R/Misc/MaxARTFinal.Sim/maxart.summary.statistics.creator.R
In wdelva/RSimpactHelp: Pre- and Post-simulation helper functions for RSimpact Cyan
Defines functions
tar.name
tar.value
#I presume this will be called through source()
#Idea is to give date and statistics. Calculated using start of the simulation - that time.
#Parameters creation
#Set up simulation parameters and initiate simulation
#just the year when simulation started.
sim.start.full <- as.Date("1970-03-31")
sim.start <- as.numeric(substr(sim.start.full,1,4))
maxart.starttime <- as.Date("2014-09-01")
maxart.endtime <- as.Date("2017-08-31")
sim.end.full <- as.Date("2019-03-31")
seed.hiv.date <- as.Date("1986-03-31")
#initial population
init.population.total <- 3000
women.frac <- 0.5253
################### ART initiation ##############################
max.art.initiated.all <- read.table(
text="PrimaryClients AllClients
all 77.6 84.6
before.eligible 4.6 2.2
within2.weeks 61 64
among.eligible 85 90
within12.months 85 91
within6months 79 87", header=TRUE, stringsAsFactors = FALSE)
#TimeTo: 2016-11
########################## ART Retention #############################
max.art.retention.all <- read.table(
text="PrimaryClients AllClients
all 72 73
at6.months 89 87
at12.months 82 79", header=TRUE, stringsAsFactors = FALSE)
#timeTo: 2016-11
########################### ViralLoadSuppression ##################
max.vl.none.suppression.all <- read.table(
text="PrimaryClients AllClients
at6.months 4 6
at12.months 6 11", header=TRUE, stringsAsFactors = FALSE)
#Check validation
#atleast6.months 4 8 2016-11
############# ViralLoadSuppression ###########################
max.mortality.all <- read.table(
text="PrimaryClients AllClients
all 1.01 1.87
aids.related 0.51 1.49", header=TRUE, stringsAsFactors = FALSE)
#timeTo 2016-11
#################### Swaziland specific statitics ##########################.
#GrowthRate for all ages and gender
swazi.growth.rate <- read.table(
text="X1985 X1990 X1995 X2000 X2005 X2010 X2015 X2016
one.year 3.63 3.39 2.00 1.50 0.98 1.86 1.83 1.81",
header=TRUE, stringsAsFactors = FALSE)
############ Incidence UNAIDS estimates (End of march each year) #############
swazi.inci.15.49 <- read.table(
text="X1990 X1992 X1995 X2000 X2005 X2010 X2015
inci.15.49 1.24 2.9 4.66 3.7 3.08 3.09 2.36",
header=TRUE, stringsAsFactors = FALSE)
############# 2011 Incidence ages specific: SHIMS 1 #############################
#consider for validation: [all 3.14 1.65 2.38 2010-12 2011-06]
swazi.inci.2011 <- read.table(
text="F.value M.value
A18.19 3.84 0.84
A20.24 4.17 1.66
A25.29 2.25 2.36
A30.34 2.78 3.12
A35.39 4.10 0.44
A40.44 2.07 1.24
A45.49 1.20 0.02",
header=TRUE, stringsAsFactors = FALSE)
########### 2006-07 prevalence ages specific: Swaziland demographics and health survey SHDS ##
# Data was collected between July 2006 and March 2007. TimeTo: 2007-03
swazi.prev.2007 <- read.table(
text="F.value M.value FM.value
A10.14 3.3 1.9 2.6
A15.19 10.1 1.9 5.8
A20.24 38.4 12.4 26.5
A25.29 49.2 27.8 39.3
A30.34 45.2 43.7 44.6
A35.39 37.7 44.9 40.8
A40.44 27.9 40.7 32.7
A45.49 21.4 27.9 24.0
A50.54 24.3 28.3 26.0
