Nothing
R/CritValues_Persistence.R
In memochange: Testing for Structural Breaks under Long Memory and Testing for Changes in Persistence
Defines functions
CV
getCV
Documented in
CV
getCV
#' function to extract critical values. For internal use only.
#' @keywords internal
getCV<-function(){
b<-matrix(c(0.239, 0.408, 0.308, 0.237, 0.409, 0.311, 0.290, 0.470, 0.376,
0.293, 0.480, 0.383, 0.290, 0.481, 0.382, 0.336, 0.538, 0.446,
0.389, 0.635, 0.548, 0.392, 0.632, 0.542, 0.431, 0.696, 0.609,
0.511, 1.062, 0.805, 0.497, 1.014, 0.771, 0.579, 1.189, 0.904,
0.595, 1.248, 0.953, 0.577, 1.187, 0.899, 0.658, 1.367, 1.046,
0.773, 1.699, 1.325, 0.714, 1.538, 1.186, 0.812, 1.738, 1.371),ncol=9,nrow=6,byrow = TRUE)
rownames(b)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(b)<-c("MSm","MEm","MXm","MSmR","MEmR","MXmR","MSmM","MEmM","MXmM")
b_min<-matrix(c(1.408, 2.162,1.722,1.390,2.187,1.746,2.115,3.118,2.617,
2.070,3.060,2.570,2.119,3.120,2.623,2.969,4.197,3.647,
4.255,5.779,5.216,4.350,6.073,5.482,5.694,7.667,7.053,
2.696,5.028,3.893,2.669,4.967,3.803,3.918,6.991,5.497,
3.845,6.847,5.408,3.828,6.738,5.271,5.285,9.182,7.399,
7.640,12.865,10.473,7.311,12.133,9.984,9.774,15.864,13.359),ncol=9,nrow=6,byrow = TRUE)
rownames(b_min)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(b_min)<-c("MSm","MEm","MXm","MSmR","MEmR","MXmR","MSmM","MEmM","MXmM")
cv_ratio_test<-matrix(c(3.56, 3.48, 12.91, 3.56, 3.48, 12.88, 4.66, 5.23, 17.00,
4.67, 5.31, 17.24, 4.64, 5.25, 17.00, 5.91, 7.38, 21.72,
7.75, 11.02, 29.38, 7.67, 10.49, 28.37, 9.26, 13.34, 34.31,
3.55, 3.48, 13.16, 3.55, 3.46, 13.15, 4.67, 5.23, 17.40,
4.66, 5.29, 17.48, 4.68, 5.27, 17.51, 5.92, 7.40, 22.16,
7.72, 10.43, 28.73, 7.73, 10.89, 29.55, 9.22, 13.30, 34.67,
3.51, 3.36, 13.14, 3.54, 3.47, 13.37, 4.62, 5.11, 17.31,
4.58, 5.06, 17.18, 4.68, 5.27, 17.65, 5.85, 7.24, 22.06,
7.56, 10.21, 28.58, 7.82, 10.69, 29.64, 9.21, 13.20, 34.82,
3.52, 3.41, 13.37, 3.59, 3.44, 13.41, 4.63, 5.11, 17.56,
4.62, 5.14, 17.64, 4.63, 5.17, 17.73, 5.80, 7.21, 22.27,
7.59, 10.37, 29.06, 7.65, 10.44, 29.40, 9.24, 13.21, 34.96,
3.51, 3.35, 13.42, 3.53, 3.46, 13.70, 4.60, 5.07, 17.69,
4.60, 5.12, 17.80, 4.60, 5.11, 17.85, 5.79, 7.10, 22.36,
7.52, 10.37, 29.75, 7.42, 10.29, 29.36, 8.94, 12.93, 35.06,
3.51, 3.41, 13.81, 3.51, 3.41, 13.81, 4.63, 5.16, 18.15,
4.61, 5.21, 18.34, 4.61, 5.21, 18.34, 5.88, 7.28, 23.15,
7.69, 10.56, 30.34, 7.69, 10.56, 30.34, 9.24, 13.14, 35.71,
2.38, 1.55, 6.71, 2.38, 1.53, 6.66, 2.91, 2.01, 8.28,
2.91, 2.02, 8.39, 2.90, 2.02, 8.28, 3.48, 2.61, 10.04,
4.28, 3.50, 12.52, 4.24, 3.49, 12.66, 4.87, 4.29, 14.50,
2.38, 1.52, 6.70, 2.37, 1.53, 6.73, 2.92, 1.98, 8.31,
2.92, 2.00, 8.30, 2.92, 1.99, 8.40, 3.47, 2.54, 10.01,
4.23, 3.43, 12.54, 4.23, 3.42, 12.46, 4.88, 4.29, 14.73,
2.35, 1.50, 6.75, 2.37, 1.51, 6.72, 2.88, 1.96, 8.34,
2.87, 1.96, 8.37, 2.90, 1.98, 8.40, 3.43, 2.50, 10.07,
4.18, 3.33, 12.46, 4.22, 3.41, 12.59, 4.80, 4.10, 14.53,
2.35, 1.50, 6.83, 2.37, 1.51, 6.86, 2.88, 1.96, 8.42,
2.89, 1.95, 8.40, 2.88, 1.98, 8.55, 3.40, 2.50, 10.23,
4.13, 3.31, 12.54, 4.17, 3.47, 12.79, 4.77, 4.09, 14.64,
2.35, 1.50, 6.89, 2.37, 1.50, 6.91, 2.87, 1.95, 8.50,
2.87, 1.96, 8.58, 2.88, 1.96, 8.51, 3.42, 2.47, 10.26,
4.20, 3.32, 12.69, 4.21, 3.36, 12.72, 4.79, 4.08, 14.59,
2.36, 1.50, 6.98, 2.36, 1.50, 6.98, 2.86, 1.95, 8.57,
2.86, 1.96, 8.62, 2.86, 1.96, 8.62, 3.42, 2.49, 10.33,
4.20, 3.30, 12.77, 4.20, 3.30, 12.77, 4.79, 4.14, 14.77),ncol=9,nrow=36,byrow=TRUE)
colnames(cv_ratio_test)<-c("MS","ME","MX","MSR","MER","MXR","MSM","MEM","MXM")
rownames(cv_ratio_test)<-c(rep(c(rep(100,3),rep(150,3),rep(200,3),rep(300,3),rep(500,3),rep(1000,3)),2))
cv_lbi_test<-matrix(c(0.729,0.987,1.590,0.297 ,0.373, 0.563,
0.374,0.505,0.822,0.151 ,0.193, 0.297,
1.224 ,1.586, 2.529,0.690 ,0.897, 1.443,
0.729,0.987,1.590,0.297 ,0.373, 0.563,
0.374,0.505,0.822,0.151 ,0.193, 0.297,
1.224 ,1.586, 2.529,0.690 ,0.897, 1.443,
0.913 ,1.214, 1.787,0.354, 0.439, 0.638,
0.473,0.631,0.940, 0.182, 0.227, 0.335,
1.561, 1.974, 2.939, 0.866, 1.120, 1.650),ncol=9,nrow=6)
