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04_b_App_Dense.R
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04_b_App_Dense.R
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source('00_system.R')
load('Datasets/app_dataset.RData')
# Initial Model (Model A)
if(T){
list_formulas <- list("mean" = as.formula(" ~ 1 + lati + long + wind + elevation + cloud_c"),
"std.dev" = as.formula(" ~ 1 + log_cloud + log_elevation + log_wind + log_long + log_lati"),
"scale" = as.formula(" ~ 1 + log_cloud + log_elevation + log_wind + log_long + log_lati"),
"aniso" = as.formula(" ~ 1 + log_cloud + log_elevation + log_wind + log_long + log_lati"),
"tilt" = as.formula(" ~ 1 + log_cloud + log_elevation + log_wind + log_long + log_lati"),
"smooth" = as.formula(" ~ 1 + log_cloud + log_elevation + log_wind + log_long + log_lati"),
"nugget" = -Inf)
test_coco <- coco(type = 'dense',
data = all_dfs[[2]],
locs = as.matrix(all_dfs[[2]][, 1:2]),
z = all_dfs[[2]]$prec,
model.list = list_formulas,
info = list('lambda' = 0.025 * sqrt(8),
'smooth_limits' = c(0.5, 2.0)
)
)
boundaries_A <- getBoundariesV2(coco.object = test_coco,
mean.limits = c(-1.5, 0, 1.5),
std.dev.limits = c(-2.5, 0, 2.5),
scale.limits = c(-2.5, 0, 2.5),
aniso.limits = c(-2, 0, 2),
tilt.limits = c(-2, 0, 2),
smooth.limits = c(-2, 0, 1.5),
nugget.limits = c(-5, -3, 1))
# Tailored boundaries
if(T){
first_var <- which(names(boundaries_A$theta_init) == "std.dev.limits")[1]
first_range <- which(names(boundaries_A$theta_init) == "scale.limits")[1]
first_smooth <- which(names(boundaries_A$theta_init) == "smooth.limits")[1]
boundaries_A$theta_upper[c(first_var, first_range)] <- c(5, 5)
boundaries_A$theta_lower[c(first_var, first_range)] <- c(-5, -1.75)
boundaries_A$theta_init[first_range] <- log(sd(c(dist(test_coco@locs))))
boundaries_A$theta_upper[first_smooth] <- 2
boundaries_A$theta_lower[first_smooth] <- -3
boundaries_A$theta_init[first_smooth] <- -3
boundaries_A$theta_init[1] <- mean(test_coco@z)
boundaries_A$theta_upper[1] <- boundaries_A$theta_init[1] + 3
boundaries_A$theta_lower[1] <- boundaries_A$theta_init[1] - 3
}
Time_A <- system.time({Model_A <- cocoOptim(coco.object = test_coco,
ncores = ncores,
boundaries = boundaries_A,
optim.type = "mle")})
plot(Model_A)
save(Time_A,Model_A, file = 'Rdata/Model_A.RData')
}
# Model B - A (better) specification of the trend
if(T){
list_formulas <- list("mean" = as.formula(" ~ 1 + wind + new_wind + BIO04 + BIO15 + cloud_c + elevation + lati + long"),
"std.dev" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation + log_lati + log_long"),
"scale" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation + log_lati + log_long"),
"aniso" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation + log_lati + log_long"),
"tilt" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation + log_lati + log_long"),
"smooth" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation + log_lati + log_long"),
"nugget" = -Inf)
test_coco_classic <- coco(type = 'dense',
data = all_dfs[[2]],
locs = as.matrix(all_dfs[[2]][, 1:2]),
