KLTN
Ngọc Trâm
2025-11-26
1 GỌI CÁC GÓI CẦN THIẾT
## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
##
## legend##
## Attaching package: 'moments'## The following objects are masked from 'package:PerformanceAnalytics':
##
## kurtosis, skewness##
## ######################### Warning from 'xts' package ##########################
## # #
## # The dplyr lag() function breaks how base R's lag() function is supposed to #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or #
## # source() into this session won't work correctly. #
## # #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## # #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
## # #
## #################################################################################
## Attaching package: 'dplyr'## The following objects are masked from 'package:xts':
##
## first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rows## corrplot 0.95 loaded## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 4.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.4 ✔ tibble 3.2.1
## ✔ purrr 1.0.4 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks xts::first()
## ✖ kableExtra::group_rows() masks dplyr::group_rows()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks xts::last()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## NOTE: Packages 'fBasics', 'timeDate', and 'timeSeries' are no longer
## attached to the search() path when 'fGarch' is attached.
##
## If needed attach them yourself in your R script by e.g.,
## require("timeSeries")
##
## Attaching package: 'fGarch'
##
## The following objects are masked from 'package:PerformanceAnalytics':
##
## ES, VaR## Loading required package: parallel
##
## Attaching package: 'rugarch'
##
## The following object is masked from 'package:purrr':
##
## reduce##
## Attaching package: 'copula'
##
## The following object is masked from 'package:lubridate':
##
## interval##
## Attaching package: 'ggpubr'
##
## The following object is masked from 'package:forecast':
##
## gghistogram##
## Attaching package: 'VineCopula'
##
## The following object is masked from 'package:copula':
##
## pobs##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combine##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smiths2 GỌI DỮ LIỆU VÀ TÍNH TỶ SUẤT LỢI NHUẬN
data <- read_xlsx("C:/Users/PC/OneDrive/Máy tính/KLTN/KLTN.xlsx")
lg1<- diff(log(data$MBB), lag = 1)
lg2<- diff(log(data$VCB), lag = 1)
lg3<- diff(log(data$HPG), lag = 1)
lg4<- diff(log(data$NKG), lag = 1)
lg5<- diff(log(data$SSI), lag = 1)
lg6<- diff(log(data$VND), lag = 1)
lg7<- diff(log(data$MSN), lag = 1)
lg8<- diff(log(data$MWG), lag = 1)
lg9<- diff(log(data$GEX), lag = 1)
lg10<- diff(log(data$VIC), lag = 1)
mhnn <- data.frame( MBB= lg1, VCB = lg2, HPG = lg3, NKG = lg4, SSI = lg5, VND = lg6, MSN = lg7, MWG = lg8, GEX = lg9, VIC = lg10)# Tạo lưới 5x2 cho 10 biểu đồ
par(mfrow=c(5,2), mar=c(3,3,2,1)) # cài margin nhỏ gọn
cols <- c("darkorange2", "deeppink3", "red", "blue", "seagreen",
"goldenrod1", "purple3", "brown2", "cyan3", "gray30")
for(i in 1:ncol(mhnn)) {
plot.ts(mhnn[[i]], col=cols[i], lwd=2,
main=colnames(mhnn)[i],
ylab="Tỷ suất sinh lợi", xlab="Phiên giao dịch")
grid()}
matplot(mhnn, type="l", lty=1, lwd=2, col=cols,
xlab="Phiên giao dịch", ylab="Tỷ suất sinh lợi logarit",
main="Biểu đồ chồng 10 cổ phiếu")
grid()
legend("topright", legend=colnames(mhnn), col=cols, lty=1, lwd=2, cex=0.8)
# Vẽ boxplot
mhnn %>%
pivot_longer(cols = everything(),
names_to = "TSLN",
values_to = "Value") %>%
ggplot(aes(x = TSLN, y = Value)) +
geom_boxplot(fill = "lightpink", color = "darkblue", outlier.color = "red") +
theme_minimal(base_size = 13) +
labs(title = "Biểu đồ hộp thể hiện phân phối tỷ suất sinh lợi của các cổ phiếu",
x = "Cổ phiếu",
y = "Tỷ suất sinh lợi logarit") +
theme(plot.title = element_text(face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1))
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value
## Warning in adf.test(na.omit(x)): p-value smaller than printed p-value## MBB VCB HPG NKG SSI VND MSN MWG GEX VIC
## 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01## MBB VCB HPG NKG SSI VND
## 6.349238e-01 2.141271e-01 2.874831e-01 3.409428e-11 5.350444e-01 5.774336e-01
## MSN MWG GEX VIC
## 1.984884e-03 2.071152e-02 1.372236e-03 1.388945e-02library(tidyverse)
library(reshape2)
library(ggplot2)
# Tính ma trận tương quan
cor_matrix <- cor(mhnn, use = "pairwise.complete.obs")
# Làm tròn giá trị (cho đẹp)
cor_matrix_rounded <- round(cor_matrix, 2)
# Chuyển sang dạng long để ggplot vẽ được
cor_melted <- melt(cor_matrix_rounded)
# Vẽ biểu đồ heatmap tương quan
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white") +
geom_text(aes(label = value), color = "black", size = 3) +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1),
name = "Hệ số\nTương quan") +
labs(title = "Biểu đồ tương quan giữa các cổ phiếu",
x = "Cổ phiếu", y = "Cổ phiếu") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
3 TẠO THỐNG KÊ MÔ TẢ DỮ LIỆU
# Tính thống kê mô tả chi tiết cho từng cổ phiếu
desc_stats <- mhnn %>%
summarise(across(everything(), list(
Mean = ~mean(.x, na.rm = TRUE),
SD = ~sd(.x, na.rm = TRUE),
Min = ~min(.x, na.rm = TRUE),
Q1 = ~quantile(.x, 0.25, na.rm = TRUE),
Median = ~median(.x, na.rm = TRUE),
Q3 = ~quantile(.x, 0.75, na.rm = TRUE),
Max = ~max(.x, na.rm = TRUE),
Skewness = ~psych::skew(.x, na.rm = TRUE),
Kurtosis = ~psych::kurtosi(.x, na.rm = TRUE)
), .names = "{.col}_{.fn}")) %>%
tidyr::pivot_longer(everything(),
names_to = c("Cổ phiếu", "Chỉ tiêu"),
names_sep = "_",
values_to = "Giá trị") %>%
tidyr::pivot_wider(names_from = "Chỉ tiêu", values_from = "Giá trị")
# Làm đẹp bảng
desc_stats %>%
mutate(across(where(is.numeric), ~ round(.x, 4))) %>%
kable("html", caption = "Bảng 1. Thống kê mô tả tỷ suất sinh lợi logarit của các cổ phiếu") %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1, bold = TRUE, color = "white", background = "#4B72B0") %>%
row_spec(0, bold = TRUE, color = "white", background = "#2E3B4E") %>%
footnote(general = "Nguồn: Tính toán từ dữ liệu giá cổ phiếu (log-return).")| Cổ phiếu | Mean | SD | Min | Q1 | Median | Q3 | Max | Skewness | Kurtosis |
|---|---|---|---|---|---|---|---|---|---|
| MBB | 8e-04 | 0.0188 | -0.0724 | -0.0072 | 0 | 0.0093 | 0.0670 | -0.3047 | 3.1460 |
| VCB | 3e-04 | 0.0193 | -0.4021 | -0.0079 | 0 | 0.0083 | 0.0672 | -3.9804 | 80.2291 |
| HPG | 8e-04 | 0.0207 | -0.0726 | -0.0095 | 0 | 0.0113 | 0.0671 | -0.0898 | 1.8463 |
| NKG | 6e-04 | 0.0287 | -0.3056 | -0.0135 | 0 | 0.0149 | 0.0859 | -0.5572 | 5.6848 |
| SSI | 5e-04 | 0.0243 | -0.2312 | -0.0099 | 0 | 0.0113 | 0.0848 | -0.4585 | 4.9330 |
| VND | 4e-04 | 0.0344 | -0.8915 | -0.0116 | 0 | 0.0139 | 0.0947 | -8.4525 | 202.1106 |
| MSN | 2e-04 | 0.0216 | -0.2098 | -0.0100 | 0 | 0.0105 | 0.0676 | -0.3717 | 5.2690 |
| MWG | 8e-04 | 0.0210 | -0.0726 | -0.0085 | 0 | 0.0106 | 0.0676 | -0.2032 | 2.2896 |
| GEX | 8e-04 | 0.0260 | -0.1450 | -0.0110 | 0 | 0.0131 | 0.1172 | -0.1446 | 1.7925 |
| VIC | 5e-04 | 0.0196 | -0.1717 | -0.0071 | 0 | 0.0070 | 0.0677 | -0.1554 | 6.0841 |
| Note: | |||||||||
| Nguồn: Tính toán từ dữ liệu giá cổ phiếu (log-return). |
4 TÍNH SỰ TƯƠNG QUAN VÀ THỂ HIỆN BÀNG BIỂU ĐỒ
# Chuyển sang dạng long
mh <- melt(cor_matrix_rounded)
ggplot(mh, aes(Var1, Var2, fill = value)) +
geom_tile(color = "white") +
geom_text(aes(label = round(value, 2)), color = "black", size = 3.5) +
scale_fill_gradient2(low = "#2166AC", mid = "white", high = "#B2182B", midpoint = 0,
limit = c(-1, 1), name = "Hệ số\nTương quan") +
labs(title = "Hình 5. Bản đồ nhiệt hệ số tương quan giữa các cổ phiếu",
x = "", y = "") +
theme_minimal(base_size = 13) +
theme(
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
plot.title = element_text(face = "bold", hjust = 0.5))
# Hàm kiểm định an toàn trả p-value (tránh lỗi)
get_stationarity_pvalues <- function(x) {
adf_p <- tryCatch({
adf.test(x, alternative = "stationary")$p.value
}, error = function(e) NA_real_)
kpss_p <- tryCatch({
kpss.test(x, null = "Level")$p.value
}, error = function(e) NA_real_)
tibble(ADF_p = adf_p, KPSS_p = kpss_p)
}
# Áp dụng cho từng cột trong mhnn
stationarity_tests <- map_dfr(names(mhnn), function(colname) {
vals <- mhnn[[colname]]
# loại NA trước khi kiểm định (nếu cần)
vals <- vals[!is.na(vals)]
get_stationarity_pvalues(vals) %>%
mutate(Stock = colname, .before = 1)})## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value## Warning in adf.test(x, alternative = "stationary"): p-value smaller than
## printed p-value## Warning in kpss.test(x, null = "Level"): p-value greater than printed p-value# Kết luận dựa trên p-value (quy ước: ADF_p < 0.05 và KPSS_p > 0.05 => dừng)
stationarity_tests <- stationarity_tests %>%
mutate(
ADF_p = round(ADF_p, 4),
KPSS_p = round(KPSS_p, 4),
Conclusion = case_when(
!is.na(ADF_p) & !is.na(KPSS_p) & (ADF_p < 0.05) & (KPSS_p > 0.05) ~ "Chuỗi dừng",
