scratch/redTime/lookatRedTimeResults_ExpRanges3.R
In CollinErickson/CGGP: Composite Grid Gaussian Processes
# This has results from ExpandedRanges3.
# Done after thesis, to be used in publication.
# ER3a used no supp, Greedy, power exp.
# Only using first 80 output dimensions.
x100 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n100_s0506_all_input.csv")[,-1]))
y100 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n100_s0506_all_output.csv")[,-1])))[,1:80]
x1000 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0429_all_input.csv")[,-1]))
y1000 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0429_all_output.csv")[,-1])))[,1:80]
x1000_2 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0505_all_input.csv")[,-1]))
y1000_2 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0505_all_output.csv")[,-1])))[,1:80]
# ER3a
ra.sggp.399 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-399.rds")
ra.sggp.599 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-599.rds")
ra.sggp.799 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-799.rds")
ra.sggp.999 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-999.rds")
ra.sggp.1199 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-1199.rds")
ra.sggp.1699 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-1699.rds")
ra.sggp.2199 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-2199.rds")
ra.sggp.2699 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-2699.rds")
ra.sggp.3195 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-3195.rds")
ra.sggp.4191 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-4191.rds")
ra.sggp.5187 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-5187.rds")
ra.sggp.6179 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-6179.rds")
ra.sggp.7171 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-7171.rds")
ra.sggp.8171 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-8171.rds")
ra.sggp.10159 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-10159.rds")
ra.sggp.12155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-12155.rds")
ra.sggp.16155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-16155.rds")
ra.sggp.18155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-18155.rds")
# ra.sggp.20159 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-20159.rds")
ra.sggp.30151 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-30151.rds")
ra.sggp.40147 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-40147.rds")
ra.sggp.50143 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-50143.rds")
ra.sggp.60139 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-60139.rds")
# ra.sggp.20159.PE<- CGGPfit(ra.sggp.20159, Y=ra.sggp.20159$Y,Xs=ra.sggp.20159$Xs,Ys=ra.sggp.20159$Ys,corr="PowerExp")
ra.sggp.30151.PE<- CGGPfit(ra.sggp.30151, Y=ra.sggp.30151$Y,Xs=ra.sggp.30151$Xs,Ys=ra.sggp.30151$Ys,corr="PowerExp")
# ra.sggp.20159.C <- CGGPfit(ra.sggp.20159, Y=ra.sggp.20159$Y,Xs=ra.sggp.20159$Xs,Ys=ra.sggp.20159$Ys,corr="Cauchy")
ra.sggp.30151.C <- CGGPfit(ra.sggp.30151, Y=ra.sggp.30151$Y,Xs=ra.sggp.30151$Xs,Ys=ra.sggp.30151$Ys,corr="Cauchy")
# Get stats
# Big 3
stats.ra.sggp.399 <- CGGPvalstats(ra.sggp.399, x1000, y1000, bydim=F)
stats.ra.sggp.599 <- CGGPvalstats(ra.sggp.599, x1000, y1000, bydim=F)