A55.59 9.6 17.4 12.7
A60.150 7.0 13.3 9.5
A15.49 31.1 19.7 25.9
A50.150 11.7 17.9 14.2",
header=TRUE, stringsAsFactors = FALSE)
#A2.4 4.8 5.5 5.1 2007-03
#A5.9 3.6 4.8 4.2 2007-03
#A2.150 22.1 14.9 18.8
#Note it is not possibloe to compute the young age incidence 2- 12 yr olds since these will
#not be sexually active more of which we would expect the infection to be through MTCT
############# Prevalence ages specific UNAIDS (End of March each year) ############
swazi.prev.age.year <- read.table(
text="F.value M.value
A15.24.1990 2.6 1.5
A15.49.1990 2.3 2.2
A15.24.1995 17.4 7.5
A15.49.1995 14.4 16.9
A15.24.2000 21.9 8.1
A15.49.2000 21.8 27.6
A15.24.2005 18.1 6.7
A15.49.2005 21.8 29.4
A15.24.2010 17.4 7.2
A15.49.2010 22.6 31.9
A15.24.2015 16.7 7.3",
header=TRUE, stringsAsFactors = FALSE)
#A15.49.2015 NA 34.2 28.8 ### Maybe for validation
#A15.49.1990 2.3 2.2 2.2
#A15.49.1995 14.4 16.9 15.7
#A15.49.2000 21.8 27.6 24.8
#A15.49.2005 21.8 29.4 25.7
#A15.49.2010 22.6 31.9 27.2
############ Hhohho prevalence ##############################################
#Data was collected between July 2006 and March 2007 [SDHS 2006-07]: TimeTo = 2007-03
hhohho.prev <- read.table(
text="F.value M.value FM.value
A0.150 24.2 17.4 21.0
A15.49 33.8 23.1 28.9
A50.150 11.2 20.7 15.4 ",
header=TRUE, stringsAsFactors = FALSE)
#A2.14 4.1 3.6 3.9 2007-03
########## Swazi Age difference ######################################################
# Data was collected between December 2010 and June 2011 [SHIMS 2011] TimeTo: 2011-06
swazi.age.diff <- read.table(
text="FM.value
mean 5.19
meadian 6.64 ", header=TRUE, stringsAsFactors = FALSE)
swazi.AD.tar.names <- paste("FM.value", row.names(swazi.age.diff),"AD", sep = ".")
################### Hhohho age diff ##########################################
# Data was collected between December 2010 and June 2011 [SHIMS 2011] TimeTo: 2011-06
hhohho.age.diff <- read.table(
text="F.value M.value FM.value
mean 7.51 5.53 6.76
meadian 6.82 5.34 5.19 ",
header=TRUE, stringsAsFactors = FALSE)
##################### ART Retention UNAIDS (End of March each year) ############
#Consider using 2015 98 and 97 % for validation
swazi.art.retention <- read.table(
text="X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014
less.15.at6.months 81 88 87 87 89 84 85 97
greateq.15.at6.months 82 85 85 86 88 87 86 96
less.15.at12.months 73 82 81 80 89 76 91 93
greateq.15.at12.months 76 78 78 80 80 77 88 92",
header=TRUE, stringsAsFactors = FALSE)
#consider validation
#less.15.at24.months 67 75 74 73 69 87 87 NA
#greateq.15.at24.months 70 71 71 71 69 82 87 NA
################ ART coverage UNAIDS (End of March each year) ####################
swazi.art.coverage <- read.table(
text="F.value M.value FM.value
over15.2012 94 77 87
over15.2013 91 76 85
over15.2014 85 89 93
over15.2015 90 73 83", header=TRUE, stringsAsFactors = FALSE)
#14.less.2015 NA NA 72 validation
################ Swazi age distribution for 1970 ####################
swazi.1970.popn <- read.table(
text="Age Percent.Male Percent.Female
0.5 4.031943296 3.654955405
1.5 4.031943296 3.654955405
2.5 4.031943296 3.654955405