rownames(cv_lbi_test)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(cv_lbi_test)<-c("MS","ME","MX","MSR","MER","MXR","MSM","MEM","MXM")
cv_cusum_test<-matrix(c(0.16, 0.28, 0.37, 2.70, 3.62, 6.24,
0.14, 0.25, 0.34, 2.86, 3.90, 6.92,
0.13, 0.24, 0.33, 2.97, 4.16, 7.61,
0.13, 0.24, 0.33, 3.01, 4.20, 7.77,
0.12, 0.23, 0.32, 3.09, 4.32, 8.01,
0.34, 0.47, 0.56, 1.78, 2.11, 2.89,
0.30, 0.43, 0.52, 1.93, 2.34, 3.29,
0.27, 0.40, 0.49, 2.03, 2.48, 3.69,
0.26, 0.39, 0.48, 2.09, 2.58, 3.76,
0.25, 0.38, 0.47, 2.13, 2.67, 4.00),nrow=10,ncol=6,byrow=TRUE)
colnames(cv_cusum_test)<-c("1%","5%","10%","90%","95%","99%")
rownames(cv_cusum_test)<-c(rep(c(50,100,250,500,1000),2))
respcurve<-matrix(c(1.063,0,0,0,-41.002,133.627,-183.98,131.206,-47.89,7.102,
1.601,0,-7.486,9.449,0,0,-17.596,25.299,-13.724,2.688,
-221.524,2316.11,-10522.512,27414.943,-45191.318,48907.541,-34769.527,15666.998,-4062.561,462.173,
5145.518,-54469.126,252323.451,-671384.183,1131196.84,-1252080.53,910897.739,-420239.255,111628.329,-13015.697,
10493.76,-110784.01,511682.48,-1357262,2279365.93,-2514370.73,1822761.11,-837851.29,221721.78,-25752.77,
-1174.527,0,58540.259,-312952.617,792898.52,-1170633.31,1062803.45,-586254.848,180679.152,-23898.266,
1.051,0,0,-4.815,0,18.496,-25.406,13.556,-2.63,0,
1.151,0,0,-9.281,21.702,-21.366,9.999,-1.824,0,0,
-0.455,0,53.424,-234.177,459.766,-499.311,310.551,-103.809,14.485,0,
1.054,0,0,0,3.328,-3.117,0.868,0,0,0,
1.008,0,0,0,8.274,-13.18,8.509,-1.971,0,0,
1.187,0,0,0,6.272,-5.03,1.557,0,0,0),nrow=12,ncol=10,byrow=TRUE)
cv_LKSN_test<-matrix(c(-2.95,-3.26,-2.72,-3.02,-2.53,-2.83,-2.41,-2.72,-2.40,-2.70,
-2.95,-3.26,-2.72,-3.02,-2.53,-2.83,-2.41,-2.72,-2.40,-2.70,
-3.23,-3.51,-3.00,-3.27,-2.81,-3.07,-2.71,-2.96,-2.69,-2.95,
-3.59,-3.93,-3.43,-3.72,-3.32,-3.59,-3.31,-3.58,-3.27,-3.55,
-3.59,-3.93,-3.43,-3.72,-3.32,-3.59,-3.31,-3.58,-3.27,-3.55,
-3.92,-4.23,-3.71,-3.98,-3.59,-3.86,-3.57,-3.81,-3.54,-3.78),ncol=10,nrow=6,byrow=TRUE)
rownames(cv_LKSN_test)<-rep(c("F","R","M"),2)
colnames(cv_LKSN_test)<-c(rep(c(100,200,500,1000,2000),each=2))
cv_MR_test<-matrix(c(-2.398,-1.815,-1.476,-2.403,-1.829,-1.530,-2.452,-1.835,-1.515,-2.319,-1.643,-1.279,
-2.562,-2.009,-1.682,-2.649,-2.046,-1.738,-2.638,-2.080,-1.768,-2.587,-1.913,-1.580,
-2.660,-2.189,-1.914,-2.751,-2.224,-1.951,-2.774,-2.250,-2.703,-2.106,-1.796,-1.967,
-2.382,-1.856,-1.509,-2.398,-1.860,-1.532,-2.437,-1.875,-1.561,-2.289,-1.693,-1.336,
-2.538,-1.943,-1.645,-2.500,-1.958,-1.677,-2.581,-2.001,-1.692,-2.492,-1.855,-1.524,
-2.620,-2.167,-1.898,-2.632,-2.169,-1.913,-2.781,-2.213,-2.606,-2.053,-1.775,-1.937,
-2.400,-1.866,-1.552,-2.404,-1.876,-1.577,-2.460,-1.905,-1.591,-2.426,-1.776,-1.435,
-2.406,-1.918,-1.623,-2.506,-1.946,-1.651,-2.500,-1.942,-1.658,-2.497,-1.888,-1.578,
-2.613,-2.143,-1.890,-2.684,-2.165,-1.912,-2.680,-2.183,-1.919,-2.696,-2.140,-1.838,
-2.454,-1.918,-1.605,-2.471,-1.920,-1.618,-2.434,-1.897,-1.592,-2.438,-1.856,-1.538,
-2.470,-1.927,-1.618,-2.491,-1.910,-1.619,-2.523,-1.953,-1.661,-2.487,-1.894,-1.601,
-2.646,-2.174,-1.919,-2.698,-2.171,-1.912,-2.639,-2.172,-1.928,-2.699,-2.132,-1.872,
-2.465,-1.935,-1.615,-2.452,-1.922,-1.617,-2.469,-1.890,-1.620,-2.508,-1.960,-1.634,
-2.453,-1.942,-1.654,-2.461,-1.917,-1.629,-2.491,-1.938,-1.630,-2.556,-1.961,-1.634,
-2.662,-2.190,-1.932,-2.656,-2.175,-1.918,-2.700,-2.191,-1.910,-2.777,-2.230,-1.956,
-2.517,-1.962,-1.669,-2.474,-1.924,-1.626,-2.490,-1.927,-1.624,-2.589,-2.030,-1.739,
-2.428,-1.959,-1.642,-2.478,-1.880,-1.598,-2.490,-1.959,-1.642,-2.679,-2.083,-1.758,
-2.630,-2.201,-1.956,-2.631,-2.176,-1.900,-2.686,-2.202,-1.940,-2.870,-2.343,-2.050,
-2.533,-1.980,-1.676,-2.545,-1.929,-1.641,-2.540,-1.928,-1.628,-2.747,-2.157,-1.856,
-2.469,-1.969,-1.675,-2.460,-1.930,-1.610,-2.516,-1.925,-1.631,-2.794,-2.187,-1.846,
-2.657,-2.207,-1.967,-2.724,-2.174,-1.927,-2.766,-2.192,-1.922,-3.011,-2.439,-2.167,
-2.493,-1.978,-1.693,-2.441,-1.900,-1.625,-2.548,-1.922,-1.642,-2.805,-2.251,-1.937,
-2.531,-2.020,-1.731,-2.503,-1.919,-1.632,-2.532,-1.967,-1.659,-2.825,-2.266,-1.951,