stats.ra.sggp.799 <- CGGPvalstats(ra.sggp.799, x1000, y1000, bydim=F)
stats.ra.sggp.999 <- CGGPvalstats(ra.sggp.999, x1000, y1000, bydim=F)
stats.ra.sggp.1199 <- CGGPvalstats(ra.sggp.1199, x1000, y1000, bydim=F)
stats.ra.sggp.1699 <- CGGPvalstats(ra.sggp.1699, x1000, y1000, bydim=F)
stats.ra.sggp.2199 <- CGGPvalstats(ra.sggp.2199, x1000, y1000, bydim=F)
stats.ra.sggp.2699 <- CGGPvalstats(ra.sggp.2699, x1000, y1000, bydim=F)
stats.ra.sggp.3195 <- CGGPvalstats(ra.sggp.3195, x1000, y1000, bydim=F)
stats.ra.sggp.4191 <- CGGPvalstats(ra.sggp.4191, x1000, y1000, bydim=F)
stats.ra.sggp.5187 <- CGGPvalstats(ra.sggp.5187, x1000, y1000, bydim=F)
stats.ra.sggp.6179 <- CGGPvalstats(ra.sggp.6179, x1000, y1000, bydim=F)
stats.ra.sggp.7171 <- CGGPvalstats(ra.sggp.7171, x1000, y1000, bydim=F)
stats.ra.sggp.8171 <- CGGPvalstats(ra.sggp.8171, x1000, y1000, bydim=F)
stats.ra.sggp.10159<- CGGPvalstats(ra.sggp.10159, x1000, y1000, bydim=F)
stats.ra.sggp.12155<- CGGPvalstats(ra.sggp.12155, x1000, y1000, bydim=F)
stats.ra.sggp.16155<- CGGPvalstats(ra.sggp.16155, x1000, y1000, bydim=F)
stats.ra.sggp.18155<- CGGPvalstats(ra.sggp.18155, x1000, y1000, bydim=F)
# stats.ra.sggp.20159<- CGGPvalstats(ra.sggp.20159, x1000, y1000, bydim=F)
stats.ra.sggp.30151<- CGGPvalstats(ra.sggp.30151, x1000, y1000, bydim=F)
stats.ra.sggp.40147<- CGGPvalstats(ra.sggp.40147, x1000, y1000, bydim=F)
stats.ra.sggp.50143<- CGGPvalstats(ra.sggp.50143, x1000, y1000, bydim=F)
stats.ra.sggp.60139<- CGGPvalstats(ra.sggp.60139, x1000, y1000, bydim=F)
# stats.ra.sggp.20159.PE<- CGGPvalstats(ra.sggp.20159.PE, x1000, y1000, bydim=F)
stats.ra.sggp.30151.PE<- CGGPvalstats(ra.sggp.30151.PE, x1000, y1000, bydim=F)
# stats.ra.sggp.20159.C <- CGGPvalstats(ra.sggp.20159.C , x1000, y1000, bydim=F)
stats.ra.sggp.30151.C <- CGGPvalstats(ra.sggp.30151.C , x1000, y1000, bydim=F)
##########################
#### Run with mlegp
##########################
mod.mlegp.50 <- mlegp::mlegp(x100[1:50,], y100[1:50,])
pred.mlegp.50 <- lapply(1:80, function(i) predict(mod.mlegp.50[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.50 <- valstats(pred.mlegp.50$fit, pred.mlegp.50$se, y1000, bydim=F)
mod.mlegp.75 <- mlegp::mlegp(x100[1:75,], y100[1:75,])
pred.mlegp.75 <- lapply(1:80, function(i) predict(mod.mlegp.75[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.75 <- valstats(pred.mlegp.75$fit, pred.mlegp.75$se, y1000, bydim=F)
mod.mlegp.100 <- mlegp::mlegp(x100, y100)
pred.mlegp.100 <- lapply(1:80, function(i) predict(mod.mlegp.100[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.100 <- valstats(pred.mlegp.100$fit, pred.mlegp.100$se, y1000, bydim=F)
mod.mlegp.150 <- sample(1:1000, 150) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.150 <- lapply(1:80, function(i) predict(mod.mlegp.150[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.150 <- valstats(pred.mlegp.150$fit, pred.mlegp.150$se^2, y1000, bydim=F)
mod.mlegp.200 <- sample(1:1000, 200) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.200 <- lapply(1:80, function(i) predict(mod.mlegp.200[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.200 <- valstats(pred.mlegp.200$fit, pred.mlegp.200$se^2, y1000, bydim=F)
mod.mlegp.250 <- sample(1:1000, 250) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.250 <- lapply(1:80, function(i) predict(mod.mlegp.250[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.250 <- valstats(pred.mlegp.250$fit, pred.mlegp.250$se^2, y1000, bydim=F)