3.5 4.031943296 3.654955405
4.5 4.031943296 3.654955405
5.5 3.186107501 2.910353131
6.5 3.186107501 2.910353131
7.5 3.186107501 2.910353131
8.5 3.186107501 2.910353131
9.5 3.186107501 2.910353131
10.5 2.687773184 2.46512879
11.5 2.687773184 2.46512879
12.5 2.687773184 2.46512879
13.5 2.687773184 2.46512879
14.5 2.687773184 2.46512879
15.5 2.200496161 2.097011771
16.5 2.200496161 2.097011771
17.5 2.200496161 2.097011771
18.5 2.200496161 2.097011771
19.5 2.200496161 2.097011771
20.5 1.58261075 1.748705272
21.5 1.58261075 1.748705272
22.5 1.58261075 1.748705272
23.5 1.58261075 1.748705272
24.5 1.58261075 1.748705272
25.5 1.08994684 1.371878456
26.5 1.08994684 1.371878456
27.5 1.08994684 1.371878456
28.5 1.08994684 1.371878456
29.5 1.08994684 1.371878456
30.5 1.006213822 1.147729262
31.5 1.006213822 1.147729262
32.5 1.006213822 1.147729262
33.5 1.006213822 1.147729262
34.5 1.006213822 1.147729262
35.5 0.924181926 0.979254458
36.5 0.924181926 0.979254458
37.5 0.924181926 0.979254458
38.5 0.924181926 0.979254458
39.5 0.924181926 0.979254458
40.5 0.77968104 0.836140535
41.5 0.77968104 0.836140535
42.5 0.77968104 0.836140535
43.5 0.77968104 0.836140535
44.5 0.77968104 0.836140535
45.5 0.677991731 0.71232479
46.5 0.677991731 0.71232479
47.5 0.677991731 0.71232479
48.5 0.677991731 0.71232479
49.5 0.677991731 0.71232479
50.5 0.584051979 0.599609767
51.5 0.584051979 0.599609767
52.5 0.584051979 0.599609767
53.5 0.584051979 0.599609767
54.5 0.584051979 0.599609767
55.5 0.452782044 0.487663255
56.5 0.452782044 0.487663255
57.5 0.452782044 0.487663255
58.5 0.452782044 0.487663255
59.5 0.452782044 0.487663255
60.5 0.330395747 0.375972914
61.5 0.330395747 0.375972914
62.5 0.330395747 0.375972914
63.5 0.330395747 0.375972914
64.5 0.330395747 0.375972914
65.5 0.217554637 0.2727362
66.5 0.217554637 0.2727362
67.5 0.217554637 0.2727362
68.5 0.217554637 0.2727362
69.5 0.217554637 0.2727362
70.5 0.136467809 0.173768994
71.5 0.136467809 0.173768994
72.5 0.136467809 0.173768994
73.5 0.136467809 0.173768994
74.5 0.136467809 0.173768994
75.5 0.072297696 0.101614301
76.5 0.072297696 0.101614301
77.5 0.072297696 0.101614301
78.5 0.072297696 0.101614301
79.5 0.072297696 0.101614301
80.5 0.029061889 0.045975199
81.5 0.029061889 0.045975199
82.5 0.029061889 0.045975199
83.5 0.029061889 0.045975199
84.5 0.029061889 0.045975199
85.5 0.008547614 0.01545207
86.5 0.008547614 0.01545207
87.5 0.008547614 0.01545207
88.5 0.008547614 0.01545207
89.5 0.008547614 0.01545207
90.5 0.001709523 0.003259752
91.5 0.001709523 0.003259752
92.5 0.001709523 0.003259752
93.5 0.001709523 0.003259752
94.5 0.001709523 0.003259752
95.5 0.000184813 0.000423344
96.5 0.000184813 0.000423344
97.5 0.000184813 0.000423344
98.5 0.000184813 0.000423344
99.5 0.000184813 0.000423344
100.5 0 0", header=TRUE, stringsAsFactors = FALSE)
##################################### END #######################################################
###################### target names
tar.name <- function(df, tar.type = "name"){
apply(expand.grid(rownames(df),".", names(df), ".",tar.type), 1, paste0,collapse="" )
}
