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RShiny_UCLouvain
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f5036761
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f5036761
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Sys.setlocale
(
"LC_ALL"
,
"fr_FR.UTF-8"
)
#to be sure that accents in text will be allowed in plots
library
(
shiny
)
library
(
plotrix
)
shinyServer
(
function
(
input
,
output
){
rv
<-
reactiveValues
()
# Create a reactiveValues object, to let us use settable reactive values
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
rv
$
lastAction
<-
'none'
# To start out, lastAction == NULL, meaning nothing clicked yet
rv
$
lastDist
<-
" "
rv
$
lastp
<
-0.5
rv
$
lastminrud
<
-1
rv
$
lastmaxrud
<
-6
rv
$
lastdf
<
-5
rv
$
lastdf1
<
-5
rv
$
lastdf2
<
-20
rv
$
lastm1
<
-8
rv
$
lastm2
<
-4
rv
$
lastsd1
<
-1.5
rv
$
lastsd2
<
-1.1
rv
$
lastN
<
-0
# An observe block for each button, to record that the action happened
observe
({
if
(
input
$
takesample
!=
0
)
{
rv
$
lastAction
<-
'takesample'
}
})
observe
({
if
(
input
$
reset
!=
0
)
{
rv
$
lastAction
<-
'reset'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
}
})
getSamples
<-
reactive
({
if
(
input
$
takesample
>
rv
$
last.takesample.value
&&
rv
$
lastAction
==
"takesample"
){
return
(
isolate
({
#Now do the expensive stuff
samples
<-
list
()
for
(
i
in
1
:
input
$
ns
){
if
(
input
$
dist
==
"DN"
)
{
samples
[[
i
]]
<-
rnorm
(
input
$
n
)}
if
(
input
$
dist
==
"DBin"
)
{
samples
[[
i
]]
<-
rbinom
(
n
=
1
,
size
=
input
$
n
,
prob
=
input
$
p
)}
#if (input$dist == "DLN"){samples[[i]]<-rlnorm(input$n)}
if
(
input
$
dist
==
"DUD"
)
{
x
<-
min
(
input
$
RUD
)
:
max
(
input
$
RUD
)
samples
[[
i
]]
<-
sample
(
x
,
input
$
n
,
replace
=
TRUE
)}
if
(
input
$
dist
==
"DU"
)
{
samples
[[
i
]]
<-
runif
(
input
$
n
)}
if
(
input
$
dist
==
"DE"
)
{
samples
[[
i
]]
<-
rexp
(
input
$
n
)}
if
(
input
$
dist
==
"DC"
)
{
samples
[[
i
]]
<-
rchisq
(
input
$
n
,
df
=
input
$
df
)}
if
(
input
$
dist
==
"DF"
)
{
samples
[[
i
]]
<-
rf
(
input
$
n
,
df1
=
input
$
df1
,
df2
=
input
$
df2
)}
if
(
input
$
dist
==
"DB"
)
{
samples
[[
i
]]
<-
c
(
rnorm
(
input
$
n
/
2
,
input
$
m1
,
input
$
sd1
),
rnorm
(
input
$
n
/
2
,
input
$
m2
,
input
$
sd2
))}
}
return
(
samples
)
}))
}
else
{
return
(
NULL
)
}})
getPlotHeight
<-
function
()
{
if
(
input
$
display
==
"default"
)
{
unit.height
<
-320
#cannot be auto because height is already "auto" in ui and double auto = conflict
}
if
(
input
$
display
==
"1024"
)
{
unit.height
<
-280
}
if
(
input
$
display
==
"800"
)
{
unit.height
<
-250
}
return
(
2
*
unit.height
)
}
getPlotWidth
<-
function
()
{
if
(
input
$
display
==
"default"
)
{
full.plot.width
<
-1310-400
#"auto"
}
if
(
input
$
display
==
"1024"
)
{
full.plot.width
<
-900-200
}
if
(
input
$
display
==
"800"
)
{
full.plot.width
<
-700-200
}
if
(
input
$
visM
&&
input
$
display
!=
"default"
){
full.plot.width
<-
full.plot.width
+400
}
return
(
full.plot.width
)
}
getInputValues
<-
reactive
({
return
(
input
)
#collect all inputs
})
getComputedValues
<-
reactive
({
samples
<-
list
()
samples
<-
getSamples
()
rv
$
samples.z
<-
c
(
rv
$
samples.z
,
samples
)
v
<-
getInputValues
()
# get all values of input list
cv
<-
list
()
#created empty computed values list
cv
$
samples.x
<-
list
()
cv
$
n.samples
<-
length
(
rv
$
samples.z
)
cv
$
vx
<-
v
$
sx
^
2
## Computation of sample related values ##
if
(
cv
$
n.samples
>
0
){
for
(
i
in
1
:
cv
$
n.samples
){
if
(
v
$
dist
==
"DN"
)
{
cv
$
samples.x
[[
i
]]
<-
round
((
rv
$
samples.z
[[
i
]]
*
v
$
sx
)
+
v
$
mx
,
2
)}
if
(
v
$
dist
==
"DBin"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]],
2
)}
#if (v$dist == "DLN"){cv$samples.x[[i]]<-round((rv$samples.z[[i]]*v$lsx)+v$lmx,2)}
if
(
v
$
dist
==
"DUD"
)
{
cv
$
samples.x
[[
i
]]
<-
rv
$
samples.z
[[
i
]]}
if
(
v
$
dist
==
"DU"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]]
*
v
$
b
,
2
)}
if
(
v
$
dist
==
"DE"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]]
*
(
1
/
v
$
Lambda
),
2
)}
if
(
v
$
dist
==
"DC"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]],
2
)}
if
(
v
$
dist
==
"DF"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]],
2
)}
if
(
v
$
dist
==
"DB"
)
{
cv
$
samples.x
[[
i
]]
<-
round
(
rv
$
samples.z
[[
i
]],
2
)}
}
## Automatic reset en cas de modification des paramètres
if
(
rv
$
lastDist
!=
v
$
dist
)
{
rv
$
lastAction
<-
'changeDist'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastN
!=
v
$
n
)
{
rv
$
lastAction
<-
'changeN'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastp
!=
v
$
p
)
{
rv
$
lastAction
<-
'changep'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastminrud
!=
min
(
v
$
RUD
))
{
rv
$
lastAction
<-
'changeminrud'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastmaxrud
!=
max
(
v
$
RUD
))