z = all_dfs[[2]]$prec,
model.list = list_formulas,
info = list('lambda' = 0.025 * sqrt(8), # before was 0.075
'smooth_limits' = c(0.5, 2.0)
)
)
# Tailored boundaries
if(T){
boundaries_B <- getBoundariesV2(coco.object = test_coco_classic,
mean.limits = c(-1.5, 0, 1.5),
std.dev.limits = c(-2.5, 0, 2.5),
scale.limits = c(-2.5, 0, 2.5),
aniso.limits = c(-2, 0, 2),
tilt.limits = c(-2, 0, 2),
smooth.limits = c(-2, 0, 1.5),
nugget.limits = c(-5, -3, 1))
first_var <- which(names(boundaries_B$theta_init) == "std.dev.limits")[1]
first_range <- which(names(boundaries_B$theta_init) == "scale.limits")[1]
first_smooth <- which(names(boundaries_B$theta_init) == "smooth.limits")[1]
boundaries_B$theta_upper[c(first_var, first_range)] <- c(5, 5)
boundaries_B$theta_lower[c(first_var, first_range)] <- c(-5, -1.75)
boundaries_B$theta_init[first_range] <- log(sd(c(dist(test_coco_classic@locs))))
boundaries_B$theta_upper[first_smooth] <- 2
boundaries_B$theta_lower[first_smooth] <- -3
boundaries_B$theta_init[first_smooth] <- -3
boundaries_B$theta_init[1] <- mean(test_coco_classic@z)
boundaries_B$theta_upper[1] <- boundaries_B$theta_init[1] + 1
boundaries_B$theta_lower[1] <- boundaries_B$theta_init[1] - 1
}
Time_B <- system.time({Model_B <- cocoOptim(coco.object = test_coco_classic,
ncores = ncores,
boundaries = boundaries_B,
optim.type = 'mle')})
HESS_B <- getHessian(Model_B, ncores = ncores)
aaa <- getCIs(coco.object = Model_B, inv.hess = solve(HESS_B), alpha = 0.2)
which(aaa[,1] < 0 & aaa[,2] > 0)
save(Time_B, Model_B, HESS_B, file = 'RData/Model_B.RData')
}
# Model C - (informally) Reduced
if(F){
list_formulas <- list("mean" = as.formula(" ~ 1 + wind + new_wind + BIO04 + BIO15 + cloud_c + elevation + lati + long"),
"std.dev" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation"),
"scale" = as.formula(" ~ 1 + log_wind + log_elevation"),
"aniso" = as.formula(" ~ 1 + log_lati + log_long"),
"tilt" = as.formula(" ~ 1 + log_cloud"),
"smooth" = as.formula(" ~ 1 + log_wind + log_long"),
"nugget" = -Inf)
test_coco_red <- coco(type = 'dense',
data = all_dfs[[2]],
locs = as.matrix(all_dfs[[2]][, 1:2]),
z = all_dfs[[2]]$prec,
model.list = list_formulas,
info = list('lambda' = 0.025 * sqrt(8),
'smooth_limits' = c(0.5, 2.0)
)
)
# Tailored boundaries
if(T){
boundaries_C <- getBoundariesV2(coco.object = test_coco_red,
mean.limits = c(-1.5, 0, 1.5),
std.dev.limits = c(-2.5, 0, 2.5),
scale.limits = c(-2.5, 0, 2.5),
aniso.limits = c(-2, 0, 2),
tilt.limits = c(-2, 0, 2),
smooth.limits = c(-2, 0, 1.5),
nugget.limits = c(-5, -3, 1))
first_var <- which(names(boundaries_C$theta_init) == "std.dev.limits")[1]
first_range <- which(names(boundaries_C$theta_init) == "scale.limits")[1]
first_smooth <- which(names(boundaries_C$theta_init) == "smooth.limits")[1]
boundaries_C$theta_upper[c(first_var, first_range)] <- c(5, 5)
boundaries_C$theta_lower[c(first_var, first_range)] <- c(-5, -1.75)
boundaries_C$theta_init[first_range] <- log(sd(c(dist(test_coco_red@locs))))
boundaries_C$theta_upper[first_smooth] <- 1.5
boundaries_C$theta_lower[first_smooth] <- -3