### get the real target values
tar.value <- function(df){
return(as.numeric(unlist(df, use.names = FALSE)))
}
#Creating target names
target.variables <- c(tar.name(max.art.initiated.all, "max.ART.init"),
tar.name(max.art.retention.all, "max.ret"),
tar.name(max.vl.none.suppression.all, "max.val"),
tar.name(max.mortality.all, "max.mort"),
tar.name(swazi.growth.rate, "growth.rate"),
tar.name(swazi.inci.15.49, "swazi"),
tar.name(swazi.inci.2011, "swazi.inci.2011"),
tar.name(swazi.prev.2007, "2007.swazi.prev"),
tar.name(swazi.prev.age.year, "swazi.prev"),
tar.name(hhohho.prev, "hho.prev"),
tar.name(swazi.age.diff, "swazi.AD"),
tar.name(hhohho.age.diff, "hho.AD"),
tar.name(swazi.art.retention, "swazi.art.ret"),
tar.name(swazi.art.coverage, "swazi.art.cov"))
#if you will be doing calibration you need the table names
target.values <- c(tar.value(max.art.initiated.all), tar.value(max.art.retention.all),
tar.value(max.vl.none.suppression.all), tar.value(max.mortality.all),
tar.value(swazi.growth.rate), tar.value(swazi.inci.15.49),
tar.value(swazi.inci.2011), tar.value(swazi.prev.2007),
tar.value(swazi.prev.age.year), tar.value(hhohho.prev),
tar.value(swazi.age.diff), tar.value(hhohho.age.diff),
tar.value(swazi.art.retention), tar.value(swazi.art.coverage))
#Testing
#target.variables <- c(max.art.retention.tar.names,"node.id")
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Defines functions tar.name tar.value
#I presume this will be called through source()
#Idea is to give date and statistics. Calculated using start of the simulation - that time.
#Parameters creation
#Set up simulation parameters and initiate simulation
#just the year when simulation started.
sim.start.full <- as.Date("1970-03-31")
sim.start <- as.numeric(substr(sim.start.full,1,4))
maxart.starttime <- as.Date("2014-09-01")
maxart.endtime <- as.Date("2017-08-31")
sim.end.full <- as.Date("2019-03-31")
seed.hiv.date <- as.Date("1986-03-31")
#initial population
init.population.total <- 3000
women.frac <- 0.5253
################### ART initiation ##############################
max.art.initiated.all <- read.table(
text="PrimaryClients AllClients
all 77.6 84.6
before.eligible 4.6 2.2
within2.weeks 61 64
among.eligible 85 90
within12.months 85 91
within6months 79 87", header=TRUE, stringsAsFactors = FALSE)
#TimeTo: 2016-11
########################## ART Retention #############################
max.art.retention.all <- read.table(
text="PrimaryClients AllClients
all 72 73
at6.months 89 87
at12.months 82 79", header=TRUE, stringsAsFactors = FALSE)
#timeTo: 2016-11
########################### ViralLoadSuppression ##################
max.vl.none.suppression.all <- read.table(
text="PrimaryClients AllClients
at6.months 4 6
at12.months 6 11", header=TRUE, stringsAsFactors = FALSE)
#Check validation
#atleast6.months 4 8 2016-11
############# ViralLoadSuppression ###########################
max.mortality.all <- read.table(
text="PrimaryClients AllClients
all 1.01 1.87
aids.related 0.51 1.49", header=TRUE, stringsAsFactors = FALSE)
#timeTo 2016-11
#################### Swaziland specific statitics ##########################.
