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generate plots.R
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# function to generate plots for paper
# can be set to run sims -or- use prepared sim data from function
make_accum_dat <- function(
sims=NULL ,
N_groups = 16,
s = 0.01,
d = 1,
e = 0.1,
u = 0.01,
v = 1,
m = 0.01,
death = 0.05,
b = 0.1,
K = log(0.1-0.05+1)/20,
f = 1,
n_behaviors = 1e4,
n_teachers = 1,
tmax = 2000,
end_range = 500,
n_sims = 1,
cores = 3,
preschedule = FALSE ,
... )
{
the_call <- match.call()
mnames <- names(the_call)[-1]
# list of arguments to recognize as graphed parameters
white_list <- c( "s","d","e","u","v","m","death","b","K","f","n_behaviors","n_teachers" )
mnames <- mnames[ mnames %in% white_list ]
print(mnames)
library(parallel)
library(rethinking)
# set up list of parameter values
seq <- expand.grid(
N_groups = N_groups,
s = s,
d = d,
e = e,
u = u,
v = v,
m = m,
f = f,
death = death,
b = b,
K = K, # gives different average group sizes
n_behaviors = n_behaviors,
n_teachers = n_teachers
)
# set up function wrapper that calls simulation and cleans results
library(compiler)
sim_cmp <- cmpfun(sim_accum_flex)
t2 <- tmax-end_range
ff <- function(i) {
p <- seq[i,]
#print(p)
ss <- with( p , sim_cmp(
tmax=tmax ,
N=100 ,
N_init=log(b-death+1)/K ,
N_groups=N_groups ,
s=s ,
d=d ,
e=e ,
u=u ,
v=v ,
m=m ,
f=p$f ,
death=death , b=b , K=K ,
n_behaviors=n_behaviors , n_teachers=n_teachers ) )
r <- list(
EHwithin = mean(ss$EHwithin[t2:tmax]),
Hbetween = mean(ss$Hbetween[t2:tmax]),
ENwithin = mean(ss$ENwithin[t2:tmax]),
q = mean(ss$q[t2:tmax]),
N = mean(ss$N[t2:tmax])
)
return(r)
}
f_cmp <- cmpfun(ff)
# dispatch to cores
num_cores <- cores
if (n_sims>1) {
# more than one replicate for each combination of parameters
# so duplicate the rows with rbind
seq_orig <- seq
for ( i in 1:n_sims ) seq <- rbind(seq,seq_orig)
}
nn <- nrow(seq)
result <- mclapply(
1:nrow(seq) ,
function(i) {
print(concat(i,"/",nn))
return(f_cmp(i))
} ,
mc.cores=num_cores , mc.preschedule=preschedule )
# simplify from list to array
result2 <- t( simplify2array(result) )
result3 <- as.data.frame(matrix(unlist(result), nrow=length(unlist(result[1])), byrow=FALSE))
result3 <- t(result3)
colnames(result3) <- colnames(result2)
rownames(result3) <- NULL
# merge with parameter values
result4 <- cbind( seq[1:nrow(result3),] , result3 )
attr(result4,"parnames") <- mnames
attr(result4,"call") <- the_call
invisible(result4)
}#
make_accum_plot <- function( sim_set , y="q" , x=NULL , z=NULL , ylim=NULL , xlim=NULL , new=TRUE , logx=FALSE , idpts=TRUE , xlab , ylab , cex=0.7 , na.rm=TRUE , ... ) {
pars <- attr(sim_set,"parnames")
if ( is.null(x) ) {
x <- pars[1]
z <- pars[2]
}
if ( y=="H" ) {
sim_set$H <- sim_set$EHwithin + sim_set$Hbetween
}
if ( y=="P" ) {
sim_set$P <- sim_set$Hbetween / ( sim_set$EHwithin + sim_set$Hbetween )
}
if ( is.null(ylim) ) ylim <- range(sim_set[[y]])
if ( new==TRUE ) blank()
if ( is.null(xlim) ) xlim <- range(sim_set[[x]])
if ( logx==TRUE ) xlim <- log(xlim)
if ( missing(xlab) ) xlab <- x
if ( missing(ylab) ) ylab <- y
plot( NULL , xlim=xlim , ylim=ylim , xlab=xlab , ylab=ylab , ... )
xu <- unique(sim_set[[x]])
zu <- unique(sim_set[[z]])
#axis( 1 , at=xu , labels=xu )
xp <- xu
if ( logx==TRUE ) xp <- log(xu)
for ( zi in zu ) {
#yu <- sim_set[[y]][sim_set[[z]]==zi]
yu <- xp # just need dimensions
for ( i in 1:length(xp) ) {
yu[i] <- mean( sim_set[[y]][ sim_set[[z]]==zi & sim_set[[x]]==xp[i] ] , na.rm=na.rm )
}
lines( xp , yu , ... )
points( xp , yu , pch=16 , ... )
}#zi
if ( idpts==TRUE ) {
labs <- paste( z , "=" , sim_set[[z]] )
identify( sim_set[[x]] , sim_set[[y]] , labels=labs , atpen=TRUE , cex=cex )
}
}
# example use
zz <- make_accum_dat(
m=c(0,0.01,0.04,0.1) , u=c(0,0.01,0.05,0.1) ,
s=0.001 , K = log(0.1-0.05+1)/20 , b=0.1 , death=0.05 , n_teachers=1 ,
tmax=5000 , end_range=1000 )
make_accum_plot( zz , y="q" , x="m" , z="u" , ylim=c(0,1) , logx=FALSE , idpts=FALSE )
############################ impact of migration
# q * m * u [and H,p]
zz <- make_accum_dat(
