################# LOAD PACKAGES
library(rstan)
library(rethinking)
library(parallel)
library(binom)

################# LOAD DATA

dataset <- read.csv("T23longdata_child.csv")

#################

predict.values <- "YES"

################# SELECT MODEL

model = mT23_child_all_by_prime_RE

################# GENERAL PARAMETERS

col0= 	rgb(0,0,0,255,max=255)
shade0= rgb(0,0,0,70,max=255) 
col1= 	rgb(255,0,0,255,max=255)
shade1= rgb(255,0,0,70,max=255) 
col2= 	rgb(0,0,255,255,max=255)
shade2= rgb(0,0,255,70,max=255) 
col3= 	rgb(0,153,0,255,max=255)
shade3= rgb(0,153,0,70,max=255) 
col4= 	rgb(204,102,0,255,max=255)
shade4= rgb(204,102,0,70,max=255) 
col5= 	rgb(127,0,255,255,max=255)
shade5= rgb(127,0,255,70,max=255) 
col6= 	rgb(153,150,76,255,max=255)
shade6= rgb(153,150,76,70,max=255) 
col7= 	rgb(216,5,202,255,max=255)
shade7= rgb(75,0,153,70,max=255) 
col8= 	rgb(100,100,100,255,max=255)
shade8= rgb(100,100,100,70,max=255) 

length=30	# Length of vector of predictions, i.e. how many predictions are plotted per line

age.seq0a <- seq( from=min(dataset[dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq1a <- seq( from=min(dataset[dataset$fieldid==1 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==1 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq2a <- seq( from=min(dataset[dataset$fieldid==2 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==2 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq3a <- seq( from=min(dataset[dataset$fieldid==3 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==3 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq4a <- seq( from=min(dataset[dataset$fieldid==4 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==4 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq5a <- seq( from=min(dataset[dataset$fieldid==5 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==5 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq6a <- seq( from=min(dataset[dataset$fieldid==6 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==6 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)

age.seq0b <- seq( from=min(dataset[dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq1b <- seq( from=min(dataset[dataset$fieldid==1 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==1 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq2b <- seq( from=min(dataset[dataset$fieldid==2 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==2 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq3b <- seq( from=min(dataset[dataset$fieldid==3 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==3 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq4b <- seq( from=min(dataset[dataset$fieldid==4 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==4 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq5b <- seq( from=min(dataset[dataset$fieldid==5 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==5 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)
age.seq6b <- seq( from=min(dataset[dataset$fieldid==6 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c), to=max(dataset[dataset$fieldid==6 & dataset$CONDITION_1_1yes==0 & dataset$CONDITION_2_1yes==0,]$age_c) , length.out=length)


################# EXTRA PARAMETERS 

aid=rep(1,length)
aid_zeros=matrix(0,10000,length(unique(dataset$aid)))
fieldid_zeros=matrix(0,10000,length(unique(dataset$fieldid)))

#################







##############################################      PUNISH-EITHER PRIME ESTIMATES, BY THIRD PARTY BEHAVIOR AND SOCIETY 


CONDITION_1_1yes = rep(0,length)
CONDITION_2_1yes = rep(0,length)

################# ESTIMATES FOR AVERAGE SITE
fieldid <- rep(1, length)
age_c=age.seq0a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros,v_fieldid_Intercept=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes=fieldid_zeros,v_fieldid_age_c=fieldid_zeros,v_fieldid_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_2_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_CONDITION_2_1yes=fieldid_zeros,v_fieldid_age_c_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_age_c_X_CONDITION_2_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c_X_CONDITION_2_1yes=fieldid_zeros))
pred0a_base <- apply(link , 2 , mean)
pred.PI0a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros,v_fieldid_Intercept=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes=fieldid_zeros,v_fieldid_age_c=fieldid_zeros,v_fieldid_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_2_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_CONDITION_2_1yes=fieldid_zeros,v_fieldid_age_c_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_age_c_X_CONDITION_2_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c_X_CONDITION_1_1yes=fieldid_zeros,v_fieldid_CONDITION_altruistic_1yes_X_age_c_X_CONDITION_2_1yes=fieldid_zeros))
pred0b_base <- apply(link , 2 , mean)
pred.PI0b_base <- apply( link , 2 , PI)}


################# ESTIMATES FOR BERLIN
fieldid <- rep(1, length)
age_c=age.seq1a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1a_base <- apply(link , 2 , mean)
pred.PI1a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1b_base <- apply(link , 2 , mean)
pred.PI1b_base <- apply( link , 2 , PI)}


################# ESTIMATES FOR LA PLATA
fieldid <- rep(2, length)
age_c=age.seq2a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2a_base <- apply(link , 2 , mean)
pred.PI2a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2b_base <- apply(link , 2 , mean)
pred.PI2b_base <- apply( link , 2 , PI)}


################# ESTIMATES FOR PHOENIX
fieldid <- rep(3, length)
age_c=age.seq3a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3a_base <- apply(link , 2 , mean)
pred.PI3a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3b_base <- apply(link , 2 , mean)
pred.PI3b_base <- apply( link , 2 , PI)}


################# ESTIMATES FOR PUNE
fieldid <- rep(4, length)
age_c=age.seq4a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4a_base <- apply(link , 2 , mean)
pred.PI4a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4b_base <- apply(link , 2 , mean)
pred.PI4b_base <- apply( link , 2 , PI)}



