_biopopulation$larvae<0,0,i_biopopulation$larvae);
i_biopopulation$pupae=ifelse(i_biopopulation$pupae<0,0,i_biopopulation$pupae);
i_biopopulation$adults=ifelse(i_biopopulation$adults<0,0,i_biopopulation$adults);
#########################################################################################################################################
# update outcomes
df_outcome_pop[i_day,2]=i_biopopulation$eggs/i_biocontainer$nrecipients;
df_outcome_pop[i_day,3]=i_biopopulation$eggs_diap/i_biocontainer$nrecipients;
df_outcome_pop[i_day,4]=i_biopopulation$larvae/i_biocontainer$nrecipients;
df_outcome_pop[i_day,5]=i_biopopulation$pupae/i_biocontainer$nrecipients;
df_outcome_pop[i_day,6]=i_biopopulation$adults/i_biocontainer$nrecipients;
df_outcome_pop[i_day,7]=df_outcome_pop[i_day,2]+neggs_diap_pop/i_biocontainer$nrecipients;
#################################################################
# update daily parameters
if (saveparameter == TRUE)
{
df_outcome_par[i_day,2]=as.numeric(f_ovo_a);
df_outcome_par[i_day,3]=as.numeric(f_trans_u2l);
df_outcome_par[i_day,4]=as.numeric(f_trans_l2p);
df_outcome_par[i_day,5]=as.numeric(f_trans_p2a);
df_outcome_par[i_day,6]=as.numeric(ma);
df_outcome_par[i_day,7]=as.numeric(mu);
df_outcome_par[i_day,8]=as.numeric(mp);
df_outcome_par[i_day,9]=as.numeric(ml);
df_outcome_par[i_day,10]=as.numeric(i_biometeo$d_emergency[i_day]);
df_outcome_par[i_day,11]=as.numeric(i_biometeo$d_induction[i_day]);
df_outcome_par[i_day,12]=as.numeric(inib_state);
}
}; # end loop days
#########################################################################################################################################
# create time series objects
ts_population=as.xts(zoo(df_outcome_pop,i_biometeo$dates))
ts_parameter=as.xts(zoo(df_outcome_par,i_biometeo$dates))
#########################################################################################################################################
object <- list(name_model="Rbiosim",
ID_sim=ID_sim,
timestep_integration = length(Time),
ts_population=ts_population,
ts_parameter=ts_parameter,
stocastic=stocastic,
n_sampling=n_sampling,
ID=i_biocontainer$ID,
site_name=i_biocontainer$site_name,
sp_obj=i_biocontainer$sp_obj,
lat=i_biocontainer$lat,
lon=i_biocontainer$lon
);
#########################################################################################################################################
attr(object,"name_model") <- "Model's name"
attr(object,"ID_sim") <- "ID label of simulation"
attr(object,"timestep_integration") <- "Time step of daily integration"
attr(object,"ts_population") <- "Model outcomes of mosquito as multivariate timeseries xts object"
attr(object,"ts_parameter") <- "Model outcomes of simulation parameters xts object"
attr(object,"ID")<-"ID label of container set"
attr(object,"site_name")<-"Name of sites"
attr(object,"sp_obj")<-"SpatialPointDataFrame of location"
attr(object,"lat")<-"latitude coordinate of cointainer"
attr(object,"lon")<-"longitude coordinates of cointainer"
class(object) <- "biomodel"
return(object)
}
alfcrisci/rAedesSim documentation built on May 10, 2019, 8:59 a.m.
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#' biomodel
#'
#' @description The biomodel function is the core function that performs simulation.It consists in a S3 object where by using the others objects the simulation is performed.
#' calculating the transitional rate of population by using daily environmental data. With these a a Runge-Kutta
#' @param i_biometeo object rAedesSim object concerning biometeorological parameter of location where mosquito population are simulated.
#' @param i_biocontainer object rAedesSim object concerning trap or habitat considered.
#' @param i_biopopulation object rAedesSim object concerning mosquito population.