{
rv
$
lastAction
<-
'changemaxrud'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastdf
!=
v
$
df
)
{
rv
$
lastAction
<-
'changedf'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastdf1
!=
v
$
df1
)
{
rv
$
lastAction
<-
'changedf1'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastdf2
!=
v
$
df2
)
{
rv
$
lastAction
<-
'changedf2'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastm1
!=
v
$
m1
)
{
rv
$
lastAction
<-
'changem1'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastm2
!=
v
$
m2
)
{
rv
$
lastAction
<-
'changem2'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastsd1
!=
v
$
sd1
)
{
rv
$
lastAction
<-
'changesd1'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
if
(
rv
$
lastsd2
!=
v
$
sd2
)
{
rv
$
lastAction
<-
'changesd2'
rv
$
last.takesample.value
<
-0
rv
$
samples.z
<-
list
()
cv
$
samples.x
<-
list
()
}
##Pour passer d'une liste à une matrice
if
(
v
$
dist
==
"DBin"
){
cv
$
samples.x.mat
<-
matrix
(
nrow
=
cv
$
n.samples
,
ncol
=
1
)
cv
$
samples.p.mat
<-
matrix
(
nrow
=
cv
$
n.samples
,
ncol
=
1
)
for
(
i
in
1
:
cv
$
n.samples
){
cv
$
samples.x.mat
[
i
,
1
]
<-
cv
$
samples.x
[[
i
]]
cv
$
samples.p.mat
[
i
,
1
]
<-
cv
$
samples.x
[[
i
]]
/
v
$
n
}
}
else
{
cv
$
samples.x.mat
<-
matrix
(
nrow
=
cv
$
n.samples
,
ncol
=
v
$
n
)
for
(
i
in
1
:
cv
$
n.samples
){
cv
$
samples.x.mat
[
i
,]
<-
cv
$
samples.x
[[
i
]]
}
}
## Computation of descriptives
cv
$
samples.x.m.vec
<-
c
()
# vector of mean values, each line a sample
cv
$
samples.x.sd.vec
<-
c
()
cv
$
samples.x.m.vec
<-
round
(
apply
(
cv
$
samples.x.mat
,
1
,
mean
),
2
)
#means of samples
cv
$
samples.x.sd.vec
<-
round
(
apply
(
cv
$
samples.x.mat
,
1
,
sd
),
2
)
#sds of samples
if
(
v
$
dist
==
"DBin"
){
cv
$
samples.p.m.vec
<-
round
(
apply
(
cv
$
samples.p.mat
,
1
,
mean
),
2
)
#means of samples
cv
$
samples.p.sd.vec
<-
round
(
apply
(
cv
$
samples.p.mat
,
1
,
sd
),
2
)
#sds of samples
}
## Define subset to plot
cv
$
samples.x.n.toshow
<
-0
cv
$
samples.x.from
<
-1
if
(
cv
$
n.samples
>
5
){
cv
$
samples.x.from
<-
cv
$
n.samples
-5+1
}
cv
$
samples.x.to
<-
cv
$
n.samples
if
(
v
$
dist
==
"DBin"
){
cv
$
samples.x.mat.toshow
<-
matrix
(
cv
$
samples.x.mat
[
cv
$
samples.x.from
:
cv
$
samples.x.to
,],
ncol
=
1
)
}
else
{
cv
$
samples.x.mat.toshow
<-
matrix
(
cv
$
samples.x.mat
[
cv
$
samples.x.from
:
cv
$
samples.x.to
,],
ncol
=
v
$
n
)
}
cv
$
samples.x.m.vec.toshow
<-
cv
$
samples.x.m.vec
[
cv
$
samples.x.from
:
cv
$
samples.x.to
]
cv
$
samples.x.sd.vec.toshow
<-
cv
$
samples.x.sd.vec
[
cv
$
samples.x.from
:
cv
$
samples.x.to
]
cv
$
samples.x.i.vec.toshow
<-
c
(
cv
$
samples.x.from
:
cv
$
samples.x.to
)
cv
$
samples.x.n.toshow
<-
length
(
cv
$
samples.x.mat.toshow
[,
1
])
cv
$
samples.x.m.m
<-
round
(
mean
(
cv
$
samples.x.m.vec
),
4
)
cv
$
samples.x.v.m
<-
round
(
var
(
cv
$
samples.x.m.vec
),
4
)
if
(
v
$
dist
==
"DBin"
){
cv
$
samples.p.m.m
<-
round
(
mean
(
cv
$
samples.p.m.vec
),
4
)
cv
$
samples.p.v.m
<-
round
(
var
(
cv
$
samples.p.m.vec
),
4
)
cv
$
vx
<-
v
$
sx
^
2
cv
$
lvx
<-
v
$
lsx
^
2
}
## Valeurs qui serviront à définir les limites des axes
hf
<-
hist
(
cv
$
samples.x.mat
,
freq
=
TRUE
,
breaks
=
50
)
freqcl
<-
hf
$
counts
cv
$
maxfreqcl
<-
max
(
freqcl
)
n.obs.tot
<-
length
(
cv
$
samples.x.mat
)
probcl
<-
freqcl
/
n.obs.tot
cv
$
maxprobcl
<-
max
(
probcl
)
#if(cv$n.samples <100){breaks =10}
#else{breaks <- sqrt(cv$n.samples)}
#hm<-hist(cv$samples.x.m.vec, freq = TRUE, breaks = breaks)
#cv$freqmcl <- unlist(hm[2])
#densm<-density(cv$samples.x.m.vec)
#cv$highdensm <- unlist(densm[2])
}
## Last takesample value
rv
$
last.takesample.value
<-
v
$
takesample
rv
$
lastDist
<-
v
$
dist
rv
$
lastp
<-
v
$
p
rv
$
lastminrud
<-
min
(
v
$
RUD
)
rv
$
lastmaxrud
<-
max
(
v
$
RUD
)
rv
$
lastdf
<-
v
$
df
rv
$
lastdf1
<-
v
$
df1
rv
$
lastdf2
<-
v
$
df2
rv
$
lastm1
<-
v
$
m1
rv
$
lastm2
<-
v
$
m2
rv
$
lastsd1
<-
v
$
sd1
rv
$
lastsd2
<-
v
$
sd2
rv
$
lastN
<-
v
$
n
return
(
cv
)
})
output
$
doublePlot
<-
renderPlot
({
v
<-
getInputValues
()
cv
<-
getComputedValues
()
par
(
mfcol
=
c
(
2
,
2
))
m
<-
matrix
(
c
(
1
,
2
,
3
,
4
),
2
,
2
,
byrow
=
TRUE
)
layout
(
m
,
width
=
c
(
4
,
3
))
#
## Set graphic parameters
if
(
v
$
display
==
"default"
)
{
cex.main.title
<
-2
cex.title
<
-1.5
cex.samples
<
-1.5
cex.axis
<
-1.1
cex.label
<
-1.2
}
if
(
v
$
display
==
"1024"
)
{
cex.main.title
<
-1.75
cex.title
<
-1.2
cex.samples
<
-1.2
cex.axis
<
-1
cex.label
<
-1
}
if
(
v
$
display
==
"800"
)
{
cex.main.title
<
-1.5
cex.title
<
-1
cex.samples
<
-1
cex.axis
<
-0.8
cex.label
<
-0.8
}
#Définition des limites de l'axe des abscisses pour le plot
if
(
v
$
dist
==
"DN"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdn
)
x.lim.sup
<-
max
(
v
$
rangeXdn
)}
if
(
v
$
dist
==
"DBin"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdbin
)