# mod.mlegp.300 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.300 <- predict(mod.mlegp.300, x1000, se=T)
# stats.mlegp.300 <- valstats(pred.mlegp.300$fit, pred.mlegp.300$se^2, y1000, bydim=F)
# mod.mlegp.400 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.400 <- predict(mod.mlegp.400, x1000, se=T)
# stats.mlegp.400 <- valstats(pred.mlegp.400$fit, pred.mlegp.400$se^2, y1000, bydim=F)
# mod.mlegp.500 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.500 <- predict(mod.mlegp.500, x1000, se=T)
# stats.mlegp.500 <- valstats(pred.mlegp.500$fit, pred.mlegp.500$se^2, y1000, bydim=F)
allstats <- list(
# CGGP
data.frame("CGGP", 0, 399, 0.1270009 , -3.61207 , 0.05555768, 0.94545 , 0.9969352, 0.9935222, 0.09223503),
data.frame("CGGP", 0, 599, 0.08656919, -4.095473, 0.03655804, 0.954425 , 0.9985132, 0.9969902, 0.06312542),
data.frame("CGGP", 0, 799, 0.07907009, -4.313236, 0.03075301, 0.9594125, 0.9987519, 0.9974891, 0.05773612),
data.frame("CGGP", 0, 999, 0.0750431 , -4.276698, 0.02822047, 0.9563 , 0.9988783, 0.9977383, 0.05491472),
data.frame("CGGP", 0, 1199, 0.06771744, -4.496644, 0.0243313 , 0.96335 , 0.9990838, 0.9981583, 0.04956006),
data.frame("CGGP", 0, 1699, 0.04583473, -5.088112, 0.0241948 , 0.9818875, 0.9995893, 0.9991563, 0.03327931),
data.frame("CGGP", 0, 1199, 0.03068066, -4.947636, 0.02252122, 0.9933375, 0.9998112, 0.999622, 0.02247564),
data.frame("CGGP", 0, 2699, 0.02704855, -5.738293, 0.01620703, 0.9936125, 0.9998545, 0.9997062, 0.01972948),
data.frame("CGGP", 0, 3195, 0.02155968, -6.241509, 0.0126585, 0.9906125, 0.9999072, 0.9998133, 0.01573683),
data.frame("CGGP", 0, 4191, 0.02113381, -6.562632, 0.01087367, 0.985575 , 0.9999103, 0.9998206, 0.01543448),
data.frame("CGGP", 0, 5187, 0.01550605, -6.789563, 0.00939717, 0.993375 , 0.9999519, 0.9999034, 0.01128335),
data.frame("CGGP", 0, 6179, 0.01353115, -6.940049, 0.008560179, 0.992275, 0.9999634, 0.9999265, 0.009878027),
data.frame("CGGP", 0, 7171, 0.01272007, -7.123897, 0.007880539,0.9931625, 0.9999676, 0.999935, 0.009295405),
data.frame("CGGP", 0, 8171, 0.01134226, -7.209546, 0.007439439,0.9928125, 0.9999742, 0.9999483, 0.008268045),
data.frame("CGGP", 0, 10159, 0.01038528, -7.561542, 0.00635735, 0.9885125, 0.9999784, 0.9999567, 0.0075761),
data.frame("CGGP", 0, 12159, 0.009005683,-7.803026, 0.005657783, 0.991825, 0.9999838, 0.9999674, 0.006588723),
data.frame("CGGP", 0, 16155, 0.007374222,-7.991828, 0.005007521,0.9932375, 0.9999891, 0.9999782, 0.005393246),
data.frame("CGGP", 0, 18155, 0.006947166,-8.225993, 0.004492074, 0.991475, 0.9999903, 0.9999806, 0.005082594),
data.frame("CGGP", 0, 30151, 0.005188444,-8.647838, 0.003515817, 0.9932, 0.9999946, 0.9999892, 0.003800047),
data.frame("CGGP", 0, 40147, 0.004770253,-8.632408, 0.003520589, 0.9935875,0.9999955, 0.9999909, 0.003494456),
data.frame("CGGP", 0, 50143, 0.003983129,-8.958887, 0.002996187, 0.9950625,0.9999968, 0.9999936, 0.002909961),
data.frame("CGGP", 0, 60139, 0.004100328,-9.040298, 0.00288365, 0.9943125,0.9999966, 0.9999932, 0.003006344),
data.frame("mlegp", 0, 50, 0.3392193, -1.446142, 0.1700039, 0.9710875, 0.9789255, 0.9537862, 0.2458831),
data.frame("mlegp", 0, 75, 0.2660584, -1.835621, 0.1350989, 0.986425, 0.9859735, 0.9715708, 0.1934031),
