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test.R
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test.R
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rm(list=ls())
library(tidyverse)
library(dplyr)
library(tidyr)
library(permuco)
#
#Rcpp::sourceCpp("src/clusterdepth.cpp")
#Rcpp::sourceCpp("src/clusterdepth.cpp")
x= sin(seq(from = 0,to = (4*pi),length.out = 200))
xmat = matrix(x[c(10:141)],nrow = 1)
cla = get_cluster_matrix(xmat,0.5,"all")
cls = get_cluster_matrix(xmat,0.5,"starting")
cle = get_cluster_matrix(xmat,0.5,"ending")
cbind(as.numeric(cla),as.numeric(cls),as.numeric(cle))
sum(permuco:::vector_extend(x,0.5)!=0)
sum(permuco:::vector_extend2(x,0.5)!=0)
permuco::compute_clusterdepth
object=clusterlm(attentionshifting_signal ~ visibility
+ Error(id/(visibility)), data = attentionshifting_design,
multcomp = c("troendle","clusterdepth","clusterdepth_tr"),
np =4000,return_distribution = T)
plot(object,multcomp = "clusterdepth")
plot(object,multcomp = "clusterdepth_tr")
plot(object,multcomp = "troendle")
object2=clusterlm(attentionshifting_signal ~ visibility
+ Error(id/(visibility)), data = attentionshifting_design,
multcomp = "clusterdepth",
np =2000,return_distribution = T)
Rcpp::sourceCpp("src/clusterdepth.cpp")
source("R/get_cluster.R")
source("R/depth_distribution.R")
source("R/compute_troendle.R")
source("R/compute_all_pvalue.R")
source("R/compute_pvalue.R")
distribution <- object$multiple_comparison[[1]]$uncorrected$distribution
threshold <- object$threshold[1]
alternative <- "two.sided"
cluster_all =permuco:::get_cluster_matrix(distribution,threshold,side="all")
cluster_head =permuco:::get_cluster_matrix(distribution,threshold,side="starting")
apply(abs((cluster_all!=0)-(cluster_head!=0)),2,sum)
depth_head = permuco:::get_clusterdepth_head(cluster_head, border = "ignore")
distr_head <- permuco:::depth_distribution(distribution, head_mat = depth_head)
plot(apply(distr_head,2,quantile,p = 0.95))
pvalue_head <- rep(NA,ncol(cluster_head))
max_cl_size <- max(table(cluster_head[1,cluster_head[1,]!=0]))
for(cli in seq_len(max(cluster_head[1,]))){
sample <- which(cluster_head[1,]==cli)
stats <- distribution[1,sample]
stats <- c(stats,rep(0,max_cl_size-length(stats)))
pvalue_head[sample] <- compute_troendle(rbind(stats,distr_head[,seq_len(max_cl_size),drop=F]),
alternative = alternative)$main[seq_along(sample),2]
}
compute_minP(rbind(stats,distr_head[,seq_len(max_cl_size),drop=F]),
alternative = alternative)$main
plot(apply(distr_head[,1:2],2,rank))
hist(apply(distr_head[,1:2],2,rank)
cdepth
permuco:::get_cluster(distribution,threshold,side ="starting")
sum(mat[,1])
sum(cdepth[,1])
x= c(rep(0,10),rep(1,10))
sum(permuco:::vector_extend(rev(x),0.5)!=0)
sum(permuco:::vector_extend2(rev(x),0.5)!=0)
#Rcpp::sourceCpp("src/code.cpp")
#source("R/get_cluster.R")
object=clusterlm(attentionshifting_signal ~ visibility
