image(thing2[,,21],zlim=c(0,1))
image(thing2[,,21],zlim=c(0,1))
image(thing2[,,20],zlim=c(0,1))
image(thing2[,,31],zlim=c(0,1))
image(thing2[,,35],zlim=c(0,1))
image(thing2[,,36],zlim=c(0,1))
image(thing2[,,37],zlim=c(0,1))
image(thing2[,,75],zlim=c(0,1))
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim_very_subtle.Rdata")
classic_roboBayes_keepers
MR_roboBayes_keepers
720/60
24*60
720*2
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim_less_subtle.Rdata")
MR_roboBayes_keepers
head(MR_roboBayes_keepers)
head(classic_roboBayes_keepers)
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim_less_subtle.Rdata")
classic_roboBayes_keepers
MR_roboBayes_keepers
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim_very_subtle.Rdata")
MR_roboBayes_keepers
86518/60
86518/60/60
classic_roboBayes_keepers
64*64
32*32
256/32
load("H:/LauraWendelberger/wrobobayes/interim_files/priors_sim_delta1.RData")
prior_list
active_priors <- prior_list
active_priors <- prior_list["LL8"]
active_priors
length(active_priors)
active_priors$LL8$priors[[1]]
names(prior_list)
prior_list$LH1
prior_list["LH1"]
prior_list[["LH1"]]
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim_delta1.Rdata")
classic_roboBayes_keepers
MR_roboBayes_keepers
load("C:/Users/ljwendel/Downloads/lauraZ.Rdata")
plot(z)
plot(zWeight)
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z))/length(1:X)
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
valLeft <- var(zWeight[1:65])
valsRight[65]
valLeft
lambda <- 0.09
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z))/length(1:X)
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
valLeft <- var(zWeight[1:65])
valsRight[65]
valLeft
pnorm(abs(zWeight))
plot(pnorm(abs(zWeight)))
plot(pnorm(abs(zWeight),sd=1))
plot(pnorm(abs(zWeight),mean=0,sd=1))
plot(pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1))
pnorm(-3)
plot(2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F)
pnorm(3)
pnorm(3,lower.tail=F)
2*pnorm(3,lower.tail=F)
1-2*pnorm(3,lower.tail=F)
plot(zWeight/sqrt(valsRight))
valsRight
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z))
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
plot(zWeight/sqrt(valsRight))
plot(zWeight/sqrt(valsRight))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
valLeft <- var(zWeight[1:65])
valsRight[65]
valLeft
valLeft <- sd(zWeight[1:65])
valsRight[65]
valLeft
lambda <- 0.09
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z))
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
plot(zWeight/sqrt(valsRight))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
valLeft <- var(zWeight[1:65])
valsRight[65]
valLeft
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
plot(1-2*pnorm(abs(zWeight),mean=0,sd=sqrt(valsRight),lower.tail=F))
sqrt(valsRight)
plot(sqrt(valsRight))
var(zWeigth)
var(zWeight)
var(zWeight)
valsRight[length(valsRight)]
1:length(zWeight)
plot(zWeight/sqrt(valsRight))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
# this should be the same as above
plot(1-2*pnorm(abs(zWeight),mean=0,sd=sqrt(valsRight),lower.tail=F))
length(z)
length(zWeigth)
length(zWeight)
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z[1:X]))
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
plot(zWeight/sqrt(valsRight))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
# this should be the same as above
plot(1-2*pnorm(abs(zWeight),mean=0,sd=sqrt(valsRight),lower.tail=F))
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (var(z))
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * valLamb * valExp)
})
valsRight <- sapply(1:length(zWeight), function(X){
varOrig <- (sd(z))
valLamb <- (lambda / (2-lambda))
valExp <- 1 - (1-lambda)^(2*X)
return(varOrig * sqrt(valLamb) * sqrt(valExp))
})
plot(zWeight/sqrt(valsRight))
plot(1-2*pnorm(abs(zWeight/sqrt(valsRight)),mean=0,sd=1,lower.tail=F))