!is.na(ADF_p) & !is.na(KPSS_p) ~ "Không dừng (hoặc mâu thuẫn)",
TRUE ~ "Không đủ dữ liệu / lỗi kiểm định"))
stationarity_tests %>%
arrange(Stock) %>%
kable("html", caption = "Bảng: Kết quả kiểm định tính dừng (ADF & KPSS)") %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))| Stock | ADF_p | KPSS_p | Conclusion |
|---|---|---|---|
| GEX | 0.01 | 0.1 | Chuỗi dừng |
| HPG | 0.01 | 0.1 | Chuỗi dừng |
| MBB | 0.01 | 0.1 | Chuỗi dừng |
| MSN | 0.01 | 0.1 | Chuỗi dừng |
| MWG | 0.01 | 0.1 | Chuỗi dừng |
| NKG | 0.01 | 0.1 | Chuỗi dừng |
| SSI | 0.01 | 0.1 | Chuỗi dừng |
| VCB | 0.01 | 0.1 | Chuỗi dừng |
| VIC | 0.01 | 0.1 | Chuỗi dừng |
| VND | 0.01 | 0.1 | Chuỗi dừng |
5 THỰC HIỆN KIỂM ĐỊNH BẬC ARIMA HNX VÀ DJ
library(forecast)
library(dplyr)
library(tibble)
library(knitr)
library(kableExtra)
# Danh sách cổ phiếu
stocks <- colnames(mhnn)
# Lặp qua tất cả cổ phiếu và chạy auto.arima
models <- lapply(mhnn, auto.arima)
names(models) <- stocks
# Lấy thông số thống kê quan trọng của từng mô hình
model_summary <- lapply(models, function(mod) {
tibble(
ARIMA = paste0("(", paste(mod$arma[c(1,6,2)], collapse=","), ")"),
AIC = round(mod$aic, 2),
BIC = round(mod$bic, 2),
logLik = round(mod$loglik, 2),
sigma2 = round(mod$sigma2, 6)
)
})
# Ghép thành bảng
model_summary_df <- bind_rows(model_summary, .id = "Stock")
# In bảng đẹp
model_summary_df %>%
kable("html", caption="Bảng: Thống kê mô hình ARIMA cho 10 cổ phiếu") %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))| Stock | ARIMA | AIC | BIC | logLik | sigma2 |
|---|---|---|---|---|---|
| MBB | (0,0,0) | -12436.59 | -12425.00 | 6220.30 | 0.000354 |
| VCB | (0,0,0) | -12309.61 | -12303.81 | 6155.81 | 0.000373 |
| HPG | (0,0,1) | -11975.69 | -11958.29 | 5990.84 | 0.000427 |
| NKG | (1,0,0) | -10420.84 | -10409.24 | 5212.42 | 0.000810 |
| SSI | (0,0,0) | -11194.73 | -11188.93 | 5598.36 | 0.000590 |
| VND | (0,0,0) | -9504.55 | -9498.75 | 4753.28 | 0.001180 |
| MSN | (0,0,4) | -11792.04 | -11763.05 | 5901.02 | 0.000461 |
| MWG | (0,0,1) | -11904.34 | -11886.95 | 5955.17 | 0.000440 |
| GEX | (2,0,1) | -10883.23 | -10854.24 | 5446.61 | 0.000669 |
| VIC | (0,0,1) | -12249.20 | -12237.61 | 6126.60 | 0.000382 |
6 Ước lượng ARIMA cho từng cổ phiếu và lấy phần dư
tickers <- colnames(mhnn)
arima_fits <- list()
resid_mat <- data.frame(matrix(NA, nrow=nrow(mhnn), ncol=length(tickers)))
colnames(resid_mat) <- tickers
for(i in tickers){
x <- na.omit(mhnn[[i]])
fit <- auto.arima(x, stationary=TRUE, seasonal=FALSE)
arima_fits[[i]] <- fit
r <- residuals(fit)
resid_mat[1:length(r), i] <- r
}7 Kiểm tra ARCH effect và fit GARCH
##
## Attaching package: 'FinTS'## The following object is masked from 'package:forecast':
##
## Acfstd_resid <- resid_mat
for(i in tickers){
r <- na.omit(resid_mat[[i]])
if(ArchTest(r)$p.value < 0.05){
spec <- ugarchspec(variance.model=list(model="sGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(0,0), include.mean=FALSE))
gfit <- ugarchfit(spec, r)
std_resid[1:length(r), i] <- residuals(gfit, standardize=TRUE)
} else {
std_resid[1:length(r), i] <- scale(r)
}
}library(FinTS)
library(rugarch)
for(i in tickers){
cat("=====================================\n")
cat("XỬ LÝ MÃ:", i, "\n")
cat("=====================================\n")
r <- na.omit(resid_mat[[i]])
test_arch <- ArchTest(r)
cat("ARCH Test p-value:", test_arch$p.value, "\n")
if(test_arch$p.value < 0.05){
cat("=> Có ARCH effect → Ước lượng GARCH(1,1)\n")
spec <- ugarchspec(
variance.model = list(model="sGARCH", garchOrder=c(1,1)),
mean.model = list(armaOrder=c(0,0), include.mean=FALSE)
)
gfit <- ugarchfit(spec, r)
# In kết quả GARCH và tham số
show(gfit)
# Lưu residual chuẩn hóa
std_resid[1:length(r), i] <- residuals(gfit, standardize=TRUE)
} else {
cat("=> Không có ARCH effect → Chuẩn hóa bằng scale()\n")
std_resid[1:length(r), i] <- scale(r)
}
cat("\n\n")
}## =====================================
## XỬ LÝ MÃ: MBB
## =====================================
## ARCH Test p-value: 1.382558e-64
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000002 4.9744 1e-06
## alpha1 0.114585 0.009658 11.8643 0e+00
## beta1 0.865595 0.008170 105.9528 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000005 1.8427 0.065372
## alpha1 0.114585 0.015668 7.3133 0.000000
## beta1 0.865595 0.021991 39.3614 0.000000
##
## LogLikelihood : 6524.655
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.3566
## Bayes -5.3495
## Shibata -5.3566
## Hannan-Quinn -5.3540
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.5005 0.4793
## Lag[2*(p+q)+(p+q)-1][2] 1.2266 0.4302
## Lag[4*(p+q)+(p+q)-1][5] 1.8423 0.6561
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2212 0.6381
## Lag[2*(p+q)+(p+q)-1][5] 1.7781 0.6717
## Lag[4*(p+q)+(p+q)-1][9] 3.0326 0.7536
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.01369 0.500 2.000 0.9069
## ARCH Lag[5] 0.16698 1.440 1.667 0.9731
## ARCH Lag[7] 0.45345 2.315 1.543 0.9824
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 3.4087
## Individual Statistics:
## omega 0.9312
## alpha1 0.1365
## beta1 0.2257
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.02172 0.9827
## Negative Sign Bias 1.20512 0.2283
## Positive Sign Bias 1.15897 0.2466
## Joint Effect 5.40075 0.1447
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 377.8 1.786e-68
## 2 30 551.7 6.321e-98
## 3 40 527.9 5.578e-87
## 4 50 801.9 9.294e-137
##
##
## Elapsed time : 0.5679469
##
##
##
## =====================================
## XỬ LÝ MÃ: VCB
## =====================================
## ARCH Test p-value: 0.9999999
## => Không có ARCH effect → Chuẩn hóa bằng scale()
##
##
## =====================================
## XỬ LÝ MÃ: HPG
## =====================================
## ARCH Test p-value: 3.026608e-51
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000021 0.000005 4.4207 1e-05
## alpha1 0.093620 0.013380 6.9968 0e+00
## beta1 0.856854 0.021395 40.0487 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000021 0.000008 2.5318 0.011349
## alpha1 0.093620 0.020410 4.5869 0.000004
## beta1 0.856854 0.037446 22.8826 0.000000
##
## LogLikelihood : 6161.499
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.0583
## Bayes -5.0512
## Shibata -5.0583
## Hannan-Quinn -5.0557
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.0154 0.9012
## Lag[2*(p+q)+(p+q)-1][2] 0.1276 0.9007
## Lag[4*(p+q)+(p+q)-1][5] 0.8694 0.8885
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.6589 0.4170
## Lag[2*(p+q)+(p+q)-1][5] 2.7742 0.4498
## Lag[4*(p+q)+(p+q)-1][9] 4.3564 0.5295
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.101 0.500 2.000 0.7506
## ARCH Lag[5] 0.448 1.440 1.667 0.8989
## ARCH Lag[7] 1.859 2.315 1.543 0.7472
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.5328
## Individual Statistics:
## omega 0.2078
## alpha1 0.1965
## beta1 0.2298
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 3.230 0.0012535 ***
## Negative Sign Bias 3.564 0.0003729 ***
## Positive Sign Bias 2.601 0.0093486 ***
## Joint Effect 19.498 0.0002157 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 122.7 3.450e-17
## 2 30 160.1 4.436e-20
## 3 40 177.8 1.244e-19
## 4 50 184.2 1.539e-17
##
##
## Elapsed time : 0.2303178
##
##
##
## =====================================
## XỬ LÝ MÃ: NKG
## =====================================
## ARCH Test p-value: 2.029502e-27
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000016 0.000004 3.9602 7.5e-05
## alpha1 0.085767 0.009497 9.0309 0.0e+00
## beta1 0.895850 0.011474 78.0777 0.0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000016 0.000007 2.3817 0.017231
## alpha1 0.085767 0.011733 7.3100 0.000000
## beta1 0.895850 0.014096 63.5532 0.000000
##
## LogLikelihood : 5428.323
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.4561
## Bayes -4.4490
## Shibata -4.4561
## Hannan-Quinn -4.4535
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.06752 0.7950
## Lag[2*(p+q)+(p+q)-1][2] 0.76974 0.5793
## Lag[4*(p+q)+(p+q)-1][5] 3.08991 0.3909
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 6.063 0.01380
## Lag[2*(p+q)+(p+q)-1][5] 6.317 0.07567
## Lag[4*(p+q)+(p+q)-1][9] 7.856 0.13748
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.1489 0.500 2.000 0.6996
## ARCH Lag[5] 0.2374 1.440 1.667 0.9564
## ARCH Lag[7] 1.3828 2.315 1.543 0.8445
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 3.3084
## Individual Statistics:
## omega 0.7337
## alpha1 0.1423
## beta1 0.1133
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.462 1.439e-01
## Negative Sign Bias 4.559 5.397e-06 ***
## Positive Sign Bias 1.366 1.722e-01
## Joint Effect 27.305 5.082e-06 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 135.0 1.667e-19
## 2 30 145.7 1.725e-17
## 3 40 151.4 3.664e-15
## 4 50 163.5 3.001e-14
##
##
## Elapsed time : 0.214535
##
##
##
## =====================================
## XỬ LÝ MÃ: SSI
## =====================================
## ARCH Test p-value: 4.131669e-46
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000004 2.2042 0.027509