+ Error(id/(visibility)), data = attentionshifting_design,
multcomp = c("clustermass"), np =200,return_distribution = T)
distribution <- object$multiple_comparison[[1]]$uncorrected$distribution
threshold <- object$threshold[1]
alternative <- "two.sided"
mt<- compute_maxT(distribution,alternative)
sdmt<- compute_stepdownmaxT(distribution,alternative)
plot(mt$main[,2],sdmt$main[,2])
E = object$multiple_comparison[[1]]$tfce$E
H = object$multiple_comparison[[1]]$tfce$H
dhi = object$multiple_comparison[[1]]$tfce$dhi
dh = dhi[2]-dhi[1]
t0 = proc.time()
out0=compute_tfce(distribution = distribution,E = E,H = H,ndh = length(dhi))
t0 =rbind(t0, proc.time())
out1=permuco:::compute_tfce2(distribution = distribution,E = E,H = H,ndh = length(dhi))
t0 =rbind(t0, proc.time())
apply(t0,2,diff)
tfcecpp <- permuco:::tfce_distribution(distribution, E, H, dh, dhi)
plot(out0$main[,1],out1$main[,1])
plot(object$multiple_comparison[[1]]$tfce$main[,1])
compute_tfce
permuco:::vector_extend(1:10,3)
print(object,multcomp= "clusterdepth")
summary(object,multcomp= "clusterdepth",table_type = "full")
object$multiple_comparison$visibility$clusterdepth
plot(object,multcomp = "clusterdepth")
multcomp = "clusterdepth"
print(object,multcomp= "tfce")
print(object,multcomp= "clusterdepth")
x = object$multiple_comparison
permuco:::get_cluster.matrix(distribution = rbind(x,x),threshold = 0.5,alternative="greater")
permuco:::compute_clustermass
get_cluster(distribution = rbind(x,x),threshold = 0.5,alternative="greater")
get_cluster(distribution = rbind(x,x),threshold = 0.5,alternative="less")
get_cluster(distribution = rbind(x,x),threshold = 0.5,alternative="two.sided")
H=function(mat){mat%*%MASS:::ginv(t(mat)%*%mat)%*%t(mat)}
set.seed(1)
mydata = expand.grid(particip = paste0("P",stringr::str_pad(1:6,2,"left","0")),
W1 = paste0("w",1:3),
W2 = paste0("w",1:2), stringsAsFactors = F)%>%
mutate(count=if_else(particip=="P01"&W1=="w1"&W2=="w1",4,1))%>%
uncount(count)%>%
nest(data=-particip)%>%
mutate(B1 = if_else(particip%in%c("P01","P02","P03"),"b1","b2"),
Cov1 = rnorm(n()),
Cov1 = Cov1-mean(Cov1))%>%
unnest(data)%>%
mutate(y = rnorm(n()))%>%
arrange(particip,W1,W2,B1)
mydata_avg = mydata%>%
group_by(particip,W1,W2,B1,Cov1)%>%
summarise(y = mean(y))%>%
ungroup()%>%
arrange(particip,W1,W2,B1)
f = y~Cov1*B1*W1*W2+Error(particip/(W1*W2))
#f = y~W1+Error(particip/(W1))
m0 = aovperm(f,data = mydata,np=5)
m1 = aovperm(f,data = mydata_avg,np=5)
m2 = aovperm(f,data = mydata,
method ="Rd_replic_kheradPajouh_renaud",
np=5)
m3=aovperm(f,data = mydata_avg,
method ="Rd_replic_kheradPajouh_renaud",
np=5)
cbind(wrong=m0$distribution[1,],
avg=m1$distribution[1,],
test =m2$distribution[1,],
test_avg =m3$distribution[1,])
mm <- argi$mm
zid <- argi$mm_id
factors = names(attr(mm,"contrasts"))
eff = colnames(argi$link)
wfact_link <- sapply(strsplit(eff,":"),function(effi){sum(!effi%in%factors)==0})