-2.694,-2.230,-1.989,-2.645,-2.157,-1.909,-2.760,-2.218,-1.938,-3.014,-2.486,-2.252,
-2.503,-2.010,-1.724,-2.479,-1.897,-1.612,-2.519,-1.945,-1.656,-2.945,-2.337,-2.036,
-2.562,-2.056,-1.789,-2.511,-1.943,-1.634,-2.507,-1.961,-1.667,-2.973,-2.405,-2.095,
-2.695,-2.271,-2.032,-2.683,-2.197,-1.919,-2.756,-2.220,-1.951,-3.156,-2.649,-2.368,
-2.513,-2.019,-1.735,-2.519,-1.916,-1.614,-2.547,-1.994,-1.675,-3.051,-2.477,-2.175,
-2.623,-2.151,-1.857,-2.510,-1.968,-1.682,-2.549,-1.987,-1.710,-3.116,-2.479,-2.206,
-2.748,-2.344,-2.081,-2.689,-2.210,-1.943,-2.763,-2.260,-1.987,-3.319,-2.746,-2.473,
-2.621,-2.069,-1.784,-2.467,-1.952,-1.641,-2.566,-1.984,-1.680,-3.211,-2.621,-2.307,
-2.714,-2.206,-1.933,-2.572,-2.019,-1.743,-2.660,-2.066,-1.788,-3.245,-2.682,-2.412,
-2.827,-2.371,-2.147,-2.736,-2.233,-1.986,-2.836,-2.311,-2.024,-3.441,-2.910,-2.645),nrow=33,ncol=12,byrow=TRUE)
rownames(cv_MR_test)<-rep(seq(-0.5,0.5,by=0.1),each=3)
colnames(cv_MR_test)<-c(rep(100,3),rep(250,3),rep(500,3),rep(750,3))
cv_MR_test_squared<-matrix(c(8.493,11.640,20.349,6.989,9.426,16.127,6.406,8.636,14.343,8.497,10.931,16.906,
8.104,12.351,23.399,5.490,7.477,14.414,5.015,6.387,10.580,6.717,8.710,14.343,
9.865,13.669,24.088,7.810,10.347,17.562,7.127,9.282,14.948,9.431,11.929,17.884,
7.043,9.636,17.753,5.406,7.114,12.250,4.964,6.370,10.225,6.578,8.207,12.187,
7.040,10.588,21.642,4.824,6.374,12.572,4.491,5.697,8.991,5.762,7.200,11.596,
8.441,11.795,21.874,6.336,8.175,13.801,5.773,7.331,11.255,7.544,9.179,13.455,
6.119,8.107,14.796,4.670,5.959,9.348,4.433,5.622,8.617,5.553,6.831,10.032,
6.263,8.861,17.844,4.682,6.104,10.146,4.293,5.365,8.351,5.272,6.661,9.831,
7.432,10.094,18.449,5.723,7.162,11.262,5.305,6.616,9.981,6.590,7.928,11.315,
5.640,7.379,12.717,4.490,5.735,8.853,4.203,5.215,7.686,4.974,6.181,8.774,
5.919,7.928,13.888,4.493,5.914,9.764,4.200,5.317,8.122,4.931,6.178,9.062,
6.845,8.941,15.019,5.533,6.955,10.576,5.089,6.234,8.910,6.054,7.212,10.051,
5.381,7.099,11.846,4.351,5.416,8.178,4.046,5.133,7.619,4.529,5.602,8.412,
5.686,7.491,13.976,4.482,5.701,8.857,4.118,5.217,7.723,4.658,5.741,8.501,
6.638,8.484,14.783,5.362,6.609,9.427,5.047,6.072,8.579,5.574,6.746,9.514,
5.143,6.519,10.833,4.353,5.560,8.553,3.987,4.997,7.525,4.433,5.576,8.002,
5.246,6.767,11.776,4.345,5.613,8.617,4.205,5.247,7.841,4.470,5.582,8.182,
6.201,7.668,12.589,5.359,6.549,9.684,4.984,6.110,8.686,5.488,6.547,9.208,
5.007,6.465,10.121,4.374,5.631,9.064,4.076,5.278,8.077,4.425,5.551,8.443,
5.009,6.358,10.341,4.317,5.486,8.971,4.062,5.183,7.726,4.416,5.457,8.275,
6.065,7.418,11.660,5.326,6.693,10.036,5.046,6.182,8.898,5.412,6.568,9.689,
4.817,6.088,9.257,4.430,5.610,8.952,4.124,5.191,7.815,4.411,5.499,8.173,
4.707,5.862,8.798,4.268,5.373,8.606,4.034,5.049,7.567,4.388,5.555,8.262,
5.726,6.895,10.498,5.251,6.545,10.310,4.956,6.035,8.741,5.441,6.537,9.226,
4.559,5.582,7.991,4.535,5.834,8.821,4.142,5.175,7.830,4.548,5.690,8.747,
4.386,5.361,7.583,4.136,5.211,7.794,3.923,4.887,7.322,4.699,5.971,8.895,
5.386,6.429,8.507,5.315,6.557,9.558,4.901,5.981,8.572,5.768,7.118,10.057,
4.399,5.463,7.673,4.497,5.845,9.205,4.300,5.401,8.076,4.926,6.247,9.283,
4.167,5.159,7.250,3.953,4.977,7.450,3.803,4.795,6.934,4.943,6.198,9.706,
5.238,6.242,8.326,5.229,6.506,9.588,4.985,6.040,8.720,6.153,7.562,10.948,
4.136,5.107,7.485,4.576,5.959,9.468,4.217,5.400,8.259,5.403,6.968,10.306,
4.166,5.153,7.478,3.900,4.901,7.431,3.781,4.859,7.216,5.831,7.220,10.506,
5.035,6.077,8.284,5.303,6.553,9.978,4.987,6.169,8.990,7.021,8.474,11.835),nrow=33,ncol=12,byrow=TRUE)
rownames(cv_MR_test_squared)<-rep(seq(-0.5,0.5,by=0.1),each=3)
colnames(cv_MR_test_squared)<-c(rep(100,3),rep(250,3),rep(500,3),rep(750,3))
Critical_values<-list(b=b,b_min=b_min,cv_ratio_test=cv_ratio_test,cv_lbi_test=cv_lbi_test,cv_cusum_test=cv_cusum_test,respcurve=respcurve,cv_LKSN_test=cv_LKSN_test,
cv_MR_test=cv_MR_test,cv_MR_test_squared=cv_MR_test_squared)
return(Critical_values)
}
#' function to generate critical values. For internal use only.