boundaries_C$theta_init[first_smooth] <- -3
boundaries_C$theta_init[1] <- mean(test_coco_red@z)
boundaries_C$theta_upper[1] <- boundaries_C$theta_init[1] + 3
boundaries_C$theta_lower[1] <- boundaries_C$theta_init[1] - 1
}
time_C <- system.time({Model_C <- cocoOptim(coco.object = test_coco_red,
ncores = ncores,
boundaries = boundaries_C,
optim.type = 'mle')})
HESS_C <- getHessian(Model_C, ncores = ncores)
aaa <- getCIs(Model_C, inv.hess = solve(HESS_C), alpha = 0.2)
which(aaa[,1] < 0 & aaa[,2] > 0)
save(Model_C, time_C, HESS_C, file = 'RData/Model_C.RData')
}
# Model D - Classic model
if(T){
list_formulas <- list("mean" = as.formula(" ~ 1 + wind + new_wind + BIO04 + BIO15 + cloud_c + elevation + lati + long"),
"std.dev" = as.formula(" ~ 1 "),
"scale" = as.formula(" ~ 1 "),
"aniso" = as.formula(" ~ 1 "),
"tilt" = as.formula(" ~ 1 "),
"smooth" = as.formula(" ~ 1 "),
"nugget" = -Inf)
test_coco_classic <- coco(type = 'dense',
data = all_dfs[[2]],
locs = as.matrix(all_dfs[[2]][, 1:2]),
z = all_dfs[[2]]$prec,
model.list = list_formulas,
info = list('smooth_limits' = c(0.5, 2.0)
))
# Tailored boundaries
if(T){
boundaries_D <- getBoundariesV2(coco.object = test_coco_classic,
mean.limits = c(-3, 0, 3),
std.dev.limits = c(-2.5, 0, 2.5),
scale.limits = c(-2.5, 0, 2.5),
aniso.limits = c(-2, 0, 2),
tilt.limits = c(-2, 0, 2),
smooth.limits = c(-3, 0, 1.5),
nugget.limits = c(-5, -3, 1))
boundaries_D$theta_upper[1] <- 6
first_var <- which(names(boundaries_D$theta_init) == "std.dev.limits")[1]
first_range <- which(names(boundaries_D$theta_init) == "scale.limits")[1]
first_smooth <- which(names(boundaries_D$theta_init) == "smooth.limits")[1]
boundaries_D$theta_init[1] <- c(mean(test_coco_classic@z))
boundaries_D$theta_upper[c(first_var, first_range)] <- c(4, 5)
boundaries_D$theta_lower[c(first_var, first_range)] <- c(-5, -3)
boundaries_D$theta_init[first_range] <- log(sd(c(dist(test_coco_classic@locs))))
boundaries_D$theta_upper[first_smooth] <- 2
}
Time_D <- system.time({Model_D <- cocoOptim(coco.object = test_coco_classic,
ncores = ncores,
boundaries = boundaries_D,
optim.type = 'mle')})
getLoglik(Model_D)
getBIC(Model_D)
save(Time_D, Model_D, file = 'RData/Model_D.RData')
}
# Model E - Global smoothness
if(T){
list_formulas <- list("mean" = as.formula(" ~ 1 + wind + new_wind + BIO04 + BIO15 + cloud_c + elevation + lati + long"),
"std.dev" = as.formula(" ~ 1 + log_cloud + log_wind + log_elevation"),
"scale" = as.formula(" ~ 1 + log_wind + log_elevation"),
"aniso" = as.formula(" ~ 1 + log_lati + log_long"),
"tilt" = as.formula(" ~ 1 + log_cloud"),
"smooth" = as.formula(" ~ 1"),
"nugget" = -Inf)
test_coco_red <- coco(type = 'dense',
data = all_dfs[[2]],
locs = as.matrix(all_dfs[[2]][, 1:2]),
z = all_dfs[[2]]$prec,
model.list = list_formulas,
info = list('lambda' = 0.025 * sqrt(8),
'smooth_limits' = c(0.5, 2.0)
))
# Tailored boundaries
if(T){
boundaries_E <- getBoundariesV2(coco.object = test_coco_red,
mean.limits = c(-1.5, 0, 1.5),
std.dev.limits = c(-2.5, 0, 2.5),
scale.limits = c(-2.5, 0, 2.5),
aniso.limits = c(-2, 0, 2),
tilt.limits = c(-2, 0, 2),