data.frame("mlegp", 0, 100, 0.2208473, -1.999161, 0.1182225, 0.9906625, 0.9901938, 0.9804118, 0.1607628),
data.frame("mlegp", 0, 150, 0.1951786, -1.795716, 0.08392041, 0.81135, 0.9923303, 0.9847006, 0.1424369),
data.frame("mlegp", 0, 200, 0.19641, -2.27177, 0.08338489,0.781575 , 0.9923615, 0.9845069, 0.1433341),
data.frame("mlegp", 0, 250, 0.1393964, -2.492744, 0.0581818, 0.7953125, 0.9961854, 0.9921961, 0.1017273)
# data.frame("mlegp", , ),
# data.frame("mlegp", , ),
)
allstats <- lapply(allstats, function(x){colnames(x) <- c("Package", 'Nsup',"Ngrid","RMSE","score","CRPscore","coverage","corr","R2","RMSEnorm");x})
allstats <- do.call(rbind, allstats)
allstats$Ntotal <- allstats$Nsup + allstats$Ngrid
allstats$Package <- as.character(allstats$Package)
library(ggplot2)
# ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Package, shape=as.factor(Nsup))) + geom_point(size=3) + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Package)) + geom_point(size=3) + scale_x_log10() + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Nsup)) + geom_point(size=3) + facet_grid(. ~ Package) + scale_x_log10() + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, score, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10()
ggplot(data=allstats, mapping=aes(Ntotal, CRPscore, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10() + scale_y_log10()
# ggplot(data=allstats %>% filter(score<1e5), mapping=aes(Ntotal, score, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10()
CollinErickson/CGGP documentation built on Feb. 6, 2024, 2:24 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.
# This has results from ExpandedRanges3.
# Done after thesis, to be used in publication.
# ER3a used no supp, Greedy, power exp.
# Only using first 80 output dimensions.
x100 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n100_s0506_all_input.csv")[,-1]))
y100 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n100_s0506_all_output.csv")[,-1])))[,1:80]
x1000 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0429_all_input.csv")[,-1]))
y1000 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0429_all_output.csv")[,-1])))[,1:80]
x1000_2 <- unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0505_all_input.csv")[,-1]))
y1000_2 <- log(unname(as.matrix(read.csv("./scratch/redTime/redTimeData/ExpandedRanges3_LHS1L_n1000_s0505_all_output.csv")[,-1])))[,1:80]
# ER3a
ra.sggp.399 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-399.rds")
ra.sggp.599 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-599.rds")
ra.sggp.799 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-799.rds")
ra.sggp.999 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-999.rds")
ra.sggp.1199 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-1199.rds")
ra.sggp.1699 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-1699.rds")
ra.sggp.2199 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-2199.rds")
ra.sggp.2699 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-2699.rds")
ra.sggp.3195 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-3195.rds")
ra.sggp.4191 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-4191.rds")
ra.sggp.5187 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-5187.rds")