#' @keywords internal
CV<-function(x,statistic,trend,type,m=0,M,d=0,tau,serial=FALSE,lmax=0){
T<-length(x)
if(trend=="linear"){
index<-1:T
trend_coef<-stats::coef(stats::lm(x~index))[2]
tre<-index*trend_coef}
else{tre<-0}
if(type=="LT" | type=="BT" | type=="HLT" | type=="HLTmin"){
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="mean"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-mean(tstat_sim)
M_R[l]<-mean(1/tstat_sim)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="max"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-max(tstat_sim)
M_R[l]<-max(1/tstat_sim)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="exp"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-log(mean(exp(.5*tstat_sim)))
M_R[l]<-log(mean(exp(.5/tstat_sim)))
M_M[l]<-max(M_N[l],M_R[l])
}
}
crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
if(type=="cusum"){
tstat_sim<-rep(NA,M)
for(l in 1:M){tstat_sim[l]<-cusum(cumsum(stats::rnorm(length(x)))+tre,trend=trend,tau=tau,m=m)$tstat}
crit<-matrix(c(stats::quantile(tstat_sim,c(0.01,0.05,0.1,.99,.95,.9))),ncol=2,nrow=3)
}
if(type=="cusum_SK"){
tstat_sim<-rep(NA,M)
for(l in 1:M){tstat_sim[l]<-cusum(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,m=m)$tstat}
crit<-matrix(c(stats::quantile(tstat_sim,c(0.01,0.05,0.1,.99,.95,.9))),ncol=2,nrow=3)
}
if(type=="MR"){
tstat_sim<-rep(NA,M)
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="standard"){
for(l in 1:M){
tstat_sim<-MR(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,serial=serial)
M_N[l]<-min(tstat_sim$tstat1)
M_R[l]<-min(tstat_sim$tstat2)
M_M[l]<-min(M_N[l],M_R[l])
}
}
else{
for(l in 1:M){
tstat_sim<-MR(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,serial=serial)
M_N[l]<-max(tstat_sim$tstat1^2)
M_R[l]<-max(tstat_sim$tstat2^2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="standard") crit<-matrix(c(stats::quantile(M_N,c(0.01,.05,.1)),stats::quantile(M_R,c(0.01,.05,.1)),stats::quantile(M_M,c(0.01,.05,.1))),nrow=3,ncol=3,byrow=TRUE)
if(statistic=="squared") crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
if(type=="LBI")
{
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="mean"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-mean(tstat_sim$tstat1)
M_R[l]<-mean(tstat_sim$tstat2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="exp"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-log(mean(exp(.5*tstat_sim$tstat1)))
M_R[l]<-log(mean(exp(.5*tstat_sim$tstat2)))
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="max"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-max(tstat_sim$tstat1)
M_R[l]<-max(tstat_sim$tstat2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
# if(type=="NM")
# {
# M_N<-rep(NA,M)
# M_R<-rep(NA,M)
# if(statistic=="mean"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,tau=tau)
# M_N[l]<-mean(tstat_sim$tstat1)
# M_R[l]<-mean(tstat_sim$tstat2)
# }
# }
# if(statistic=="exp"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,tau=tau)
# M_N[l]<-log(mean(exp(.5*tstat_sim$tstat1)))
# M_R[l]<-log(mean(exp(.5*tstat_sim$tstat2)))
# }
# }
# if(statistic=="max"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,m=m,tau=tau)
# M_N[l]<-max(tstat_sim$tstat1)
# M_R[l]<-max(tstat_sim$tstat2)
# }
# }
# crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99))),nrow=2,ncol=3,byrow=TRUE)
# }
if(type=="LKSN"){
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
for(l in 1:M){
tstat_sim<-LKSN(cumsum(stats::rnorm(length(x)))+tre,trend=trend,tau=tau,lmax=lmax)
M_N[l]<-min(tstat_sim$tstat1)
M_R[l]<-min(tstat_sim$tstat2)
M_M[l]<-min(M_N[l],M_R[l])
}
crit<-matrix(c(stats::quantile(M_N,c(0.1,.05)),stats::quantile(M_R,c(0.1,.05)),stats::quantile(M_M,c(0.1,.05))),nrow=3,ncol=2,byrow=TRUE)
}
return(crit)
}
Try the memochange package in your browser
Any scripts or data that you put into this service are public.
memochange documentation built on July 27, 2020, 1:09 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions CV getCV
Documented in CV getCV
#' function to extract critical values. For internal use only.