## alpha1 0.107081 0.014028 7.6336 0.000000
## beta1 0.880688 0.010336 85.2045 0.000000
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000017 0.56701 0.570710
## alpha1 0.107081 0.044440 2.40956 0.015972
## beta1 0.880688 0.023059 38.19239 0.000000
##
## LogLikelihood : 5957.951
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.8911
## Bayes -4.8840
## Shibata -4.8911
## Hannan-Quinn -4.8885
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 4.919 0.02656
## Lag[2*(p+q)+(p+q)-1][2] 5.312 0.03356
## Lag[4*(p+q)+(p+q)-1][5] 6.292 0.07673
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.04452 0.8329
## Lag[2*(p+q)+(p+q)-1][5] 0.32254 0.9813
## Lag[4*(p+q)+(p+q)-1][9] 1.12481 0.9805
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.0192 0.500 2.000 0.8898
## ARCH Lag[5] 0.2377 1.440 1.667 0.9563
## ARCH Lag[7] 0.8007 2.315 1.543 0.9437
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 2.292
## Individual Statistics:
## omega 0.5432
## alpha1 0.2942
## beta1 0.1983
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.2512 0.801702
## Negative Sign Bias 1.7036 0.088578 *
## Positive Sign Bias 1.8530 0.064010 *
## Joint Effect 13.6901 0.003359 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 269.0 4.346e-46
## 2 30 366.3 4.672e-60
## 3 40 471.9 1.030e-75
## 4 50 577.5 2.223e-91
##
##
## Elapsed time : 0.269922
##
##
##
## =====================================
## XỬ LÝ MÃ: VND
## =====================================
## ARCH Test p-value: 1
## => Không có ARCH effect → Chuẩn hóa bằng scale()
##
##
## =====================================
## XỬ LÝ MÃ: MSN
## =====================================
## ARCH Test p-value: 8.905524e-11
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000031 0.000005 5.5837 0
## alpha1 0.127931 0.015836 8.0786 0
## beta1 0.811236 0.021896 37.0499 0
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000031 0.000008 3.8789 0.000105
## alpha1 0.127931 0.021688 5.8986 0.000000
## beta1 0.811236 0.027331 29.6818 0.000000
##
## LogLikelihood : 6053.198
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.9694
## Bayes -4.9622
## Shibata -4.9694
## Hannan-Quinn -4.9668
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.05084 0.8216
## Lag[2*(p+q)+(p+q)-1][2] 0.05137 0.9553
## Lag[4*(p+q)+(p+q)-1][5] 0.53318 0.9524
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.7045 0.4013
## Lag[2*(p+q)+(p+q)-1][5] 3.8021 0.2798
## Lag[4*(p+q)+(p+q)-1][9] 6.5585 0.2390
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.02824 0.500 2.000 0.8665
## ARCH Lag[5] 0.12479 1.440 1.667 0.9820
## ARCH Lag[7] 2.04447 2.315 1.543 0.7079
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.3762
## Individual Statistics:
## omega 0.2558
## alpha1 0.1134
## beta1 0.1788
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.2163 0.22398
## Negative Sign Bias 2.2355 0.02547 **
## Positive Sign Bias 0.7561 0.44964
## Joint Effect 5.6541 0.12971
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 229.8 3.738e-38
## 2 30 261.6 2.816e-39
## 3 40 277.9 8.413e-38
## 4 50 306.3 6.466e-39
##
##
## Elapsed time : 0.2676971
##
##
##
## =====================================
## XỬ LÝ MÃ: MWG
## =====================================
## ARCH Test p-value: 3.304668e-65
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000017 0.000004 4.7087 2e-06
## alpha1 0.080461 0.008379 9.6032 0e+00
## beta1 0.878420 0.014207 61.8321 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000017 0.000007 2.4456 0.014462
## alpha1 0.080461 0.011226 7.1674 0.000000
## beta1 0.878420 0.024004 36.5942 0.000000
##
## LogLikelihood : 6144.679
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.0445
## Bayes -5.0374
## Shibata -5.0445
## Hannan-Quinn -5.0419
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.5158 0.4726
## Lag[2*(p+q)+(p+q)-1][2] 0.5224 0.6832
## Lag[4*(p+q)+(p+q)-1][5] 3.7263 0.2902
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 5.827 0.01578
## Lag[2*(p+q)+(p+q)-1][5] 6.466 0.06972
## Lag[4*(p+q)+(p+q)-1][9] 8.053 0.12588
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.4105 0.500 2.000 0.5217
## ARCH Lag[5] 1.3825 1.440 1.667 0.6237
## ARCH Lag[7] 2.7401 2.315 1.543 0.5639
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 6.8531
## Individual Statistics:
## omega 0.54509
## alpha1 0.11304
## beta1 0.08973
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.0282 0.3039730
## Negative Sign Bias 3.2975 0.0009894 ***
## Positive Sign Bias 0.7336 0.4632425
## Joint Effect 13.0933 0.0044391 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 175.3 2.608e-27
## 2 30 207.6 6.993e-29
## 3 40 224.3 6.973e-28
## 4 50 226.4 1.258e-24
##
##
## Elapsed time : 0.223556
##
##
##
## =====================================
## XỬ LÝ MÃ: GEX
## =====================================
## ARCH Test p-value: 6.773923e-73
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.00002 0.000004 5.0408 0
## alpha1 0.13473 0.015527 8.6769 0
## beta1 0.83741 0.017786 47.0813 0
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.00002 0.000007 2.8418 0.004486
## alpha1 0.13473 0.021936 6.1418 0.000000
## beta1 0.83741 0.026893 31.1390 0.000000
##
## LogLikelihood : 5734.389
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.7075
## Bayes -4.7004
## Shibata -4.7075
## Hannan-Quinn -4.7049
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2062 0.6498
## Lag[2*(p+q)+(p+q)-1][2] 0.2150 0.8450
## Lag[4*(p+q)+(p+q)-1][5] 0.6931 0.9242
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 2.666 0.1025
## Lag[2*(p+q)+(p+q)-1][5] 3.555 0.3150
## Lag[4*(p+q)+(p+q)-1][9] 4.401 0.5223
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.6928 0.500 2.000 0.4052
## ARCH Lag[5] 1.4085 1.440 1.667 0.6167
## ARCH Lag[7] 1.8695 2.315 1.543 0.7449
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.8629
## Individual Statistics:
## omega 0.3527
## alpha1 0.5672
## beta1 0.6728
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.9561 0.3391
## Negative Sign Bias 1.1535 0.2488
## Positive Sign Bias 0.5157 0.6061
## Joint Effect 1.6374 0.6509
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 132.3 5.457e-19
## 2 30 152.0 1.280e-18
## 3 40 155.5 7.650e-16
## 4 50 168.1 5.715e-15
##
##
## Elapsed time : 0.2040031
##
##
##
## =====================================
## XỬ LÝ MÃ: VIC
## =====================================
## ARCH Test p-value: 9.165337e-28
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000048 0.000010 4.9248 1e-06
## alpha1 0.230541 0.038345 6.0124 0e+00
## beta1 0.666652 0.052031 12.8125 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000048 0.000031 1.5471 0.121837
## alpha1 0.230541 0.108134 2.1320 0.033007
## beta1 0.666652 0.159933 4.1683 0.000031
##
## LogLikelihood : 6375.35
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.2340
## Bayes -5.2268
## Shibata -5.2340
## Hannan-Quinn -5.2314
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2015 0.6535
## Lag[2*(p+q)+(p+q)-1][2] 0.2216 0.8410
## Lag[4*(p+q)+(p+q)-1][5] 0.5291 0.9531
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.1622 0.6872
## Lag[2*(p+q)+(p+q)-1][5] 1.8102 0.6639
## Lag[4*(p+q)+(p+q)-1][9] 2.5517 0.8300
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.6735 0.500 2.000 0.4118
## ARCH Lag[5] 0.8493 1.440 1.667 0.7781
## ARCH Lag[7] 1.3811 2.315 1.543 0.8449
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.3924
## Individual Statistics:
## omega 0.05699
## alpha1 0.05790
## beta1 0.04625
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.7362 0.08266 *
## Negative Sign Bias 1.0677 0.28578
## Positive Sign Bias 0.2797 0.77972
## Joint Effect 3.3791 0.33679
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 689.7 5.448e-134
## 2 30 803.0 2.682e-150
## 3 40 799.4 1.295e-142
## 4 50 844.8 1.543e-145
##
##
## Elapsed time : 0.2510321library(forecast)
library(FinTS)
library(rugarch)
tickers <- colnames(mhnn)
run_garch_analysis <- function(series, name){
cat("\n\n=====================================\n")
cat(paste("XỬ LÝ MÃ:", name, "\n"))
cat("=====================================\n")
x <- na.omit(series)
# 1. ARIMA
fit_arima <- auto.arima(x, stationary=TRUE, seasonal=FALSE)
resid_arima <- residuals(fit_arima)
# 2. ARCH Test
arch_p <- ArchTest(resid_arima)$p.value
cat("ARCH Test p-value:", arch_p, "\n")
if(arch_p < 0.05){
cat("=> Có ARCH effect → Ước lượng GARCH(1,1)\n\n")
spec <- ugarchspec(
variance.model=list(model="sGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(0,0), include.mean=FALSE),
distribution.model="norm"
)
fit_g <- ugarchfit(spec, resid_arima)
show(fit_g)
} else {
cat("=> Không có ARCH effect → KHÔNG cần GARCH\n")
cat("=> Dùng residuals ARIMA đã chuẩn hóa.\n")
}
}
# LOOP cho tất cả 8 mã
for(tk in tickers){
run_garch_analysis(mhnn[[tk]], tk)
