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
|
pscore.test <- function(obj, seg.Z, k = 10, alternative = c("two.sided", "less", "greater"),
values=NULL, dispersion=NULL, df.t=NULL, more.break=FALSE, n.break=1,
only.term=FALSE, break.type=c("break","jump")) {
#-------------------------------------------------------------------------------
test.Sc2<-function(y, z, xreg, sigma=NULL, values=NULL, fn="pmax(x-p,0)", df.t="Inf", alternative, w=NULL, offs=NULL,
nbreaks=1, ties.ok=FALSE, only.term=FALSE){
#xreg: la matrice del disegno del modello nullo. Se mancante viene assunta solo l'intercetta.
#Attenzione che se invXtX e xx vengono entrambe fornite, non viene fatto alcun controllo
#invXtX: {X'X}^{-1}. if missing it is computed from xreg
#sigma: the sd. If missing it is computed from data (under the *null* model)
#values: the values with respect to ones to compute the average term. If NULL 10 values from min(z) to max(z) are taken.
if(!is.null(offs)) y<-y-offs
n<-length(y)
if(missing(xreg)) xreg<-cbind(rep(1,n))
id.ok<-complete.cases(cbind(y,z,xreg))
y<-y[id.ok]
z<-z[id.ok]
xreg<-xreg[id.ok,,drop=FALSE]
idv <- which(apply(xreg,2,sd)!=0)
xreg[,idv]<-scale(xreg[,idv])
n<-length(y)
k=ncol(xreg) #per un modello ~1+x
if(is.null(values)) values<-seq(min(z), max(z), length=10)
n1<-length(values)
PSI<-matrix(values, nrow=n, ncol=n1, byrow=TRUE) #(era X2) matrice di valori di psi
if(is.matrix(z)) {
X1<-matrix(z[,1], nrow=n, ncol=n1, byrow=FALSE)
X2<-matrix(z[,2], nrow=n, ncol=n1, byrow=FALSE)
X<-eval(parse(text=fn), list(x=X1, y=X2, p=PSI)) #X<-pmax(X1-X2,0)
pmaxMedio<-rowMeans(X)
} else {
X1<-matrix(z, nrow=n, ncol=n1, byrow=FALSE) #matrice della variabile Z
if(length(fn)<=1){
X<-eval(parse(text=fn), list(x=X1, p=PSI)) #X<-pmax(X1-PSI,0)
pmaxMedio <- rowMeans(X)
if(nbreaks>1){
XX<-sapply(1:length(values), function(.x) X[,-(1:.x), drop=FALSE])
XX<-do.call("cbind", XX)
if(ties.ok) XX<-cbind(X, XX)
pmaxMedio2 <- rowMeans(XX)
pmaxMedio <- cbind(pmaxMedio, pmaxMedio2)
}
} else {
pmaxMedio<-matrix(NA,n,length(fn))
#list.X<-vector("list", length=length(fn))
for(j in 1:length(fn)){
#list.X[[j]]<-eval(parse(text=fn[j]), list(x=X1, p=PSI))
X<-eval(parse(text=fn[[j]]), list(x=X1, p=PSI))
pmaxMedio[,j]<-rowMeans(X)
}
}
}
if(only.term) return(pmaxMedio)
if(is.null(w)) {
invXtX<-solve(crossprod(xreg))
IA<- -xreg%*%tcrossprod(invXtX, xreg)
.a1<-1+diag(IA)
id<-col(IA)==row(IA)
IA[id]<-.a1
pIA<- drop(crossprod(pmaxMedio,IA))
sc<- drop(pIA %*% y)
v.s<- pIA %*% pmaxMedio #pIA%*% pmaxMedio #tcrossprod(pIA, pmaxMedio)
} else {
invXtX<-solve(crossprod(sqrt(w)*xreg))
#I-hat matrix
#dovrebbe essere diag(n) - xreg%*%tcrossprod(invXtX, xreg*w) ma non funziona se n e' grande (12000)
IA<- xreg%*%tcrossprod(invXtX, xreg*w)
.a1<-1-diag(IA)
id<-col(IA)==row(IA)
IA[id]<-.a1
pIA<- drop(crossprod(pmaxMedio*w,IA))
#sc<-t(pmaxMedio*w) %*% IA %*% y
sc<- drop(pIA %*% y)
pIA<-crossprod(pmaxMedio*w,IA/sqrt(w))
v.s<- pIA %*% (pmaxMedio*w) #crossprod(pIA, pmaxMedio*w) #t(pmaxMedio*w) %*% crossprod(t(IA)/sqrt(w))%*%(w*pmaxMedio)
}
ris<-if(nbreaks==1) drop(sc/(sigma*sqrt(v.s))) else drop(crossprod(sc,solve(v.s,sc)))/(sigma^2)
#if(length(fn)<=1 && cadj) ris<- sign(ris)*sqrt((ris^2)*(1-(3-(ris^2))/(2*n)))
#passa alla F..