#' @keywords internal
getCV<-function(){
b<-matrix(c(0.239, 0.408, 0.308, 0.237, 0.409, 0.311, 0.290, 0.470, 0.376,
0.293, 0.480, 0.383, 0.290, 0.481, 0.382, 0.336, 0.538, 0.446,
0.389, 0.635, 0.548, 0.392, 0.632, 0.542, 0.431, 0.696, 0.609,
0.511, 1.062, 0.805, 0.497, 1.014, 0.771, 0.579, 1.189, 0.904,
0.595, 1.248, 0.953, 0.577, 1.187, 0.899, 0.658, 1.367, 1.046,
0.773, 1.699, 1.325, 0.714, 1.538, 1.186, 0.812, 1.738, 1.371),ncol=9,nrow=6,byrow = TRUE)
rownames(b)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(b)<-c("MSm","MEm","MXm","MSmR","MEmR","MXmR","MSmM","MEmM","MXmM")
b_min<-matrix(c(1.408, 2.162,1.722,1.390,2.187,1.746,2.115,3.118,2.617,
2.070,3.060,2.570,2.119,3.120,2.623,2.969,4.197,3.647,
4.255,5.779,5.216,4.350,6.073,5.482,5.694,7.667,7.053,
2.696,5.028,3.893,2.669,4.967,3.803,3.918,6.991,5.497,
3.845,6.847,5.408,3.828,6.738,5.271,5.285,9.182,7.399,
7.640,12.865,10.473,7.311,12.133,9.984,9.774,15.864,13.359),ncol=9,nrow=6,byrow = TRUE)
rownames(b_min)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(b_min)<-c("MSm","MEm","MXm","MSmR","MEmR","MXmR","MSmM","MEmM","MXmM")
cv_ratio_test<-matrix(c(3.56, 3.48, 12.91, 3.56, 3.48, 12.88, 4.66, 5.23, 17.00,
4.67, 5.31, 17.24, 4.64, 5.25, 17.00, 5.91, 7.38, 21.72,
7.75, 11.02, 29.38, 7.67, 10.49, 28.37, 9.26, 13.34, 34.31,
3.55, 3.48, 13.16, 3.55, 3.46, 13.15, 4.67, 5.23, 17.40,
4.66, 5.29, 17.48, 4.68, 5.27, 17.51, 5.92, 7.40, 22.16,
7.72, 10.43, 28.73, 7.73, 10.89, 29.55, 9.22, 13.30, 34.67,
3.51, 3.36, 13.14, 3.54, 3.47, 13.37, 4.62, 5.11, 17.31,
4.58, 5.06, 17.18, 4.68, 5.27, 17.65, 5.85, 7.24, 22.06,
7.56, 10.21, 28.58, 7.82, 10.69, 29.64, 9.21, 13.20, 34.82,
3.52, 3.41, 13.37, 3.59, 3.44, 13.41, 4.63, 5.11, 17.56,
4.62, 5.14, 17.64, 4.63, 5.17, 17.73, 5.80, 7.21, 22.27,
7.59, 10.37, 29.06, 7.65, 10.44, 29.40, 9.24, 13.21, 34.96,
3.51, 3.35, 13.42, 3.53, 3.46, 13.70, 4.60, 5.07, 17.69,
4.60, 5.12, 17.80, 4.60, 5.11, 17.85, 5.79, 7.10, 22.36,
7.52, 10.37, 29.75, 7.42, 10.29, 29.36, 8.94, 12.93, 35.06,
3.51, 3.41, 13.81, 3.51, 3.41, 13.81, 4.63, 5.16, 18.15,
4.61, 5.21, 18.34, 4.61, 5.21, 18.34, 5.88, 7.28, 23.15,
7.69, 10.56, 30.34, 7.69, 10.56, 30.34, 9.24, 13.14, 35.71,
2.38, 1.55, 6.71, 2.38, 1.53, 6.66, 2.91, 2.01, 8.28,
2.91, 2.02, 8.39, 2.90, 2.02, 8.28, 3.48, 2.61, 10.04,
4.28, 3.50, 12.52, 4.24, 3.49, 12.66, 4.87, 4.29, 14.50,
2.38, 1.52, 6.70, 2.37, 1.53, 6.73, 2.92, 1.98, 8.31,
2.92, 2.00, 8.30, 2.92, 1.99, 8.40, 3.47, 2.54, 10.01,
4.23, 3.43, 12.54, 4.23, 3.42, 12.46, 4.88, 4.29, 14.73,
2.35, 1.50, 6.75, 2.37, 1.51, 6.72, 2.88, 1.96, 8.34,
2.87, 1.96, 8.37, 2.90, 1.98, 8.40, 3.43, 2.50, 10.07,
4.18, 3.33, 12.46, 4.22, 3.41, 12.59, 4.80, 4.10, 14.53,
2.35, 1.50, 6.83, 2.37, 1.51, 6.86, 2.88, 1.96, 8.42,
2.89, 1.95, 8.40, 2.88, 1.98, 8.55, 3.40, 2.50, 10.23,
4.13, 3.31, 12.54, 4.17, 3.47, 12.79, 4.77, 4.09, 14.64,
2.35, 1.50, 6.89, 2.37, 1.50, 6.91, 2.87, 1.95, 8.50,
2.87, 1.96, 8.58, 2.88, 1.96, 8.51, 3.42, 2.47, 10.26,
4.20, 3.32, 12.69, 4.21, 3.36, 12.72, 4.79, 4.08, 14.59,
2.36, 1.50, 6.98, 2.36, 1.50, 6.98, 2.86, 1.95, 8.57,
2.86, 1.96, 8.62, 2.86, 1.96, 8.62, 3.42, 2.49, 10.33,
4.20, 3.30, 12.77, 4.20, 3.30, 12.77, 4.79, 4.14, 14.77),ncol=9,nrow=36,byrow=TRUE)
colnames(cv_ratio_test)<-c("MS","ME","MX","MSR","MER","MXR","MSM","MEM","MXM")
rownames(cv_ratio_test)<-c(rep(c(rep(100,3),rep(150,3),rep(200,3),rep(300,3),rep(500,3),rep(1000,3)),2))
cv_lbi_test<-matrix(c(0.729,0.987,1.590,0.297 ,0.373, 0.563,
0.374,0.505,0.822,0.151 ,0.193, 0.297,
1.224 ,1.586, 2.529,0.690 ,0.897, 1.443,
0.729,0.987,1.590,0.297 ,0.373, 0.563,
0.374,0.505,0.822,0.151 ,0.193, 0.297,
1.224 ,1.586, 2.529,0.690 ,0.897, 1.443,
0.913 ,1.214, 1.787,0.354, 0.439, 0.638,
0.473,0.631,0.940, 0.182, 0.227, 0.335,
1.561, 1.974, 2.939, 0.866, 1.120, 1.650),ncol=9,nrow=6)
rownames(cv_lbi_test)<-c("Mean 10%","Mean 5%","Mean 1%","Trend 10%","Trend 5%","Trend 1%")
colnames(cv_lbi_test)<-c("MS","ME","MX","MSR","MER","MXR","MSM","MEM","MXM")
