Newer
Older
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
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
## simpleIC Shiny/R app server.R
##
## Author(s) :
## -----------
## J.J.
## Orginal version by Grégoire Vincke http://www.uclouvain.be/gregoire.vincke
## For Statistical eLearning Tools http://sites.uclouvain.be/selt/
##
## Licences :
## ---------
## CC-BY for the web page http://sites.uclouvain.be/selt/shiny/testhypic
## see http://creativecommons.org/licenses/by/2.0/be/ for more informations
##
## GPLv2 for source code on https://github.com/uclouvain-selt/shiny
## See LICENCE.tx or http://www.gnu.org/licenses/old-licenses/gpl-2.0.html for more informations
Sys.setlocale("LC_ALL", "fr_FR.UTF-8")#to be sure that accents in text will be allowed in plots
library(shiny)
# library(plotrix)
# library(xtable)
# library(ggplot2)
debug<-0
color.blue<-rgb(0,0,0.9)
color.true<-rgb(0,0.7,0)
color.false<-rgb(1,0,0,0.9)
oui.color.true<-rgb(0.3,0.3,0.3)
oui.color.false<-rgb(.6,.6,.6)
text.color.true<-rgb(0.2,0.2,0.2)
text.color.false<-rgb(.5,.5,.5)
non.color.true<-rgb(0.8,0.8,0.8)
non.color.false<-rgb(0.93,0.93,0.93)
density.true<-10
density.false<-25
y.delta<-0.1 #factor to set delta between rows of datas in plots
hypoth.text.levels<-c(1,0.7,0.4,0.1)
x.lim.min<-0
x.lim.max<-60
x.amp<-x.lim.max-x.lim.min
# possible values for the mean, cf ui.R mx1, mx0, mx
mu.vec<-c(x.lim.min:x.lim.max)#c(25:75)
shinyServer(function(input, output,session){
rv <- reactiveValues()# Create a reactiveValues object, to let us use settable reactive values
rv$last.takesample.value<-0
#rv$samples.z<-list() # all observations
rv$samples.mat<-c() # matrix of all observations, each line one sample
rv$new.sample<-c() # new matrix of observations, each line one sample
rv$cv.ls<-list() # calculated values
rv$lastAction <- 'none' # To start out, lastAction == NULL, meaning nothing clicked yet
# An observe block for each button, to record that the action happened
# Calculations only needed if one of these values are changed, so observe them
rv$mx.c<-0 # population mean
rv$sx.c<-0 # population deviation
rv$IC.k.c<-0 # IC length
rv$typIC.c<-'' # Type 'eCVk' empiric, 'vCVk' variance connue, 'sCVk' variance inconnue
# Create all samples new if a change is made in sample size
rv$n.c<-0 # sample size as in ui
rv$tn.c<-0 # total numer of samples
observe({
if(input$visM){
js_string <- '$(".span8").width(500);'
session$sendCustomMessage(type='jsCode', list(value = js_string))
}
})
# if take sample
observe({
if (input$takesample != 0) {
rv$lastAction <- 'takesample'
}
})
# if reset all new
observe({
if(input$reset !=0){
rv$lastAction <- 'reset'
rv$last.takesample.value<-0
rv$samples.mat<-c()
rv$cv.ls<-list()
rv$mx.c<-0 # population mean
rv$sx.c<-0 # population deviation
rv$IC.k.c<-0 # IC length
rv$typIC.c<-'' # Type 'eCVk' empiric, 'vCVk' variance connue, 'sCVk' variance inconnue
rv$n.c<-0 # sample size as in ui
rv$tn.c<-0 # total numer of samples
updateSliderInput(session, "mx1",value = sample(c(31:35),1))
updateSliderInput(session, "sx",value = sample(seq(from = 2, to = 3.5, by = 0.5),1))
updateCheckboxInput(session, "muKn",value=FALSE)
updateCheckboxInput(session, "sigKn",value=FALSE)
}
})