df.t<-eval(parse(text=df.t))
p2<- if(nbreaks==1) 2*pt(abs(ris), df=df.t, lower.tail=FALSE) else pchisq(ris, df=nbreaks, lower.tail=FALSE)#pf((ris/nbreaks)/(sigma^2), df1=nbreaks, df2=df.t, lower.tail =FALSE)#
pvalue<-switch(alternative,
less = pt(ris, df=df.t, lower.tail =TRUE) ,
greater = pt(ris, df=df.t, lower.tail =FALSE) ,
two.sided = p2)
#pvalue<- 2*pt(abs(ris), df=df.t, lower.tail =FALSE)
r<-c(ris, pvalue)#, pmaxMedio)
r
#return(pmaxMedio)
}
#-------------------------------------------------------------------------------
scGLM<-function(y, z, xreg, family, values = NULL, size=1, weights.var,
fn="pmax(x-p,0)", alternative=alternative){
#score test for GLM (NON USATO!)
#size (only if family=binomial())
#weights.var: weights to be used for variance computations. If missing the weights come from the null fit
output<-match.arg(output)
n<-length(y)
if(missing(xreg)) xreg<-cbind(rep(1,n))
id.ok<-complete.cases(cbind(y,z,xreg))
y<-y[id.ok]
z<-z[id.ok]
xreg<-xreg[id.ok,,drop=FALSE]
n<-length(y)
if(family$family=="poisson") size=1
if(length(size)==1) size<-rep(size,n)
yN<-y/size
k=ncol(xreg) #per un modello ~1+x
if(is.null(values)) values<-seq(min(z), max(z), length=10)
n1<-length(values)
PSI<-matrix(values, nrow=n, ncol=n1, byrow=TRUE) #(era X2) matrice di valori di psi
X1<-matrix(z, nrow=n, ncol=n1, byrow=FALSE) #matrice della variabile Z
X<-eval(parse(text=fn), list(x=X1, p=PSI)) #X<-pmax(X1-X2,0)
pmaxMedio<-rowMeans(X)
o<-glm.fit(yN, x=xreg, weights=size, family=family)
r<-y-(o$fitted*size)
sc<-drop(crossprod(r, pmaxMedio))
# if(output=="unst.score") return(drop(sc))
p <- o$rank
Qr <- o$qr
COV <- chol2inv(Qr$qr[1:p, 1:p, drop = FALSE]) #vcov(glm(y~x, family=poisson))
A<-xreg%*%COV%*%crossprod(xreg, diag(o$weights))
h<- drop(tcrossprod(pmaxMedio, diag(n)- A))
if(missing(weights.var)) weights.var<-o$weights
v.s<- drop(crossprod(h*sqrt(weights.var))) #t(h)%*%diag(exp(lp))%*%h
ris<-if(length(fn)<=1) sc/sqrt(v.s) else drop(crossprod(sc,solve(v.s,sc)))
# if(output=="score") return(drop(ris))
pvalue<- switch(alternative,
less = pnorm(ris, lower.tail =TRUE) ,
greater = pnorm(ris, lower.tail =FALSE) ,
two.sided = 2*pnorm(abs(ris), lower.tail =FALSE)
)
# pvalue<- if(length(fn)<=1) 2*pnorm(abs(ris), lower.tail =FALSE) else pchisq(ris,df=length(fn), lower.tail =FALSE)
# NB: se calcoli ris<-drop(t(sc)%*%solve(v.s,sc))/(length(fn)*sigma^2) devi usare pf(ris,df1=length(fn),df2=df.t, lower.tail =FALSE)
return(c(ris, pvalue))
}
#----------------------------------------------------
if(!inherits(obj, "lm")) stop("A '(g)lm', or 'segmented-(g)lm' model is requested")
break.type<-match.arg(break.type)