}##
##
## =====================================
## XỬ LÝ MÃ: MBB
## =====================================
## ARCH Test p-value: 1.382558e-64
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000002 4.9744 1e-06
## alpha1 0.114585 0.009658 11.8643 0e+00
## beta1 0.865595 0.008170 105.9528 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000005 1.8427 0.065372
## alpha1 0.114585 0.015668 7.3133 0.000000
## beta1 0.865595 0.021991 39.3614 0.000000
##
## LogLikelihood : 6524.655
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.3566
## Bayes -5.3495
## Shibata -5.3566
## Hannan-Quinn -5.3540
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.5005 0.4793
## Lag[2*(p+q)+(p+q)-1][2] 1.2266 0.4302
## Lag[4*(p+q)+(p+q)-1][5] 1.8423 0.6561
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2212 0.6381
## Lag[2*(p+q)+(p+q)-1][5] 1.7781 0.6717
## Lag[4*(p+q)+(p+q)-1][9] 3.0326 0.7536
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.01369 0.500 2.000 0.9069
## ARCH Lag[5] 0.16698 1.440 1.667 0.9731
## ARCH Lag[7] 0.45345 2.315 1.543 0.9824
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 3.4087
## Individual Statistics:
## omega 0.9312
## alpha1 0.1365
## beta1 0.2257
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.02172 0.9827
## Negative Sign Bias 1.20512 0.2283
## Positive Sign Bias 1.15897 0.2466
## Joint Effect 5.40075 0.1447
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 377.8 1.786e-68
## 2 30 551.7 6.321e-98
## 3 40 527.9 5.578e-87
## 4 50 801.9 9.294e-137
##
##
## Elapsed time : 0.2160759
##
##
##
## =====================================
## XỬ LÝ MÃ: VCB
## =====================================
## ARCH Test p-value: 0.9999999
## => Không có ARCH effect → KHÔNG cần GARCH
## => Dùng residuals ARIMA đã chuẩn hóa.
##
##
## =====================================
## XỬ LÝ MÃ: HPG
## =====================================
## ARCH Test p-value: 3.026608e-51
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000021 0.000005 4.4207 1e-05
## alpha1 0.093620 0.013380 6.9968 0e+00
## beta1 0.856854 0.021395 40.0487 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000021 0.000008 2.5318 0.011349
## alpha1 0.093620 0.020410 4.5869 0.000004
## beta1 0.856854 0.037446 22.8826 0.000000
##
## LogLikelihood : 6161.499
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.0583
## Bayes -5.0512
## Shibata -5.0583
## Hannan-Quinn -5.0557
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.0154 0.9012
## Lag[2*(p+q)+(p+q)-1][2] 0.1276 0.9007
## Lag[4*(p+q)+(p+q)-1][5] 0.8694 0.8885
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.6589 0.4170
## Lag[2*(p+q)+(p+q)-1][5] 2.7742 0.4498
## Lag[4*(p+q)+(p+q)-1][9] 4.3564 0.5295
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.101 0.500 2.000 0.7506
## ARCH Lag[5] 0.448 1.440 1.667 0.8989
## ARCH Lag[7] 1.859 2.315 1.543 0.7472
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.5328
## Individual Statistics:
## omega 0.2078
## alpha1 0.1965
## beta1 0.2298
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 3.230 0.0012535 ***
## Negative Sign Bias 3.564 0.0003729 ***
## Positive Sign Bias 2.601 0.0093486 ***
## Joint Effect 19.498 0.0002157 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 122.7 3.450e-17
## 2 30 160.1 4.436e-20
## 3 40 177.8 1.244e-19
## 4 50 184.2 1.539e-17
##
##
## Elapsed time : 0.1780291
##
##
##
## =====================================
## XỬ LÝ MÃ: NKG
## =====================================
## ARCH Test p-value: 2.029502e-27
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000016 0.000004 3.9602 7.5e-05
## alpha1 0.085767 0.009497 9.0309 0.0e+00
## beta1 0.895850 0.011474 78.0777 0.0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000016 0.000007 2.3817 0.017231
## alpha1 0.085767 0.011733 7.3100 0.000000
## beta1 0.895850 0.014096 63.5532 0.000000
##
## LogLikelihood : 5428.323
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.4561
## Bayes -4.4490
## Shibata -4.4561
## Hannan-Quinn -4.4535
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.06752 0.7950
## Lag[2*(p+q)+(p+q)-1][2] 0.76974 0.5793
## Lag[4*(p+q)+(p+q)-1][5] 3.08991 0.3909
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 6.063 0.01380
## Lag[2*(p+q)+(p+q)-1][5] 6.317 0.07567
## Lag[4*(p+q)+(p+q)-1][9] 7.856 0.13748
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.1489 0.500 2.000 0.6996
## ARCH Lag[5] 0.2374 1.440 1.667 0.9564
## ARCH Lag[7] 1.3828 2.315 1.543 0.8445
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 3.3084
## Individual Statistics:
## omega 0.7337
## alpha1 0.1423
## beta1 0.1133
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.462 1.439e-01
## Negative Sign Bias 4.559 5.397e-06 ***
## Positive Sign Bias 1.366 1.722e-01
## Joint Effect 27.305 5.082e-06 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 135.0 1.667e-19
## 2 30 145.7 1.725e-17
## 3 40 151.4 3.664e-15
## 4 50 163.5 3.001e-14
##
##
## Elapsed time : 0.1459711
##
##
##
## =====================================
## XỬ LÝ MÃ: SSI
## =====================================
## ARCH Test p-value: 4.131669e-46
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000004 2.2042 0.027509
## alpha1 0.107081 0.014028 7.6336 0.000000
## beta1 0.880688 0.010336 85.2045 0.000000
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000009 0.000017 0.56701 0.570710
## alpha1 0.107081 0.044440 2.40956 0.015972
## beta1 0.880688 0.023059 38.19239 0.000000
##
## LogLikelihood : 5957.951
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.8911
## Bayes -4.8840
## Shibata -4.8911
## Hannan-Quinn -4.8885
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 4.919 0.02656
## Lag[2*(p+q)+(p+q)-1][2] 5.312 0.03356
## Lag[4*(p+q)+(p+q)-1][5] 6.292 0.07673
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.04452 0.8329
## Lag[2*(p+q)+(p+q)-1][5] 0.32254 0.9813
## Lag[4*(p+q)+(p+q)-1][9] 1.12481 0.9805
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.0192 0.500 2.000 0.8898
## ARCH Lag[5] 0.2377 1.440 1.667 0.9563
## ARCH Lag[7] 0.8007 2.315 1.543 0.9437
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 2.292
## Individual Statistics:
## omega 0.5432
## alpha1 0.2942
## beta1 0.1983
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.2512 0.801702
## Negative Sign Bias 1.7036 0.088578 *
## Positive Sign Bias 1.8530 0.064010 *
## Joint Effect 13.6901 0.003359 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 269.0 4.346e-46
## 2 30 366.3 4.672e-60
## 3 40 471.9 1.030e-75
## 4 50 577.5 2.223e-91
##
##
## Elapsed time : 0.3375199
##
##
##
## =====================================
## XỬ LÝ MÃ: VND
## =====================================
## ARCH Test p-value: 1
## => Không có ARCH effect → KHÔNG cần GARCH
## => Dùng residuals ARIMA đã chuẩn hóa.
##
##
## =====================================
## XỬ LÝ MÃ: MSN
## =====================================
## ARCH Test p-value: 8.905524e-11
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000031 0.000005 5.5837 0
## alpha1 0.127931 0.015836 8.0786 0
## beta1 0.811236 0.021896 37.0499 0
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000031 0.000008 3.8789 0.000105
## alpha1 0.127931 0.021688 5.8986 0.000000
## beta1 0.811236 0.027331 29.6818 0.000000
##
## LogLikelihood : 6053.198
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.9694
## Bayes -4.9622
## Shibata -4.9694
## Hannan-Quinn -4.9668
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.05084 0.8216
## Lag[2*(p+q)+(p+q)-1][2] 0.05137 0.9553
## Lag[4*(p+q)+(p+q)-1][5] 0.53318 0.9524
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.7045 0.4013
## Lag[2*(p+q)+(p+q)-1][5] 3.8021 0.2798
## Lag[4*(p+q)+(p+q)-1][9] 6.5585 0.2390
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.02824 0.500 2.000 0.8665
## ARCH Lag[5] 0.12479 1.440 1.667 0.9820
## ARCH Lag[7] 2.04447 2.315 1.543 0.7079
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.3762
## Individual Statistics:
## omega 0.2558
## alpha1 0.1134
## beta1 0.1788
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.2163 0.22398
## Negative Sign Bias 2.2355 0.02547 **
## Positive Sign Bias 0.7561 0.44964
## Joint Effect 5.6541 0.12971
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 229.8 3.738e-38
## 2 30 261.6 2.816e-39
## 3 40 277.9 8.413e-38
## 4 50 306.3 6.466e-39
##
##
## Elapsed time : 0.298466
##
##
##
## =====================================
## XỬ LÝ MÃ: MWG
## =====================================
## ARCH Test p-value: 3.304668e-65
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000017 0.000004 4.7087 2e-06
## alpha1 0.080461 0.008379 9.6032 0e+00
## beta1 0.878420 0.014207 61.8321 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000017 0.000007 2.4456 0.014462
## alpha1 0.080461 0.011226 7.1674 0.000000
## beta1 0.878420 0.024004 36.5942 0.000000
##
## LogLikelihood : 6144.679
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.0445
## Bayes -5.0374
## Shibata -5.0445
## Hannan-Quinn -5.0419
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.5158 0.4726
## Lag[2*(p+q)+(p+q)-1][2] 0.5224 0.6832
## Lag[4*(p+q)+(p+q)-1][5] 3.7263 0.2902
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 5.827 0.01578
## Lag[2*(p+q)+(p+q)-1][5] 6.466 0.06972
## Lag[4*(p+q)+(p+q)-1][9] 8.053 0.12588
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.4105 0.500 2.000 0.5217
## ARCH Lag[5] 1.3825 1.440 1.667 0.6237
## ARCH Lag[7] 2.7401 2.315 1.543 0.5639
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 6.8531
## Individual Statistics:
## omega 0.54509
## alpha1 0.11304
## beta1 0.08973
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.0282 0.3039730
## Negative Sign Bias 3.2975 0.0009894 ***
## Positive Sign Bias 0.7336 0.4632425
## Joint Effect 13.0933 0.0044391 ***
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 175.3 2.608e-27
## 2 30 207.6 6.993e-29
## 3 40 224.3 6.973e-28
## 4 50 226.4 1.258e-24
##
##
## Elapsed time : 0.4917281
##
##
##
## =====================================
## XỬ LÝ MÃ: GEX
## =====================================
## ARCH Test p-value: 6.773923e-73
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.00002 0.000004 5.0408 0
## alpha1 0.13473 0.015527 8.6769 0
## beta1 0.83741 0.017786 47.0813 0
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.00002 0.000007 2.8418 0.004486