link_fact <- argi$link[,wfact_link,drop=F]
wfact_mm <- (attr(mm,"assign"))%in%c(0,link_fact[2,drop=F]+link_fact[3,drop=F])
ZE = permuco:::khatrirao(zid,mm[,wfact_mm,drop=F])
mm_gr=cbind(mm[,wfact_mm,drop=F],ZE)
qr_gr = qr(mm_gr)
#hii = diag(qr.fitted(qr_gr,diag(length(argi$y))))
duplic = duplicated.matrix(mm_gr)
###method
assign = attr(mm,"assign")
select_x = assign==argi$i
#select_between = assign%in%args$link[2,]
select_within = assign == (argi$link[3,argi$i])
z = permuco:::khatrirao(a = argi$mm_id, b = mm[,select_within,drop=F])
z = qr.resid(qr(mm),z)
qr_z = qr(z)
qr_d = qr(mm[,!select_x,drop=F])
rdx = qr.resid(qr_d,mm[,select_x,drop=F])
qr_rdx = qr(rdx)
qr_rdz = qr(qr.resid(qr_d,z))
###method
pry = qr.fitted(qr_gr,qr.resid(qr_d,argi$y))
pry[!duplic]
qr_dd = qr(mm[!duplic,!select_x,drop=F])
qr.resid(qr_dd,mm[!duplic,select_x,drop=F])
qr_rdx = qr(qr.resid(qr_d,mm[,select_x,drop=F]))
qr_rdz = qr(qr.resid(qr_d,z))
den = sum(qr.fitted(qr_rdz,pry)^2)/qr_rdz$rank
num = sum(qr.fitted(qr_rdx,pry)^2)/qr_rdx$rank
den
qr.fyi - m3$y
qr_gr = qr(mm_gr)
Q_gr = qr.Q(qr_gr)
qr.fitted(qr_gr,diag(length(argi$y)))-H(mm_gr)
-as.numeric(H(mm_gr)%*%argi$y)
H(mm_gr)%*%argi$y
sum(abs(Q_gr-Q_grc)>1e-10)
qr_gr$rank
rowSums(Q_gr-Q_grc)
sum(abs(qr.fitted(qr_gr,argi$y)-as.numeric(H(mm_gr)%*%argi$y))> 1e-10)
H(mm_gr)- t(Q_gr)%*%Q_gr
diag(H(mm_gr))
qr.Q(qr_gr)%*%t(qr.Q(qr_gr))-H(mm_gr)
DXZE <- cbind(mm,zid,permuco:::khatrirao(mm,zid))
image(Matrix(round(H(mm_gr),6)))
image(Matrix(H(mm)))
rowSums(qr.Q(qr(DXZE))^2)
# mydata = expand.grid(particip = paste0("P",stringr::str_pad(1:6,2,"left","0")),
# W1 = paste0("w",1:3),
# W2 = paste0("w",1:2), stringsAsFactors = F)%>%
# mutate(count=if_else(particip=="P01"&W1=="w1"&W2=="w1",4,1))%>%
# uncount(count)%>%
# nest(data=-particip)%>%
# mutate(B1 = if_else(particip%in%c("P01","P02","P03"),"b1","b2"),
# Cov1 = rnorm(n()),
# Cov1 = Cov1-mean(Cov1))%>%
# unnest(data)%>%
# mutate(y = rnorm(n()))%>%
# arrange(particip,W1,W2,B1)
#
#
# mydata_avg = mydata%>%
# group_by(particip,W1,W2,B1,Cov1)%>%
# summarise(y = mean(y))%>%
# ungroup()%>%
# arrange(particip,W1,W2,B1)
# library(permuco)
# library(Matrix)
rm(list=ls())
set.seed(1)
y = rnorm(10)
x = matrix(rnorm(20),ncol=2)
lf= list.files("R")
for(lfi in lf){source(paste0("R/",lfi))}
pm = Pmat(np = 5000, n = 19,type="s",counting = "all")
dim(pm)
n = 15
mat = t(as.matrix(expand.grid(as.data.frame(t(cbind(rep(1,n),rep(-1,n)))))))
rownames(mat)<-NULL
dim(mat)
expand.grid(A = c(1,-1),B = c(1,-1))
ys = permuco::attentionshifting_signal[,(1:50)*16]
df = permuco::attentionshifting_design
lf= list.files("R/")
for(lfi in lf){source(paste0("R/",lfi))}
pm = Pmat(np = 2000, n = nrow(ys),type="s")
plot(hj_p,effect = "visibility")
plot(cl_hj)
cl_hj<-clusterlm(ys~visibility+emotion,df,P=pm,method ="huh_jhun")
hj_p <- aovperm(ys[,1]~visibility+emotion,df,P=pm,method ="huh_jhun")