## observe({
## if (input$n != rv$n.c) {
## isolate({#Now do the expensive stuff
## rv$n.c<-input$n
## rv$samples.mat<-matrix(rnorm((rv$tn.c+input$ns)*rv$n.c),ncol=rv$n.c)
## rv$tn.c<-length(rv$samples.mat[,1]) # new total number of samples
## })
## }
## })
getSamples<-reactive({#créee n valeurs aléatoires N(0;1) quand input$takesample est implémenté (quand le bouton takesample est pressé)
if(input$takesample > rv$last.takesample.value && rv$lastAction == "takesample"){
return(isolate({#Now do the expensive stuff
rv$new.sample<-matrix(rnorm(input$ns*input$n),ncol=input$n)
return(TRUE)
}))
} else {
return(FALSE)
}
})
getPlotHeight <- function() {
if(input$display=="default") {
unit.height<-250 #cannot be auto because height is already "auto" in ui and double auto = conflict
}
if(input$display=="1024") {
unit.height<-180
}
if(input$display=="800") {
unit.height<-140 #160 for real full page
}
return(3*unit.height)
}
getPlotWidth <- function() {
if(input$display=="default") {
full.plot.width<-1310-310#"auto"
}
if(input$display=="1024") {
full.plot.width<-900-310
}
if(input$display=="800") {
full.plot.width<-700-310
}
if(input$visM && input$display!="default"){
full.plot.width<-full.plot.width+310
}
return(full.plot.width)
}
getInputValues<-reactive({
return(input)#collect all inputs
})
getComputedValues<-reactive({
# returns TRUE if new calculations other wise FALSE
# results hold in rv$cv.ls
# gives TRUE back if new values calculated otherwise FALSE
calc.new<-FALSE # we do not want to calculate all values again
# did we create new samples?
sample.new<-getSamples() # if TRUE then append new observations from rv$new.sample
v<-getInputValues() # get all values of input list
# check if sample size was changed
if (v$n != rv$n.c){# if changed create a new observation matrix of correct size
rv$n.c<-v$n
if(rv$tn.c>0){
rv$samples.mat<-matrix(rnorm(rv$tn.c*rv$n.c),ncol=rv$n.c)
}
calc.new<-TRUE # we have to calculate all values again
}
if(sample.new){#if new observations created, append them
sample.mat<-mat.or.vec(rv$tn.c + v$ns,v$n)
if(rv$tn.c>0){
sample.mat[1:rv$tn.c,]<-rv$samples.mat
}
sample.mat[(rv$tn.c+1):(rv$tn.c+v$ns),]<-rv$new.sample
rv$samples.mat<-sample.mat # new observations
rv$tn.c<-length(rv$samples.mat[,1]) # new total number of samples
calc.new<-TRUE # we have to calculate all values again
}
# check if caluations are needed due to parameter changes
# could be still optimized since a change in rv$IC.k.c does not needs a whole new calcul
if (v$mx1 != rv$mx.c){# population mean changed
rv$mx.c<-v$mx1 # update
calc.new<-TRUE
}
if (v$sx != rv$sx.c){# population sd changed
rv$sx.c<-v$sx # update
calc.new<-TRUE
}
if (v$k != rv$IC.k.c){# IC length changed
rv$IC.k.c<-v$k # update
calc.new<-TRUE
}
if (v$CVk != rv$typIC.c){# type IC changed
rv$typIC.c<-v$CVk # update
calc.new<-TRUE
}
# new calulations if new obsarvations or
if(calc.new){
cv<-list()#created empty computed values list
## Define reality parameters
cv$vx<-v$sx^2#compute variance of Reality distribution
## Computation of x y coordinates for Normal curve of Reality
z<-seq(-5,5,length=100)
cv$xr<-(z*v$sx)+v$mx1 #x for Reality
cv$yr<-dnorm(cv$xr,mean=v$mx1,sd=v$sx)#y for Reality
## Computation of sample related values ##
cv$samples.x.mat<-c() # matrix of observations, each line a sample
cv$samples.x.m.vec<-c() # vector of mean values, each line a sample
cv$samples.x.sd.vec<-c() # vector of sd values, each line a sample
