-
Notifications
You must be signed in to change notification settings - Fork 0
/
ptII_quan_Bayes_Fisher_LadyBristol-BUGS_helpfuncs.r
210 lines (181 loc) · 5.77 KB
/
ptII_quan_Bayes_Fisher_LadyBristol-BUGS_helpfuncs.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
### (C) 2005-2023 by Leo Guertler
### R-code supplement
### to the book
###
### "Subjektive Ansichten und objektive Betrachtungen"
###
### written by Gürtler & Huber (2023)
###
### All R-code is published under the GPL v3 license:
###
### https://www.gnu.org/licenses/gpl-3.0.en.html
###
### except for 'borrowed' code - see links and references.
### For this R-code the original license of the respective
### authors is valid.
###
### R-code published on
###
### https://osdn.net/projects/mixedmethod-rcode
### https://github.com/abcnorio/mixedmethod-rcode
# file:
# ptII_quan_Bayes_Fisher_LadyBristol-BUGS_helpfuncs.r
# location:
# chap. 6 [6.13.5]
# Fisher reloaded — mehr Tee
# HELPER FUNCTIONS
###### function to do Lady Muriel experiment via Bayes-Theorem
ladymuriel.BT <- function(pr1, pr2, si, Ni, prout=TRUE)
{
pr1pr2.sum <- pr1 + pr2
if(pr1pr2.sum != 1)
{
pr1.adj <- pr1/pr1pr2.sum
pr2.adj <- pr2/pr1pr2.sum
adj <- TRUE
} else
{
pr1.adj <- pr1
pr2.adj <- pr2
adj <- FALSE
}
BF.pr12 <- pr1.adj/pr2.adj
BF.pr21 <- pr2.adj/pr1.adj
const <- choose(Ni,si)
fs <- Ni - si
L1 <- const * pr1.adj^si * (1-pr1.adj)^(fs)
L2 <- const * pr2.adj^si * (1-pr2.adj)^(fs)
evi <- pr1.adj*L1 + pr2.adj*L2
LR12 <- L1/L2
LR21 <- L2/L1
post1 <- pr1.adj * L1 / evi
post2 <- pr2.adj * L2 / evi
postOR12 <- post1/post2
postOR21 <- post2/post1
# update post OR by BF and L
postOR12.alt <- pr1.adj/pr2.adj * LR12
stopifnot(postOR12.alt == BF.pr12*LR12)
if(adj) note <- c("Prior values adjusted to sum up to 1") else note <- NULL
res <- structure(list(prior1=pr1.adj,
prior2=pr2.adj,
BF.pr12=BF.pr12,
BF.pr21=BF.pr21,
const=const,
successes=si,
failures=fs,
trials=Ni,
L1=L1,
L2=L2,
evidence=evi,
LR12=LR12,
LR21=LR21,
post1=post1,
post2=post2,
postOR12=postOR12,
postOR21=postOR21,
note=note)
)
if(prout)
{
cat("\nLady Muriel via Bayes Theorem\n\n")
res[c("method", "note")] <- NULL
cat(paste(format(names(res), width = 15L, justify = "right"),
format(res, digits = digits), sep = " = "), sep = "\n")
if (!is.null(note))
cat("\n", "NOTE: ", note, "\n\n", sep = "")
else cat("\n")
invisible()
} else return(res)
}
# call:
# ladymuriel.BT(pr1=0.6, pr2=0.9, si=8, Ni=8)
# res <- ladymuriel.BT(pr1=0.6, pr2=0.9, si=8, Ni=8, prout=FALSE)
########################## END OF FUNCTION
###### function to plot s/N
plot.siNi <- function(theta, beta.dens, pbl.dens, pjc.dens, a.prior=NA, b.prior=NA, critbelo=1e-50, PRnos=1:3)
{
par(oma=c(2,1,1,1), "cex.axis"=1, bty="l")
plot(theta, beta.dens,type="l", col="red", pre.plot=grid(), bty="n", xlab=expression(paste(theta)), ylab="Density", cex.lab=1.2, lwd=2)
lines(theta, pbl.dens, col="steelblue", lty=2, lwd=2)
if(is.nan(mean(pjc.dens)) || is.infinite(mean(pjc.dens[!is.nan(pjc.dens)])))
{
cat("\npjc has not enough valid values and won't be printed, please see here:\n\n")
print(pjc.dens)
cat("\n")
PRnos <- c(1,2)
} else
{
lines(theta, pjc.dens, col="skyblue", lty=2, lwd=2)
}
if(!is.na(a.prior) & !is.na(b.prior))
{
lines(theta, dbeta(theta, shape1=a.prior, shape2=b.prior), col="violetred3", lwd=2, lty=3)
PRnos <- c(PRnos,4)
}
mtext("Lady tea taste Beta update", outer=TRUE, line=-2, cex=1.5, side=3)
par(fig=c(0,1,0,1), oma=c(1,0,0,0), mar=c(0,0,0,0), new=TRUE)
plot(1, type="n", bty="n", xaxt="n", yaxt="n")
categs <- c("Beta update", "UMS (pbl)", "UMS (pjc)","prior beta")
colos <- c("red","steelblue","skyblue","violetred3")
legend("bottom", legend=categs[PRnos], lty=1, lwd=2, xpd=TRUE, horiz=TRUE, col=colos[PRnos], bty="n", cex=.9)
}
# call:
# plot.siNi(theta, beta.dens=beta.dens, pbl.dens=muriel.pbl, pjc.dens=muriel.pjc)
# circumvent to write our own HDI code by creating a proper density object and use 'hdi' from 'HDInterval'
hdi.densi <- function(theta, densis)
{
require(HDInterval)
densi <- list(x=theta, y=densis, bw=NA, n=length(densis), call=NA, data.name="density over theta", has.na=FALSE)
attr(densi, "class") <- "density"
return(hdi(densi))
}
# call:
# hdi.densi(theta=theta, densis=beta.dens)
########################## END OF FUNCTION
###### function to calculate MAP
MAP <- function(theta, dens)
{
theta[dens == max(dens)]
}
# call:
# MAP(theta, dens)
########################## END OF FUNCTION
###### function to collect steps to run a BUGs model from R with BRugs
# taken and adopted from
# http://www.di.fc.ul.pt/~jpn/r/bugs/bugs_tutorial.html
run.model <- function(model, samps, dats=list(), chainL=1e+5, burning=0.10,
initfun=NULL, numC=3, thin=1)
{
# cL = chainLength
# thin = thinning
# burning = burn in steps
# samps = samples
writeLines(model, con="model.txt")
writeLines(dats, con="data.txt")
modelCheck("model.txt")
modelData(fileName="data.txt")
modelCompile(numChains=numC)
if( is.null(initfun) )
{
# randomly inititalize model generation
modelGenInits()
} else {
for(chain in 1:n.chains)
{
# use user data als initial starting point
modelInits(bugsInits(initfun))
}
}
# drop burn ins
modelUpdate(chainL * burning)
# keep record
samplesSet(samps)
# thinning
samplesSetThin(thin)
# randomly initialize a chain
modelUpdate(chainL)
# thin = 1 = k = samples used of every k_th iteration for inference
}
# call:
# run.model(model=modelstrng, samples=c("post","y","p","n"), dats=dats)
########################## END OF FUNCTION