ra.sggp.6179 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-6179.rds")
ra.sggp.7171 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-7171.rds")
ra.sggp.8171 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-8171.rds")
ra.sggp.10159 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-10159.rds")
ra.sggp.12155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-12155.rds")
ra.sggp.16155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-16155.rds")
ra.sggp.18155 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-18155.rds")
# ra.sggp.20159 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-20159.rds")
ra.sggp.30151 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-30151.rds")
ra.sggp.40147 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-40147.rds")
ra.sggp.50143 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-50143.rds")
ra.sggp.60139 <- readRDS("./scratch/redTime/redTimeData/out_ER3a_SGGP-60139.rds")
# ra.sggp.20159.PE<- CGGPfit(ra.sggp.20159, Y=ra.sggp.20159$Y,Xs=ra.sggp.20159$Xs,Ys=ra.sggp.20159$Ys,corr="PowerExp")
ra.sggp.30151.PE<- CGGPfit(ra.sggp.30151, Y=ra.sggp.30151$Y,Xs=ra.sggp.30151$Xs,Ys=ra.sggp.30151$Ys,corr="PowerExp")
# ra.sggp.20159.C <- CGGPfit(ra.sggp.20159, Y=ra.sggp.20159$Y,Xs=ra.sggp.20159$Xs,Ys=ra.sggp.20159$Ys,corr="Cauchy")
ra.sggp.30151.C <- CGGPfit(ra.sggp.30151, Y=ra.sggp.30151$Y,Xs=ra.sggp.30151$Xs,Ys=ra.sggp.30151$Ys,corr="Cauchy")
# Get stats
# Big 3
stats.ra.sggp.399 <- CGGPvalstats(ra.sggp.399, x1000, y1000, bydim=F)
stats.ra.sggp.599 <- CGGPvalstats(ra.sggp.599, x1000, y1000, bydim=F)
stats.ra.sggp.799 <- CGGPvalstats(ra.sggp.799, x1000, y1000, bydim=F)
stats.ra.sggp.999 <- CGGPvalstats(ra.sggp.999, x1000, y1000, bydim=F)
stats.ra.sggp.1199 <- CGGPvalstats(ra.sggp.1199, x1000, y1000, bydim=F)
stats.ra.sggp.1699 <- CGGPvalstats(ra.sggp.1699, x1000, y1000, bydim=F)
stats.ra.sggp.2199 <- CGGPvalstats(ra.sggp.2199, x1000, y1000, bydim=F)
stats.ra.sggp.2699 <- CGGPvalstats(ra.sggp.2699, x1000, y1000, bydim=F)
stats.ra.sggp.3195 <- CGGPvalstats(ra.sggp.3195, x1000, y1000, bydim=F)
stats.ra.sggp.4191 <- CGGPvalstats(ra.sggp.4191, x1000, y1000, bydim=F)
stats.ra.sggp.5187 <- CGGPvalstats(ra.sggp.5187, x1000, y1000, bydim=F)
stats.ra.sggp.6179 <- CGGPvalstats(ra.sggp.6179, x1000, y1000, bydim=F)
stats.ra.sggp.7171 <- CGGPvalstats(ra.sggp.7171, x1000, y1000, bydim=F)
stats.ra.sggp.8171 <- CGGPvalstats(ra.sggp.8171, x1000, y1000, bydim=F)
stats.ra.sggp.10159<- CGGPvalstats(ra.sggp.10159, x1000, y1000, bydim=F)
stats.ra.sggp.12155<- CGGPvalstats(ra.sggp.12155, x1000, y1000, bydim=F)
stats.ra.sggp.16155<- CGGPvalstats(ra.sggp.16155, x1000, y1000, bydim=F)
stats.ra.sggp.18155<- CGGPvalstats(ra.sggp.18155, x1000, y1000, bydim=F)
# stats.ra.sggp.20159<- CGGPvalstats(ra.sggp.20159, x1000, y1000, bydim=F)
stats.ra.sggp.30151<- CGGPvalstats(ra.sggp.30151, x1000, y1000, bydim=F)
stats.ra.sggp.40147<- CGGPvalstats(ra.sggp.40147, x1000, y1000, bydim=F)
stats.ra.sggp.50143<- CGGPvalstats(ra.sggp.50143, x1000, y1000, bydim=F)
stats.ra.sggp.60139<- CGGPvalstats(ra.sggp.60139, x1000, y1000, bydim=F)
# stats.ra.sggp.20159.PE<- CGGPvalstats(ra.sggp.20159.PE, x1000, y1000, bydim=F)
stats.ra.sggp.30151.PE<- CGGPvalstats(ra.sggp.30151.PE, x1000, y1000, bydim=F)