x.lim.sup
<-
max
(
v
$
rangeXdbin
)}
#if(v$dist=="DLN"&& v$range =="SameRange"){x.lim.inf<-min(v$rangeXdln)
# x.lim.sup<-max(v$rangeXdln)}
if
(
v
$
dist
==
"DUD"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdud
)
x.lim.sup
<-
max
(
v
$
rangeXdud
)}
if
(
v
$
dist
==
"DU"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdu
)
x.lim.sup
<-
max
(
v
$
rangeXdu
)}
if
(
v
$
dist
==
"DE"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXde
)
x.lim.sup
<-
max
(
v
$
rangeXde
)}
if
(
v
$
dist
==
"DC"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdc
)
x.lim.sup
<-
max
(
v
$
rangeXdc
)}
if
(
v
$
dist
==
"DF"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdf
)
x.lim.sup
<-
max
(
v
$
rangeXdf
)}
if
(
v
$
dist
==
"DB"
&&
v
$
range
==
"SameRange"
)
{
x.lim.inf
<-
min
(
v
$
rangeXdb
)
x.lim.sup
<-
max
(
v
$
rangeXdb
)}
if
(
v
$
dist
==
"DN"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdn
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdn
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardn
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardn
)}
if
(
v
$
dist
==
"DBin"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdbin
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdbin
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardbin
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardbin
)}
#if(v$dist=="DLN"&& v$range =="DifRange"){Obs.lim.inf<-min(v$rangeObsdln)
# Obs.lim.sup<-max(v$rangeObsdln)
# Xbar.lim.inf<-min(v$rangeXbardln)
# Xbar.lim.sup<-max(v$rangeXbardln)}
if
(
v
$
dist
==
"DUD"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdud
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdud
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardud
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardud
)}
if
(
v
$
dist
==
"DU"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdu
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdu
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardu
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardu
)}
if
(
v
$
dist
==
"DE"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsde
)
Obs.lim.sup
<-
max
(
v
$
rangeObsde
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbarde
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbarde
)}
if
(
v
$
dist
==
"DC"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdc
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdc
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardc
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardc
)}
if
(
v
$
dist
==
"DF"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdf
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdf
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardf
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardf
)}
if
(
v
$
dist
==
"DB"
&&
v
$
range
==
"DifRange"
)
{
Obs.lim.inf
<-
min
(
v
$
rangeObsdb
)
Obs.lim.sup
<-
max
(
v
$
rangeObsdb
)
Xbar.lim.inf
<-
min
(
v
$
rangeXbardb
)
Xbar.lim.sup
<-
max
(
v
$
rangeXbardb
)}
#Définition des X conditionnellement à la distribution
if
(
v
$
dist
==
"DN"
){
X
=
seq
(
-10
,
40
,
length
=
1000
)}
if
(
v
$
dist
==
"DBin"
){
X
=
0
:
v
$
n
}
#if(v$dist=="DLN"){X=seq(-10,40, length=1000)}
if
(
v
$
dist
==
"DUD"
){
X
=
min
(
v
$
RUD
)
:
max
(
v
$
RUD
)}
if
(
v
$
dist
==
"DU"
){
X
=
seq
(
-5
,
25
,
length
=
1000
)}
if
(
v
$
dist
==
"DE"
){
X
=
seq
(
-5
,
20
,
length
=
1000
)}
if
(
v
$
dist
==
"DC"
){
X
=
seq
(
-5
,
60
,
length
=
1000
)}
if
(
v
$
dist
==
"DF"
){
X
=
seq
(
-5
,
10
,
length
=
1000
)}
#Définition de la densité théorique
getY
<-
reactive
({
if
(
v
$
dist
==
"DN"
)
return
(
dnorm
(
X
,
mean
=
v
$
mx
,
sd
=
v
$
sx
))
if
(
v
$
dist
==
"DBin"
)
return
(
dbinom
(
X
,
size
=
v
$
n
,
prob
=
v
$
p
))
#if (v$dist=="DLN")
# return(dlnorm(X,meanlog=v$lmx, sdlog =v$lsx))
if
(
v
$
dist
==
"DU"
)
return
(
dunif
(
X
,
min
=
0
,
max
=
v
$
b
))
if
(
v
$
dist
==
"DE"
)
return
(
dexp
(
X
,
rate
=
v
$
Lambda
))
if
(
v
$
dist
==
"DC"
)
return
(
dchisq
(
X
,
df
=
v
$
df
))
if
(
v
$
dist
==
"DF"
)
return
(
df
(
X
,
df1
=
v
$
df1
,
df2
=
v
$
df2
))
if
(
v
$
dist
==
"DB"
)
return
(
density
(
c
(
rnorm
(
1000000
/
2
,
v
$
m1
,
v
$
sd1
),
rnorm
(
1000000
/
2
,
v
$
m2
,
v
$
sd2
))))
})
#------------------- Output 1 : ------------------------------
#Afficher les observations pour 5 échantillons prélevés
#Afficher la distribution théorique d'origine (optionnel
#-------------------------------------------------------------
if
(
v
$
dist
==
"DB"
){
dens
<-
getY
()
d
<-
unlist
(
dens
[
2
])
y.delta
<-
max
(
d
)
}
else
{
if
(
v
$
dist
==
"DUD"
){
p
<-
rep
(
1
/
length
(
X
),
length
(
X
))
y.delta
<-
p
[
1
]
+
p
[
1
]
/
length
(
X
)
}
else
{
y.delta
<-
max
(
getY
())
}
}
cv
$
samples.y.mat.toshow
<-
c
()
if
(
cv
$
n.samples
>
0
&&
cv
$
samples.x.n.toshow
>
0
){
if
(
v
$
dist
==
"DBin"