cv$ic.k.limit.mat<-c() # matrix of limits, columns lower and upper bound , lines by sample
cv$ic.k.inc.allmu.mat<-c() # matrix of TRUE/FALSE if mu in IC columns all mu.vec=c(20:60) and lines by sample
cv$pc.ic.k.inc.allmu.vec<-c() # for all mu increment percentage covered by IC
cv$n.ic.k.inc.allmu.vec<-c() # for all mu increment number covered by IC
cv$n.samples<-rv$tn.c # number of samples
cv$samples.x.n.toshow<-0
if(cv$n.samples>0){
cv$samples.x.mat<-mat.or.vec(cv$n.samples,v$n)
cv$ic.k.limit.mat<-mat.or.vec(cv$n.samples,2)
cv$vect.n.samples<-c(1:cv$n.samples)
cv$samples.x.mat<-round((rv$samples.mat*v$sx)+v$mx1,2)#Then sample values are compute with mx1 mean and standard deviation
## Computation of descriptives
cv$samples.x.m.vec<-round(apply(cv$samples.x.mat,1,mean),2)#means of samples
cv$samples.x.sd.vec<-round(apply(cv$samples.x.mat,1,sd),2)#sds of samples
## Computation of confidence intervals for the mean µ ##
if(v$CVk == 'vCVk'){#compute the CI limits with k value and known variance
cv$ic.k.limit.mat[,1]<-round(cv$samples.x.m.vec-v$k*v$sx*(v$n)^(-.5),2)
cv$ic.k.limit.mat[,2]<-round(cv$samples.x.m.vec+v$k*v$sx*(v$n)^(-.5),2)
}
if(v$CVk == 'sCVk'){#compute the CI limits with k value and unknown variance
cv$ic.k.limit.mat[,1]<-round(cv$samples.x.m.vec-v$k*cv$samples.x.sd.vec*(v$n)^(-.5),2)
cv$ic.k.limit.mat[,2]<-round(cv$samples.x.m.vec+v$k*cv$samples.x.sd.vec*(v$n)^(-.5),2)
}
if(v$CVk == 'eCVk'){#compute the CI limits with empiric k value
cv$ic.k.limit.mat[,1]<-round(cv$samples.x.m.vec-v$k,2)
cv$ic.k.limit.mat[,2]<-round(cv$samples.x.m.vec+v$k,2)
}
## Check for all values in mu.vec if in IC
cv$ic.k.inc.allmu.mat<-sapply(mu.vec,function(x){return (cv$ic.k.limit.mat[,1] <=x & x<=cv$ic.k.limit.mat[,2])})
## Calculate for all values in mu.vec frequencies absolute and relative
cv$n.ic.k.inc.allmu.vec<-apply(matrix(cv$ic.k.inc.allmu.mat,ncol=length(mu.vec)),2,sum)
cv$pc.ic.k.inc.allmu.vec<-round(cv$n.ic.k.inc.allmu.vec/cv$n.samples,3)*100
## Define colors
cv$ic.k.inc.mu.color.mat<-matrix(ifelse(cv$ic.k.inc.allmu.mat,oui.color.true,oui.color.false),ncol=length(mu.vec))
cv$ic.k.inc.mu0.color.mat<-matrix(ifelse(cv$ic.k.inc.allmu.mat,color.false,color.true),ncol=length(mu.vec))
cv$ic.k.inc.mu1.color.mat<-matrix(ifelse(cv$ic.k.inc.allmu.mat,color.true,color.false),ncol=length(mu.vec))
## Define subset to plot
cv$samples.x.from<-1
if(cv$n.samples>v$nss){
cv$samples.x.from<-cv$n.samples-v$nss+1
}
cv$samples.x.to<-cv$n.samples
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$ic.k.limit.mat.toshow<-matrix(cv$ic.k.limit.mat[cv$samples.x.from:cv$samples.x.to,],ncol=2)
cv$samples.y.mat.toshow<-c() # plot line by line the values, here corresponding y-values
cv$samples.x.n.toshow<-length(cv$samples.x.mat.toshow[,1])
cv$ic.k.inc.mu.color.vec.toshow<-c() # color the IC for mu
cv$ic.k.inc.mu0.color.vec.toshow<-c() # color the IC for mu0
cv$ic.k.inc.mu1.color.vec.toshow<-c() # color the IC for mu1
if(cv$samples.x.n.toshow>0){
cv$samples.y.mat.toshow<-matrix(rep(y.delta/(v$nss+1)*c(1:cv$samples.x.n.toshow),length(cv$samples.x.mat.toshow[1,])),nrow=length(cv$samples.x.mat.toshow[,1]))
## ## Define colors if IC covers µ or µ0 or µ1
cv$ic.k.inc.mu.color.vec.toshow<-cv$ic.k.inc.mu.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx-mu.vec[1]+1]