hj_s <- aovperm(ys[,1]~visibility+emotion,df,P=hj_s$P,method ="huh_jhun")
aovperm(ys[,1]~visibility+emotion,df,type="sign",method ="huh_jhun")
lmperm(ys[,1]~visibility+emotion,df,np=2000,method ="huh_jhun")
aovperm(ys[,1]~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm)
aovperm(ys[,1]~visibility*emotion+Error(id/(visibility*emotion)),df,type="sign")
aovperm(ys[,1]~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm,method ="Rde")
aovperm(ys[,1]~visibility*emotion+Error(id/(visibility*emotion)),df,type="sign",method ="Rde")
param= expand.grid(method = c("freedman_lane","kennedy","huh_jhun","manly",
"terBraak","draper_stoneman","dekker"),stringsAsFactors = F)
model_permuco = list()
for(i in 1:nrow(param)){
set.seed(1)
print(i)
model_permuco[[i]]<- clusterlm(ys~visibility+emotion,df,type="sign",method = param$method[i])}
pm = Pmat(np = 2000, n = nrow(ys),type = "permuta")
a = clusterlm(ys~visibility*emotion,df,P=pm)
a$multiple_comparison$visibility$uncorrected$test_info
sa = summary(a)
class(sa[[1]])
attributes(sa[[1]])
a = clusterlm(ys~visibility*emotion,df,type="sign")
attributes(sa[[1]])
pm = Pmat(np = 2000, n = nrow(ys),type = "permuta")
clusterlm(ys~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm,method = "Rd_")
clusterlm(ys~visibility*emotion+Error(id/(visibility*emotion)),df,type="sign",method = "Rd_")
clusterlm(ys~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm,method = "Rde_")
pm = Pmat(np = 2000, n = nrow(ys),type = "signflip")
clusterlm(ys~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm,method = "Rd_")
clusterlm(ys~visibility*emotion+Error(id/(visibility*emotion)),df,P=pm,method = "Rde_")
model_list = list()
param= expand.grid(method = c("freedman_lane","kennedy","huh_jhun","manly",
"terBraak","draper_stoneman","dekker"),
type=c("permutation","signflip"),stringsAsFactors = F)
for(i in 1:nrow(param)){
set.seed(1)
print(i)
pm = Pmat(np = 2000, n = nrow(ys),type = param$type[i])
model_list[[i]]<- clusterlm(ys~visibility+emotion,df,P=pm,method = param$method[i])}
i=1
model_list[[8]]
summary(model_permuco[[i]])[[1]]
summary(model_list[[i]])[[1]]
summary(model_list[[i+7]])[[1]]
model_permuco = list()
param = expand.grid(method = c("freedman_lane","kennedy","huh_jhun","manly",
"terBraak","draper_stoneman","dekker"),stringsAsFactors = F)
for(i in 1:nrow(param)){
set.seed(1)
pm = permuco:::Pmat(np = 2000, n = 10)
model_permuco[[i]] = permuco:::aovperm(y~x[,1]+x[,2], P = pm, method = param$method[i])}
lf= list.files("R")
for(lfi in lf){source(paste0("R/",lfi))}
model_list = list()
param= expand.grid(method = c("freedman_lane","kennedy","huh_jhun","manly",
"terBraak","draper_stoneman","dekker"),
type=c("permutation","signflip"),stringsAsFactors = F)
for(i in 1:nrow(param)){
set.seed(1)
pm = Pmat(np = 2000, n = 10,type = param$type[i])
model_list[[i]] = aovperm(y~x[,1]+x[,2], P = pm, method = param$method[i])}