cv_cusum_test<-matrix(c(0.16, 0.28, 0.37, 2.70, 3.62, 6.24,
0.14, 0.25, 0.34, 2.86, 3.90, 6.92,
0.13, 0.24, 0.33, 2.97, 4.16, 7.61,
0.13, 0.24, 0.33, 3.01, 4.20, 7.77,
0.12, 0.23, 0.32, 3.09, 4.32, 8.01,
0.34, 0.47, 0.56, 1.78, 2.11, 2.89,
0.30, 0.43, 0.52, 1.93, 2.34, 3.29,
0.27, 0.40, 0.49, 2.03, 2.48, 3.69,
0.26, 0.39, 0.48, 2.09, 2.58, 3.76,
0.25, 0.38, 0.47, 2.13, 2.67, 4.00),nrow=10,ncol=6,byrow=TRUE)
colnames(cv_cusum_test)<-c("1%","5%","10%","90%","95%","99%")
rownames(cv_cusum_test)<-c(rep(c(50,100,250,500,1000),2))
respcurve<-matrix(c(1.063,0,0,0,-41.002,133.627,-183.98,131.206,-47.89,7.102,
1.601,0,-7.486,9.449,0,0,-17.596,25.299,-13.724,2.688,
-221.524,2316.11,-10522.512,27414.943,-45191.318,48907.541,-34769.527,15666.998,-4062.561,462.173,
5145.518,-54469.126,252323.451,-671384.183,1131196.84,-1252080.53,910897.739,-420239.255,111628.329,-13015.697,
10493.76,-110784.01,511682.48,-1357262,2279365.93,-2514370.73,1822761.11,-837851.29,221721.78,-25752.77,
-1174.527,0,58540.259,-312952.617,792898.52,-1170633.31,1062803.45,-586254.848,180679.152,-23898.266,
1.051,0,0,-4.815,0,18.496,-25.406,13.556,-2.63,0,
1.151,0,0,-9.281,21.702,-21.366,9.999,-1.824,0,0,
-0.455,0,53.424,-234.177,459.766,-499.311,310.551,-103.809,14.485,0,
1.054,0,0,0,3.328,-3.117,0.868,0,0,0,
1.008,0,0,0,8.274,-13.18,8.509,-1.971,0,0,
1.187,0,0,0,6.272,-5.03,1.557,0,0,0),nrow=12,ncol=10,byrow=TRUE)
cv_LKSN_test<-matrix(c(-2.95,-3.26,-2.72,-3.02,-2.53,-2.83,-2.41,-2.72,-2.40,-2.70,
-2.95,-3.26,-2.72,-3.02,-2.53,-2.83,-2.41,-2.72,-2.40,-2.70,
-3.23,-3.51,-3.00,-3.27,-2.81,-3.07,-2.71,-2.96,-2.69,-2.95,
-3.59,-3.93,-3.43,-3.72,-3.32,-3.59,-3.31,-3.58,-3.27,-3.55,
-3.59,-3.93,-3.43,-3.72,-3.32,-3.59,-3.31,-3.58,-3.27,-3.55,
-3.92,-4.23,-3.71,-3.98,-3.59,-3.86,-3.57,-3.81,-3.54,-3.78),ncol=10,nrow=6,byrow=TRUE)
rownames(cv_LKSN_test)<-rep(c("F","R","M"),2)
colnames(cv_LKSN_test)<-c(rep(c(100,200,500,1000,2000),each=2))
cv_MR_test<-matrix(c(-2.398,-1.815,-1.476,-2.403,-1.829,-1.530,-2.452,-1.835,-1.515,-2.319,-1.643,-1.279,
-2.562,-2.009,-1.682,-2.649,-2.046,-1.738,-2.638,-2.080,-1.768,-2.587,-1.913,-1.580,
-2.660,-2.189,-1.914,-2.751,-2.224,-1.951,-2.774,-2.250,-2.703,-2.106,-1.796,-1.967,
-2.382,-1.856,-1.509,-2.398,-1.860,-1.532,-2.437,-1.875,-1.561,-2.289,-1.693,-1.336,
-2.538,-1.943,-1.645,-2.500,-1.958,-1.677,-2.581,-2.001,-1.692,-2.492,-1.855,-1.524,
-2.620,-2.167,-1.898,-2.632,-2.169,-1.913,-2.781,-2.213,-2.606,-2.053,-1.775,-1.937,
-2.400,-1.866,-1.552,-2.404,-1.876,-1.577,-2.460,-1.905,-1.591,-2.426,-1.776,-1.435,
-2.406,-1.918,-1.623,-2.506,-1.946,-1.651,-2.500,-1.942,-1.658,-2.497,-1.888,-1.578,
-2.613,-2.143,-1.890,-2.684,-2.165,-1.912,-2.680,-2.183,-1.919,-2.696,-2.140,-1.838,
-2.454,-1.918,-1.605,-2.471,-1.920,-1.618,-2.434,-1.897,-1.592,-2.438,-1.856,-1.538,
-2.470,-1.927,-1.618,-2.491,-1.910,-1.619,-2.523,-1.953,-1.661,-2.487,-1.894,-1.601,
-2.646,-2.174,-1.919,-2.698,-2.171,-1.912,-2.639,-2.172,-1.928,-2.699,-2.132,-1.872,
-2.465,-1.935,-1.615,-2.452,-1.922,-1.617,-2.469,-1.890,-1.620,-2.508,-1.960,-1.634,
-2.453,-1.942,-1.654,-2.461,-1.917,-1.629,-2.491,-1.938,-1.630,-2.556,-1.961,-1.634,
-2.662,-2.190,-1.932,-2.656,-2.175,-1.918,-2.700,-2.191,-1.910,-2.777,-2.230,-1.956,
-2.517,-1.962,-1.669,-2.474,-1.924,-1.626,-2.490,-1.927,-1.624,-2.589,-2.030,-1.739,
-2.428,-1.959,-1.642,-2.478,-1.880,-1.598,-2.490,-1.959,-1.642,-2.679,-2.083,-1.758,
-2.630,-2.201,-1.956,-2.631,-2.176,-1.900,-2.686,-2.202,-1.940,-2.870,-2.343,-2.050,
-2.533,-1.980,-1.676,-2.545,-1.929,-1.641,-2.540,-1.928,-1.628,-2.747,-2.157,-1.856,
-2.469,-1.969,-1.675,-2.460,-1.930,-1.610,-2.516,-1.925,-1.631,-2.794,-2.187,-1.846,
-2.657,-2.207,-1.967,-2.724,-2.174,-1.927,-2.766,-2.192,-1.922,-3.011,-2.439,-2.167,
-2.493,-1.978,-1.693,-2.441,-1.900,-1.625,-2.548,-1.922,-1.642,-2.805,-2.251,-1.937,
-2.531,-2.020,-1.731,-2.503,-1.919,-1.632,-2.532,-1.967,-1.659,-2.825,-2.266,-1.951,