){
cv
$
samples.y.mat.toshow
<-
matrix
(
rep
(
y.delta
/
(
5+1
)
*
c
(
1
:
cv
$
samples.x.n.toshow
),
length
(
cv
$
samples.x.mat.toshow
[,
1
])),
nrow
=
length
(
cv
$
samples.x.mat.toshow
[,
1
]),
ncol
=
1
)
}
else
{
cv
$
samples.y.mat.toshow
<-
matrix
(
rep
(
y.delta
/
(
5+1
)
*
c
(
1
:
cv
$
samples.x.n.toshow
),
length
(
cv
$
samples.x.mat.toshow
[,
1
])),
nrow
=
length
(
cv
$
samples.x.mat.toshow
[,
1
]),
ncol
=
v
$
n
)
}
}
## Définition de pour l'axe des X
if
(
v
$
range
==
"SameRange"
){
lim.inf
<-
x.lim.inf
lim.sup
<-
x.lim.sup
}
if
(
v
$
range
==
"DifRange"
){
lim.inf
<-
Obs.lim.inf
lim.sup
<-
Obs.lim.sup
}
range
<-
lim.sup
-
lim.inf
## Définition du nb de graduations pour l'axe des X
if
(
v
$
dist
==
"DBin"
){
nbgrad
<
-10
}
else
{
if
(
v
$
dist
==
"DUD"
){
nbgrad
<-
range
}
else
{
if
(
range
>
10
){
nbgrad
<-
range
}
if
(
range
>
5
&
range
<=
10
){
nbgrad
<-
range
*
2
}
if
(
range
<=
5
){
nbgrad
<-
range
*
4
}
}}
## Test about range of 'x'
if
(
cv
$
n.samples
>
0
){
if
(
max
(
cv
$
samples.x.mat
)
>
lim.sup
||
min
(
cv
$
samples.x.mat
)
<
lim.inf
)
{
error
<
-1
}
else
{
error
<
-0
}
}
##############PLOT########################
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
))
### CAS N°1 : Si aucune donnée :
if
(
is.null
(
cv
$
samples.x.mat
)){
### CAS N°1.1 : Si l'option "afficher la distribution théorique est cochée :
if
(
v
$
showreality
){
#Si distribution Binomiale
if
(
v
$
dist
==
"DBin"
){
Y
<-
getY
()
plot
(
Y
,
type
=
"h"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
"x"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
ylab
=
expression
(
P
(
x
)),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
lwd
=
2
,
col
=
"red"
,
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
mtext
(
bquote
(
paste
(
X
*
"~"
*
Bin
(
n
*
","
*
p
),
" "
,
X
*
"~"
*
Bin
(
.
(
v
$
n
)
*
","
*
.
(
v
$
p
)),
sep
=
''
)),
side
=
3
,
line
=
-1
,
adj
=
0.05
,
cex
=
cex.label
)
}
else
{
#Si distribution Uniforme Discrète
if
(
v
$
dist
==
"DUD"
){
plot
(
X
,
p
,
col
=
"red"
,
type
=
"h"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlab
=
" "
,
ylab
=
" "
,
lwd
=
2
,
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
ylim
=
c
(
0
,
y.delta
),
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
mtext
(
bquote
(
paste
(
X
*
"~"
*
U
*
"{"
*
.
(
min
(
v
$
RUD
))
*
",...,"
*
.
(
max
(
v
$
RUD
))
*
"}"
,
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)
points
(
X
,
p
,
col
=
"red"
,
lwd
=
2
,
pch
=
19
)
lines
(
X
,
p
,
lty
=
3
)
}
#Pour les autres distributions
else
{
plot
(
c
(
0
),
c
(
-5
),
lty
=
1
,
lwd
=
1
,
col
=
"black"
,
yaxt
=
"n"
,
bty
=
"n"
,
las
=
1
,
xaxs
=
"i"
,
yaxs
=
"i"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
" "
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
main
=
""
)
axis
(
2
,
las
=
2
,
yaxp
=
c
(
0
,
signif
(
y.delta
,
1
),
5
),
cex.axis
=
cex.axis
)
if
(
v
$
dist
==
"DB"
){
dens
<-
getY
()
lines
(
dens
)
}
else
{
Y
<-
getY
()
points
(
X
,
Y
,
type
=
"l"
)
}
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
if
(
v
$
dist
==
"DN"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
N
(
mu
*
","
*
sigma
^
2
)
,
" "
,
X
*
"~"
*
N
(
.
(
v
$
mx
)
*
","
*
.
(
cv
$
vx
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DU"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
U
(
theta
[
1
]
*
","
*
theta
[
2
])
,
" "
,
X
*
"~"
*
U
(
.0
*
","
*
.
(
v
$
b
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DE"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
E
(
lambda
)
,
" "
,
X
*
"~"
*
E
(
.
(
v
$
Lambda
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DC"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
chi
^
2
,
(
nu
),
" "
,
X
*
"~"
*
chi
^
2
,(
.
(
v
$
df
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DF"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
F
[
nu
[
1
]
*
","
*
nu
[
2
]]
,
" "
,
X
*
"~"
*
F
[
.
(
v
$
df1
)
*
","
*
.
(
v
$
df2
)],
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
}}
}
###CAS N°1.2 : Si l'option "afficher la distribution théorique" n'est pas cochée :
else
{
#par(mai=c(0.5,0.8,0.5,0.5))
plot
(
c
(
0
),
c
(
-5
),
lty
=
1
,
lwd
=
1
,
col
=
"black"
,
yaxt
=
"n"
,
bty
=
"n"
,
las
=
1
,
xaxs
=
"i"
,
yaxs
=
"i"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
" "
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
}
}
##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
else
{
### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
if
(
error
==
1
){
plot
(
1
:
10
,
1
:
10
,
col
=
"white"
,
xlab
=
""
,
ylab
=
""
,
xaxt
=
"n"
,
yaxt
=
"n"
,
bty
=
"n"
,
type
=
'l'
)
text
(
5
,
8
,
labels
=
bquote
(
"Certaines valeurs dépassent les limites défines en abscisse."
),
cex
=
cex.label
,
col
=
"red"
)
text
(
5
,
7
,
labels
=
bquote
(
"Modifiez le choix de l'étendue au moyen du slider adéquat."