#if(!(break.type %in% 1:2)) stop(" 'break.type' should be 1 or 2")
fn=if(break.type=="break") "pmax(x-p,0)" else "1*(x>p)"
ties.ok=FALSE
if(missing(seg.Z)){
if(inherits(obj, "segmented") && length(obj$nameUV$Z)==1) seg.Z<- as.formula(paste("~", obj$nameUV$Z ))
if(!inherits(obj, "segmented") && length(all.vars(formula(obj)))==2) seg.Z<- as.formula(paste("~", all.vars(formula(obj))[2]))
} else {
#if(class(seg.Z)!="formula") stop("'seg.Z' should be an one-sided formula")
#if(!is(seg.Z,"formula")) stop("'seg.Z' should be an one-sided formula")
if(!inherits(seg.Z,"formula")) stop("'seg.Z' should be an one-sided formula")
}
if(any(c("$","[") %in% all.names(seg.Z))) stop(" '$' or '[' not allowed in 'seg.Z' ")
name.Z <- all.vars(seg.Z)
if(length(name.Z)>1) stop("Only one variable can be specified in 'seg.Z' ")
nomiU.term<-grep(name.Z, obj$nameUV$U, value=TRUE) #termini U per relativi alla variabile nomeZ
#se length(nomiU.term)==0 la variabile in seg.Z non e' nel modello (si sta assumendo che la left slope ==0)
if(length(nomiU.term)==0 && more.break) warning(paste("variable", name.Z, "has no breakpoint.. 'more.break=TRUE' ignored"), call.=FALSE)
#browser()
if(k<=1) stop("k>1 requested! k>=10 is recommended")
if(k<10) warnings("k>=10 is recommended")
alternative <- match.arg(alternative)
if(!n.break%in%1:2) stop(" 'n.break' should be 1 or 2", call. = FALSE)
if(n.break==2) alternative<-"two.sided"
isGLM<-"glm"%in%class(obj) #is(obj, "glm")
#==================================================================
if(isGLM){
if (is.null(dispersion)) dispersion <- summary.glm(obj)$dispersion
if(inherits(obj, "segmented")){ #============se e' GLM segmented
if(more.break && !name.Z %in% obj$nameUV$Z) stop(" 'more.break' is meaningful only if at least 1 breakpoint has been estimated")
Call<-mf<-obj$orig.call #del GLM
formulaSeg <-formula(obj) #contiene le variabili U e le psi
formulaNull<- update.formula(formulaSeg, paste("~.-",paste(obj$nameUV$V, collapse="-"))) #rimuovi le variabili "psi.."
#se length(nomiU.term)==0 la variabile in seg.Z non e' nel modello (si sta assumendo che la left slope ==0)
if(!more.break && length(nomiU.term)>0){
if(length(nomiU.term)>1) stop(" 'more.break=FALSE' does not work with multiple breakpoints referring to the same variable specified in seg.Z", call. = FALSE)
formulaNull <-update.formula(formulaNull,paste("~.-",paste(nomiU.term, collapse="-")))
#non contiene U del termine di interesse, MA contiene eventuali altri termini U
}
mf$formula<-formulaNull
mf$formula<-update.formula(mf$formula,paste(seg.Z,collapse=".+")) #se il modello inziale non contiene seg.Z..
