def calc_probability_LL_migration_zvoleff(person, time):
"""
Calculates the probability of local-local migration for an agent, using the
results of Alex Zvoleff's empirical analysis of the CVFS data, as presented
in chapter 3 of his dissertation.
"""
household = person.get_parent_agent()
neighborhood = household.get_parent_agent()
#########################################################################
# Intercept
inner = rcParams['migration.ll.zv.coef.(Intercept)']
#######################################################################
# Neighborhood level variables
#
# Note that the EVI measures are based off 2 year mean EVI for the change,
# so calculate the 2 year mean EVI.
EVI_2yr_mean = np.mean(neighborhood._EVI_ts[-2:])
# Note that the EVI coefficients are expressed for EVI/1000 (given the need
# to get smaller betas for lmer to converge when estimating the model)
inner += rcParams['migration.ll.zv.coef.mean_Sinteg_500m_24mth_2002_div_1000'] * (neighborhood._EVI_t0/1000)
inner += rcParams['migration.ll.zv.coef.mean_Sinteg_500m_24mth_chg_2002_div_1000'] * ((EVI_2yr_mean - neighborhood._EVI_t0)/1000)
inner += rcParams['migration.ll.zv.coef.NEAR_R_EVD_reversed'] * neighborhood._elevation_above_river
inner += rcParams['migration.ll.zv.coef.SCHLFT_2001'] * neighborhood.NFOs['school_min_ft']
inner += rcParams['migration.ll.zv.coef.MARFT_2001'] * neighborhood.NFOs['market_min_ft']
inner += rcParams['migration.ll.zv.coef.EMPFT_2001'] * neighborhood.NFOs['employer_min_ft']
inner += rcParams['migration.ll.zv.coef.num_groups_2001'] * neighborhood._num_groups
#######################################################################
# Household level variables
inner += rcParams['migration.ll.zv.coef.own_total_2001'] * household._total_possessions
inner += rcParams['migration.ll.zv.coef.any_farming_2001TRUE'] * household._any_farming
inner += rcParams['migration.ll.zv.coef.TLU_livestock_2001'] * household._TLU_livestock
#########################################################################
# Individual level variables
if person.get_sex() == "female":
# Male is the reference class
inner += rcParams['migration.ll.zv.coef.genderfemale']
age_decades = person.get_age_years() / 10.
inner += age_decades * rcParams['migration.ll.zv.coef.agedecades']
inner += (age_decades**2) * rcParams['migration.ll.zv.coef.I(agedecades^2)']
#########################################################################
# Baseline hazard
month_num = int(np.mod(np.round(time*12, 0), 12) + 1)
if month_num in [1, 2, 3, 4]:
# Reference class
pass
elif month_num in [5, 6, 7, 8]:
inner += rcParams['migration.ll.zv.coef.SeasonMonsoon (MJJA)']
elif month_num in [9, 10, 11, 12]:
inner += rcParams['migration.ll.zv.coef.SeasonWinter (SOND)']
else:
raise StatisticsError("Month number is %s. Should not be outside range of [1, 12]"%month_num)
ethnicity = person.get_ethnicity()
assert ethnicity!=None, "Ethnicity must be defined"
if ethnicity == "HighHindu":
# This was the reference level
pass
elif ethnicity == "HillTibeto":
inner += rcParams['migration.ll.zv.coef.ethnicHillTibeto']
elif ethnicity == "LowHindu":
inner += rcParams['migration.ll.zv.coef.ethnicLowHindu']
elif ethnicity == "Newar":
inner += rcParams['migration.ll.zv.coef.ethnicNewar']
elif ethnicity == "TeraiTibeto":
inner += rcParams['migration.ll.zv.coef.ethnicTeraiTibeto']
prob = 1./(1 + np.exp(-inner))
if rcParams['log_stats_probabilities']:
logger.debug("Person %s local-local migration probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
return prob
def calc_probability_marriage_zvoleff(person, time):
"""
Calculates the probability of marriage for an agent, using the results of
Alex Zvoleff's empirical analysis of the CVFS data, following the results
of the analysis conducted by Yabiku (2006).
"""
inner = rcParams['marrtime.zv.coef.(Intercept)']
ethnicity = person.get_ethnicity()
assert ethnicity!=None, "Ethnicity must be defined"
if ethnicity == "HighHindu":
# This was the reference level
pass
elif ethnicity == "HillTibeto":
inner += rcParams['marrtime.zv.coef.ethnicHillTibeto']
elif ethnicity == "LowHindu":
inner += rcParams['marrtime.zv.coef.ethnicLowHindu']
elif ethnicity == "Newar":
inner += rcParams['marrtime.zv.coef.ethnicNewar']
elif ethnicity == "TeraiTibeto":
inner += rcParams['marrtime.zv.coef.ethnicTeraiTibeto']
# Gender
if person.get_sex() == "female":
inner += rcParams['marrtime.zv.coef.genderfemale']
age = person.get_age_years()
inner += rcParams['marrtime.zv.coef.age'] * age
inner += rcParams['marrtime.zv.coef.I(age^2)'] * (age ** 2)
# Neighborhood characteristics
neighborhood = person.get_parent_agent().get_parent_agent()
log_percent_agveg = np.log((neighborhood._land_agveg / neighborhood._land_total)*100 + 1)
inner += rcParams['marrtime.zv.coef.interp_logpercagveg'] * log_percent_agveg
inner += rcParams['marrtime.zv.coef.SCHLFT'] * neighborhood.NFOs['school_min_ft']
inner += rcParams['marrtime.zv.coef.HLTHFT'] * neighborhood.NFOs['health_min_ft']
inner += rcParams['marrtime.zv.coef.BUSFT'] * neighborhood.NFOs['bus_min_ft']
inner += rcParams['marrtime.zv.coef.MARFT'] * neighborhood.NFOs['market_min_ft']
inner += rcParams['marrtime.zv.coef.EMPFT'] * neighborhood.NFOs['employer_min_ft']
# Schooling
inner += rcParams['marrtime.zv.coef.schooling_yrs'] * person.get_years_schooling()
if person.is_in_school():
inner += rcParams['marrtime.zv.coef.in_school']
# Account for monthly differences in marriage rates - some days (and
# months) are more auspicious for marriage than others.
month_num = int(np.mod(np.round(time*12, 0), 12) + 1)
if month_num == 1:
# This was the reference level
pass
elif month_num == 2:
inner += rcParams['marrtime.zv.coef.month2']
elif month_num == 3:
inner += rcParams['marrtime.zv.coef.month3']
elif month_num == 4:
inner += rcParams['marrtime.zv.coef.month4']
elif month_num == 5:
inner += rcParams['marrtime.zv.coef.month5']
elif month_num == 6:
inner += rcParams['marrtime.zv.coef.month6']
elif month_num == 7:
inner += rcParams['marrtime.zv.coef.month7']
elif month_num == 8:
inner += rcParams['marrtime.zv.coef.month8']
elif month_num == 9:
inner += rcParams['marrtime.zv.coef.month9']
elif month_num == 10:
inner += rcParams['marrtime.zv.coef.month10']
elif month_num == 11:
inner += rcParams['marrtime.zv.coef.month11']
elif month_num == 12:
inner += rcParams['marrtime.zv.coef.month12']
prob = 1./(1 + np.exp(-inner))
if rcParams['log_stats_probabilities']:
logger.debug("Person %s marriage probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
return prob