),
cex
=
cex.label
,
col
=
"red"
)
}
### CAS N°2.2 : Si pas d'erreur :
if
(
error
==
0
){
### CAS N°2.2.1 : Si l'option "afficher la distribution théorique" est cochée :
if
(
v
$
showreality
){
##Si la distribution est binomiale :
if
(
v
$
dist
==
"DBin"
){
Y
<-
getY
()
plot
(
Y
,
type
=
"h"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
"x"
,
ylab
=
expression
(
P
(
x
)),
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
lwd
=
2
,
col
=
"red"
,
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
mtext
(
bquote
(
paste
(
X
*
"~"
*
Bin
(
n
*
","
*
p
),
" "
,
X
*
"~"
*
Bin
(
.
(
v
$
n
)
*
","
*
.
(
v
$
p
)),
sep
=
''
)),
side
=
3
,
line
=
-1
,
adj
=
0.05
,
cex
=
cex.label
)
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
points
(
cv
$
samples.x.mat.toshow
[
i
,],
cv
$
samples.y.mat.toshow
[
i
,],
cex
=
cex.samples
*
0.8
)
text
(
cv
$
samples.x.m.vec.toshow
[
i
],
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
x
[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]),
cex
=
cex.samples
*
1.2
,
col
=
"blue"
)
}
}
else
{
##Si la distribution est uniforme discète :
if
(
v
$
dist
==
"DUD"
){
plot
(
X
,
p
,
col
=
"red"
,
type
=
"h"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
ylab
=
" "
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
lwd
=
2
,
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
ylim
=
c
(
0
,
y.delta
),
main
=
""
)
mtext
(
bquote
(
paste
(
"Distribution théorique"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
X
*
"~"
*
U
*
"{"
*
.
(
min
(
v
$
RUD
))
*
",...,"
*
.
(
max
(
v
$
RUD
))
*
"}"
,
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)
points
(
X
,
p
,
col
=
"red"
,
lwd
=
2
,
pch
=
19
)
lines
(
X
,
p
,
lty
=
3
)
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
points
(
jitter
(
cv
$
samples.x.mat.toshow
[
i
,],
0.5
),
jitter
(
cv
$
samples.y.mat.toshow
[
i
,],
0.5
),
cex
=
cex.samples
*
0.8
)
text
(
cv
$
samples.x.m.vec.toshow
[
i
],
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
bar
(
x
)[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]),
cex
=
cex.samples
*
1.2
,
col
=
"blue"
)
}
}
##Pour toutes les autres distributions :
else
{
plot
(
c
(
0
),
c
(
-5
),
lty
=
1
,
lwd
=
1
,
col
=
"black"
,
yaxt
=
"n"
,
bty
=
"n"
,
las
=
1
,
xaxs
=
"i"
,
yaxs
=
"i"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
" "
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
points
(
cv
$
samples.x.mat.toshow
[
i
,],
cv
$
samples.y.mat.toshow
[
i
,],
cex
=
cex.samples
*
0.8
)
text
(
cv
$
samples.x.m.vec.toshow
[
i
],
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
bar
(
x
)[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]),
cex
=
cex.samples
*
1.2
,
col
=
"blue"
)
}
axis
(
2
,
las
=
2
,
yaxp
=
c
(
0
,
signif
(
y.delta
,
1
),
5
),
cex.axis
=
cex.axis
)
if
(
v
$
dist
==
"DB"
){
dens
<-
getY
()
lines
(
dens
)
}
else
{
Y
<-
getY
()
points
(
X
,
Y
,
type
=
"l"
)
}
if
(
v
$
dist
==
"DN"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
N
(
mu
*
","
*
sigma
^
2
)
,
" "
,
X
*
"~"
*
N
(
.
(
v
$
mx
)
*
","
*
.
(
v
$
sx
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DU"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
U
(
theta
[
1
]
*
","
*
theta
[
2
])
,
" "
,
X
*
"~"
*
U
(
.0
*
","
*
.
(
v
$
b
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DE"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
E
(
lambda
)
,
" "
,
X
*
"~"
*
E
(
.
(
v
$
Lambda
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DC"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
chi
^
2
,
(
nu
),
" "
,
X
*
"~"
*
chi
^
2
,(
.
(
v
$
df
)),
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
if
(
v
$
dist
==
"DF"
){
mtext
(
bquote
(
paste
(
X
*
"~"
*
F
[
nu
[
1
]
*
","
*
nu
[
2
]]
,
" "
,
X
*
"~"
*
F
[
.
(
v
$
df1
)
*
","
*
.
(
v
$
df2
)],
sep
=
''
)),
side
=
3
,
line
=
1
,
adj
=
-0.1
,
cex
=
cex.label
)}
}
}
}
###CAS N°2.2.2 : Si la case "Afficher la distribution théorique n'est pas cochée":
else
{
##Si la distribution est binomiale :
if
(
v
$
dist
==
"DBin"
){
plot
(
c
(
0
),
c
(
-5
),
lty
=
1
,
lwd
=
1
,
col
=
"black"
,
yaxt
=
"n"
,
bty
=
"n"
,
las
=
1
,
xaxs
=
"i"
,
yaxs
=
"i"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
" "
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
main
=
""
)
#,
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
points
(
cv
$
samples.x.mat.toshow
[
i
,],
cv
$
samples.y.mat.toshow
[
i
,],
cex
=
cex.samples
*
0.8
)
text
(
cv
$
samples.x.m.vec.toshow
[
i
],
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
x
[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]),
cex
=
cex.samples
*
1.2
,
col
=
"blue"
)
}
}
else
{
plot
(
c
(
0
),
c
(
-5
),
lty
=
1
,
lwd
=
1
,
col
=
"black"
,
yaxt
=
"n"
,
bty
=
"n"
,
las
=
1
,
xaxs
=
"i"
,
yaxs
=
"i"
,
cex.lab
=
cex.label
,
cex.axis
=
cex.axis
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
" "
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
main
=
""
)
# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
if
(
v
$
dist
==
"DUD"
){
points
(
jitter
(
cv
$
samples.x.mat.toshow
[
i
,],
0.5
),
jitter
(
cv
$
samples.y.mat.toshow
[
i
,],
0.5
),
cex
=
cex.samples
*
0.8
)}
else
{
points
(
cv
$
samples.x.mat.toshow
[
i
,],
cv
$
samples.y.mat.toshow
[
i
,],
cex
=
cex.samples
*
0.8
)}
text
(
cv
$
samples.x.m.vec.toshow
[
i
],
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
bar
(
x
)[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]),
cex
=
cex.samples
*
1.2
,
col
=
"blue"
)
}
}}}
}
#------------------- Output 2 : --------------------------------------
#Afficher les stats descriptives des échantillons prélevés (optionnel)
#---------------------------------------------------------------------
### CAS n°1 : Si aucune donnée :
if
(
is.null
(
cv
$
samples.x.mat
)){
par
(
mai
=
c
(
0.5
,
0
,
0.5
,
0
))
plot
(
c
(
0
,
1
),
c
(
0
,
0
),
col
=
"white"
,
xaxt
=
"n"
,
yaxt
=
"n"
,
xlab
=
""
,
ylab
=
""
,
ylim
=
c
(
0
,
y.delta
),
bty
=
"n"
,
las
=
1
)
mtext
(
bquote
(
paste
(
"Descriptives : "
,
N
==
.