## alpha1 0.13473 0.021936 6.1418 0.000000
## beta1 0.83741 0.026893 31.1390 0.000000
##
## LogLikelihood : 5734.389
##
## Information Criteria
## ------------------------------------
##
## Akaike -4.7075
## Bayes -4.7004
## Shibata -4.7075
## Hannan-Quinn -4.7049
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2062 0.6498
## Lag[2*(p+q)+(p+q)-1][2] 0.2150 0.8450
## Lag[4*(p+q)+(p+q)-1][5] 0.6931 0.9242
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 2.666 0.1025
## Lag[2*(p+q)+(p+q)-1][5] 3.555 0.3150
## Lag[4*(p+q)+(p+q)-1][9] 4.401 0.5223
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.6928 0.500 2.000 0.4052
## ARCH Lag[5] 1.4085 1.440 1.667 0.6167
## ARCH Lag[7] 1.8695 2.315 1.543 0.7449
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.8629
## Individual Statistics:
## omega 0.3527
## alpha1 0.5672
## beta1 0.6728
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 0.9561 0.3391
## Negative Sign Bias 1.1535 0.2488
## Positive Sign Bias 0.5157 0.6061
## Joint Effect 1.6374 0.6509
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 132.3 5.457e-19
## 2 30 152.0 1.280e-18
## 3 40 155.5 7.650e-16
## 4 50 168.1 5.715e-15
##
##
## Elapsed time : 0.2385938
##
##
##
## =====================================
## XỬ LÝ MÃ: VIC
## =====================================
## ARCH Test p-value: 9.165337e-28
## => Có ARCH effect → Ước lượng GARCH(1,1)
##
##
## *---------------------------------*
## * GARCH Model Fit *
## *---------------------------------*
##
## Conditional Variance Dynamics
## -----------------------------------
## GARCH Model : sGARCH(1,1)
## Mean Model : ARFIMA(0,0,0)
## Distribution : norm
##
## Optimal Parameters
## ------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000048 0.000010 4.9248 1e-06
## alpha1 0.230541 0.038345 6.0124 0e+00
## beta1 0.666652 0.052031 12.8125 0e+00
##
## Robust Standard Errors:
## Estimate Std. Error t value Pr(>|t|)
## omega 0.000048 0.000031 1.5471 0.121837
## alpha1 0.230541 0.108134 2.1320 0.033007
## beta1 0.666652 0.159933 4.1683 0.000031
##
## LogLikelihood : 6375.35
##
## Information Criteria
## ------------------------------------
##
## Akaike -5.2340
## Bayes -5.2268
## Shibata -5.2340
## Hannan-Quinn -5.2314
##
## Weighted Ljung-Box Test on Standardized Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.2015 0.6535
## Lag[2*(p+q)+(p+q)-1][2] 0.2216 0.8410
## Lag[4*(p+q)+(p+q)-1][5] 0.5291 0.9531
## d.o.f=0
## H0 : No serial correlation
##
## Weighted Ljung-Box Test on Standardized Squared Residuals
## ------------------------------------
## statistic p-value
## Lag[1] 0.1622 0.6872
## Lag[2*(p+q)+(p+q)-1][5] 1.8102 0.6639
## Lag[4*(p+q)+(p+q)-1][9] 2.5517 0.8300
## d.o.f=2
##
## Weighted ARCH LM Tests
## ------------------------------------
## Statistic Shape Scale P-Value
## ARCH Lag[3] 0.6735 0.500 2.000 0.4118
## ARCH Lag[5] 0.8493 1.440 1.667 0.7781
## ARCH Lag[7] 1.3811 2.315 1.543 0.8449
##
## Nyblom stability test
## ------------------------------------
## Joint Statistic: 0.3924
## Individual Statistics:
## omega 0.05699
## alpha1 0.05790
## beta1 0.04625
##
## Asymptotic Critical Values (10% 5% 1%)
## Joint Statistic: 0.846 1.01 1.35
## Individual Statistic: 0.35 0.47 0.75
##
## Sign Bias Test
## ------------------------------------
## t-value prob sig
## Sign Bias 1.7362 0.08266 *
## Negative Sign Bias 1.0677 0.28578
## Positive Sign Bias 0.2797 0.77972
## Joint Effect 3.3791 0.33679
##
##
## Adjusted Pearson Goodness-of-Fit Test:
## ------------------------------------
## group statistic p-value(g-1)
## 1 20 689.7 5.448e-134
## 2 30 803.0 2.682e-150
## 3 40 799.4 1.295e-142
## 4 50 844.8 1.543e-145
##
##
## Elapsed time : 0.4085098 Chuyển phần dư thành pseudo-observations (Uniform(0,1))
9 Ước lượng Copula song phương hoặc đa biến (Vine Copula)
#So sánh logLik và AIC để chọn Copula tốt hơn.
u <- u_mat[, c("MBB", "VCB")]
# Gaussian Copula
cop.gauss <- normalCopula(dim=2)
fit.gauss <- fitCopula(cop.gauss, u, method="ml")
summary(fit.gauss)## Call: fitCopula(cop.gauss, u, method = "ml")
## Fit based on "maximum likelihood" and 2435 2-dimensional observations.
## Normal copula, dim. d = 2
## Estimate Std. Error
## rho.1 0.4626 0.015
## The maximized loglikelihood is 290.7
## Optimization converged
## Number of loglikelihood evaluations:
## function gradient
## 6 6## Call: fitCopula(cop.t, u, method = "ml")
## Fit based on "maximum likelihood" and 2435 2-dimensional observations.
## t-copula, dim. d = 2
## Estimate Std. Error
## rho.1 0.4622 0.016
## df 13.8107 4.470
## The maximized loglikelihood is 296.4
## Optimization converged
## Number of loglikelihood evaluations:
## function gradient
## 20 20=====
d <- read_xlsx("C:/Users/PC/OneDrive/Máy tính/KLTN/KLTN.xlsx")
d <- xts(d[,-1],order.by = d$Ngày)
data_train <- d["2015-10-30/2025-07-31"]
library(FinTS)
arma_mbb <- auto.arima(y = data_train$MBB)
arma_vcb <- auto.arima(y = data_train$VCB)
arma_hpg <- auto.arima(y = data_train$HPG)
arma_nkg <- auto.arima(y = data_train$NKG)
arma_msn <- auto.arima(y = data_train$MSN)
arma_mwg <- auto.arima(y = data_train$MWG)
arma_vic <- auto.arima(y = data_train$VIC)
arma_ssi <- auto.arima(y = data_train$SSI)
arma_vnd <- auto.arima(y = data_train$VND)
arma_gex <- auto.arima(y = data_train$GEX)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_mbb)
## Chi-squared = 292.95, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vcb)
## Chi-squared = 0.083452, df = 10, p-value = 1##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_hpg)
## Chi-squared = 386.58, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_nkg)
## Chi-squared = 625.37, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_msn)
## Chi-squared = 358.5, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_mwg)
## Chi-squared = 381.22, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vic)
## Chi-squared = 238.08, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_ssi)
## Chi-squared = 482.52, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vnd)
## Chi-squared = 89.574, df = 10, p-value = 6.505e-15##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_gex)
## Chi-squared = 1033.2, df = 10, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_mbb)
## Chi-squared = 298.32, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vcb)
## Chi-squared = 0.10783, df = 15, p-value = 1##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_hpg)
## Chi-squared = 395.2, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_nkg)
## Chi-squared = 682.09, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_msn)
## Chi-squared = 381.73, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_mwg)
## Chi-squared = 412.64, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vic)
## Chi-squared = 250.85, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_ssi)
## Chi-squared = 496.36, df = 15, p-value < 2.2e-16##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_vnd)
## Chi-squared = 97.633, df = 15, p-value = 3.66e-14##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: residuals(arma_gex)
## Chi-squared = 1035, df = 15, p-value < 2.2e-16#MBB
##Norm
garchspec_mbb_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_mbb_norm <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_norm)
##Snorm
garchspec_mbb_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_mbb_snorm <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_snorm)
##std
garchspec_mbb_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_mbb_std <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_std)
##sstd
garchspec_mbb_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_mbb_sstd <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_sstd)
##ged
garchspec_mbb_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_mbb_ged <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_ged)
##sged
garchspec_mbb_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_mbb_sged <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_sged)
##ghyp
garchspec_mbb_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_mbb_ghyp <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_ghyp)
##jsu
garchspec_mbb_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_mbb_jsu <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_jsu)
##nig
garchspec_mbb_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_mbb)[1],arimaorder(arma_mbb)[3])),
variance.model=list(model = "gjrGARCH",garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_mbb_nig <- ugarchfit(d=data_train$MBB,spec=garchspec_mbb_nig)
#Lựa chọn mô hình tối ưu
mbb.model.list <- list(garch11n = garchfit_mbb_norm, garch11sn = garchfit_mbb_snorm, garch11t = garchfit_mbb_std, garch11st = garchfit_mbb_sstd, garch11g = garchfit_mbb_ged, garch11sg = garchfit_mbb_sged, garch11nig = garchfit_mbb_nig,garch11ghyp = garchfit_mbb_ghyp, garch11jsu = garchfit_mbb_jsu)
# Lấy AIC/BIC/AICc của các mô hình MBB
mbb.info.mat <- sapply(mbb.model.list, infocriteria)
# Gán tên dòng cho bảng
rownames(mbb.info.mat) <- rownames(infocriteria(garchfit_mbb_norm))
# Tìm mô hình có AIC nhỏ nhất
mbb.inds <- which(mbb.info.mat == min(mbb.info.mat[1,]), arr.ind = TRUE)
# Lấy tên mô hình tối ưu cho MBB
model.mbb <- colnames(mbb.info.mat)[mbb.inds[,2]]# VCB
## Norm
garchspec_vcb_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_vcb_norm <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_norm)
## Snorm
garchspec_vcb_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_vcb_snorm <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_snorm)
## Std
garchspec_vcb_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_vcb_std <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_std)
## Sstd
garchspec_vcb_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_vcb_sstd <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_sstd)
## Ged
garchspec_vcb_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_vcb_ged <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_ged)
## Sged
garchspec_vcb_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_vcb_sged <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_sged)
## Ghyp
garchspec_vcb_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_vcb_ghyp <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_ghyp)