cv$ic.k.inc.mu0.color.vec.toshow<-cv$ic.k.inc.mu0.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx0-mu.vec[1]+1]
cv$ic.k.inc.mu1.color.vec.toshow<-cv$ic.k.inc.mu1.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx1-mu.vec[1]+1]
}
}
rv$cv.ls<-cv # set new values
}
## Last takesample value
rv$last.takesample.value<-v$takesample
return(calc.new)
})
output$plotEmp <- renderPlot({
v<-getInputValues()
calc.new<-getComputedValues() # TRUE if new values have been calculated
cv<-rv$cv.ls # holds calculated values
# if graphic values of parameter are changed without new calculus, so here calculate new values
if(v$display=="default") {
cex.samples<-2.2 #size of text describing samples (2.2)
cex.param<-3.5 #size of text of parameter µ µ'', etc (3.5)
cex.title<-2.2
y.delta<-0.1 #factor to set delta between rows of datas in plots
ic.bar.half.height<-0.004
}
if(v$display=="1024") {
cex.samples<-1.7 #size of text describing samples (2.2)
cex.param<-2.5 #size of text of parameter µ µ'', etc (3.5)
cex.title<-1.7
y.delta<-0.1 #factor to set delta between rows of datas in plots
ic.bar.half.height<-0.004
}
if(v$display=="800") {
cex.samples<-1.5 #size of text describing samples (2.2)
cex.param<-2 #size of text of parameter µ µ'', etc (3.5)
cex.title<-1.7
y.delta<-0.1 #factor to set delta between rows of datas in plots
ic.bar.half.height<-0.004
}
## Define subset to plot
if(cv$n.samples>0){
cv$samples.x.from<-1
if(cv$n.samples>v$nss){
cv$samples.x.from<-cv$n.samples-v$nss+1
}
cv$samples.x.to<-cv$n.samples
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$ic.k.limit.mat.toshow<-matrix(cv$ic.k.limit.mat[cv$samples.x.from:cv$samples.x.to,],ncol=2)
cv$samples.y.mat.toshow<-c() # plot line by line the values, here corresponding y-values
cv$samples.x.n.toshow<-length(cv$samples.x.mat.toshow[,1])
cv$ic.k.inc.mu.color.vec.toshow<-c() # color the IC for mu
cv$ic.k.inc.mu0.color.vec.toshow<-c() # color the IC for mu0
cv$ic.k.inc.mu1.color.vec.toshow<-c() # color the IC for mu1
if(cv$samples.x.n.toshow>0){
cv$samples.y.mat.toshow<-matrix(rep(y.delta/(v$nss+1)*c(1:cv$samples.x.n.toshow),length(cv$samples.x.mat.toshow[1,])),nrow=length(cv$samples.x.mat.toshow[,1]))
## ## Define colors if IC covers µ or µ0 or µ1
cv$ic.k.inc.mu.color.vec.toshow<-cv$ic.k.inc.mu.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx-mu.vec[1]+1]
cv$ic.k.inc.mu0.color.vec.toshow<-cv$ic.k.inc.mu0.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx0-mu.vec[1]+1]
cv$ic.k.inc.mu1.color.vec.toshow<-cv$ic.k.inc.mu1.color.mat[cv$samples.x.from:cv$samples.x.to,v$mx1-mu.vec[1]+1]
}
}
m<-matrix(c(1,2,3,4,5,6),3,2,byrow=TRUE)
layout(m,width=c(3,1))
##-------------------------------------------
## Plot always Reality ##
##-------------------------------------------
cv$maxdmx=0.05
par(mai=c(0.3,0.6,0.5,0))
label<-""
if(v$showreality){
label<-"Density"
}
plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=1,cex.axis=1.5,xlim=c(x.lim.min,x.lim.max),ylim=c(0,cv$maxdmx*2.1),xlab="",ylab=label,xaxp=c(x.lim.min,x.lim.max,20),main=bquote(paste("Echantillons prélevés :")),cex.main=cex.title)
if(debug){
box(which="outer",lty = 'dotted', col = 'red')
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
if(cv$samples.x.n.toshow>0){
for(i in 1:cv$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$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col=color.blue)