(
0
),
sep
=
""
)),
side
=
3
,
line
=
1
,
adj
=
0
,
at
=
0.00
,
cex
=
cex.label
)
}
### CAS n°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
else
{
par
(
mai
=
c
(
0.5
,
0
,
0.5
,
0
))
plot
(
c
(
0
,
1
),
c
(
0
,
0
),
col
=
"white"
,
xaxt
=
"n"
,
yaxt
=
"n"
,
xlab
=
""
,
ylab
=
""
,
ylim
=
c
(
0
,
y.delta
),
bty
=
"n"
,
las
=
1
)
### CAS N°2.1 : Si l'option "afficher les stat descr" est cochée :
if
(
v
$
empPl
){
mtext
(
bquote
(
paste
(
"Descriptives : "
,
N
==
.
(
cv
$
n.samples
),
sep
=
""
)),
side
=
3
,
line
=
1
,
adj
=
0
,
at
=
0.00
,
cex
=
cex.label
)
if
(
cv
$
samples.x.n.toshow
>
0
){
if
(
v
$
dist
==
"DBin"
){
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
text
(
0
,
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
paste
(
x
[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]
==
.
(
sprintf
(
"%.2f"
,
cv
$
samples.x.mat.toshow
[
i
])),
sep
=
""
)),
col
=
"blue"
,
pos
=
4
,
cex
=
cex.samples
)
text
(
0.3
,
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
paste
(
p
[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]
==
.
(
sprintf
(
"%.2f"
,
cv
$
samples.x.mat.toshow
[
i
]
/
v
$
n
)),
sep
=
""
)),
pos
=
4
,
cex
=
cex.samples
)
}
}
else
{
for
(
i
in
1
:
cv
$
samples.x.n.toshow
){
text
(
0
,
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
paste
(
bar
(
x
)[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]
==
.
(
sprintf
(
"%.2f"
,
cv
$
samples.x.m.vec.toshow
[
i
])),
sep
=
""
)),
col
=
"blue"
,
pos
=
4
,
cex
=
cex.samples
)
text
(
0.3
,
cv
$
samples.y.mat.toshow
[
i
,
1
],
labels
=
bquote
(
paste
(
s
[
.
(
cv
$
samples.x.i.vec.toshow
[
i
])]
==
.
(
sprintf
(
"%.2f"
,
cv
$
samples.x.sd.vec.toshow
[
i
])),
sep
=
""
)),
pos
=
4
,
cex
=
cex.samples
)
}
}
if
(
cv
$
n.samples
>
1
&&
v
$
dist
!=
"DBin"
){
mtext
(
bquote
(
paste
(
"E("
,
bar
(
X
),
")"
==
.
(
cv
$
samples.x.m.m
),
sep
=
""
)),
side
=
1
,
line
=
1
,
adj
=
0
,
at
=
0.01
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
"V("
,
bar
(
X
),
")"
==
.
(
cv
$
samples.x.v.m
),
sep
=
""
)),
side
=
1
,
line
=
1
,
adj
=
0
,
at
=
0.31
,
cex
=
cex.label
)
}
if
(
cv
$
n.samples
>
1
&&
v
$
dist
==
"DBin"
){
mtext
(
bquote
(
paste
(
"E("
,
X
,
")"
==
.
(
cv
$
samples.x.m.m
),
sep
=
""
)),
side
=
1
,
line
=
-1
,
adj
=
0
,
at
=
0.01
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
"V("
,
X
,
")"
==
.
(
cv
$
samples.x.v.m
),
sep
=
""
)),
side
=
1
,
line
=
1
,
adj
=
0
,
at
=
0.01
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
"E("
,
p
,
")"
==
.
(
cv
$
samples.p.m.m
),
sep
=
""
)),
side
=
1
,
line
=
-1
,
adj
=
0
,
at
=
0.31
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
"V("
,
p
,
")"
==
.
(
cv
$
samples.p.v.m
),
sep
=
""
)),
side
=
1
,
line
=
1
,
adj
=
0
,
at
=
0.31
,
cex
=
cex.label
)
}
}}
### CAS N°2.2 : Si l'option "afficher les stat descr" n'est pas cochée :
else
{}
}
#------------------- Output 3 : --------------------------------------
#Histogramme des données d'échantillonnage
#Afficher leur distribution (optionnel)
#---------------------------------------------------------------------
##Définition des limites pour l'axe des X
if
(
v
$
range
==
"SameRange"
){
lim.inf
<-
x.lim.inf
lim.sup
<-
x.lim.sup
}
if
(
v
$
range
==
"DifRange"
){
lim.inf
<-
Obs.lim.inf
lim.sup
<-
Obs.lim.sup
}
range
<-
lim.sup
-
lim.inf
## Définition du nb de graduations pour l'axe des X
if
(
v
$
dist
==
"DBin"
){
nbgrad
<
-10
}
else
{
if
(
v
$
dist
==
"DUD"
){
nbgrad
<-
range
}
else
{
if
(
range
>
10
){
nbgrad
<-
range
}
if
(
range
>
5
&
range
<=
10
){
nbgrad
<-
range
*
2
}
if
(
range
<=
5
){
nbgrad
<-
range
*
4
}
}}
## Test about range of 'x'
if
(
cv
$
n.samples
>
0
){
if
(
max
(
cv
$
samples.x.mat
)
>
lim.sup
||
min
(
cv
$
samples.x.mat
)
<
lim.inf
)
{
error
<
-1
}
else
{
error
<
-0
}
}
##############PLOT########################
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
))
### CAS N°1 : Si aucune donnée :
if
(
is.null
(
cv
$
samples.x.mat
)){
Y
<-
c
()
X
<-
c
()
plot
(
X
,
Y
,
main
=
""
,
yaxt
=
"n"
,
bty
=
"n"
,
cex.axis
=
cex.axis
,
cex.lab
=
cex.label
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
xlab
=
""
,
ylab
=
""
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
))
if
(
v
$
dist
==
"DBin"
){
mtext
(
bquote
(
paste
(
"Distribution du nombre de succès (N tentatives)"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)}
else
{
if
(
v
$
dist
==
"DUD"
){
mtext
(
bquote
(
paste
(
"Distribution des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)}
else
{
mtext
(
bquote
(
paste
(
"Histogramme des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)}
}
}
##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
else
{
### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
if
(
error
==
1
){
plot
(
1
:
10
,
1
:
10
,
col
=
"white"
,
xlab
=
""
,
ylab
=
""
,
xaxt
=
"n"
,
yaxt
=
"n"
,
bty
=
"n"
,
type
=
'l'
)
text
(
5
,
8
,
labels
=
bquote
(
"Certaines valeurs dépassent les limites défines en abscisse."