## Jsu
garchspec_vcb_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_vcb_jsu <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_jsu)
## NIG
garchspec_vcb_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vcb)[1], arimaorder(arma_vcb)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_vcb_nig <- ugarchfit(data=data_train$VCB, spec=garchspec_vcb_nig)
# Danh sách mô hình
vcb.safe.list <- list(
garch11n = garchfit_vcb_norm,
garch11t = garchfit_vcb_std,
garch11g = garchfit_vcb_ged,
garch11sn = garchfit_vcb_snorm,
garch11st = garchfit_vcb_sstd,
garch11sg = garchfit_vcb_sged
)
vcb.info.mat <- sapply(vcb.safe.list, function(mod){
if(!is.null(mod)) infocriteria(mod) else NA
})
rownames(vcb.info.mat) <- rownames(infocriteria(garchfit_vcb_norm))
# Tìm mô hình AIC nhỏ nhất
vcb.inds <- which(vcb.info.mat[1,] == min(vcb.info.mat[1,], na.rm=TRUE))
model.vcb <- colnames(vcb.info.mat)[vcb.inds]#HPG
##Norm
garchspec_hpg_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_hpg_norm <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_norm)
##Snorm
garchspec_hpg_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_hpg_snorm <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_snorm)
##std
garchspec_hpg_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_hpg_std <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_std)
##sstd
garchspec_hpg_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_hpg_sstd <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_sstd)
##ged
garchspec_hpg_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_hpg_ged <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_ged)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##sged
garchspec_hpg_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_hpg_sged <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_sged)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##ghyp
garchspec_hpg_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_hpg_ghyp <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_ghyp)
##jsu
garchspec_hpg_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_hpg_jsu <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_jsu)
##nig
garchspec_hpg_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_hpg)[1], arimaorder(arma_hpg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_hpg_nig <- ugarchfit(d = data_train$HPG, spec = garchspec_hpg_nig)
# Lựa chọn mô hình tối ưu
hpg.model.list <- list(
garch11n = garchfit_hpg_norm,
garch11sn = garchfit_hpg_snorm,
garch11t = garchfit_hpg_std,
garch11st = garchfit_hpg_sstd,
garch11g = garchfit_hpg_ged,
garch11sg = garchfit_hpg_sged,
garch11nig = garchfit_hpg_nig,
garch11ghyp = garchfit_hpg_ghyp,
garch11jsu = garchfit_hpg_jsu
)
# Lấy AIC/BIC/AICc
hpg.info.mat <- sapply(hpg.model.list, infocriteria)
rownames(hpg.info.mat) <- rownames(infocriteria(garchfit_hpg_norm))
# Tìm mô hình AIC nhỏ nhất
hpg.inds <- which(hpg.info.mat == min(hpg.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho HPG
model.hpg <- colnames(hpg.info.mat)[hpg.inds[,2]]#NKG
##Norm
garchspec_nkg_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_nkg_norm <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_norm)
##Snorm
garchspec_nkg_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_nkg_snorm <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_snorm)
##std
garchspec_nkg_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_nkg_std <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_std)
##sstd
garchspec_nkg_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_nkg_sstd <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_sstd)
##ged
garchspec_nkg_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_nkg_ged <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_ged)
##sged
garchspec_nkg_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_nkg_sged <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_sged)
##ghyp
garchspec_nkg_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_nkg_ghyp <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_ghyp)
##jsu
garchspec_nkg_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_nkg_jsu <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_jsu)
##nig
garchspec_nkg_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_nkg)[1], arimaorder(arma_nkg)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_nkg_nig <- ugarchfit(d = data_train$NKG, spec = garchspec_nkg_nig)
# Lựa chọn mô hình tối ưu
nkg.model.list <- list(
garch11n = garchfit_nkg_norm,
garch11sn = garchfit_nkg_snorm,
garch11t = garchfit_nkg_std,
garch11st = garchfit_nkg_sstd,
garch11g = garchfit_nkg_ged,
garch11sg = garchfit_nkg_sged,
garch11nig = garchfit_nkg_nig,
garch11ghyp = garchfit_nkg_ghyp,
garch11jsu = garchfit_nkg_jsu
)
# Lấy AIC/BIC/AICc
nkg.info.mat <- sapply(nkg.model.list, infocriteria)
rownames(nkg.info.mat) <- rownames(infocriteria(garchfit_nkg_norm))
# Tìm mô hình AIC nhỏ nhất
nkg.inds <- which(nkg.info.mat == min(nkg.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho NKG
model.nkg <- colnames(nkg.info.mat)[nkg.inds[,2]]#SSI
##Norm
garchspec_ssi_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_ssi_norm <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_norm)
##Snorm
garchspec_ssi_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_ssi_snorm <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_snorm)
##std
garchspec_ssi_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_ssi_std <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_std)
##sstd
garchspec_ssi_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_ssi_sstd <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_sstd)
##ged
garchspec_ssi_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_ssi_ged <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_ged)
##sged
garchspec_ssi_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_ssi_sged <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_sged)
##ghyp
garchspec_ssi_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_ssi_ghyp <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_ghyp)
##jsu
garchspec_ssi_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_ssi_jsu <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_jsu)
##nig
garchspec_ssi_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_ssi)[1], arimaorder(arma_ssi)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_ssi_nig <- ugarchfit(d = data_train$SSI, spec = garchspec_ssi_nig)
# Lựa chọn mô hình tối ưu
ssi.model.list <- list(
garch11n = garchfit_ssi_norm,
garch11sn = garchfit_ssi_snorm,
garch11t = garchfit_ssi_std,
garch11st = garchfit_ssi_sstd,
garch11g = garchfit_ssi_ged,
garch11sg = garchfit_ssi_sged,
garch11nig = garchfit_ssi_nig,
garch11ghyp = garchfit_ssi_ghyp,
garch11jsu = garchfit_ssi_jsu
)
# Lấy AIC/BIC/AICc
ssi.info.mat <- sapply(ssi.model.list, infocriteria)
rownames(ssi.info.mat) <- rownames(infocriteria(garchfit_ssi_norm))
# Tìm mô hình AIC nhỏ nhất
ssi.inds <- which(ssi.info.mat == min(ssi.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho SSI
model.ssi <- colnames(ssi.info.mat)[ssi.inds[,2]]#VND
##Norm
garchspec_vnd_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_vnd_norm <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_norm)
##Snorm
garchspec_vnd_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_vnd_snorm <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_snorm)
##std
garchspec_vnd_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_vnd_std <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_std)
##sstd
garchspec_vnd_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_vnd_sstd <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_sstd)
##ged
garchspec_vnd_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_vnd_ged <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_ged)
##sged
garchspec_vnd_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_vnd_sged <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_sged)
##ghyp
garchspec_vnd_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_vnd_ghyp <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_ghyp)
##jsu
garchspec_vnd_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_vnd_jsu <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_jsu)
##nig
garchspec_vnd_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_vnd)[1], arimaorder(arma_vnd)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_vnd_nig <- ugarchfit(d = data_train$VND, spec = garchspec_vnd_nig)
# Lựa chọn mô hình tối ưu
vnd.model.list <- list(
garch11n = garchfit_vnd_norm,
garch11sn = garchfit_vnd_snorm,
garch11t = garchfit_vnd_std,
garch11st = garchfit_vnd_sstd,
garch11g = garchfit_vnd_ged,
garch11sg = garchfit_vnd_sged,
garch11nig = garchfit_vnd_nig,
garch11ghyp = garchfit_vnd_ghyp,
garch11jsu = garchfit_vnd_jsu
)
# Lấy AIC/BIC/AICc
vnd.info.mat <- sapply(vnd.model.list, infocriteria)
rownames(vnd.info.mat) <- rownames(infocriteria(garchfit_vnd_norm))
# Tìm mô hình AIC nhỏ nhất
vnd.inds <- which(vnd.info.mat == min(vnd.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho VND
model.vnd <- colnames(vnd.info.mat)[vnd.inds[,2]]#MSN
##Norm
garchspec_msn_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_msn_norm <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_norm)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##Snorm
garchspec_msn_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_msn_snorm <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_snorm)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##std
garchspec_msn_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_msn_std <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_std)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##sstd
garchspec_msn_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_msn_sstd <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_sstd)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##ged
garchspec_msn_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_msn_ged <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_ged)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##sged
garchspec_msn_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_msn_sged <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_sged)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##ghyp