smooth.limits = c(-2, 0, 1.5),
nugget.limits = c(-5, -3, 1))
first_var <- which(names(boundaries_E$theta_init) == "std.dev.limits")[1]
first_range <- which(names(boundaries_E$theta_init) == "scale.limits")[1]
first_smooth <- which(names(boundaries_E$theta_init) == "smooth.limits")[1]
boundaries_E$theta_upper[c(first_var, first_range)] <- c(5, 5)
boundaries_E$theta_lower[c(first_var, first_range)] <- c(-5, -1.75)
boundaries_E$theta_init[first_range] <- log(sd(c(dist(test_coco_red@locs))))
boundaries_E$theta_upper[first_smooth] <- 1.5
boundaries_E$theta_lower[first_smooth] <- -3
boundaries_E$theta_init[first_smooth] <- -3
boundaries_E$theta_init[1] <- mean(test_coco_red@z)
boundaries_E$theta_upper[1] <- boundaries_E$theta_init[1] + 3
boundaries_E$theta_lower[1] <- boundaries_E$theta_init[1] - 3
}
time_E <- system.time({Model_E <- cocoOptim(coco.object = test_coco_red,
ncores = ncores,
boundaries = boundaries_E,
optim.type = 'mle')})
save(Model_E, time_E, file = 'RData/Model_E.RData')
}
# Predictions
if(T){
newdataset <- all_dfs[[1]]
newlocs <- all_dfs[[1]][ ,1:2]
z_values <- all_dfs[[1]]$prec
# Create holdouts
if(T){
set.seed(100621)
hetero_holdouts <- kmeans(as.data.frame(scale(newdataset[,c(1,2,4:9)])),centers = 100,iter.max = 100)
groups <- as.factor(hetero_holdouts$cluster)
quilt.plot(newlocs, hetero_holdouts$cluster, nx = 150, ny = 150)
}
Pred_B <- cocoPredict(coco.object = Model_B,
newdataset = newdataset,
newlocs = newlocs,
type = 'pred')
Pred_C <- cocons::cocoPredict(coco.object = Model_C,
newdataset = newdataset,
newlocs = newlocs,
type = 'pred')
Pred_D <- cocons::cocoPredict(coco.object = Model_D,
newdataset = newdataset,
newlocs = newlocs,
type = 'pred')
Pred_E <- cocons::cocoPredict(coco.object = Model_E,
newdataset = newdataset,
newlocs = newlocs,
type = 'pred')
CRPS_B <- getCRPS(z_values,mean.pred = Pred_B$trend + Pred_B$mean,sd.pred = Pred_B$sd.pred)
CRPS_C <- getCRPS(z_values,mean.pred = Pred_C$trend + Pred_C$mean,sd.pred = Pred_C$sd.pred)
CRPS_D <- getCRPS(z_values,mean.pred = Pred_D$trend + Pred_D$mean,sd.pred = Pred_D$sd.pred)
CRPS_E <- getCRPS(z_values,mean.pred = Pred_E$trend + Pred_E$mean,sd.pred = Pred_E$sd.pred)
Logscore_B <- getLogRank(z_values,mean.pred = Pred_B$trend + Pred_B$mean,sd.pred = Pred_B$sd.pred)
Logscore_C <- getLogRank(z_values,mean.pred = Pred_C$trend + Pred_C$mean,sd.pred = Pred_C$sd.pred)
Logscore_D <- getLogRank(z_values,mean.pred = Pred_D$trend + Pred_D$mean,sd.pred = Pred_D$sd.pred)
Logscore_E <- getLogRank(z_values,mean.pred = Pred_E$trend + Pred_E$mean,sd.pred = Pred_E$sd.pred)
z_std_B <- (Pred_B$trend + Pred_B$mean - z_values) / Pred_B$sd.pred
z_std_C <- (Pred_C$trend + Pred_C$mean - z_values) / Pred_C$sd.pred
z_std_D <- (Pred_D$trend + Pred_D$mean - z_values) / Pred_D$sd.pred
z_std_E <- (Pred_E$trend + Pred_E$mean - z_values) / Pred_E$sd.pred
save(Pred_B, Pred_C, Pred_D, Pred_E,
CRPS_B, CRPS_C, CRPS_D, CRPS_E,
Logscore_B, Logscore_C, Logscore_D, Logscore_E,
z_std_B, z_std_C, z_std_D, z_std_E,
file = 'RData/Pred_dense.RData')
}
# Metrics
load('RData/Pred_dense.RData')
if(T){