# stats.ra.sggp.20159.C <- CGGPvalstats(ra.sggp.20159.C , x1000, y1000, bydim=F)
stats.ra.sggp.30151.C <- CGGPvalstats(ra.sggp.30151.C , x1000, y1000, bydim=F)
##########################
#### Run with mlegp
##########################
mod.mlegp.50 <- mlegp::mlegp(x100[1:50,], y100[1:50,])
pred.mlegp.50 <- lapply(1:80, function(i) predict(mod.mlegp.50[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.50 <- valstats(pred.mlegp.50$fit, pred.mlegp.50$se, y1000, bydim=F)
mod.mlegp.75 <- mlegp::mlegp(x100[1:75,], y100[1:75,])
pred.mlegp.75 <- lapply(1:80, function(i) predict(mod.mlegp.75[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.75 <- valstats(pred.mlegp.75$fit, pred.mlegp.75$se, y1000, bydim=F)
mod.mlegp.100 <- mlegp::mlegp(x100, y100)
pred.mlegp.100 <- lapply(1:80, function(i) predict(mod.mlegp.100[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.100 <- valstats(pred.mlegp.100$fit, pred.mlegp.100$se, y1000, bydim=F)
mod.mlegp.150 <- sample(1:1000, 150) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.150 <- lapply(1:80, function(i) predict(mod.mlegp.150[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.150 <- valstats(pred.mlegp.150$fit, pred.mlegp.150$se^2, y1000, bydim=F)
mod.mlegp.200 <- sample(1:1000, 200) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.200 <- lapply(1:80, function(i) predict(mod.mlegp.200[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.200 <- valstats(pred.mlegp.200$fit, pred.mlegp.200$se^2, y1000, bydim=F)
mod.mlegp.250 <- sample(1:1000, 250) %>% {mlegp::mlegp(x1000_2[.,], y1000_2[.,])}
pred.mlegp.250 <- lapply(1:80, function(i) predict(mod.mlegp.250[[i]], x1000, se=T)) %>% {list(fit={do.call(cbind, lapply(., function(i) i$fit))}, se.fit={do.call(cbind, lapply(., function(i) i$se.fit))})}
stats.mlegp.250 <- valstats(pred.mlegp.250$fit, pred.mlegp.250$se^2, y1000, bydim=F)
# mod.mlegp.300 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.300 <- predict(mod.mlegp.300, x1000, se=T)
# stats.mlegp.300 <- valstats(pred.mlegp.300$fit, pred.mlegp.300$se^2, y1000, bydim=F)
# mod.mlegp.400 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.400 <- predict(mod.mlegp.400, x1000, se=T)
# stats.mlegp.400 <- valstats(pred.mlegp.400$fit, pred.mlegp.400$se^2, y1000, bydim=F)
# mod.mlegp.500 <- mlegp::mlegp(x1000_2, y1000_2)
# pred.mlegp.500 <- predict(mod.mlegp.500, x1000, se=T)
# stats.mlegp.500 <- valstats(pred.mlegp.500$fit, pred.mlegp.500$se^2, y1000, bydim=F)
allstats <- list(
# CGGP
data.frame("CGGP", 0, 399, 0.1270009 , -3.61207 , 0.05555768, 0.94545 , 0.9969352, 0.9935222, 0.09223503),
data.frame("CGGP", 0, 599, 0.08656919, -4.095473, 0.03655804, 0.954425 , 0.9985132, 0.9969902, 0.06312542),
data.frame("CGGP", 0, 799, 0.07907009, -4.313236, 0.03075301, 0.9594125, 0.9987519, 0.9974891, 0.05773612),
data.frame("CGGP", 0, 999, 0.0750431 , -4.276698, 0.02822047, 0.9563 , 0.9988783, 0.9977383, 0.05491472),
data.frame("CGGP", 0, 1199, 0.06771744, -4.496644, 0.0243313 , 0.96335 , 0.9990838, 0.9981583, 0.04956006),
data.frame("CGGP", 0, 1699, 0.04583473, -5.088112, 0.0241948 , 0.9818875, 0.9995893, 0.9991563, 0.03327931),
data.frame("CGGP", 0, 1199, 0.03068066, -4.947636, 0.02252122, 0.9933375, 0.9998112, 0.999622, 0.02247564),
data.frame("CGGP", 0, 2699, 0.02704855, -5.738293, 0.01620703, 0.9936125, 0.9998545, 0.9997062, 0.01972948),