#' @param i_bioparameters object rAedesSim object concerning biological parameter of mosquito population.
#' @param state numeric The state vector used in time simulation.The values are number of individuals L1 : Ovideposition rate, L3 : Eggs Mortality rate,
#' L4: Eggs2larvae transition rate, L5 : Pupae2Larvae transition rate, L6 Larvae Mortality,L7 Pupae Mortality
#' L8 Pupae2Adult transition rate, L10 Adult mortality Default: L1=0,L3=0,L4=0,L5=0,L6=0,L7=0,L8=0,L10=0
#' @param time_interval numeric Interval times of simulation.
#' @param stocastic logical performs stocastic elaboration by poisson sampling at the end of simulation for each state.
#' @param n_sampling=10 numeric : number of resampling.
#' @param inibition logical taking into account density larvae inibition.Default is FALSE.
#' @param ID_sim character ID of simulation.Default is FALSE.
#' @param saveparameter logical Save results about paraemters. Default is FALSE.
#' @return biomodel
#' @author Istituto di Biometeorologia Firenze Italy Alfonso crisci \email{a.crisci@@ibimet.cnr.it} ASL LUCCA Marco Selmi \email{marco.selmi@@uslnordovest.toscana.it }
#' @keywords daily simulation modeling
#'
#' @import deSolve
#' @import lubridate
#' @import xts
#'
#' @export
biomodel <- function(i_biometeo,
i_biocontainer,
i_biopopulation,
i_bioparameters,
state=c(L1=0,L2=0,L3=0,L4=0,L5=0,L6=0,L7=0,L8=0),
time_interval=24,
stocastic=TRUE,
n_sampling=10,
inibition=FALSE,
ID_sim="",
saveparameter=FALSE
)
{
#########################################################################################à
# Check arguments of object.
if (class(i_biopopulation)!="biopopulation") { stop("Object i_biopopulation argument must be of class biopopulation" )};
if (class(i_bioparameters)!="bioparameters") { stop("Object i_bioparameters argument must be of class bioparameters" )};
if (class(i_biometeo)!="biometeo") { stop("Object i_biometeo argument must be of class biocontainer" )};
if (class(i_biocontainer)!="biocontainer") { stop("Object i_biometeo don't have a biocontainer object defined" )};
months=as.numeric(format(i_biometeo$dates,"%m"));
#########################################################################################
# Define time vector interval and instance del loop to build outcome.
Time <- c(0:(time_interval-1));
df_outcome_pop=data.frame(index_day=as.vector(1:i_biometeo$ndays),
eggs=NA,
diapausant_eggs=NA,
larvae=NA,
pupae=NA,
adult=NA,
full_eggs=NA
);
df_outcome_pop$eggs[1]=i_biopopulation$eggs;
df_outcome_pop$diapausant_eggs[1]=i_biopopulation$eggs_diap;
df_outcome_pop$larvae[1]=i_biopopulation$larvae;
df_outcome_pop$pupae[1]=i_biopopulation$pupae;
df_outcome_pop$adult[1]=i_biopopulation$adults;
df_outcome_pop$full_eggs[1]=i_biopopulation$eggs+i_biopopulation$eggs_diap;
df_outcome_par=data.frame(index_day=as.vector(1:i_biometeo$ndays),
f_ovo_a = NA,
f_trans_u2l = NA,
f_trans_l2p = NA,
f_trans_p2a = NA,
ma = NA,
mu = NA,
mp = NA,
ml = NA,
d_emergency=NA,
d_induction=NA,
inib_state=NA
);
#########################################################################################
# Main loop.
for ( i_day in 2:i_biometeo$ndays)
{
#########################################################################################