# RMSPE
if(T){
RMSPE_C <- numeric(100)
for(ii in 1:100){
RMSPE_C[ii] <- sqrt(mean((Pred_C$mean[groups == levels(groups)[ii]] + Pred_C$trend[groups == levels(groups)[ii]] - z_values[groups == levels(groups)[ii]])^2))
}
mean(RMSPE_C)
sd(RMSPE_C)
RMSPE_D <- numeric(100)
for(ii in 1:100){
RMSPE_D[ii] <- sqrt(mean((Pred_D$mean[groups == levels(groups)[ii]] + Pred_D$trend[groups == levels(groups)[ii]] - z_values[groups == levels(groups)[ii]])^2))
}
mean(RMSPE_D)
sd(RMSPE_D)
RMSPE_E <- numeric(100)
for(ii in 1:100){
RMSPE_E[ii] <- sqrt(mean((Pred_E$mean[groups == levels(groups)[ii]] +
Pred_E$trend[groups == levels(groups)[ii]] -
z_values[groups == levels(groups)[ii]])^2))
}
mean(RMSPE_E)
sd(RMSPE_E)
matrix_to_show <- t(round(cbind(c(mean(RMSPE_D),mean(RMSPE_C),mean(RMSPE_E)),
c(sd(RMSPE_D),sd(RMSPE_C),sd(RMSPE_E))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('RMSPE\n')
print(matrix_to_show)
cat('------\n')
}
# CRPS
if(T){
CRPS_C_holdout <- numeric(100)
for(ii in 1:100){
CRPS_C_holdout[ii] <- mean(CRPS_C[groups == levels(groups)[ii]])
}
mean(CRPS_C_holdout)
sd(CRPS_C_holdout)
CRPS_D_holdout <- numeric(100)
for(ii in 1:100){
CRPS_D_holdout[ii] <- mean(CRPS_D[groups == levels(groups)[ii]])
}
mean(CRPS_D_holdout)
sd(CRPS_D_holdout)
CRPS_E_holdout <- numeric(100)
for(ii in 1:100){
CRPS_E_holdout[ii] <- mean(CRPS_E[groups == levels(groups)[ii]])
}
mean(CRPS_E_holdout)
sd(CRPS_E_holdout)
matrix_to_show <- t(round(cbind(c(mean(CRPS_D_holdout),mean(CRPS_C_holdout),mean(CRPS_E_holdout)),
c(sd(CRPS_D_holdout),sd(CRPS_C_holdout),sd(CRPS_E_holdout))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('CRPS\n')
print(matrix_to_show)
cat('------\n')
}
# Q_0.9 CRPS
if(T){
Q_CRPS_C <- numeric(100)
for(ii in 1:100){
Q_CRPS_C[ii] <- quantile(CRPS_C[groups == levels(groups)[ii]],probs = 0.95)
}
mean(Q_CRPS_C)
sd(Q_CRPS_C)
Q_CRPS_D <- numeric(100)
for(ii in 1:100){
Q_CRPS_D[ii] <- quantile(CRPS_D[groups == levels(groups)[ii]],probs = 0.95)
}
mean(Q_CRPS_D)
sd(Q_CRPS_D)
Q_CRPS_E <- numeric(100)
for(ii in 1:100){
Q_CRPS_E[ii] <- quantile(CRPS_E[groups == levels(groups)[ii]],probs=0.95)
}
mean(Q_CRPS_E)
sd(Q_CRPS_E)
matrix_to_show <- t(round(cbind(c(mean(Q_CRPS_D),mean(Q_CRPS_C),mean(Q_CRPS_E)),
c(sd(Q_CRPS_D),sd(Q_CRPS_C),sd(Q_CRPS_E))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('0.95 Quantile CRPS\n')
print(matrix_to_show)
cat('---------\n')
}
# D_n
if(T){
KS_C <- numeric(100)
for(ii in 1:100){
KS_C[ii] <- ks.test(z_std_C[groups == levels(groups)[ii]],y = pnorm)$statistic
}
mean(KS_C)
sd(KS_C)
KS_D <- numeric(100)
for(ii in 1:100){
KS_D[ii] <- ks.test(z_std_D[groups == levels(groups)[ii]],
y = pnorm)$statistic
}
mean(KS_D)
sd(KS_D)
KS_E <- numeric(100)
for(ii in 1:100){
KS_E[ii] <- ks.test(z_std_E[groups == levels(groups)[ii]],
y = pnorm)$statistic
}
mean(KS_E)
sd(KS_E)
matrix_to_show <- t(round(cbind(c(mean(KS_D),mean(KS_C),mean(KS_E)),
c(sd(KS_D),sd(KS_C),sd(KS_E))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('KS statistic \n')
print(matrix_to_show)
cat('--------\n')
}
# Coverage_prob
if(T){
Cov_prob_C <- numeric(100)