if(!is.null(obj$orig.call$offset) || !is.null(obj$orig.call$weights) || !is.null(obj$orig.call$subset)){
mf$formula <- update.formula(mf$formula,
paste(".~.+", paste(c(all.vars(obj$orig.call$offset),
all.vars(obj$orig.call$weights),
all.vars(obj$orig.call$subset)),
collapse = "+")))
}
m <- match(c("formula", "data", "subset", "weights", "na.action","offset"), names(mf), 0L)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
#browser()
#for(i in 1:length(obj$nameUV$U)) assign(obj$nameUV$U[i], obj$model[,obj$nameUV$U[i]], envir=parent.frame())
#mf <- eval(mf, parent.frame())
mf$data <- quote(obj$model)
mf <- eval(mf)
mt <- attr(mf, "terms")
#interc<-attr(mt,"intercept")
y <- model.response(mf, "any")
X0<- if (!is.empty.model(mt)) model.matrix(mt, mf)
Z<-X0[ ,match(name.Z, colnames(X0))]
n<-length(Z)
if(is.null(values)) values<-seq(min(Z), max(Z), length=k) #values<-seq(sort(Z)[2], sort(Z)[(n - 1)], length = k)
n1<-length(values)
X1<-matrix(Z, nrow=n, ncol=n1, byrow=FALSE) #matrice della variabile Z
PSI<-matrix(values, nrow=n, ncol=n1, byrow=TRUE)
X<-eval(parse(text=fn), list(x=X1, p=PSI)) # fn t.c. length(fn)<=1; fn="pmax(x-p,0)" definita sopra..
pmaxMedio <-as.matrix(rowMeans(X))
if(n.break>1){
XX<-sapply(1:length(values), function(.x) X[,-(1:.x), drop=FALSE])
XX<-do.call("cbind", XX)
if(ties.ok) XX<-cbind(X, XX)
pmaxMedio2 <- rowMeans(XX)
pmaxMedio <- cbind(pmaxMedio, pmaxMedio2)
}
if(only.term) return(pmaxMedio)
#necessario salvare pmaxMedio in mf???
mf$pmaxMedio<-pmaxMedio
Call$formula<- formulaNull
Call$data<-quote(mf)
obj0<-eval(Call)
# pos<-1
# assign("mf", mf, envir=as.environment(pos))
# r<-as.numeric(as.matrix(add1(obj0, ~.+pmaxMedio, scale=dispersion, test="Rao"))[2,c("scaled Rao sc.", "Pr(>Chi)")])
ws <- sqrt(obj0$weights[obj0$weights>0])
res<-obj0$residuals[obj0$weights>0]
zw <- ws * res
A <- qr.resid(obj0$qr, ws * pmaxMedio[obj0$weights>0,])
u<-t(A)%*% zw
v<-crossprod(A)
r<-if(n.break==1) u/sqrt(v*dispersion) else t(u)%*% solve(v) %*%u/dispersion
#r<- (colSums(as.matrix(A * zw))/sqrt(colSums(as.matrix(A * A)))/sqrt(dispersion))
p2<- if(n.break==1) 2*pnorm(abs(r), lower.tail=FALSE) else pchisq(r, df=n.break, lower.tail=FALSE)
pvalue<- switch(alternative,
less = pnorm(r, lower.tail =TRUE) ,
greater = pnorm(r, lower.tail =FALSE) ,
two.sided = p2)
r<-c(r, pvalue)
# ================fine se e' GLM+segmented.
} else {
#=================Se e' GLM NON segmented
Call<-mf<-obj$call
mf$formula<-formula(obj)
m <- match(c("formula", "data", "subset", "weights", "na.action","offset"), names(mf), 0L)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
formulaNull <- formula(obj)
mf$formula<-update.formula(mf$formula,paste(seg.Z,collapse=".+"))
#aggiunto 12/03/18 (non trovava la variable weights perche' era salvata in mf come "(weights)")
if(!is.null(obj$call$offset) || !is.null(obj$call$weights) || !is.null(obj$call$subset)){
mf$formula <-update.formula(mf$formula,
paste(".~.+", paste(
c(all.vars(obj$call$offset),
all.vars(obj$call$weights),
all.vars(obj$call$subset)),
collapse = "+")))
}
mf <- eval(mf, parent.frame())
mt <- attr(mf, "terms")
XREG <- if (!is.empty.model(mt)) model.matrix(mt, mf)
n <- nrow(XREG)
Z<- XREG[,match(name.Z, colnames(XREG))]
if(!name.Z %in% names(coef(obj))) XREG<-XREG[,-match(name.Z, colnames(XREG)),drop=FALSE]
#questo preso da LM...funziona?