#GrowthRate for all ages and gender
swazi.growth.rate <- read.table(
text="X1985 X1990 X1995 X2000 X2005 X2010 X2015 X2016
one.year 3.63 3.39 2.00 1.50 0.98 1.86 1.83 1.81",
header=TRUE, stringsAsFactors = FALSE)
############ Incidence UNAIDS estimates (End of march each year) #############
swazi.inci.15.49 <- read.table(
text="X1990 X1992 X1995 X2000 X2005 X2010 X2015
inci.15.49 1.24 2.9 4.66 3.7 3.08 3.09 2.36",
header=TRUE, stringsAsFactors = FALSE)
############# 2011 Incidence ages specific: SHIMS 1 #############################
#consider for validation: [all 3.14 1.65 2.38 2010-12 2011-06]
swazi.inci.2011 <- read.table(
text="F.value M.value
A18.19 3.84 0.84
A20.24 4.17 1.66
A25.29 2.25 2.36
A30.34 2.78 3.12
A35.39 4.10 0.44
A40.44 2.07 1.24
A45.49 1.20 0.02",
header=TRUE, stringsAsFactors = FALSE)
########### 2006-07 prevalence ages specific: Swaziland demographics and health survey SHDS ##
# Data was collected between July 2006 and March 2007. TimeTo: 2007-03
swazi.prev.2007 <- read.table(
text="F.value M.value FM.value
A10.14 3.3 1.9 2.6
A15.19 10.1 1.9 5.8
A20.24 38.4 12.4 26.5
A25.29 49.2 27.8 39.3
A30.34 45.2 43.7 44.6
A35.39 37.7 44.9 40.8
A40.44 27.9 40.7 32.7
A45.49 21.4 27.9 24.0
A50.54 24.3 28.3 26.0
A55.59 9.6 17.4 12.7
A60.150 7.0 13.3 9.5
A15.49 31.1 19.7 25.9
A50.150 11.7 17.9 14.2",
header=TRUE, stringsAsFactors = FALSE)
#A2.4 4.8 5.5 5.1 2007-03
#A5.9 3.6 4.8 4.2 2007-03
#A2.150 22.1 14.9 18.8
#Note it is not possibloe to compute the young age incidence 2- 12 yr olds since these will
#not be sexually active more of which we would expect the infection to be through MTCT
############# Prevalence ages specific UNAIDS (End of March each year) ############
swazi.prev.age.year <- read.table(
text="F.value M.value
A15.24.1990 2.6 1.5
A15.49.1990 2.3 2.2
A15.24.1995 17.4 7.5
A15.49.1995 14.4 16.9
A15.24.2000 21.9 8.1
A15.49.2000 21.8 27.6
A15.24.2005 18.1 6.7
A15.49.2005 21.8 29.4
A15.24.2010 17.4 7.2
A15.49.2010 22.6 31.9
A15.24.2015 16.7 7.3",
header=TRUE, stringsAsFactors = FALSE)
#A15.49.2015 NA 34.2 28.8 ### Maybe for validation
#A15.49.1990 2.3 2.2 2.2
#A15.49.1995 14.4 16.9 15.7
#A15.49.2000 21.8 27.6 24.8
#A15.49.2005 21.8 29.4 25.7
#A15.49.2010 22.6 31.9 27.2
############ Hhohho prevalence ##############################################
#Data was collected between July 2006 and March 2007 [SDHS 2006-07]: TimeTo = 2007-03
hhohho.prev <- read.table(
text="F.value M.value FM.value
A0.150 24.2 17.4 21.0
A15.49 33.8 23.1 28.9
A50.150 11.2 20.7 15.4 ",
header=TRUE, stringsAsFactors = FALSE)
#A2.14 4.1 3.6 3.9 2007-03
########## Swazi Age difference ######################################################
# Data was collected between December 2010 and June 2011 [SHIMS 2011] TimeTo: 2011-06
swazi.age.diff <- read.table(
text="FM.value
mean 5.19
meadian 6.64 ", header=TRUE, stringsAsFactors = FALSE)
swazi.AD.tar.names <- paste("FM.value", row.names(swazi.age.diff),"AD", sep = ".")