data.frame("CGGP", 0, 3195, 0.02155968, -6.241509, 0.0126585, 0.9906125, 0.9999072, 0.9998133, 0.01573683),
data.frame("CGGP", 0, 4191, 0.02113381, -6.562632, 0.01087367, 0.985575 , 0.9999103, 0.9998206, 0.01543448),
data.frame("CGGP", 0, 5187, 0.01550605, -6.789563, 0.00939717, 0.993375 , 0.9999519, 0.9999034, 0.01128335),
data.frame("CGGP", 0, 6179, 0.01353115, -6.940049, 0.008560179, 0.992275, 0.9999634, 0.9999265, 0.009878027),
data.frame("CGGP", 0, 7171, 0.01272007, -7.123897, 0.007880539,0.9931625, 0.9999676, 0.999935, 0.009295405),
data.frame("CGGP", 0, 8171, 0.01134226, -7.209546, 0.007439439,0.9928125, 0.9999742, 0.9999483, 0.008268045),
data.frame("CGGP", 0, 10159, 0.01038528, -7.561542, 0.00635735, 0.9885125, 0.9999784, 0.9999567, 0.0075761),
data.frame("CGGP", 0, 12159, 0.009005683,-7.803026, 0.005657783, 0.991825, 0.9999838, 0.9999674, 0.006588723),
data.frame("CGGP", 0, 16155, 0.007374222,-7.991828, 0.005007521,0.9932375, 0.9999891, 0.9999782, 0.005393246),
data.frame("CGGP", 0, 18155, 0.006947166,-8.225993, 0.004492074, 0.991475, 0.9999903, 0.9999806, 0.005082594),
data.frame("CGGP", 0, 30151, 0.005188444,-8.647838, 0.003515817, 0.9932, 0.9999946, 0.9999892, 0.003800047),
data.frame("CGGP", 0, 40147, 0.004770253,-8.632408, 0.003520589, 0.9935875,0.9999955, 0.9999909, 0.003494456),
data.frame("CGGP", 0, 50143, 0.003983129,-8.958887, 0.002996187, 0.9950625,0.9999968, 0.9999936, 0.002909961),
data.frame("CGGP", 0, 60139, 0.004100328,-9.040298, 0.00288365, 0.9943125,0.9999966, 0.9999932, 0.003006344),
data.frame("mlegp", 0, 50, 0.3392193, -1.446142, 0.1700039, 0.9710875, 0.9789255, 0.9537862, 0.2458831),
data.frame("mlegp", 0, 75, 0.2660584, -1.835621, 0.1350989, 0.986425, 0.9859735, 0.9715708, 0.1934031),
data.frame("mlegp", 0, 100, 0.2208473, -1.999161, 0.1182225, 0.9906625, 0.9901938, 0.9804118, 0.1607628),
data.frame("mlegp", 0, 150, 0.1951786, -1.795716, 0.08392041, 0.81135, 0.9923303, 0.9847006, 0.1424369),
data.frame("mlegp", 0, 200, 0.19641, -2.27177, 0.08338489,0.781575 , 0.9923615, 0.9845069, 0.1433341),
data.frame("mlegp", 0, 250, 0.1393964, -2.492744, 0.0581818, 0.7953125, 0.9961854, 0.9921961, 0.1017273)
# data.frame("mlegp", , ),
# data.frame("mlegp", , ),
)
allstats <- lapply(allstats, function(x){colnames(x) <- c("Package", 'Nsup',"Ngrid","RMSE","score","CRPscore","coverage","corr","R2","RMSEnorm");x})
allstats <- do.call(rbind, allstats)
allstats$Ntotal <- allstats$Nsup + allstats$Ngrid
allstats$Package <- as.character(allstats$Package)
library(ggplot2)
# ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Package, shape=as.factor(Nsup))) + geom_point(size=3) + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Package)) + geom_point(size=3) + scale_x_log10() + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, RMSE, color=Nsup)) + geom_point(size=3) + facet_grid(. ~ Package) + scale_x_log10() + scale_y_log10()
ggplot(data=allstats, mapping=aes(Ntotal, score, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10()
ggplot(data=allstats, mapping=aes(Ntotal, CRPscore, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10() + scale_y_log10()
# ggplot(data=allstats %>% filter(score<1e5), mapping=aes(Ntotal, score, color=(Package), shape=as.factor(Nsup))) + geom_point(size=3) + scale_x_log10()
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.