# Call<-mf<-obj$call
# mf$formula<-formula(obj)
# m <- match(c("formula", "data", "subset", "weights", "na.action","offset"), names(mf), 0L)
# mf <- mf[c(1, m)]
# mf$drop.unused.levels <- TRUE
# mf[[1L]] <- as.name("model.frame")
# formulaNull <- formula(obj)
# mf$formula<-update.formula(mf$formula,paste(seg.Z,collapse=".+"))
# mf<-eval(mf) #parent.frame()?
# Z<- mf[[name.Z]]
# y <- model.response(mf, "any")
# weights <- as.vector(model.weights(mf))
# offset <- as.vector(model.offset(mf))
# #XREG <-model.matrix(formula(obj), data=build.mf(obj))
# #XREG<-XREG[,colnames(XREG0)]
# XREG<- model.matrix(update.formula(formula(obj),~.), mf)
#
# if(length(weights)>0) formulaNull <- update.formula(formulaNull,
# paste(".~.+ weights(", obj$call$weights, ")",sep=""))
#======================================================================
if(is.null(values)) values<-seq(min(Z), max(Z), length=k) #values<-seq(sort(Z)[2], sort(Z)[(n - 1)], length = k)
n1<-length(values)
PSI<-matrix(values, nrow=n, ncol=n1, byrow=TRUE) #(era X2) matrice di valori di psi
X1<-matrix(Z, nrow=n, ncol=n1, byrow=FALSE) #matrice della variabile Z
X<-eval(parse(text=fn), list(x=X1, p=PSI)) # fn t.c. length(fn)<=1
pmaxMedio<-as.matrix(rowMeans(X))
if(n.break>1){
XX<-sapply(1:length(values), function(.x) X[,-(1:.x), drop=FALSE])
XX<-do.call("cbind", XX)
if(ties.ok) XX<-cbind(X, XX)
pmaxMedio2 <- rowMeans(XX)
pmaxMedio <- cbind(pmaxMedio, pmaxMedio2)
}
if(only.term) return(pmaxMedio)
#r<-as.numeric(as.matrix(add1(update(obj, data=mf), ~.+pmaxMedio, scale=dispersion, test="Rao"))[2,c("scaled Rao sc.", "Pr(>Chi)")])
#Call$formula<- formulaNull
#Call$data<-quote(mf)
#obj0<-eval(Call)
ws <- sqrt(obj$weights[obj$weights>0])
res<-obj$residuals[obj$weights>0]
zw <- ws * res
A <- qr.resid(obj$qr, ws * pmaxMedio[obj$weights>0,])
u<-t(A)%*% zw
v<-crossprod(A)
r<-if(n.break==1) u/sqrt(v*dispersion) else t(u)%*% solve(v) %*%u/dispersion
#r<- (colSums(as.matrix(A * zw))/sqrt(colSums(as.matrix(A * A)))/sqrt(dispersion))
p2<- if(n.break==1) 2*pnorm(abs(r), lower.tail=FALSE) else pchisq(r, df=n.break, lower.tail=FALSE)
pvalue<- switch(alternative,
less = pnorm(r, lower.tail =TRUE) ,
greater = pnorm(r, lower.tail =FALSE) ,
two.sided = p2)
r<-c(r, pvalue)
#fine se e' un GLM NON segmented
}
} else { ##============================== Se e' un LM..
if(is.null(dispersion)) dispersion<- summary(obj)$sigma^2
if(is.null(df.t)) df.t <- obj$df.residual
#df.ok<- if(!is.null(df.t)) df.t else obj$df.residual
if(inherits(obj, "segmented")){ #===== se e' LM+segmented
if(more.break && !name.Z %in% obj$nameUV$Z) stop(" stop 'more.break' is meaningful only if at least 1 breakpoint has been estimated", call.=FALSE )