m=c(0,0.01,0.04,0.1) , u=c(0.001,0.01,0.1) ,
s=0.001 , K = log(0.1-0.01+1)/10 , b=0.1 , death=0.01 , n_teachers=1 ,
tmax=5000 , end_range=2000 , cores=60 , n_sims=10 )
save( zz , file="zz_qmu.rda" )
# load( "zz_qmu.rda" )
blank(w=3,ex=0.7)
par(mfrow=c(1,3))
make_accum_plot( zz , y="q" , x="m" , z="u" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion successful" )
make_accum_plot( zz , y="H" , x="m" , z="u" , ylim=c(0,2.5) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="total diversity (H)" )
make_accum_plot( zz , y="P" , x="m" , z="u" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion diversity between groups" )
############################ impact of group size AND migration
# q * N * m [and H,p]
zz <- make_accum_dat(
m=c(0.001,0.01,0.1) , K = log(0.1-0.01+1)/c(10,20,50) ,
u=0.01 , s=0.001 , b=0.1 , death=0.01 , n_teachers=1 ,
tmax=5000 , end_range=2000 , cores=60 , n_sims=10 )
save( zz , file="zz_qNm.rda" )
# load( "zz_qNm.rda" )
blank(w=3,ex=0.7)
par(mfrow=c(1,3))
make_accum_plot( zz , y="q" , x="K" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion successful" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
make_accum_plot( zz , y="H" , x="K" , z="m" , ylim=c(0,3) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="total diversity (H)" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
make_accum_plot( zz , y="P" , x="K" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion diversity between groups" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
############################ impact of number of teachers
# q * N * m with 10 teachers [and H,p]
zz <- make_accum_dat(
m=c(0.001,0.01,0.1) , K = log(0.1-0.01+1)/c(10,20,50) ,
u=0.01 , s=0.001 , b=0.1 , death=0.01 , n_teachers=10 ,
tmax=5000 , end_range=2000 , cores=60 , n_sims=10 )
save( zz , file="zz_qNm10t.rda" )
# load( "zz_qNm10t.rda" )
blank(w=3,ex=0.7)
par(mfrow=c(1,3))
make_accum_plot( zz , y="q" , x="K" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion successful" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
make_accum_plot( zz , y="H" , x="K" , z="m" , ylim=c(0,3) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="total diversity (H)" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
make_accum_plot( zz , y="P" , x="K" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion diversity between groups" , xaxt="n" , xlab="N" )
axis( 1 , at=log(0.1-0.01+1)/c(10,20,50) , labels=c(10,20,50) )
############################
# q * s * m [and H,p]
zz <- make_accum_dat(
s=c(0.001,0.005,0.01,0.05,0.1) , m=c(0.001,0.01,0.1) ,
u=0.01 , K = log(0.1-0.01+1)/10 , b=0.1 , death=0.01 , n_teachers=1 ,
tmax=5000 , end_range=2000 , cores=60 , n_sims=10 )
save( zz , file="zz_qsm.rda" )
# load( "zz_qsm.rda" )
blank(w=3,ex=0.7)
par(mfrow=c(1,3))
make_accum_plot( zz , y="q" , x="s" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion successful" , xlab="innovation rate (s)" )
make_accum_plot( zz , y="H" , x="s" , z="m" , ylim=c(0,3) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="total diversity (H)" , xlab="innovation rate (s)" )
make_accum_plot( zz , y="P" , x="s" , z="m" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion diversity between groups" , xlab="innovation rate (s)" )
############################
# q * s * e [and H,p]
zz <- make_accum_dat(
s=c(0.001,0.01,0.1) , e=c(0.1,0.2,0.5) , m=0.01 ,
u=0.01 , K = log(0.1-0.01+1)/10 , b=0.1 , death=0.01 , n_teachers=1 ,
tmax=5000 , end_range=2000 , cores=60 , n_sims=10 )
save( zz , file="zz_qse.rda" )
# load( "zz_qse.rda" )
blank(w=3,ex=0.7)
par(mfrow=c(1,3))
make_accum_plot( zz , y="q" , x="s" , z="e" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion successful" , xlab="innovation rate (s)" )
make_accum_plot( zz , y="H" , x="s" , z="e" , ylim=c(0,3) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="total diversity (H)" , xlab="innovation rate (s)" )
make_accum_plot( zz , y="P" , x="s" , z="e" , ylim=c(0,1) , logx=FALSE , idpts=TRUE , new=FALSE , ylab="proportion diversity between groups" , xlab="innovation rate (s)" )