################### Hhohho age diff ##########################################
# Data was collected between December 2010 and June 2011 [SHIMS 2011] TimeTo: 2011-06
hhohho.age.diff <- read.table(
text="F.value M.value FM.value
mean 7.51 5.53 6.76
meadian 6.82 5.34 5.19 ",
header=TRUE, stringsAsFactors = FALSE)
##################### ART Retention UNAIDS (End of March each year) ############
#Consider using 2015 98 and 97 % for validation
swazi.art.retention <- read.table(
text="X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014
less.15.at6.months 81 88 87 87 89 84 85 97
greateq.15.at6.months 82 85 85 86 88 87 86 96
less.15.at12.months 73 82 81 80 89 76 91 93
greateq.15.at12.months 76 78 78 80 80 77 88 92",
header=TRUE, stringsAsFactors = FALSE)
#consider validation
#less.15.at24.months 67 75 74 73 69 87 87 NA
#greateq.15.at24.months 70 71 71 71 69 82 87 NA
################ ART coverage UNAIDS (End of March each year) ####################
swazi.art.coverage <- read.table(
text="F.value M.value FM.value
over15.2012 94 77 87
over15.2013 91 76 85
over15.2014 85 89 93
over15.2015 90 73 83", header=TRUE, stringsAsFactors = FALSE)
#14.less.2015 NA NA 72 validation
################ Swazi age distribution for 1970 ####################
swazi.1970.popn <- read.table(
text="Age Percent.Male Percent.Female
0.5 4.031943296 3.654955405
1.5 4.031943296 3.654955405
2.5 4.031943296 3.654955405
3.5 4.031943296 3.654955405
4.5 4.031943296 3.654955405
5.5 3.186107501 2.910353131
6.5 3.186107501 2.910353131
7.5 3.186107501 2.910353131
8.5 3.186107501 2.910353131
9.5 3.186107501 2.910353131
10.5 2.687773184 2.46512879
11.5 2.687773184 2.46512879
12.5 2.687773184 2.46512879
13.5 2.687773184 2.46512879
14.5 2.687773184 2.46512879
15.5 2.200496161 2.097011771
16.5 2.200496161 2.097011771
17.5 2.200496161 2.097011771
18.5 2.200496161 2.097011771
19.5 2.200496161 2.097011771
20.5 1.58261075 1.748705272
21.5 1.58261075 1.748705272
22.5 1.58261075 1.748705272
23.5 1.58261075 1.748705272
24.5 1.58261075 1.748705272
25.5 1.08994684 1.371878456
26.5 1.08994684 1.371878456
27.5 1.08994684 1.371878456
28.5 1.08994684 1.371878456
29.5 1.08994684 1.371878456
30.5 1.006213822 1.147729262
31.5 1.006213822 1.147729262
32.5 1.006213822 1.147729262
33.5 1.006213822 1.147729262
34.5 1.006213822 1.147729262
35.5 0.924181926 0.979254458
36.5 0.924181926 0.979254458
37.5 0.924181926 0.979254458
38.5 0.924181926 0.979254458
39.5 0.924181926 0.979254458
40.5 0.77968104 0.836140535
41.5 0.77968104 0.836140535
42.5 0.77968104 0.836140535
43.5 0.77968104 0.836140535
44.5 0.77968104 0.836140535
45.5 0.677991731 0.71232479
46.5 0.677991731 0.71232479
47.5 0.677991731 0.71232479
48.5 0.677991731 0.71232479
49.5 0.677991731 0.71232479
50.5 0.584051979 0.599609767
51.5 0.584051979 0.599609767
52.5 0.584051979 0.599609767
53.5 0.584051979 0.599609767
54.5 0.584051979 0.599609767
55.5 0.452782044 0.487663255
56.5 0.452782044 0.487663255