# this should be the same as above
plot(1-2*pnorm(abs(zWeight),mean=0,sd=sqrt(valsRight),lower.tail=F))
plot(1-2*pnorm(abs(zWeight/(valsRight)),mean=0,sd=1,lower.tail=F))
# this should be the same as above
plot(1-2*pnorm(abs(zWeight),mean=0,sd=(valsRight),lower.tail=F))
(valsRight)
2^7
?roboBayes
library(roboBayes)
?roboBayes
X <- rep(1,50)
Y <- matrix(rnorm(50,0,1))
X <- as.matrix(rep(1,50))
X
roboBayes(Y,X)
thing <- roboBayes(Y,X)
thing$outliers
Y[c(15,29)] <- 5
thing <- roboBayes(Y,X)
thing
thing$outliers
plot(Y)
thing <- roboBayes(Y,X)
thing$outliers
prs <- list(Lambda=diag(c(1)),B=matrix(0),V=matrix(1),nu=2.1)
thing <- roboBayes(Y,X,par_inits=prs)
thing$outliers
thing <- roboBayes(Y,X,par_inits=prs,getOUtliers=T)
thing <- roboBayes(Y,X,par_inits=prs,getOutliers=T)
thing$outliers
Y[c(15,29)] <- 7
thing <- roboBayes(Y,X,par_inits=prs,getOutliers=T)
thing$outliers
prs <- list(Lambda=diag(c(1)),B=matrix(0),V=matrix(3),nu=2.1)
thing <- roboBayes(Y,X,par_inits=prs,getOutliers=T)
thing$outliers
thing$R
plot(thing$RL)
plot(c(thing$RL))
plot(c(thing$R))
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T)
thing$outliers
plot(c(thing$R))
prs <- list(Lambda=diag(c(.1)),B=matrix(0),V=matrix(3),nu=2.1)
plot(c(thing$R))
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T)
prs <- list(Lambda=matrix(c(.1)),B=matrix(0),V=matrix(3),nu=2.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T)
plot(c(thing$R))
thing$outliers
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
plot(c(thing$R))
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
plot(c(thing$R))
thing$outliers
Y[c(15,29)] <- 5
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing$outliers
prs <- list(Lambda=matrix(c(.01)),B=matrix(0),V=matrix(3),nu=2.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing$outliers
plot(Y)
thing$cpInds
prs <- list(Lambda=matrix(c(.01)),B=matrix(0),V=matrix(7),nu=6.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing
thing$outliers
thing$cpInds
prs <- list(Lambda=matrix(c(.01)),B=matrix(0),V=matrix(4.1),nu=6.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing$outliers
thing$cpInds
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(4.1),nu=6.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing$outliers
thing$cpInds
prs <- list(Lambda=matrix(c(100)),B=matrix(0),V=matrix(6.1),nu=8.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.5)
thing$outliers
thing
plot(c(thing$RL))
plot(c(thing$R))
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2)
thing$outliers
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(13.1),nu=15.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2)
thing$outliers
thing$cpInds
plot(c(thing$R))
prs <- list(Lambda=matrix(c(1)),B=matrix(0),V=matrix(13.1),nu=15.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2)
thing
thing$outliers
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15)
thing
thing$outliers
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5))
thing$outliers
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),getR=T)
thing
plot(thing$RFull[,49])
plot(thing$RFull[,14])
plot(thing$RFull[,15])
plot(thing$RFull[,16])
plot(thing$RFull[,17])
prs <- list(Lambda=matrix(c(1)),B=matrix(0),V=matrix(60.1),nu=62.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),getR=T)
thing
thing$outliers
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(60.1),nu=62.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),getR=T)
thing$outliers
thing$cpInds
dim(Y)
dim(X)
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(20.1),nu=22.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),getR=T)
thing$cpInds
thing$outliers
plot(Y)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=0.5,,getR=T)
thing$outliers
thing$cpInds
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=0.5,getR=T)
thing$outliers
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(10.1),nu=12.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=0.5,getR=T)