garchspec_msn_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_msn_ghyp <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_ghyp)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##jsu
garchspec_msn_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_msn_jsu <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_jsu)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian##nig
garchspec_msn_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_msn)[1], arimaorder(arma_msn)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_msn_nig <- ugarchfit(d = data_train$MSN, spec = garchspec_msn_nig)## Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
## rugarch-->warning: failed to invert hessian# Lựa chọn mô hình tối ưu
msn.model.list <- list(
garch11n = garchfit_msn_norm,
garch11sn = garchfit_msn_snorm,
garch11t = garchfit_msn_std,
garch11st = garchfit_msn_sstd,
garch11g = garchfit_msn_ged,
garch11sg = garchfit_msn_sged,
garch11nig = garchfit_msn_nig,
garch11ghyp = garchfit_msn_ghyp,
garch11jsu = garchfit_msn_jsu
)
# Lấy AIC/BIC/AICc
msn.info.mat <- sapply(msn.model.list, infocriteria)
rownames(msn.info.mat) <- rownames(infocriteria(garchfit_msn_norm))
# Tìm mô hình AIC nhỏ nhất
msn.inds <- which(msn.info.mat == min(msn.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho MSN
model.msn <- colnames(msn.info.mat)[msn.inds[,2]]#GEX
##Norm
garchspec_gex_norm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "norm")
garchfit_gex_norm <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_norm)
##Snorm
garchspec_gex_snorm <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "snorm")
garchfit_gex_snorm <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_snorm)
##std
garchspec_gex_std <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "std")
garchfit_gex_std <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_std)
##sstd
garchspec_gex_sstd <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sstd")
garchfit_gex_sstd <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_sstd)
##ged
garchspec_gex_ged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ged")
garchfit_gex_ged <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_ged)
##sged
garchspec_gex_sged <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "sged")
garchfit_gex_sged <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_sged)
##ghyp
garchspec_gex_ghyp <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "ghyp")
garchfit_gex_ghyp <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_ghyp)
##jsu
garchspec_gex_jsu <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "jsu")
garchfit_gex_jsu <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_jsu)
##nig
garchspec_gex_nig <- ugarchspec(
mean.model=list(armaOrder=c(arimaorder(arma_gex)[1], arimaorder(arma_gex)[3])),
variance.model=list(model = "gjrGARCH", garchOrder = c(1, 1)),
distribution.model = "nig")
garchfit_gex_nig <- ugarchfit(d = data_train$GEX, spec = garchspec_gex_nig)
# Lựa chọn mô hình tối ưu
gex.model.list <- list(
garch11n = garchfit_gex_norm,
garch11sn = garchfit_gex_snorm,
garch11t = garchfit_gex_std,
garch11st = garchfit_gex_sstd,
garch11g = garchfit_gex_ged,
garch11sg = garchfit_gex_sged,
garch11nig = garchfit_gex_nig,
garch11ghyp = garchfit_gex_ghyp,
garch11jsu = garchfit_gex_jsu
)
# Lấy AIC/BIC/AICc
gex.info.mat <- sapply(gex.model.list, infocriteria)
rownames(gex.info.mat) <- rownames(infocriteria(garchfit_gex_norm))
# Tìm mô hình AIC nhỏ nhất
gex.inds <- which(gex.info.mat == min(gex.info.mat[1,]), arr.ind = TRUE)
# Tên mô hình tối ưu cho GEX
model.gex <- colnames(gex.info.mat)[gex.inds[,2]]## [1] "garch11sg"## [1] "garch11sg"## [1] "garch11jsu"## [1] "garch11g"## [1] "garch11sg"## [1] "garch11ghyp"## [1] "garch11g"## [1] "garch11g"library(rugarch)
library(FinTS)
# --- Bước 1: Chuẩn hóa residuals ---
standardize_residuals <- list(
mbb = residuals(garchfit_mbb_sged)/sigma(garchfit_mbb_sged),
vcb = residuals(garchfit_vcb_sged)/sigma(garchfit_vcb_sged),
hpg = residuals(garchfit_hpg_jsu)/sigma(garchfit_hpg_jsu),
nkg = residuals(garchfit_nkg_ged)/sigma(garchfit_nkg_ged),
ssi = residuals(garchfit_ssi_sged)/sigma(garchfit_ssi_sged),
vnd = residuals(garchfit_vnd_ghyp)/sigma(garchfit_vnd_ghyp),
gex = residuals(garchfit_gex_sged)/sigma(garchfit_gex_sged),
msn = residuals(garchfit_msn_ged)/sigma(garchfit_msn_ged)
)
# --- Bước 2: Kiểm định Ljung-Box ---
LJtest_residual <- sapply(standardize_residuals, function(res) {
test <- Box.test(as.numeric(res), lag = 5, type = "Ljung-Box")
c(TS = test$statistic, p.value = test$p.value)
})
# Chuyển sang ma trận 2 hàng, 8 cột
LJtest_residual <- matrix(LJtest_residual, nrow = 2)
dimnames(LJtest_residual) <- list(
c("Ljung-Box TS", "Ljung-Box p-value"),
names(standardize_residuals)
)
# --- Bước 3: Hiển thị kết quả ---
LJtest_residual## mbb vcb hpg nkg ssi vnd
## Ljung-Box TS 11591.86 11375.64 5.6356796 25.207347898 10503.22 9493.469
## Ljung-Box p-value 0.00 0.00 0.3432973 0.000127058 0.00 0.000
## gex msn
## Ljung-Box TS 127.8506 103.0062
## Ljung-Box p-value 0.0000 0.0000# --- Bước 1: chuẩn hoá (giữ nguyên cách bạn tạo list) ---
standardize_residuals <- list(
mbb = residuals(garchfit_mbb_sged)/sigma(garchfit_mbb_sged),
vcb = residuals(garchfit_vcb_sged)/sigma(garchfit_vcb_sged),
hpg = residuals(garchfit_hpg_jsu)/sigma(garchfit_hpg_jsu),
nkg = residuals(garchfit_nkg_ged)/sigma(garchfit_nkg_ged),
ssi = residuals(garchfit_ssi_sged)/sigma(garchfit_ssi_sged),
vnd = residuals(garchfit_vnd_ghyp)/sigma(garchfit_vnd_ghyp),
gex = residuals(garchfit_gex_sged)/sigma(garchfit_gex_sged),
msn = residuals(garchfit_msn_ged)/sigma(garchfit_msn_ged)
)
# --- Bước 2: Kiểm định Ljung-Box ---
# lưu ý: loại bỏ NA và ép sang numeric trước khi test
LJtest_residual <- sapply(standardize_residuals, function(res) {
r <- na.omit(as.numeric(res))
# nếu độ dài dữ liệu quá nhỏ so với lag, Box.test sẽ lỗi -> kiểm tra trước
if(length(r) <= 5) {
c(TS = NA_real_, p.value = NA_real_)
} else {
test <- Box.test(r, lag = 5, type = "Ljung-Box")
c(TS = as.numeric(test$statistic), p.value = as.numeric(test$p.value))
}
})
# sapply trả về ma trận 2 x 8 (hàng: TS, p.value). Đổi tên hàng cho rõ:
rownames(LJtest_residual) <- c("Ljung-Box TS", "Ljung-Box p-value")
LJtest_residual # in kết quả## mbb vcb hpg nkg ssi vnd
## Ljung-Box TS 11591.86 11375.64 5.6356796 25.207347898 10503.22 9493.469
## Ljung-Box p-value 0.00 0.00 0.3432973 0.000127058 0.00 0.000
## gex msn
## Ljung-Box TS 127.8506 103.0062
## Ljung-Box p-value 0.0000 0.0000# --- Bước 3: Chuyển sang uniform (u = Φ(z)) ---
# dùng do.call + cbind trên pnorm áp lên từng phần tử của list
u_data <- do.call(cbind, lapply(standardize_residuals, function(res) {
r <- na.omit(as.numeric(res))
pnorm(r)
}))
colnames(u_data) <- names(standardize_residuals)
# Nếu bạn cần u_data có cùng số hàng cho tất cả cột, đảm bảo tất cả residuals có cùng length / cùng index.
# In nhanh kích thước và 1 vài quan sát:
dim(u_data)## [1] 2436 8## mbb vcb hpg nkg ssi vnd
## [1,] 0.32300025 0.06251269 1.211520e-170 1.457417e-76 0.5539691 0.1397537
## [2,] 0.24201791 0.07540661 4.837826e-01 4.876845e-01 0.5833287 0.1661169
## [3,] 0.16580276 0.08498553 5.004269e-01 5.000000e-01 0.6377377 0.1693145
## [4,] 0.11340082 0.08926891 4.932515e-01 5.070333e-01 0.7086173 0.1828328
## [5,] 0.09213999 0.09092893 4.945753e-01 5.058828e-01 0.7650929 0.1972723
## [6,] 0.08370333 0.09336336 4.952157e-01 4.709782e-01 0.7569257 0.1944945
## gex msn
## [1,] 1.878942e-40 3.581605e-37
## [2,] 4.250838e-49 3.895541e-38
## [3,] 4.043640e-46 3.895541e-38
## [4,] 9.098227e-01 4.888986e-01
## [5,] 2.850555e-01 4.876556e-01
## [6,] 5.590434e-01 5.142189e-01library(FinTS)
archtest_results <- lapply(standardize_residuals, function(res) {
x <- na.omit(as.numeric(res))
test <- FinTS::ArchTest(x, lags = 5) # đúng tên hàm!
c(Statistic = as.numeric(test$statistic),
p.value = as.numeric(test$p.value))
})
archtest_results <- do.call(cbind, archtest_results)
colnames(archtest_results) <- names(standardize_residuals)
rownames(archtest_results) <- c("ARCH Statistic", "ARCH p-value")
archtest_results## mbb vcb hpg nkg ssi
## ARCH Statistic 1.131310e+03 8.594589e+02 19.867232659 4.581465e+01 1872.383
## ARCH p-value 2.215818e-242 1.579570e-183 0.001323485 9.906395e-09 0.000
## vnd gex msn
## ARCH Statistic 6.608342e+02 2.5592881 9.83051773
## ARCH p-value 1.440778e-140 0.7675409 0.08018238#kiểm định Anderson–Darling
library(goftest)
u_list <- as.data.frame(u_data)
AD_results <- lapply(u_list, function(u) {
x <- na.omit(as.numeric(u)) # loại NA
test <- ad.test(x, null = "punif") # kiểm định U~Uniform(0,1)
c(statistic = unname(test$statistic),
p.value = unname(test$p.value))
})
# Chuyển sang bảng
AD_results <- do.call(cbind, AD_results)
colnames(AD_results) <- colnames(u_list)
rownames(AD_results) <- c("AD Statistic", "AD p-value")
AD_results## mbb vcb hpg nkg ssi
## AD Statistic 1.125666e+03 5.297628e+02 1.297745e+02 1.001611e+02 1.311700e+03
## AD p-value 2.463054e-07 2.463054e-07 2.463054e-07 2.463054e-07 2.463054e-07
## vnd gex msn
## AD Statistic 4.489238e+02 6.107440e+01 5.585325e+01
## AD p-value 2.463054e-07 2.463054e-07 2.463054e-07# Kiểm định CvM test
CvM_results <- lapply(u_list, function(u) {
x <- na.omit(as.numeric(u))
test <- cvm.test(x, null = "punif")
c(statistic = unname(test$statistic),
p.value = unname(test$p.value))
})
# chuyển sang bảng kết quả
CvM_results <- do.call(cbind, CvM_results)
colnames(CvM_results) <- colnames(u_list)
rownames(CvM_results) <- c("CvM Statistic", "CvM p-value")
CvM_results## mbb vcb hpg nkg ssi vnd gex
## CvM Statistic 175.1404 80.87733 23.70781 18.68105 280.8333 88.18952 11.2491
## CvM p-value 0.0000 0.00000 0.00000 0.00000 0.0000 0.00000 0.0000
## msn
## CvM Statistic 10.214
## CvM p-value 0.000#kiểm định Kolmogorov–Smirnov (KS-test)
KS_results <- lapply(u_list, function(u) {
x <- na.omit(as.numeric(u))
x <- jitter(x, factor = 1e-06) # xử lý ties
test <- ks.test(x, "punif")
c(statistic = unname(test$statistic),
p.value = unname(test$p.value))
})
KS_results <- do.call(cbind, KS_results)
colnames(KS_results) <- colnames(u_list)
rownames(KS_results) <- c("KS Statistic", "KS p-value")
KS_results## mbb vcb hpg nkg ssi