for(ii in 1:100){
upper_bound <- Pred_C$trend[groups == levels(groups)[ii]] +
Pred_C$mean[groups == levels(groups)[ii]] +
qnorm(1 - 0.025) * Pred_C$sd.pred[groups == levels(groups)[ii]]
lower_bound <- Pred_C$trend[groups == levels(groups)[ii]] +
Pred_C$mean[groups == levels(groups)[ii]] -
qnorm(1 - 0.025) * Pred_C$sd.pred[groups == levels(groups)[ii]]
Cov_prob_C[ii] <- 1 - length(which(z_values[groups == levels(groups)[ii]] < lower_bound |
z_values[groups == levels(groups)[ii]] > upper_bound )) /
length(z_values[groups == levels(groups)[ii]])
}
mean(Cov_prob_C)
sd(Cov_prob_C)
Cov_prob_D <- numeric(100)
for(ii in 1:100){
upper_bound <- Pred_D$trend[groups == levels(groups)[ii]] +
Pred_D$mean[groups == levels(groups)[ii]] +
qnorm(1 - 0.025) * Pred_D$sd.pred[groups == levels(groups)[ii]]
lower_bound <- Pred_D$trend[groups == levels(groups)[ii]] +
Pred_D$mean[groups == levels(groups)[ii]] -
qnorm(1 - 0.025) * Pred_D$sd.pred[groups == levels(groups)[ii]]
Cov_prob_D[ii] <- 1 - length(which(z_values[groups == levels(groups)[ii]] < lower_bound |
z_values[groups == levels(groups)[ii]] > upper_bound )) /
length(z_values[groups == levels(groups)[ii]])
}
Cov_prob_E <- numeric(100)
for(ii in 1:100){
upper_bound <- Pred_E$trend[groups == levels(groups)[ii]] +
Pred_E$mean[groups == levels(groups)[ii]] +
qnorm(1 - 0.025) * Pred_E$sd.pred[groups == levels(groups)[ii]]
lower_bound <- Pred_E$trend[groups == levels(groups)[ii]] +
Pred_E$mean[groups == levels(groups)[ii]] -
qnorm(1 - 0.025) * Pred_E$sd.pred[groups == levels(groups)[ii]]
Cov_prob_E[ii] <- 1 - length(which(z_values[groups == levels(groups)[ii]] < lower_bound |
z_values[groups == levels(groups)[ii]] > upper_bound )) /
length(z_values[groups == levels(groups)[ii]])
}
mean(Cov_prob_E)
sd(Cov_prob_E)
matrix_to_show <- t(round(cbind(c(mean(Cov_prob_D),mean(Cov_prob_C),mean(Cov_prob_E)),
c(sd(Cov_prob_D),sd(Cov_prob_C),sd(Cov_prob_E))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('coverage probability\n')
print(matrix_to_show)
cat('--------------\n')
}
# log-score
if(T){
LS_C <- numeric(100)
for(ii in 1:100){
LS_C[ii] <- mean(Logscore_C[groups == levels(groups)[ii]])
}
mean(LS_C)
sd(LS_C)
LS_D <- numeric(100)
for(ii in 1:100){
LS_D[ii] <- mean(Logscore_D[groups == levels(groups)[ii]])
}
LS_E <- numeric(100)
for(ii in 1:100){
LS_E[ii] <- mean(Logscore_E[groups == levels(groups)[ii]])
}
mean(LS_E)
sd(LS_E)
matrix_to_show <- t(round(cbind(c(mean(LS_D),mean(LS_C),mean(LS_E)),
c(sd(LS_D),sd(LS_C),sd(LS_E))),3))
rownames(matrix_to_show) <- c('mean','se')
colnames(matrix_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('logscore\n')
print(matrix_to_show)
cat('--------\n')
}
# logliks
vector_to_show <- c(getLoglik(Model_D),getLoglik(Model_C),getLoglik(Model_E))
names(vector_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('Logliks\n')
print(vector_to_show)
cat('--------\n')
# times
vector_to_show <- round(c(Time_D[3]/60,time_C[3]/60,time_E[3]/60),2)
names(vector_to_show) <- c('M-STAT','M-NS','M-NS-G')
cat('system times\n')
print(vector_to_show)
cat('--------\n')
}