thing$outliers
prs <- list(Lambda=matrix(c(1)),B=matrix(0),V=matrix(10.1),nu=12.1)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=0.5,getR=T)
thing$outliers
thing$cpInds
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T)
thing$outliers
Y <- rnorm(50,0,0.5)
Y[c(20,45),] <- 5
Y[c(20,45)] <- 5
Y <- as.matrix(Y)
thing <- roboBayes(Y,X,lambda=50,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T)
thing$outliers
plot(Y)
X
lambda
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.2,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T)
thing
thing$outliers
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T)
thing$outliers
prs <- list(Lambda=matrix(c(1)),B=matrix(0),V=matrix(4.1),nu=6.1)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T)
thing$outliers
plot(thing$RFull[,22])
plot(thing$RFull[,25])
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8)
thing$outliers
thing$cpInds
prs <- list(Lambda=matrix(c(10)),B=matrix(0),V=matrix(4.1),nu=6.1)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8)
thing$outliers
thing$cpInds
matrix(c(10))
B=matrix(0)
prs <- list(Lambda=matrix(c(10)),B=matrix(0.1),V=matrix(4.1),nu=6.1)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8)
thing$cpInds
prs <- list(Lambda=matrix(c(10)),B=matrix(0.1),V=matrix(10.1),nu=12.1)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8)
thing$cpInds
prs <- list(Lambda=matrix(c(.10)),B=matrix(0.1),V=matrix(10.1),nu=12.1)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8)
thing$cpInds
thing$outliers
plot(thing$lastLs)
plot(Y)
length(thing$lastDataPt)
length(thing$lastLs)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8,getModels=T)
thing$mods
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8,getModels=T,cp_Delay=6)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=6,cptimemin=8,getModels=T,cp_delay=6)
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,outlier_mean=c(5),outlier_var=c(0.5),getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6)
thing$outliers
thing$cpInds
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.8,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6)
thing
thing$cpInds
thing$outliers
plot(thing$RFull[,22])
plot(thing$RFull[,23])
plot(thing$RFull[,24])
plot(thing$RFull[,25])
plot(thing$RFull[,26])
plot(thing$RFull[,27])
thing <- roboBayes(Y,X,lambda=10,par_inits=prs,getOutliers=T,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6)
thing$outliers
thing$cpInds
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=T,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6)
thing
thing$outliers
thing$alpha <- 0.5
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=T,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6,alpha=0.5)
thing
thing$outliers
Y <- rnorm(50,0.5,0.05)
plot(Y)
Y[c(20,40)] <- 1
Y <- as.matrix(Y)
plot(Y)
prs <- list(Lambda=matrix(c(.10)),B=matrix(0.1),V=matrix(.51),nu=12.1)
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=T,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6,alpha=0.5)
thing$cpInds
thing$outliers
plot(thing$RFull[,22])
thing$model0
thing$currentModel
thing$lastLs
plot(thing$lastLs)
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6,alpha=0.5)
plot(thing$lastLs)
prs
.51/10.1
.501/10.1
.51/10.1
prs$B <- 0.5
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6,alpha=0.5)
prs$B <- matrix(0.5)
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=6,alpha=0.5)
thing$out
thing$outliers
thing
thing$cpInds
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=3,alpha=0.5)
thing$cpInds
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=3,alpha=0.5)
plot(thing$RFull[,23])
thing$cpInds
plot(thing$RFull[,26])
plot(thing$RFull[,29])
prs <- list(Lambda=matrix(c(10)),B=matrix(0.1),V=matrix(.51),nu=12.1)
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=3,alpha=0.5)
thing$cpInds
plot(thing$RFull[,29])