57.5 0.452782044 0.487663255
58.5 0.452782044 0.487663255
59.5 0.452782044 0.487663255
60.5 0.330395747 0.375972914
61.5 0.330395747 0.375972914
62.5 0.330395747 0.375972914
63.5 0.330395747 0.375972914
64.5 0.330395747 0.375972914
65.5 0.217554637 0.2727362
66.5 0.217554637 0.2727362
67.5 0.217554637 0.2727362
68.5 0.217554637 0.2727362
69.5 0.217554637 0.2727362
70.5 0.136467809 0.173768994
71.5 0.136467809 0.173768994
72.5 0.136467809 0.173768994
73.5 0.136467809 0.173768994
74.5 0.136467809 0.173768994
75.5 0.072297696 0.101614301
76.5 0.072297696 0.101614301
77.5 0.072297696 0.101614301
78.5 0.072297696 0.101614301
79.5 0.072297696 0.101614301
80.5 0.029061889 0.045975199
81.5 0.029061889 0.045975199
82.5 0.029061889 0.045975199
83.5 0.029061889 0.045975199
84.5 0.029061889 0.045975199
85.5 0.008547614 0.01545207
86.5 0.008547614 0.01545207
87.5 0.008547614 0.01545207
88.5 0.008547614 0.01545207
89.5 0.008547614 0.01545207
90.5 0.001709523 0.003259752
91.5 0.001709523 0.003259752
92.5 0.001709523 0.003259752
93.5 0.001709523 0.003259752
94.5 0.001709523 0.003259752
95.5 0.000184813 0.000423344
96.5 0.000184813 0.000423344
97.5 0.000184813 0.000423344
98.5 0.000184813 0.000423344
99.5 0.000184813 0.000423344
100.5 0 0", header=TRUE, stringsAsFactors = FALSE)
##################################### END #######################################################
###################### target names
tar.name <- function(df, tar.type = "name"){
apply(expand.grid(rownames(df),".", names(df), ".",tar.type), 1, paste0,collapse="" )
}
### get the real target values
tar.value <- function(df){
return(as.numeric(unlist(df, use.names = FALSE)))
}
#Creating target names
target.variables <- c(tar.name(max.art.initiated.all, "max.ART.init"),
tar.name(max.art.retention.all, "max.ret"),
tar.name(max.vl.none.suppression.all, "max.val"),
tar.name(max.mortality.all, "max.mort"),
tar.name(swazi.growth.rate, "growth.rate"),
tar.name(swazi.inci.15.49, "swazi"),
tar.name(swazi.inci.2011, "swazi.inci.2011"),
tar.name(swazi.prev.2007, "2007.swazi.prev"),
tar.name(swazi.prev.age.year, "swazi.prev"),
tar.name(hhohho.prev, "hho.prev"),
tar.name(swazi.age.diff, "swazi.AD"),
tar.name(hhohho.age.diff, "hho.AD"),
tar.name(swazi.art.retention, "swazi.art.ret"),
tar.name(swazi.art.coverage, "swazi.art.cov"))
#if you will be doing calibration you need the table names
target.values <- c(tar.value(max.art.initiated.all), tar.value(max.art.retention.all),
tar.value(max.vl.none.suppression.all), tar.value(max.mortality.all),
tar.value(swazi.growth.rate), tar.value(swazi.inci.15.49),
tar.value(swazi.inci.2011), tar.value(swazi.prev.2007),
tar.value(swazi.prev.age.year), tar.value(hhohho.prev),
tar.value(swazi.age.diff), tar.value(hhohho.age.diff),
tar.value(swazi.art.retention), tar.value(swazi.art.coverage))
#Testing
#target.variables <- c(max.art.retention.tar.names,"node.id")
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