# Call<-mf<-obj$orig.call
# mf$formula<-formula(obj)
# m <- match(c("formula", "data", "subset", "weights", "na.action","offset"), names(mf), 0L)
# mf <- mf[c(1, m)]
# mf$drop.unused.levels <- TRUE
# mf[[1L]] <- as.name("model.frame")
# mf$formula<-update.formula(mf$formula,paste(seg.Z,collapse=".+"))
# #formulaOrig<-formula(obj)
# if(!is.null(obj$orig.call$offset) || !is.null(obj$orig.call$weights) || !is.null(obj$orig.call$subset)){
# mf$formula <- update.formula(mf$formula,
# paste(".~.+", paste(c(all.vars(obj$orig.call$offset),
# all.vars(obj$orig.call$weights),
# all.vars(obj$orig.call$subset)),
# collapse = "+")))
# }
#
# browser()
#
#
# mf$formula<-update.formula(mf$formula,paste("~.-",paste(obj$nameUV$V, collapse="-"))) #rimuovi le variabili "psi.."
# if(!more.break) {
# if(length(nomiU.term)>1) stop(" 'more.break=FALSE' does not work with multiple breakpoints referring to the same variable specified in seg.Z", call. = FALSE)
# #ovvero il test funziona per un solo breakpoint..
# mf$formula<-update.formula(mf$formula,paste("~.-",paste(nomiU.term, collapse="-"))) #rimuovi il termine U in questione, cioe' solo per una variabile
# #altre variabili "U" relative a piu' variabili devono rimanere..
# }
# formulaNull <- formula(mf)
###############
#PERCHE' NON estrarre direttamente la model.matrix(obj)?
#===> se la variabile NON e' nel modello (perche' left slope=0) non so come recuperarla
#X <-model.matrix(mf) #non funziona se c'e' log(y)..
#X <- X[, !(colnames(X) %in% obj$nameUV$V), drop=FALSE]
#perche' poi
# if(more.break) {
# #ATTENZIONE... A QUANTO PARE SE LA RISPOSTA E' CON log(y), non la trova perche' in model.frame(obj) e' salvata come log(y),
#mentre lui cerca y.. Quindi cambiare i nomi?
# eval.parent(obj$orig.call$data, n=1)
#mf$data<-quote(model.frame(obj))
# mf<-eval(mf)
# } else {
# mf <- eval(mf, parent.frame())
# }
#for(i in 1:length(obj$nameUV$U)) assign(obj$nameUV$U[i], obj$model[,obj$nameUV$U[i]], envir=parent.frame())
if(!is.null(obj$formulaLin)) stop(" 'pscore()' does not work with objects returned by segreg()")
mf<-model.frame(obj)
y <- model.response(mf, "any")
weights <- as.vector(model.weights(mf))
offset <- as.vector(model.offset(mf))
fo<-formula(obj) #NB formula(mf), stranamente, e' del modello lineare di partenza.. non include le U e le V!!
fo <-update.formula(fo ,paste("~.-",paste(obj$nameUV$V, collapse="-"))) #escludi tutte le V
if(!more.break) {
if(length(nomiU.term)>1) stop(" 'more.break=FALSE' does not work with multiple breakpoints referring to the same variable specified in seg.Z", call. = FALSE)
#ovvero il test funziona per un solo breakpoint..
fo <-update.formula(fo,paste("~.-",paste(nomiU.term, collapse="-"))) #rimuovi il termine U in questione, cioe' solo per una variabile
#altre variabili "U" relative a piu' variabili devono rimanere..
}
formulaNull <- fo
if(length(weights)>0) formulaNull <- update.formula(formulaNull,
paste(".~.+ weights(", obj$orig.call$weights, ")",sep=""))
X0<-model.matrix(fo , mf)
#Z<-X0[ ,match(name.Z, colnames(X0))]
Z<- mf[[name.Z]]
#COSA SUCCEDE SE IL MODELLO DI PARTENZA NON INCLUDE IL TERMINE LINEARE?
#X0<- if (!is.empty.model(mt)) model.matrix(mt, mf) #E' quella del modello lineare di partenza.. strano!
#Z<-X0[ ,match(name.Z, colnames(X0))]
#browser()
#mt <- attr(mf, "terms")
#interc<-attr(mt,"intercept")
n<-length(Z)
if(is.null(values)) values<-seq(min(Z), max(Z), length=k) #values<-seq(sort(Z)[2], sort(Z)[(n - 1)], length = k)
# n1<-length(values)
# X1<-matrix(Z, nrow=n, ncol=n1, byrow=FALSE) #matrice della variabile Z
# PSI<-matrix(values, nrow=n, ncol=n1, byrow=TRUE)
# X<-eval(parse(text=fn), list(x=X1, p=PSI)) # fn t.c. length(fn)<=1; fn="pmax(x-p,0)" definita sopra..
# mf$pmaxMedio<- pmaxMedio <-rowMeans(X)
r<-test.Sc2(y=y, z=Z, xreg=X0, sigma=sqrt(dispersion), values=values, fn=fn, df.t=df.t, alternative=alternative,
w=weights, offs=offset, nbreaks=n.break, ties.ok=FALSE, only.term=only.term)
#---fine se e' LM+segmented.