}
}
if(v$showreality){
axis(2,las=2,yaxp=c(0,signif(cv$maxdmx,1),5),cex.axis=1.2)
points(cv$xr,cv$yr,type="l")
text(1,signif(cv$maxdmx,1)*0.75,labels=bquote(paste(N *"~"* ( mu *","* sigma^2 ) ," ", N *"~"* (.(v$mx1)*","*.(cv$vx)) ,sep='')),cex=1.4, pos=4)
}
if(v$muKn){
## Plot true mean only if known
lines(x<-c(v$mx1,v$mx1),y <- c(0,cv$maxdmx*1.8),lty=1,lwd=2)
text(v$mx1,cv$maxdmx*1.95,labels=bquote(mu),cex=cex.param,col=color.blue)
}
## empty plot for layout
par(mai=c(0.3,0,0.5,0))
plot(c(0,1),c(0,0),col="white",xaxt="n",yaxt="n",xlab="",ylab="",ylim=c(0,cv$maxdmx*2.1),bty="n",las=1)
if(debug){
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
if(v$empPl){
mtext(bquote(paste("Descriptives : ", N == .(cv$n.samples), sep="")),side=1,line=1,at=0.05,adj=0)
if(cv$samples.x.n.toshow>0){
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="")),cex=cex.samples,col=color.blue,pos=4)
text(0.5,cv$samples.y.mat.toshow[i,1],labels=bquote(paste(s[.(cv$samples.x.i.vec.toshow[i])] == .(sprintf("%.2f",cv$samples.x.sd.vec.toshow[i])),sep="")),cex=cex.samples,pos=4)
}
}
}
if(v$icPl){
##-------------------------------------------
## Plot IC ##
##-------------------------------------------
cv$maxdmx=0.05
par(mai=c(0.3,0.6,0.5,0))
plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=1,cex.axis=1.5,xlim=c(x.lim.min,x.lim.max),ylim=c(0,cv$maxdmx*2.1),ylab="",xlab="",xaxp=c(x.lim.min,x.lim.max,20),main=bquote(paste("Intervalles de confiance:")),cex.main=cex.title)
if(debug){
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
if(v$cvPl != "non"){
if(v$cvPl == "oui"){
## Plot mean mx
lines(x<-c(v$mx,v$mx),y <- c(0,cv$maxdmx*1.8),lty=1,lwd=3,col=oui.color.true)
text(v$mx,cv$maxdmx*1.95,labels=v$mx,cex=cex.param*0.75,col=oui.color.true)
help.color.vec<-cv$ic.k.inc.mu.color.vec.toshow
}
if(v$cvPl == "parOri" || (v$cvPl == "parAlt" && v$mx1 == v$mx0)){
## Plot mean mx1
lines(x<-c(v$mx1,v$mx1),y <- c(0,cv$maxdmx*1.8),lty=1,lwd=3,col=color.true)
text(v$mx1,cv$maxdmx*1.95,labels=bquote(mu),cex=cex.param,col=color.true)
help.color.vec<-cv$ic.k.inc.mu1.color.vec.toshow
}
if(v$cvPl == "parAlt" && v$mx1 != v$mx0){
lines(x<-c(v$mx0,v$mx0),y <- c(0,cv$maxdmx*1.8),lty=1,lwd=3,col=color.false)
text(v$mx0,cv$maxdmx*1.95,labels=bquote(paste(mu,"''",sep="")),cex=cex.param,col=color.false)
help.color.vec<-cv$ic.k.inc.mu0.color.vec.toshow
}
} else {
help.color.vec<-ifelse(cv$ic.k.inc.mu0.color.vec.toshow,"black","black")
}
if(cv$samples.x.n.toshow>0){
for(i in 1:cv$samples.x.n.toshow){
polygon(c(cv$ic.k.limit.mat.toshow[i,1],cv$ic.k.limit.mat.toshow[i,1],cv$ic.k.limit.mat.toshow[i,2],cv$ic.k.limit.mat.toshow[i,2]),c(cv$samples.y.mat.toshow[i,1]-ic.bar.half.height,cv$samples.y.mat.toshow[i,1]+ic.bar.half.height,cv$samples.y.mat.toshow[i,1]+ic.bar.half.height,cv$samples.y.mat.toshow[i,1]-ic.bar.half.height),col=help.color.vec[i])
text(cv$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")
lines(x<-c(cv$ic.k.limit.mat.toshow[i,1],cv$samples.x.m.vec.toshow[i]-1),y <- c(cv$samples.y.mat.toshow[i,1],cv$samples.y.mat.toshow[i,1]),lwd=1,lty=2,col="black")
lines(x<-c(cv$samples.x.m.vec.toshow[i]+1,cv$ic.k.limit.mat.toshow[i,2]),y <- c(cv$samples.y.mat.toshow[i,1],cv$samples.y.mat.toshow[i,1]),lwd=1,lty=2,col="black")
}
}
## empty plot for layout
par(mai=c(0.3,0,0.5,0))