# Define necessary i_bioparameters in the loop. Here eventual time serial modifications.
# Eggs - Larval - Pupae - Adult are the stage considered in differential system
alfa_lar = i_bioparameters$alfa_l; # Density dependent competition parameter for larvae
alfa_ad = i_bioparameters$alfa_a; # Density dependent competition parameter for adults
par_egn = i_bioparameters$egn; # Mean eggs for individual female cycle
par_ef = i_bioparameters$ef; # Pupal emergency factor
inib_state = flag_inib(N_larvae=i_biopopulation$larvae,
vol_water=i_biocontainer$pooled_volume_current,
critical_density=i_bioparameters$l_density); # inibition state
inib_state=ifelse(inibition==FALSE,0,inib_state)
f_ovo_a = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Ovideposition");
f_trans_u2l = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Eggs2larvae");
f_trans_l2p = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Larvae2Pupae");
f_trans_p2a = trans_rates(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
transition = "Pupae2Adult");
urel=ifelse(is.na(i_biometeo$rhum[i_day]),60,i_biometeo$rhum[i_day]);
ma = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Adult")
mu = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Eggs")
mp = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Pupae")
ml = mortality_rate(as.numeric(i_biometeo$tmed_est[i_day]),
as.numeric(i_biometeo$twater_est[i_day]),
urel,
stage="Larval")
##############################################################################################################################
# Define parameter for daily simulation
Parameter <- c(f_ovo_a=f_ovo_a,
f_trans_u2l=f_trans_u2l,
f_trans_l2p=f_trans_l2p,
f_trans_p2a=f_trans_p2a,
ma=ma,
mu=mu,
mp=mp,
ml=ml,
alfa_l=alfa_lar,
alfa_a=alfa_ad,
ef=par_ef,
egn=par_egn,
N_ini_a=i_biopopulation$adults,
N_ini_p=i_biopopulation$pupae,
N_ini_l=i_biopopulation$larvae,
N_ini_u=i_biopopulation$eggs,
sexratio=i_bioparameters$sex_ratio,
deltatime=time_interval
);
#########################################################################################################################################
# Model calling C interface library
aedesmodelday <- suppressWarnings(ode(func = "derivs",
y = state,
parms = Parameter,
times=Time,
initfunc = "initmod",
dllname = "rAedesSim",
method = rkMethod("ode45"),
nout=1
))
##########################################################################################################################################
# Take sum of daily derivative
aedesmodelday=as.list(aedesmodelday[nrow(aedesmodelday),][2:9])
if (stocastic == TRUE)
{
for ( j in 2:(length(aedesmodelday)-1)) {
aedesmodelday[[j]]=ifelse(aedesmodelday[j]<=0,0,round(mean(rpois(n_sampling,aedesmodelday[[j]]))));
}
}
#########################################################################################################################################
if (inib_state == 1) { par_egn=ifelse(inib_state == 1,0,par_egn)}
dadults=as.numeric(aedesmodelday$L7 - aedesmodelday$L8);
dlarvae=as.numeric(aedesmodelday$L3-aedesmodelday$L5-aedesmodelday$L4);
dpupae=as.numeric(aedesmodelday$L4-aedesmodelday$L6-aedesmodelday$L7);
deggs_pop=as.numeric(par_egn*aedesmodelday$L1-aedesmodelday$L2)+i_biopopulation$eggs_diap*i_biometeo$d_emergency[i_day]
neggs_diap_pop=i_biopopulation$adults*i_bioparameters$sex_ratio*par_egn*f_ovo_a*i_biometeo$d_induction[i_day];
#########################################################################################################################################
# update population
i_biopopulation$larvae=i_biopopulation$larvae+dlarvae;
i_biopopulation$pupae=i_biopopulation$pupae+dpupae;
i_biopopulation$adults=i_biopopulation$adults+dadults;
i_biopopulation$eggs=i_biopopulation$eggs+deggs_pop;
i_biopopulation$eggs_diap=ifelse(i_biometeo$d_emergency[i_day]>0,
i_biopopulation$eggs_diap*(1-i_biometeo$d_emergency[i_day]),