),
cex
=
cex.label
,
col
=
"red"
)
text
(
5
,
7
,
labels
=
bquote
(
"Modifiez le choix de l'étendue au moyen du slider adéquat."
),
cex
=
cex.label
,
col
=
"red"
)
}
### CAS N°2.2 : Si pas d'erreur
if
(
error
==
0
){
#Si la distribution est Binomiale & que l'option "afficher la densité normale" est cochée :
if
(
v
$
dist
==
"DBin"
&&
v
$
showNdensity
)
{
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.mat
,
probability
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Densité"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
#, ylim=c(0,cv$maxfreqcl*1.1)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution du nombre de succès (N tentatives)"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
if
(
cv
$
n.samples
>
1
){
mtext
(
bquote
(
paste
(
X
%~~%
N
(
np
*
","
*
np
(
1
-
p
)),
sep
=
''
)),
side
=
3
,
line
=
-1
,
adj
=
0.05
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
X
%~~%
N
(
.
(
cv
$
samples.x.m.m
)
*
","
*
.
(
cv
$
samples.x.v.m
)),
sep
=
''
)),
side
=
3
,
line
=
-3
,
adj
=
0.05
,
cex
=
cex.label
)
lim_dens_inf
<-
min
(
cv
$
samples.x.mat
)
-1
lim_dens_sup
<-
max
(
cv
$
samples.x.mat
)
+1
xfit
<-
seq
(
lim_dens_inf
,
lim_dens_sup
,
length
=
1000
)
yfit
<-
dnorm
(
xfit
,
mean
=
mean
(
cv
$
samples.x.mat
),
sd
=
sd
(
cv
$
samples.x.mat
))
lines
(
xfit
,
yfit
,
col
=
"blue"
,
type
=
'l'
,
lwd
=
2
)
}}
#Si la distribution est Binomiale mais que l'option "afficher la densité normale" n'est pas cochée :
else
{
if
(
v
$
dist
==
"DBin"
){
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.mat
,
freq
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
cv
$
maxfreqcl
*
1.1
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution du nombre de succès (N tentatives)"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
}
#Si la distribution est Uniforme discrète et que l'option "afficher la distribution théorique" est cochée:
else
{
if
(
v
$
dist
==
"DUD"
){
if
(
v
$
showreality
){
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
b
<-
barplot
(
prop.table
(
table
(
cv
$
samples.x.mat
)),
bty
=
"n"
,
yaxt
=
"n"
,
col
=
'grey'
,
main
=
""
,
space
=
2
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences relatives"
),
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
abline
(
h
=
p
,
lty
=
3
,
lwd
=
2
)
}
#Si la distribution est Uniforme discrète et que l'option "afficher la distribution théorique" n'est pas cochée:
else
{
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
b
<-
barplot
(
table
(
cv
$
samples.x.mat
),
bty
=
"n"
,
yaxt
=
"n"
,
col
=
'grey'
,
main
=
""
,
space
=
2
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences"
),
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
}
}
#Pour toutes les autres distributions quand l'option "afficher la distribution théorique" est cochée:
else
{
if
(
v
$
showreality
){
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.mat
,
probability
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Densité"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
#,ylim =c(0, max(c(y.delta, cv$maxprobcl))*1.1)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Histogramme des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
#afficher la distribution théorique
if
(
v
$
dist
==
"DB"
){
lines
(
getY
())}
else
{
lines
(
X
,
getY
(),
type
=
'l'
)}
}
#Pour toutes les autres distributions quand l'option "afficher la distribution théorique" n'est pas cochée:
else
{
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.mat
,
freq
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
cv
$
maxfreqcl
*
1.1
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Histogramme des données d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
}
}}
}
}}
#------------------- Output 4 : --------------------------------------
#Histogramme des moyennes d'échantillonnage
#Afficher leur distribution (optionnel)
#---------------------------------------------------------------------
##Définition des limites pour l'axe des X
if
(
v
$
range
==
"SameRange"
){
lim.inf
<-
x.lim.inf
lim.sup
<-
x.lim.sup
}
if
(
v
$
range
==
"DifRange"
){
lim.inf
<-
Xbar.lim.inf
lim.sup
<-
Xbar.lim.sup
}
range
<-
lim.sup
-
lim.inf
## Définition du nb de graduations pour l'axe des X
if
(
v
$
dist
==
"DBin"
){
nbgrad
<
-10
}
else
{
if
(
v
$
dist
==
"DUD"
){
nbgrad
<-
range
}
else
{
if
(
range
>
10
){
nbgrad
<-
range
}
if
(
range
>
5
&
range
<=
10
){
nbgrad
<-
range
*
2
}
if
(
range
<=
5
){
nbgrad
<-
range
*
4
}
}
}
##Définition du nb d'intervalles pour l'histogramme
if
(
v
$
dist
==
"DE"
||
v
$
dist
==
"DF"
)
{
breaks
<-
seq
(
lim.inf
,
lim.sup
,
0.01
)
}
else
{
breaks
<-
seq
(
lim.inf
,
lim.sup
,
0.05
)
}
## Test about range of 'x'
if
(
v
$
dist
==
"DBin"
){
if
(
cv
$
n.samples
>
0
){
if
(
max
(
cv
$
samples.p.mat
)
>
lim.sup
||
min
(
cv
$
samples.p.mat
)
<
lim.inf
)
{
error
<
-1
}
else
{
error
<
-0
}
}
}
else
{
if
(
cv
$
n.samples
>
0
){
#for (i in 1: length(cv$samples.x.m.vec)){
if
(
max
(
cv
$
samples.x.m.vec
)
>
lim.sup
||
min
(
cv
$
samples.x.m.vec
)
<
lim.inf
)
{
error
<
-1
}
else
{
error
<
-0
}
#}
}
}
##############PLOT########################
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
))
### CAS N°1 : Si aucune donnée :
if
(
is.null
(
cv
$
samples.x.mat
)){
Y
<-
c
()
X
<-
c
()
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0
),
xaxs
=
"i"
,
yaxs
=
"i"
)
plot
(
X
,
Y
,
main
=
""
,
yaxt
=
"n"
,
bty
=
"n"
,
xlab
=
""
,
ylab
=
""
,
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
y.delta
),
cex.axis
=
cex.axis
,
cex.lab
=
cex.label
,
,
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
))
if
(
v
$
dist
==
"DBin"
){
mtext
(
bquote
(
paste
(
"Distribution de la proportion de succès"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)}
else
{
mtext
(
bquote
(
paste
(
"Histogramme des moyennes d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)}
}
##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
else
{
### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
if
(
error
==
1
){
plot
(
1
:
10
,
1
:
10
,
col
=
"white"
,
xlab
=
""
,
ylab
=
""
,
xaxt
=
"n"
,
yaxt
=
"n"
,
bty
=
"n"
,
type
=
'l'
)
text
(
5
,
8
,
labels
=
bquote
(
"Certaines valeurs dépassent les limites défines en abscisse."