plot(c(0,1),c(0,0),col="white",xaxt="n",yaxt="n",xlab="",ylab="",ylim=c(0,cv$maxdmx*2.1),bty="n",las=1)
if(debug){
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
if(v$empPl){
mtext(bquote(paste("Intervalles : ", N == .(cv$n.samples), sep="")),side=1,line=1,at=0.05,adj=0)
if(cv$samples.x.n.toshow>0){
for(i in 1:cv$samples.x.n.toshow){
if(v$cvPl != "non"){
if(v$cvPl == "oui"){
help.color.vec<-cv$ic.k.inc.mu.color.vec.toshow
}
if(v$cvPl == "parOri" || (v$cvPl == "parAlt" && v$mx1 == v$mx0)){
## Plot mean mx1
help.color.vec<-cv$ic.k.inc.mu1.color.vec.toshow
}
if(v$cvPl == "parAlt" && v$mx1 != v$mx0){
help.color.vec<-cv$ic.k.inc.mu0.color.vec.toshow
}
} else {
help.color.vec<-ifelse(cv$ic.k.inc.mu0.color.vec.toshow,"black","black")
}
if(v$thresholds == "formula"){
if(v$CVk == 'eCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(bar(x)[.(cv$samples.x.i.vec.toshow[i])]-c,bar(x)[.(cv$samples.x.i.vec.toshow[i])]+c),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
if(v$CVk == 'vCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(bar(x)[.(cv$samples.x.i.vec.toshow[i])]-c*'*'*sigma/sqrt(n),bar(x)[.(cv$samples.x.i.vec.toshow[i])]+c*'*'*sigma/sqrt(n)),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
if(v$CVk == 'sCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(bar(x)[.(cv$samples.x.i.vec.toshow[i])]-c*'*'*s[.(cv$samples.x.i.vec.toshow[i])]/sqrt(n),bar(x)[.(cv$samples.x.i.vec.toshow[i])]+c*'*'*s[.(cv$samples.x.i.vec.toshow[i])]/sqrt(n)),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
}
if(v$thresholds == "calcul"){
if(v$CVk == 'eCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(.(sprintf("%.2f",cv$samples.x.m.vec.toshow[i]))-.(v$k),.(sprintf("%.2f",cv$samples.x.m.vec.toshow[i]))+.(v$k)),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
if(v$CVk == 'vCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(.(sprintf("%.1f",cv$samples.x.m.vec.toshow[i]))-.(v$k)*'*'*.(v$sx)/.(sprintf("%.1f",v$n^.5)),.(sprintf("%.1f",cv$samples.x.m.vec.toshow[i]))+.(v$k)*'*'*.(v$sx)/.(sprintf("%.1f",v$n^.5))),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
if(v$CVk == 'sCVk'){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(.(sprintf("%.1f",cv$samples.x.m.vec.toshow[i]))-.(v$k)*'*'*.(sprintf("%.1f",cv$samples.x.sd.vec.toshow[i]))/.(sprintf("%.1f",v$n^.5)),.(sprintf("%.1f",cv$samples.x.m.vec.toshow[i]))+.(v$k)*'*'*.(sprintf("%.1f",cv$samples.x.sd.vec.toshow[i]))/.(sprintf("%.1f",v$n^.5))),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
}
if(v$thresholds == "result"){
text(0,cv$samples.y.mat.toshow[i,1],bquote(paste(group("[",list(.(sprintf("%.2f",cv$ic.k.limit.mat.toshow[i,1])),.(sprintf("%.2f",cv$ic.k.limit.mat.toshow[i,2]))),"]"),sep="")),las=2,col=help.color.vec[i],cex=cex.samples,pos=4)
}
}
}
}
if(v$cvPl != "non" && v$freqPl == "freqPloui"){
par(mai=c(0.3,0.6,0.5,0))
## Plot bar plot of includes 2 class %
if(cv$n.samples>0){
includes<-t(matrix(c(100-cv$pc.ic.k.inc.allmu.vec,cv$pc.ic.k.inc.allmu.vec),ncol=2))
} else {
includes<-t(matrix(c(rep(0,length(mu.vec)),100-rep(0,length(mu.vec))),ncol=2))
}
barplot.kH1<-barplot(includes,names.arg=mu.vec,ylim=c(0,100),col=c(non.color.false,non.color.true),cex.names=1.25,cex.axis=1.5,beside=FALSE,xaxs="i",space=0,yaxt="n",las=2)
axis(2,las=2,yaxp=c(0,100,2),cex.axis=1.5)#to have las=2 for horizontal labels on y axis