i_biopopulation$eggs_diap+neggs_diap_pop);
i_biopopulation$eggs_diap=ifelse(as.numeric(format(i_biometeo$dates[i_day],"%j"))==i_biometeo$datemax,0,i_biopopulation$eggs_diap)
#########################################################################################################################################
# to avoid complete extiction
i_biopopulation$eggs_diap =ifelse(i_biopopulation$eggs_diap<0,0,i_biopopulation$eggs_diap)
i_biopopulation$eggs=ifelse(i_biopopulation$eggs<0,0.1,i_biopopulation$eggs);
i_biopopulation$eggs=ifelse(i_biopopulation$eggs<2 &as.numeric(format(i_biometeo$dates[i_day],"%j")) >274,i_biopopulation$larvae*1.15,i_biopopulation$eggs)
i_biopopulation$larvae=ifelse(i_biopopulation$larvae<0,0,i_biopopulation$larvae);
i_biopopulation$pupae=ifelse(i_biopopulation$pupae<0,0,i_biopopulation$pupae);
i_biopopulation$adults=ifelse(i_biopopulation$adults<0,0,i_biopopulation$adults);
#########################################################################################################################################
# update outcomes
df_outcome_pop[i_day,2]=i_biopopulation$eggs/i_biocontainer$nrecipients;
df_outcome_pop[i_day,3]=i_biopopulation$eggs_diap/i_biocontainer$nrecipients;
df_outcome_pop[i_day,4]=i_biopopulation$larvae/i_biocontainer$nrecipients;
df_outcome_pop[i_day,5]=i_biopopulation$pupae/i_biocontainer$nrecipients;
df_outcome_pop[i_day,6]=i_biopopulation$adults/i_biocontainer$nrecipients;
df_outcome_pop[i_day,7]=df_outcome_pop[i_day,2]+neggs_diap_pop/i_biocontainer$nrecipients;
#################################################################
# update daily parameters
if (saveparameter == TRUE)
{
df_outcome_par[i_day,2]=as.numeric(f_ovo_a);
df_outcome_par[i_day,3]=as.numeric(f_trans_u2l);
df_outcome_par[i_day,4]=as.numeric(f_trans_l2p);
df_outcome_par[i_day,5]=as.numeric(f_trans_p2a);
df_outcome_par[i_day,6]=as.numeric(ma);
df_outcome_par[i_day,7]=as.numeric(mu);
df_outcome_par[i_day,8]=as.numeric(mp);
df_outcome_par[i_day,9]=as.numeric(ml);
df_outcome_par[i_day,10]=as.numeric(i_biometeo$d_emergency[i_day]);
df_outcome_par[i_day,11]=as.numeric(i_biometeo$d_induction[i_day]);
df_outcome_par[i_day,12]=as.numeric(inib_state);
}
}; # end loop days
#########################################################################################################################################
# create time series objects
ts_population=as.xts(zoo(df_outcome_pop,i_biometeo$dates))
ts_parameter=as.xts(zoo(df_outcome_par,i_biometeo$dates))
#########################################################################################################################################
object <- list(name_model="Rbiosim",
ID_sim=ID_sim,
timestep_integration = length(Time),
ts_population=ts_population,
ts_parameter=ts_parameter,
stocastic=stocastic,
n_sampling=n_sampling,
ID=i_biocontainer$ID,
site_name=i_biocontainer$site_name,
sp_obj=i_biocontainer$sp_obj,
lat=i_biocontainer$lat,
lon=i_biocontainer$lon
);
#########################################################################################################################################
attr(object,"name_model") <- "Model's name"
attr(object,"ID_sim") <- "ID label of simulation"
attr(object,"timestep_integration") <- "Time step of daily integration"
attr(object,"ts_population") <- "Model outcomes of mosquito as multivariate timeseries xts object"
attr(object,"ts_parameter") <- "Model outcomes of simulation parameters xts object"
attr(object,"ID")<-"ID label of container set"
attr(object,"site_name")<-"Name of sites"
attr(object,"sp_obj")<-"SpatialPointDataFrame of location"
attr(object,"lat")<-"latitude coordinate of cointainer"
attr(object,"lon")<-"longitude coordinates of cointainer"
class(object) <- "biomodel"
return(object)
}
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