################# ESTIMATES FOR SHUAR
fieldid <- rep(5, length)
age_c=age.seq5a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5a_base <- apply(link , 2 , mean)
pred.PI5a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5b_base <- apply(link , 2 , mean)
pred.PI5b_base <- apply( link , 2 , PI)}


################# ESTIMATES FOR WÍCHI
fieldid <- rep(6, length)
age_c=age.seq6a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6a_base <- apply(link , 2 , mean)
pred.PI6a_base <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6b_base <- apply(link , 2 , mean)
pred.PI6b_base <- apply( link , 2 , PI)}








##############################################      PUNISH-SELFISH PRIME ESTIMATES, BY THIRD PARTY BEHAVIOR AND SOCIETY 

CONDITION_1_1yes = rep(1,length)
CONDITION_2_1yes = rep(0,length)

################# ESTIMATES FOR BERLIN
fieldid <- rep(1, length)
age_c=age.seq1a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1a_gen <- apply(link , 2 , mean)
pred.PI1a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1b_gen <- apply(link , 2 , mean)
pred.PI1b_gen <- apply( link , 2 , PI)}


################# ESTIMATES FOR LA PLATA
fieldid <- rep(2, length)
age_c=age.seq2a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2a_gen <- apply(link , 2 , mean)
pred.PI2a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2b_gen <- apply(link , 2 , mean)
pred.PI2b_gen <- apply( link , 2 , PI)}


################# ESTIMATES FOR PHOENIX
fieldid <- rep(3, length)
age_c=age.seq3a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3a_gen <- apply(link , 2 , mean)
pred.PI3a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3b_gen <- apply(link , 2 , mean)
pred.PI3b_gen <- apply( link , 2 , PI)}


################# ESTIMATES FOR PUNE
fieldid <- rep(4, length)
age_c=age.seq4a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4a_gen <- apply(link , 2 , mean)
pred.PI4a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4b_gen <- apply(link , 2 , mean)
pred.PI4b_gen <- apply( link , 2 , PI)}



################# ESTIMATES FOR SHUAR
fieldid <- rep(5, length)
age_c=age.seq5a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5a_gen <- apply(link , 2 , mean)
pred.PI5a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5b_gen <- apply(link , 2 , mean)
pred.PI5b_gen <- apply( link , 2 , PI)}


################# ESTIMATES FOR WÍCHI
fieldid <- rep(6, length)
age_c=age.seq6a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6a_gen <- apply(link , 2 , mean)
pred.PI6a_gen <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6b_gen <- apply(link , 2 , mean)
pred.PI6b_gen <- apply( link , 2 , PI)}




##############################################      PUNISH-PROSOCIAL PRIME ESTIMATES, BY THIRD PARTY BEHAVIOR AND SOCIETY 

CONDITION_1_1yes = rep(0,length)
CONDITION_2_1yes = rep(1,length)

################# ESTIMATES FOR BERLIN
fieldid <- rep(1, length)
age_c=age.seq1a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1a_self <- apply(link , 2 , mean)
pred.PI1a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred1b_self <- apply(link , 2 , mean)
pred.PI1b_self <- apply( link , 2 , PI)}


################# ESTIMATES FOR LA PLATA
fieldid <- rep(2, length)
age_c=age.seq2a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2a_self <- apply(link , 2 , mean)
pred.PI2a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred2b_self <- apply(link , 2 , mean)
pred.PI2b_self <- apply( link , 2 , PI)}


################# ESTIMATES FOR PHOENIX
fieldid <- rep(3, length)
age_c=age.seq3a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3a_self <- apply(link , 2 , mean)
pred.PI3a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred3b_self <- apply(link , 2 , mean)
pred.PI3b_self <- apply( link , 2 , PI)}


################# ESTIMATES FOR PUNE
fieldid <- rep(4, length)
age_c=age.seq4a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4a_self <- apply(link , 2 , mean)
pred.PI4a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred4b_self <- apply(link , 2 , mean)
pred.PI4b_self <- apply( link , 2 , PI)}



################# ESTIMATES FOR SHUAR
fieldid <- rep(5, length)
age_c=age.seq5a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5a_self <- apply(link , 2 , mean)
pred.PI5a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred5b_self <- apply(link , 2 , mean)
pred.PI5b_self <- apply( link , 2 , PI)}


################# ESTIMATES FOR WÍCHI
fieldid <- rep(6, length)
age_c=age.seq6a
age_2c=age_c^2	
CONDITION_altruistic_1yes = rep(1,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6a_self <- apply(link , 2 , mean)
pred.PI6a_self <- apply( link , 2 , PI)}

CONDITION_altruistic_1yes = rep(0,length)
d.pred <- list( 
	CONDITION_altruistic_1yes=CONDITION_altruistic_1yes,
	CONDITION_1_1yes=CONDITION_1_1yes,
	CONDITION_2_1yes=CONDITION_2_1yes,
	age_c=age_c,
	age_2c=age_2c,
	aid=aid,
	fieldid=fieldid)
if(predict.values=="YES"){
link <- link(model , n=10000 , data=d.pred , replace=list(v_aid_Intercept=aid_zeros))
pred6b_self <- apply(link , 2 , mean)
pred.PI6b_self <- apply( link , 2 , PI)}