} else {
#browser()
#=================Se e' LM (non segmented)
Call<-mf<-obj$call
mf$formula<-formula(obj)
m <- match(c("formula", "data", "subset", "weights", "na.action","offset"), names(mf), 0L)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
formulaNull <- formula(obj)
mf$formula<-update.formula(mf$formula,paste(seg.Z,collapse=".+"))
#modifiche sett 2021
#XREG0 <-model.matrix(obj)
mf<-eval(mf) #parent.frame()?
Z<- mf[[name.Z]]
y <- model.response(mf, "any")
weights <- as.vector(model.weights(mf))
offset <- as.vector(model.offset(mf))
#XREG <-model.matrix(formula(obj), data=build.mf(obj))
#XREG<-XREG[,colnames(XREG0)]
XREG<- model.matrix(update.formula(formula(obj),~.), mf)
if(length(weights)>0) formulaNull <- update.formula(formulaNull,
paste(".~.+ weights(", obj$call$weights, ")",sep=""))
# #aggiunto 12/03/18 (non trovava la variable weights perche' era salvata in mf come "(weights)")
# if(!is.null(obj$call$offset) || !is.null(obj$call$weights) || !is.null(obj$call$subset)){
# mf$formula <-update.formula(mf$formula,
# paste(".~.+", paste(
# c(all.vars(obj$call$offset),
# all.vars(obj$call$weights),
# all.vars(obj$call$subset)),
# collapse = "+")))
# }
# mf <- eval(mf, parent.frame())
# y <- model.response(mf, "any")
# weights <- as.vector(model.weights(mf))
# offset <- as.vector(model.offset(mf))
#
# mt <- attr(mf, "terms")
# XREG <- if (!is.empty.model(mt)) model.matrix(mt, mf)
# n <- nrow(XREG)
# Z<- XREG[,match(name.Z, colnames(XREG))]
# if(!name.Z %in% names(coef(obj))) XREG<-XREG[,-match(name.Z, colnames(XREG)),drop=FALSE]
if(is.null(values)) values<-seq(min(Z), max(Z), length=k) #values<-seq(sort(Z)[2], sort(Z)[(n - 1)], length = k)
r<-test.Sc2(y=y, z=Z, xreg=XREG, sigma=sqrt(dispersion), values=values, fn=fn, df.t=df.t, alternative=alternative,
w=weights, offs=offset, nbreaks=n.break, ties.ok=FALSE, only.term=only.term)
#r<-as.numeric(as.matrix(add1(update(obj, data=mf), ~.+pmaxMedio, scale=dispersion, test="Rao"))[2,c("scaled Rao sc.", "Pr(>Chi)")])
}
} #end se LM
#################################################
if(only.term) return(r)
#################################################
if(is.null(obj$family$family)) {
famiglia<-"gaussian"
legame<-"identity"
} else {
famiglia<-obj$family$family
legame<-obj$family$link
}
#browser()
if(break.type=="break"){
msg.alt <- if(n.break==1) " breakpoint) " else " breakpoints) "
} else {
msg.alt <- if(n.break==1) " jumpoint) " else " jumpoints) "
}
if(more.break) msg.alt <- paste(" additional", msg.alt,sep="")
msg.alt <- paste(alternative," (",n.break , msg.alt , sep="")
out <- list(method = "Score test for one/two changes in the slope",
data.name=paste("formula =", as.expression(formulaNull), "\nbreakpoint for variable =", name.Z,
"\nmodel =",famiglia,", link =", legame ,", method =", obj$call[[1]]),
statistic = c(`observed value` = r[1]),
parameter = c(n.points = length(values)), p.value = r[2],
#alternative = paste(alternative, " (",n.break ,"breakpoint ) ")
#alternative = paste(alternative," (",n.break ,if(n.break==1) " breakpoint) " else " breakpoints) ", sep="")
alternative=msg.alt)
class(out) <- "htest"
return(out)
}
|