-2.694,-2.230,-1.989,-2.645,-2.157,-1.909,-2.760,-2.218,-1.938,-3.014,-2.486,-2.252,
-2.503,-2.010,-1.724,-2.479,-1.897,-1.612,-2.519,-1.945,-1.656,-2.945,-2.337,-2.036,
-2.562,-2.056,-1.789,-2.511,-1.943,-1.634,-2.507,-1.961,-1.667,-2.973,-2.405,-2.095,
-2.695,-2.271,-2.032,-2.683,-2.197,-1.919,-2.756,-2.220,-1.951,-3.156,-2.649,-2.368,
-2.513,-2.019,-1.735,-2.519,-1.916,-1.614,-2.547,-1.994,-1.675,-3.051,-2.477,-2.175,
-2.623,-2.151,-1.857,-2.510,-1.968,-1.682,-2.549,-1.987,-1.710,-3.116,-2.479,-2.206,
-2.748,-2.344,-2.081,-2.689,-2.210,-1.943,-2.763,-2.260,-1.987,-3.319,-2.746,-2.473,
-2.621,-2.069,-1.784,-2.467,-1.952,-1.641,-2.566,-1.984,-1.680,-3.211,-2.621,-2.307,
-2.714,-2.206,-1.933,-2.572,-2.019,-1.743,-2.660,-2.066,-1.788,-3.245,-2.682,-2.412,
-2.827,-2.371,-2.147,-2.736,-2.233,-1.986,-2.836,-2.311,-2.024,-3.441,-2.910,-2.645),nrow=33,ncol=12,byrow=TRUE)
rownames(cv_MR_test)<-rep(seq(-0.5,0.5,by=0.1),each=3)
colnames(cv_MR_test)<-c(rep(100,3),rep(250,3),rep(500,3),rep(750,3))
cv_MR_test_squared<-matrix(c(8.493,11.640,20.349,6.989,9.426,16.127,6.406,8.636,14.343,8.497,10.931,16.906,
8.104,12.351,23.399,5.490,7.477,14.414,5.015,6.387,10.580,6.717,8.710,14.343,
9.865,13.669,24.088,7.810,10.347,17.562,7.127,9.282,14.948,9.431,11.929,17.884,
7.043,9.636,17.753,5.406,7.114,12.250,4.964,6.370,10.225,6.578,8.207,12.187,
7.040,10.588,21.642,4.824,6.374,12.572,4.491,5.697,8.991,5.762,7.200,11.596,
8.441,11.795,21.874,6.336,8.175,13.801,5.773,7.331,11.255,7.544,9.179,13.455,
6.119,8.107,14.796,4.670,5.959,9.348,4.433,5.622,8.617,5.553,6.831,10.032,
6.263,8.861,17.844,4.682,6.104,10.146,4.293,5.365,8.351,5.272,6.661,9.831,
7.432,10.094,18.449,5.723,7.162,11.262,5.305,6.616,9.981,6.590,7.928,11.315,
5.640,7.379,12.717,4.490,5.735,8.853,4.203,5.215,7.686,4.974,6.181,8.774,
5.919,7.928,13.888,4.493,5.914,9.764,4.200,5.317,8.122,4.931,6.178,9.062,
6.845,8.941,15.019,5.533,6.955,10.576,5.089,6.234,8.910,6.054,7.212,10.051,
5.381,7.099,11.846,4.351,5.416,8.178,4.046,5.133,7.619,4.529,5.602,8.412,
5.686,7.491,13.976,4.482,5.701,8.857,4.118,5.217,7.723,4.658,5.741,8.501,
6.638,8.484,14.783,5.362,6.609,9.427,5.047,6.072,8.579,5.574,6.746,9.514,
5.143,6.519,10.833,4.353,5.560,8.553,3.987,4.997,7.525,4.433,5.576,8.002,
5.246,6.767,11.776,4.345,5.613,8.617,4.205,5.247,7.841,4.470,5.582,8.182,
6.201,7.668,12.589,5.359,6.549,9.684,4.984,6.110,8.686,5.488,6.547,9.208,
5.007,6.465,10.121,4.374,5.631,9.064,4.076,5.278,8.077,4.425,5.551,8.443,
5.009,6.358,10.341,4.317,5.486,8.971,4.062,5.183,7.726,4.416,5.457,8.275,
6.065,7.418,11.660,5.326,6.693,10.036,5.046,6.182,8.898,5.412,6.568,9.689,
4.817,6.088,9.257,4.430,5.610,8.952,4.124,5.191,7.815,4.411,5.499,8.173,
4.707,5.862,8.798,4.268,5.373,8.606,4.034,5.049,7.567,4.388,5.555,8.262,
5.726,6.895,10.498,5.251,6.545,10.310,4.956,6.035,8.741,5.441,6.537,9.226,
4.559,5.582,7.991,4.535,5.834,8.821,4.142,5.175,7.830,4.548,5.690,8.747,
4.386,5.361,7.583,4.136,5.211,7.794,3.923,4.887,7.322,4.699,5.971,8.895,
5.386,6.429,8.507,5.315,6.557,9.558,4.901,5.981,8.572,5.768,7.118,10.057,
4.399,5.463,7.673,4.497,5.845,9.205,4.300,5.401,8.076,4.926,6.247,9.283,
4.167,5.159,7.250,3.953,4.977,7.450,3.803,4.795,6.934,4.943,6.198,9.706,
5.238,6.242,8.326,5.229,6.506,9.588,4.985,6.040,8.720,6.153,7.562,10.948,
4.136,5.107,7.485,4.576,5.959,9.468,4.217,5.400,8.259,5.403,6.968,10.306,
4.166,5.153,7.478,3.900,4.901,7.431,3.781,4.859,7.216,5.831,7.220,10.506,
5.035,6.077,8.284,5.303,6.553,9.978,4.987,6.169,8.990,7.021,8.474,11.835),nrow=33,ncol=12,byrow=TRUE)
rownames(cv_MR_test_squared)<-rep(seq(-0.5,0.5,by=0.1),each=3)
colnames(cv_MR_test_squared)<-c(rep(100,3),rep(250,3),rep(500,3),rep(750,3))
Critical_values<-list(b=b,b_min=b_min,cv_ratio_test=cv_ratio_test,cv_lbi_test=cv_lbi_test,cv_cusum_test=cv_cusum_test,respcurve=respcurve,cv_LKSN_test=cv_LKSN_test,
cv_MR_test=cv_MR_test,cv_MR_test_squared=cv_MR_test_squared)
return(Critical_values)
}
#' function to generate critical values. For internal use only.