mtext("%",side=2,line=3,at=50)
title(main=bquote(paste("% de couverture par les intervalles de confiance pour ",sep=" ")),adj=1,cex.main=cex.title)
if(debug){
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
# barplot.kH1 is the vector of positions of th bars which we use next
if(v$cvPl == "oui" && cv$n.samples>0){
barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)]),ncol=1),col=c(oui.color.false,oui.color.true), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx-mu.vec[1]+1)]-0.5),axes=FALSE)
}
if(v$cvPl == "parOri" && cv$n.samples>0 || (v$cvPl == "parAlt" && v$mx1 == v$mx0)){
barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)]),ncol=1),col=c(color.false,color.true), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx1-mu.vec[1]+1)]-0.5),axes=FALSE)
}
if(v$cvPl == "parAlt" && cv$n.samples>0 && v$mx1 != v$mx0){
barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)]),ncol=1),col=c(color.true,color.false), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx0-mu.vec[1]+1)]-0.5),axes=FALSE)
}
## empty plot for layout
par(mai=c(0.3,0,0.5,0))
plot(c(0,1),c(0,0),col="white",xaxt="n",yaxt="n",xlab="",ylab="",ylim=c(0,cv$maxdmx*2.1),bty="n",las=1)
if(debug){
box(which="figure",lty = 'dotted', col = 'blue')
box(which="plot",lty = 'dotted', col = 'blue')
}
if(v$cvPl == "oui"){
title(main=bquote(paste("la valeur ",.(v$mx)," :",sep=" ")),adj=0,cex.main=cex.title)
y.max<-cv$maxdmx*2.1
text(0.425,y.max*0.8,bquote(paste("IC couvrent ",.(v$mx)," ? ",sep=" ")),cex=cex.samples)
text(0.425,y.max*0.6,bquote(paste("n",sep=" ")),cex=cex.samples)
text(0.675,y.max*0.6,bquote(paste("%",sep=" ")),cex=cex.samples)
text(0.175,y.max*0.4,"Oui",col=text.color.true,cex=cex.samples*0.85)
text(0.175,y.max*0.2,"Non",col=text.color.false,cex=cex.samples*0.85)
if(cv$n.samples>0){
# barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)]),ncol=1),col=c(oui.color.false,oui.color.true), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx-mu.vec[1]+1)]-0.5),axes=FALSE)
ICvsmu0.mat<-matrix(c(cv$n.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)],cv$n.samples-cv$n.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)],100-cv$pc.ic.k.inc.allmu.vec[(v$mx-mu.vec[1]+1)]),ncol=2)
ICvsmu0.mat<-round(ICvsmu0.mat,0)
text(0.425,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,1]),sep=" ")),col=text.color.true,cex=cex.samples)
text(0.675,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,2]),sep=" ")),col=text.color.true,cex=cex.samples)
text(0.425,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,1]),sep=" ")),col=text.color.false,cex=cex.samples)
text(0.675,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,2]),sep=" ")),col=text.color.false,cex=cex.samples)
}
}
if(v$cvPl == "parOri" || (v$cvPl == "parAlt" && v$mx1 == v$mx0)){
title(main=bquote(paste(mu," = ",.(v$mx1)," :",sep=" ")),col.main=color.true,adj=0,cex.main=cex.title)#"la moyenne de la population d'origine ",
y.max<-cv$maxdmx*2.1
text(0.425,y.max*0.8,bquote(paste("IC couvrent ",mu," = ",.(v$mx1)," ?",sep=" ")),cex=cex.samples)
text(0.425,y.max*0.6,bquote(paste("n",sep=" ")),cex=cex.samples)
text(0.675,y.max*0.6,bquote(paste("%",sep=" ")),cex=cex.samples)
text(0.175,y.max*0.4,"Oui",col=color.true,cex=cex.samples*0.85)
text(0.175,y.max*0.2,"Non",col=color.false,cex=cex.samples*0.85)