i =2
model_permuco[[i]]
model_list[[i]]
Pmat_product(x,P = as.matrix(pm[,2]),type = "signflip")
x
pis = lapply(1:np(pm),function(pi){pm[,pi]})
lmp = lmperm(y~x,P = pm)
lmp$table
n = 10
npp =20
pm = Pmat(np = npp, n = n,type = "sign")
Pmat_product(1:n,pm)
pm = Pmat(np = npp, n = n,type = "perm")
Pmat_product(1:8,pm)
pm = Pmat(10,10,type = "signflip","all")
pm[,2,drop=F]
as.matrix(pm)
pm[,2,drop=F]
as.matrix(pm)
attributes(a)
attentionshifting_design2 = attentionshifting_design
contrasts(attentionshifting_design2$visibility) = contr.sum
attentionshifting_design2$visibility = as.numeric(attentionshifting_design2$visibility)
cluster_fisher_myfunp = permuco:::cluster_kennedy
npe = 400
t0=proc.time()
signi = attentionshifting_signal[,350:370]
fx <- clusterlm(signi ~ visibility*emotion,
data = attentionshifting_design2,method = "myfunp",new_method=T,
np = npe,test="fisher",multcomp = c("clustermass", "troendl","tfce","minP"),return_distribution = T)
plot(fx,multcomp = "troendle",distinctDVs=T)
a = c("12345")
b = c("12")
nchar(b)<-5
d = fx$multiple_comparison$visibility$uncorrected$distribution
compute_minP(d,alternative = "two.sided")
summary(fx,multcomp = "minP",table_type="full")[[1]]
summary(fx,multcomp = "troendle",table_type="full")[[3]]
fx$multiple_comparison$visibility$
rnd <- clusterlm(signi ~ visibility*emotion+Error(id/(visibility*emotion)),
data = attentionshifting_design2,
np = npe,test="fisher",multcomp = c("clustermass", "troendl","tfce"))
a = summary(fx)
attributes(a$visibility)
electrod_O1$multiple_comparison$visibility$uncorrected$test_info
a = summary(electrod_O1,multcomp = "troendle",table_type = "full")
summary(electrod_O1,multcomp = "troendle")
summary(electrod_O1,table_type = "full",multcomp = "tfce")
full_table(electrod_O1$multiple_comparison)
class(a$visibility)
electrod_O1$multiple_comparison$visibility$uncorrected$test_info
cm = cluster_table(electrod_O1$multiple_comparison,"troendle")
x = electrod_O1$multiple_comparison
multcomp= "troendle"
cluster_table(electrod_O1$multiple_comparison,"clustermass")
attributes(cm$visibility)
cm$visibility
multcomp="troendle"
proc.time()-t0
t0=proc.time()
electrod_O1p <- clusterlm(attentionshifting_signal ~ visibility*emotion*direction, data = attentionshifting_design2,
test="t",multcomp = "troendle2",P=electrod_O1$P)
proc.time()-t0
unique(summary(electrod_O1,multcomp="troendle")[,2])
unique(summary(electrod_O1p,multcomp="troendle2")[,2])
cbind(electrod_O1p$multiple_comparison$visibility$troendle$main[,2],electrod_O1$multiple_comparison$visibility$troendle$main[,2])
sum(summary(electrod_O1,multcomp="troendle")[,2]<0.05)
plot(electrod_O1,multcomp = "troendle")
plot(electrod_O1,alternative = "less",multcomp = "troendle")
plot(electrod_O1,alternative = "greater",multcomp = "troendle")
plot(electrod_O1, nbbaselinepts = 200, nbptsperunit = 1024)