#' @keywords internal
CV<-function(x,statistic,trend,type,m=0,M,d=0,tau,serial=FALSE,lmax=0){
T<-length(x)
if(trend=="linear"){
index<-1:T
trend_coef<-stats::coef(stats::lm(x~index))[2]
tre<-index*trend_coef}
else{tre<-0}
if(type=="LT" | type=="BT" | type=="HLT" | type=="HLTmin"){
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="mean"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-mean(tstat_sim)
M_R[l]<-mean(1/tstat_sim)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="max"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-max(tstat_sim)
M_R[l]<-max(1/tstat_sim)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="exp"){
for(l in 1:M){
tstat_sim<-BT(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-log(mean(exp(.5*tstat_sim)))
M_R[l]<-log(mean(exp(.5/tstat_sim)))
M_M[l]<-max(M_N[l],M_R[l])
}
}
crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
if(type=="cusum"){
tstat_sim<-rep(NA,M)
for(l in 1:M){tstat_sim[l]<-cusum(cumsum(stats::rnorm(length(x)))+tre,trend=trend,tau=tau,m=m)$tstat}
crit<-matrix(c(stats::quantile(tstat_sim,c(0.01,0.05,0.1,.99,.95,.9))),ncol=2,nrow=3)
}
if(type=="cusum_SK"){
tstat_sim<-rep(NA,M)
for(l in 1:M){tstat_sim[l]<-cusum(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,m=m)$tstat}
crit<-matrix(c(stats::quantile(tstat_sim,c(0.01,0.05,0.1,.99,.95,.9))),ncol=2,nrow=3)
}
if(type=="MR"){
tstat_sim<-rep(NA,M)
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="standard"){
for(l in 1:M){
tstat_sim<-MR(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,serial=serial)
M_N[l]<-min(tstat_sim$tstat1)
M_R[l]<-min(tstat_sim$tstat2)
M_M[l]<-min(M_N[l],M_R[l])
}
}
else{
for(l in 1:M){
tstat_sim<-MR(LongMemoryTS::FI.sim(length(x),1,0,d)+tre,trend=trend,tau=tau,serial=serial)
M_N[l]<-max(tstat_sim$tstat1^2)
M_R[l]<-max(tstat_sim$tstat2^2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="standard") crit<-matrix(c(stats::quantile(M_N,c(0.01,.05,.1)),stats::quantile(M_R,c(0.01,.05,.1)),stats::quantile(M_M,c(0.01,.05,.1))),nrow=3,ncol=3,byrow=TRUE)
if(statistic=="squared") crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
if(type=="LBI")
{
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
if(statistic=="mean"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-mean(tstat_sim$tstat1)
M_R[l]<-mean(tstat_sim$tstat2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="exp"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-log(mean(exp(.5*tstat_sim$tstat1)))
M_R[l]<-log(mean(exp(.5*tstat_sim$tstat2)))
M_M[l]<-max(M_N[l],M_R[l])
}
}
if(statistic=="max"){
for(l in 1:M){
tstat_sim<-LBI(stats::rnorm(length(x))+tre,trend=trend,tau=tau)
M_N[l]<-max(tstat_sim$tstat1)
M_R[l]<-max(tstat_sim$tstat2)
M_M[l]<-max(M_N[l],M_R[l])
}
}
crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99)),stats::quantile(M_M,c(0.9,.95,.99))),nrow=3,ncol=3,byrow=TRUE)
}
# if(type=="NM")
# {
# M_N<-rep(NA,M)
# M_R<-rep(NA,M)
# if(statistic=="mean"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,tau=tau)
# M_N[l]<-mean(tstat_sim$tstat1)
# M_R[l]<-mean(tstat_sim$tstat2)
# }
# }
# if(statistic=="exp"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,tau=tau)
# M_N[l]<-log(mean(exp(.5*tstat_sim$tstat1)))
# M_R[l]<-log(mean(exp(.5*tstat_sim$tstat2)))
# }
# }
# if(statistic=="max"){
# for(l in 1:M){
# tstat_sim<-NM(stats::rnorm(length(x)),trend=trend,m=m,tau=tau)
# M_N[l]<-max(tstat_sim$tstat1)
# M_R[l]<-max(tstat_sim$tstat2)
# }
# }
# crit<-matrix(c(stats::quantile(M_N,c(0.9,.95,.99)),stats::quantile(M_R,c(0.9,.95,.99))),nrow=2,ncol=3,byrow=TRUE)
# }
if(type=="LKSN"){
M_N<-rep(NA,M)
M_R<-rep(NA,M)
M_M<-rep(NA,M)
for(l in 1:M){
tstat_sim<-LKSN(cumsum(stats::rnorm(length(x)))+tre,trend=trend,tau=tau,lmax=lmax)
M_N[l]<-min(tstat_sim$tstat1)
M_R[l]<-min(tstat_sim$tstat2)
M_M[l]<-min(M_N[l],M_R[l])
}
crit<-matrix(c(stats::quantile(M_N,c(0.1,.05)),stats::quantile(M_R,c(0.1,.05)),stats::quantile(M_M,c(0.1,.05))),nrow=3,ncol=2,byrow=TRUE)
}
return(crit)
}
Try the memochange package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.