plot(thing$RFull[,23])
prs <- list(Lambda=matrix(c(10)),B=matrix(0.5),V=matrix(5.1),nu=22.1)
prs
5/20
thing <- roboBayes(Y,X,lambda=100,par_inits=prs,getOutliers=F,cpthresh=0.5,Lm=15,getR=T,Lgroup=3,cptimemin=8,getModels=T,cp_delay=3,alpha=0.5)
thing$cpInds
thing <- roboBayes(Y,X,getOutliers=F)
thing$cpInds
plot(Y)
X
Y <- rnorm(50,c(rep(1,30),rep(2,20)))
plot(Y)
Y <- rnorm(50,c(rep(1,30),rep(5,20)))
thing <- roboBayes(Y,X,getOutliers=F)
thing$cpInds
thing <- roboBayes(Y,X,getOutliers=T)
thing$outliers
thing$cpInds
Y
Y[30] <- -3
thing <- roboBayes(Y,X,getOutliers=T)
thing$outliers
thing$cpInds
plot(Y)
Y[30] <- -7
thing <- roboBayes(Y,X,getOutliers=T)
thing$cpInds
Y[15] <- -7
Y[15] <- 7
thing <- roboBayes(Y,X,getOutliers=T)
thing$cpInds
thing$outliers
thing <- roboBayes(Y,X,getOutliers=F)
thing$cpInds
thing <- roboBayes(Y,X,getOutliers=F,cp_delay=2)
thing <- roboBayes(Y,X,getOutliers=F,cp_delay=1)
thing$out
thing$cpInds
prs <- list(Lambda=matrix(c(.10)),B=matrix(0.5),V=matrix(4.1),nu=2.1)
thing <- roboBayes(Y,X,getOutliers=F,cp_delay=1,par_inits=prs)
thing$cpInds
prs <- list(Lambda=matrix(c(.10)),B=matrix(0.5),V=matrix(2.1),nu=.1)
thing <- roboBayes(Y,X,getOutliers=F,cp_delay=1,par_inits=prs)
thing$cpInds
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim2b.Rdata")
classic_roboBayes_keepers
MR_roboBayes_keepers
MR_roboBayes_keepers_count_subset[,,1]
MR_roboBayes_keepers_count_subset[1:10,,1]
MR_roboBayes_keepers_count_subset[1:30,,1]
MR_roboBayes_keepers_count_subset[1:30,,2]
MR_roboBayes_keepers_count_subset[1:30,,3]
MR_roboBayes_keepers_count_subset[1:30,,4]
load("H:/LauraWendelberger/wrobobayes/results/metrics_count3_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics
results_metrics$final_metrics
results_metrics$metrics
plot(results_metrics$metrics$FN_agg)
plot(results_metrics$metrics$FP_agg)
plot(results_metrics$metrics$FP)
naems(results_metrics$metrics$FP_sf)
plot(results_metrics$metrics$FP_sf)
plot(results_metrics$metrics$FP)
plot(results_metrics$metrics$FP_sf)
load("H:/LauraWendelberger/wrobobayes/results/metrics_count5_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
plot(results_metrics$metrics$FP_sf)
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count6_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count12_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count10_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count9_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count8_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
load("H:/LauraWendelberger/wrobobayes/results/metrics_count7_US_crop_vscale10_nu10_lambda1_Lsearch30.Rdata")
results_metrics$final_metrics
plot(results_metrics$me\trics$FP)
plot(results_metrics$metrics$FP)
plot(results_metrics$metrics$TP)
load("H:/LauraWendelberger/wrobobayes/results/metrics_count7_US_crop_vscale1_nu10_lambda1_Lsearch30.Rdata")
plot(results_metrics$metrics$FP)
results_metrics$final_metrics
plot(results_metrics$metrics$FP_sf)
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim4.Rdata")
head(classic_roboBayes_metrics)
head(classic_roboBayes_keepers)
load("H:/LauraWendelberger/wrobobayes/results/metrics_sim4.Rdata")
classic_roboBayes_keepers[,2]
MR_roboBayes_keepers[,2]
MR_roboBayes_keepers1[,,2]
MR_roboBayes_keepers1[,2]
1:100
load("H:/LauraWendelberger/wrobobayes/interim_files/priors_sim5.RData")
prior_list$LH1
MR_roboBayes_keepers_count
tvals
MR_roboBayes_keepers_count[1:6,,1]
MR_roboBayes_keepers_count[1:6,,2]
MR_roboBayes_keepers_count[1:6,,3]
MR_roboBayes_keepers_count[1:6,,4]
sds <- sample(1e6,100)
sds
?gp
library(spectralGP)
?gp
gp(c(8,8),matern.specdens,c(1,0.1))
simulate(gpm)
gpm = gp(c(8,8),matern.specdens,c(1,0.1))
simulate(gpm)
mnval <- matrix(gpm$process,dx,dx)
dx <- 8
mnval <- matrix(gpm$process,dx,dx)
mnval
gpm=gp(c(dx,dx),matern.specdens,c(1,0.1))
simulate(gpm)
mnval <- matrix(gpm$process,dx,dx)
mnval