),
cex
=
cex.label
*
3
/
4
,
col
=
"red"
)
text
(
5
,
7
,
labels
=
bquote
(
"Modifiez le choix de l'étendue au moyen du slider adéquat."
),
cex
=
cex.label
*
3
/
4
,
col
=
"red"
)
}
### CAS N°2.2 : Si pas d'erreur
if
(
error
==
0
){
#Si la distribution est Binomiale & que l'option "afficher la densité normale" est cochée :
if
(
v
$
dist
==
"DBin"
){
if
(
v
$
showNdensity
){
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.p.mat
,
probability
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Densité"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
#, ylim=c(0,cv$maxfreqcl*1.1)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution de la proportion de succès"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
if
(
cv
$
n.samples
>
1
){
lim_dens_inf
<-
min
(
cv
$
samples.p.mat
)
-0.1
lim_dens_sup
<-
max
(
cv
$
samples.p.mat
)
+0.1
xfit
<-
seq
(
lim_dens_inf
,
lim_dens_sup
,
length
=
1000
)
yfit
<-
dnorm
(
xfit
,
mean
=
mean
(
cv
$
samples.p.mat
),
sd
=
sd
(
cv
$
samples.p.mat
))
lines
(
xfit
,
yfit
,
col
=
"blue"
,
type
=
'l'
,
lwd
=
2
)
mtext
(
bquote
(
paste
(
bar
(
X
)
%~~%
N
(
p
*
","
*
p
(
1
-
p
)
/
n
),
sep
=
''
)),
side
=
3
,
line
=
-1
,
adj
=
0.05
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
bar
(
X
)
%~~%
N
(
.
(
cv
$
samples.p.m.m
)
*
","
*
.
(
cv
$
samples.p.v.m
)),
sep
=
''
)),
side
=
3
,
line
=
-3
,
adj
=
0.05
,
cex
=
cex.label
)
}
}
#Si la distribution est Binomiale mais que l'option "afficher la densité normale" n'est pas cochée :
else
{
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.mat
/
v
$
n
,
freq
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
ylim
=
c
(
0
,
cv
$
maxfreqcl
*
1.1
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
50
,
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Distribution de la proportion de succès"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
}
}
#Pour toutes les autres distributions quand l'option "afficher la densité normale sur l'histogramme des moyennes" est cochée:
else
{
if
(
v
$
showMdensity
){
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
hist
(
cv
$
samples.x.m.vec
,
probability
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Densité"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
breaks
,
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Histogramme des moyennes d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
if
(
cv
$
n.samples
>
1
){
mtext
(
bquote
(
paste
(
bar
(
X
)
%~~%
N
(
E
(
bar
(
X
))
*
","
*
V
(
bar
(
X
))),
sep
=
''
)),
side
=
3
,
line
=
-1
,
adj
=
0.05
,
cex
=
cex.label
)
mtext
(
bquote
(
paste
(
bar
(
X
)
%~~%
N
(
.
(
cv
$
samples.x.m.m
)
*
","
*
.
(
cv
$
samples.x.v.m
)),
sep
=
''
)),
side
=
3
,
line
=
-3
,
adj
=
0.05
,
cex
=
cex.label
)
lim_inf
<-
min
(
cv
$
samples.x.m.vec
)
-1
lim_sup
<-
max
(
cv
$
samples.x.m.vec
)
+1
xfit
<-
seq
(
lim_inf
,
lim_sup
,
length
=
1000
)
yfit
<-
dnorm
(
xfit
,
mean
=
mean
(
cv
$
samples.x.m.vec
),
sd
=
sd
(
cv
$
samples.x.m.vec
))
lines
(
xfit
,
yfit
,
col
=
"blue"
,
type
=
'l'
,
lwd
=
2
)
}
}
#Pour toutes les autres distributions quand l'option "afficher la densité normale sur l'histogramme des moyennes" n'est pas cochée:
else
{
par
(
mai
=
c
(
0.5
,
0.8
,
0.5
,
0.5
),
xaxs
=
"i"
,
yaxs
=
"i"
)
h
<-
hist
(
cv
$
samples.x.m.vec
,
freq
=
TRUE
,
yaxt
=
"n"
,
bty
=
"n"
,
xaxs
=
"i"
,
yaxs
=
"i"
,
xlab
=
""
,
ylab
=
HTML
(
"Fréquences"
),
xlim
=
c
(
lim.inf
,
lim.sup
),
xaxp
=
c
(
lim.inf
,
lim.sup
,
nbgrad
),
col
=
'grey'
,
main
=
""
,
breaks
=
breaks
,
cex.lab
=
cex.label
)
axis
(
2
,
las
=
2
,
cex.axis
=
cex.axis
)
mtext
(
bquote
(
paste
(
"Histogramme des moyennes d'échantillonnage"
)),
side
=
3
,
line
=
1
,
adj
=
0.5
,
cex
=
cex.label
)
}
}
}}
},
height
=
getPlotHeight
,
width
=
getPlotWidth
)
})
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