if(cv$n.samples>0) {
# barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)]),ncol=1),col=c(color.false,color.true), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx1-mu.vec[1]+1)]-0.5),axes=FALSE)
ICvsmu0.mat<-matrix(c(cv$n.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)],cv$n.samples-cv$n.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)],100-cv$pc.ic.k.inc.allmu.vec[(v$mx1-mu.vec[1]+1)]),ncol=2)
ICvsmu0.mat<-round(ICvsmu0.mat,0)
text(0.425,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,1]),sep=" ")),col=color.true,cex=cex.samples)
text(0.675,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,2]),sep=" ")),col=color.true,cex=cex.samples)
text(0.425,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,1]),sep=" ")),col=color.false,cex=cex.samples)
text(0.675,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,2]),sep=" ")),col=color.false,cex=cex.samples)
}
}
if(v$cvPl == "parAlt" && v$mx1 != v$mx0){
title(main=bquote(paste("une valeur alternative ",mu,"'' = ",.(v$mx0)," :",sep="")),col.main=color.false,adj=0,cex.main=cex.title)
y.max<-cv$maxdmx*2.1
text(0.425,y.max*0.8,bquote(paste("IC couvrent ",mu,"'' = ",.(v$mx0)," ?",sep=" ")),cex=cex.samples)
text(0.425,y.max*0.6,bquote(paste("n",sep=" ")),cex=cex.samples)
text(0.675,y.max*0.6,bquote(paste("%",sep=" ")),cex=cex.samples)
text(0.175,y.max*0.4,"Oui",col=color.false,cex=cex.samples*0.85)
text(0.175,y.max*0.2,"Non",col=color.true,cex=cex.samples*0.85)
if(cv$n.samples>0) {
#barplot.spp<-barplot(matrix(c(100-cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)]),ncol=1),col=c(color.true,color.false), add=TRUE,beside=FALSE,space=(barplot.kH1[(v$mx0-mu.vec[1]+1)]-0.5),axes=FALSE)
ICvsmu0.mat<-matrix(c(cv$n.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)],cv$n.samples-cv$n.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)],cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)],100-cv$pc.ic.k.inc.allmu.vec[(v$mx0-mu.vec[1]+1)]),ncol=2)
ICvsmu0.mat<-round(ICvsmu0.mat,0)
text(0.425,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,1]),sep=" ")),col=color.false,cex=cex.samples)
text(0.675,y.max*0.4,bquote(paste(.(ICvsmu0.mat[1,2]),sep=" ")),col=color.false,cex=cex.samples)
text(0.425,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,1]),sep=" ")),col=color.true,cex=cex.samples)
text(0.675,y.max*0.2,bquote(paste(.(ICvsmu0.mat[2,2]),sep=" ")),col=color.true,cex=cex.samples)
}
}
}
}
}, height = getPlotHeight, width=getPlotWidth)
###################################################################
output$DataTable <- renderTable({
v<-getInputValues()
calc.new<-getComputedValues()
cv<-rv$cv.ls
## Transpose the sample list
if(cv$n.samples>0){
samples.as.list<-list()
for(i in 1:cv$n.samples){
samples.as.list[[i]]<-c(round(cv$samples.x.mat[i,],2),c(""),round(cv$samples.x.m.vec[i],2),round(cv$samples.x.sd.vec[i],2),c(""),round(cv$ic.k.limit.mat[i,1],2),round(cv$ic.k.limit.mat[i,2],2))
}
samples.as.matrix<- do.call(rbind,samples.as.list)
transposed.samples<-lapply(seq_len(ncol(samples.as.matrix)),function(i) samples.as.matrix[,i])
d<-data.frame(transposed.samples)
colnames(d)<-c(paste("X",1:v$n,sep="")," ","Moy","Sd"," ","LiICk","LsICk")
d
}
})
###################################################################
output$test1 <- renderText({
paste("Tab",input$Tabset,sep=" ")
})
})