## KS Statistic 0.5094198 3.386949e-01 1.534378e-01 1.319259e-01 0.6157944
## KS p-value 0.0000000 3.793952e-243 3.065449e-50 2.987321e-37 0.0000000
## vnd gex msn
## KS Statistic 3.873463e-01 1.066327e-01 9.409779e-02
## KS p-value 6.917808e-318 1.747046e-24 3.682735e-19tests <- lapply(names(u_list), function(nm) {
u <- na.omit(as.numeric(u_list[[nm]]))
r <- na.omit(as.numeric(standardize_residuals[[nm]]))
LB <- Box.test(r, lag = 5, type = "Ljung-Box")
ARCH <- ArchTest(r, lags = 5)
AD <- ad.test(u, "punif")
CvM <- cvm.test(u, "punif")
KS <- ks.test(jitter(u, 1e-6), "punif")
c(
LB_stat = LB$statistic, LB_p = LB$p.value,
ARCH_stat = ARCH$statistic, ARCH_p = ARCH$p.value,
AD_stat = AD$statistic, AD_p = AD$p.value,
CvM_stat = CvM$statistic, CvM_p = CvM$p.value,
KS_stat = KS$statistic, KS_p = KS$p.value
)
})
Test_Summary <- do.call(cbind, tests)
colnames(Test_Summary) <- names(u_list)
Test_Summary## mbb vcb hpg nkg
## LB_stat.X-squared 1.159186e+04 1.137564e+04 5.635680e+00 2.520735e+01
## LB_p 0.000000e+00 0.000000e+00 3.432973e-01 1.270580e-04
## ARCH_stat.Chi-squared 1.131310e+03 8.594589e+02 1.986723e+01 4.581465e+01
## ARCH_p.Chi-squared 2.215818e-242 1.579570e-183 1.323485e-03 9.906395e-09
## AD_stat.An 1.125666e+03 5.297628e+02 1.297745e+02 1.001611e+02
## AD_p 2.463054e-07 2.463054e-07 2.463054e-07 2.463054e-07
## CvM_stat.omega2 1.751404e+02 8.087733e+01 2.370781e+01 1.868105e+01
## CvM_p 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## KS_stat.D 5.094198e-01 3.386949e-01 1.534378e-01 1.319259e-01
## KS_p 0.000000e+00 3.793953e-243 3.065449e-50 2.987321e-37
## ssi vnd gex msn
## LB_stat.X-squared 1.050322e+04 9.493469e+03 1.278506e+02 1.030062e+02
## LB_p 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## ARCH_stat.Chi-squared 1.872383e+03 6.608342e+02 2.559288e+00 9.830518e+00
## ARCH_p.Chi-squared 0.000000e+00 1.440778e-140 7.675409e-01 8.018238e-02
## AD_stat.An 1.311700e+03 4.489238e+02 6.107440e+01 5.585325e+01
## AD_p 2.463054e-07 2.463054e-07 2.463054e-07 2.463054e-07
## CvM_stat.omega2 2.808333e+02 8.818952e+01 1.124910e+01 1.021400e+01
## CvM_p 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## KS_stat.D 6.157944e-01 3.873463e-01 1.066327e-01 9.409779e-02
## KS_p 0.000000e+00 6.917808e-318 1.747046e-24 3.682735e-19## mbb vcb hpg nkg ssi vnd gex
## mbb 1.0000000 0.66524911 0.21057951 0.16522551 0.4937130 0.36780851 0.16327409
## vcb 0.6652491 1.00000000 0.14020424 0.10708417 0.2589813 0.13405839 0.09540924
## hpg 0.2105795 0.14020424 1.00000000 0.58612524 0.3300228 0.07166515 0.34403073
## nkg 0.1652255 0.10708417 0.58612524 1.00000000 0.2976696 0.08266511 0.29596937
## ssi 0.4937130 0.25898127 0.33002284 0.29766960 1.0000000 0.29077095 0.27693708
## vnd 0.3678085 0.13405839 0.07166515 0.08266511 0.2907710 1.00000000 0.14208878
## gex 0.1632741 0.09540924 0.34403073 0.29596937 0.2769371 0.14208878 1.00000000
## msn 0.1294712 0.08849349 0.32358950 0.21832354 0.2184492 0.08666873 0.24512175
## msn
## mbb 0.12947119
## vcb 0.08849349
## hpg 0.32358950
## nkg 0.21832354
## ssi 0.21844924
## vnd 0.08666873
## gex 0.24512175
## msn 1.00000000## mbb vcb hpg nkg ssi vnd gex
## mbb 1.00000000 0.47592478 0.13662550 0.10613404 0.3658753 0.24069687 0.10462569
## vcb 0.47592478 1.00000000 0.09697184 0.07214583 0.1756925 0.09005776 0.06389173
## hpg 0.13662550 0.09697184 1.00000000 0.42228158 0.2197422 0.04999882 0.23947293
## nkg 0.10613404 0.07214583 0.42228158 1.00000000 0.1974170 0.05886036 0.20603441
## ssi 0.36587532 0.17569247 0.21974220 0.19741700 1.0000000 0.18773564 0.18651238
## vnd 0.24069687 0.09005776 0.04999882 0.05886036 0.1877356 1.00000000 0.09890992
## gex 0.10462569 0.06389173 0.23947293 0.20603441 0.1865124 0.09890992 1.00000000
## msn 0.08657983 0.06019395 0.22404019 0.14996518 0.1456850 0.05963896 0.16832222
## msn
## mbb 0.08657983
## vcb 0.06019395
## hpg 0.22404019
## nkg 0.14996518
## ssi 0.14568504
## vnd 0.05963896
## gex 0.16832222
## msn 1.00000000## [1] 2436 8## [1] "mbb" "vcb" "hpg" "nkg" "ssi" "vnd" "gex" "msn"library(copula)
# u_data: ma trận residuals đã chuẩn hóa (standardized residuals)
# Chuyển sang pseudo-observations [0,1]
U <- pobs(u_data)
# Fit copula 8 chiều
dim_copula <- ncol(U)
joe_cop <- joeCopula(dim = dim_copula)
normal_cop <- normalCopula(dim = dim_copula, dispstr = "un")
t_cop <- tCopula(dim = dim_copula, dispstr = "un")
# Fit bằng Maximum Likelihood
fit_joe <- fitCopula(joe_cop, U, method = "ml")
fit_normal <- fitCopula(normal_cop, U, method = "ml")
fit_t <- fitCopula(t_cop, U, method = "ml")
# Xem kết quả
summary(fit_joe)## Call: fitCopula(joe_cop, U, method = "ml")
## Fit based on "maximum likelihood" and 2436 8-dimensional observations.
## Joe copula, dim. d = 8
## Estimate Std. Error
## alpha 1.241 0.009
## The maximized loglikelihood is 801.8
## Optimization converged
## Number of loglikelihood evaluations:
## function gradient
## 8 8## Call: fitCopula(normal_cop, U, method = "ml")
## Fit based on "maximum likelihood" and 2436 8-dimensional observations.
## Normal copula, dim. d = 8
## Estimate Std. Error
## rho.1 0.58683 0.011
## rho.2 0.23310 0.019
## rho.3 0.17340 0.019
## rho.4 0.48537 0.014
## rho.5 0.32297 0.017
## rho.6 0.17154 0.019
## rho.7 0.14782 0.020
## rho.8 0.16718 0.019
## rho.9 0.11804 0.020
## rho.10 0.25741 0.018
## rho.11 0.10926 0.020
## rho.12 0.11998 0.020
## rho.13 0.11850 0.020
## rho.14 0.62735 0.010
## rho.15 0.31707 0.017
## rho.16 0.09897 0.020
## rho.17 0.38227 0.016
## rho.18 0.35116 0.017
## rho.19 0.27884 0.018
## rho.20 0.10861 0.020
## rho.21 0.32166 0.017
## rho.22 0.24156 0.018
## rho.23 0.24895 0.018
## rho.24 0.26234 0.018
## rho.25 0.21581 0.019
## rho.26 0.16817 0.019
## rho.27 0.10616 0.020
## rho.28 0.26553 0.018
## The maximized loglikelihood is 2210
## Optimization converged
## Number of loglikelihood evaluations:
## function gradient
## 198 35## Call: fitCopula(t_cop, U, method = "ml")
## Fit based on "maximum likelihood" and 2436 8-dimensional observations.
## t-copula, dim. d = 8
## Estimate Std. Error
## rho.1 0.6073 0.012
## rho.2 0.2253 0.019
## rho.3 0.1696 0.020
## rho.4 0.4944 0.015
## rho.5 0.3178 0.018
## rho.6 0.1671 0.020
## rho.7 0.1407 0.020
## rho.8 0.1633 0.020
## rho.9 0.1163 0.020
## rho.10 0.2680 0.018
## rho.11 0.1100 0.020
## rho.12 0.1140 0.020
## rho.13 0.1114 0.020
## rho.14 0.6308 0.011
## rho.15 0.3108 0.018
## rho.16 0.1027 0.020
## rho.17 0.3824 0.017
## rho.18 0.3527 0.017
## rho.19 0.2744 0.018
## rho.20 0.1149 0.020
## rho.21 0.3297 0.018
## rho.22 0.2431 0.019
## rho.23 0.2551 0.019
## rho.24 0.2590 0.019
## rho.25 0.2099 0.019
## rho.26 0.1687 0.020
## rho.27 0.1056 0.021
## rho.28 0.2666 0.019
## df 23.1569 2.525
## The maximized loglikelihood is 2266
## Optimization converged
## Number of loglikelihood evaluations:
## function gradient
## 200 43library(copula)
# Pseudo-observations
U <- pobs(u_data)
# Copula list
copulas <- list(
Joe = joeCopula(dim = 8),
Gaussian = normalCopula(dim = 8, dispstr = "un"),
t = tCopula(dim = 8, dispstr = "un")
)
# Fit và lấy kết quả
fits <- lapply(copulas, function(c) fitCopula(c, data = U, method = "ml"))
# Tóm tắt log-likelihood, AIC, BIC
results <- data.frame(
Copula = names(fits),
LogLik = sapply(fits, logLik),
AIC = sapply(fits, AIC),
BIC = sapply(fits, BIC)
)
# Tham số
params <- lapply(fits, coef)
results| Copula | LogLik | AIC | BIC | |
|---|---|---|---|---|
| Joe | Joe | 801.7599 | -1601.520 | -1595.722 |
| Gaussian | Gaussian | 2209.5055 | -4363.011 | -4200.664 |
| t | t | 2265.5058 | -4473.012 | -4304.866 |
## $Joe
## alpha
## 1.241458
##
## $Gaussian
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7
## 0.58682981 0.23310004 0.17340318 0.48536912 0.32297366 0.17154481 0.14782303
## rho.8 rho.9 rho.10 rho.11 rho.12 rho.13 rho.14
## 0.16717522 0.11803901 0.25741093 0.10925605 0.11997824 0.11850120 0.62734962
## rho.15 rho.16 rho.17 rho.18 rho.19 rho.20 rho.21
## 0.31706873 0.09897162 0.38227134 0.35116298 0.27884206 0.10860950 0.32166070
## rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28
## 0.24155838 0.24894766 0.26234497 0.21581222 0.16817217 0.10615546 0.26552979
##
## $t
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7
## 0.6072848 0.2252730 0.1695576 0.4943796 0.3177688 0.1670895 0.1406589
## rho.8 rho.9 rho.10 rho.11 rho.12 rho.13 rho.14
## 0.1632934 0.1163060 0.2680463 0.1099936 0.1140016 0.1114461 0.6308023
## rho.15 rho.16 rho.17 rho.18 rho.19 rho.20 rho.21
## 0.3107731 0.1027268 0.3823995 0.3527425 0.2743807 0.1149470 0.3297296
## rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28
## 0.2430706 0.2550990 0.2590383 0.2098643 0.1686945 0.1056371 0.2666302
## df
## 23.1568845library(copula)
# Pseudo-observations
U <- pobs(u_data)
U_adj <- jitter(U, factor = 1e-6)
U_adj[U_adj==0] <- 1e-6
U_adj[U_adj==1] <- 1-1e-6
n <- ncol(U_adj)
n_rho <- n*(n-1)/2
# Khởi tạo copula với tham số hợp lý
copulas <- list(
Joe = joeCopula(dim=n, param=1.0),
Gaussian = normalCopula(param=rep(0.2, n_rho), dim=n, dispstr="un"),
t = tCopula(param=rep(0.2, n_rho), dim=n, dispstr="un")
)## parameter at boundary ==> returning indepCopula()# Fit copula robust
fits <- lapply(copulas, function(c) {
tryCatch(
fitCopula(c, data=U_adj, method="ml"),
error = function(e) {message("Fit failed: ", e$message); NULL}
)
})## Fit failed: any(isFree(copula)) is not TRUE# Tóm tắt
results <- data.frame(
Copula = names(fits),
LogLik = sapply(fits, function(f) if(!is.null(f)) logLik(f) else NA),
AIC = sapply(fits, function(f) if(!is.null(f)) AIC(f) else NA),
BIC = sapply(fits, function(f) if(!is.null(f)) BIC(f) else NA)
)
params <- lapply(fits, function(f) if(!is.null(f)) coef(f) else NA)
results| Copula | LogLik | AIC | BIC | |
|---|---|---|---|---|
| Joe | Joe | NA | NA | NA |
| Gaussian | Gaussian | 2209.505 | -4363.011 | -4200.664 |
| t | t | 2265.506 | -4473.012 | -4304.866 |
## $Joe
## [1] NA
##
## $Gaussian
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7
## 0.58683091 0.23309968 0.17340377 0.48536931 0.32297498 0.17154482 0.14782301
## rho.8 rho.9 rho.10 rho.11 rho.12 rho.13 rho.14
## 0.16717526 0.11803884 0.25741088 0.10925508 0.11997656 0.11850150 0.62734911
## rho.15 rho.16 rho.17 rho.18 rho.19 rho.20 rho.21
## 0.31706985 0.09897124 0.38227064 0.35116388 0.27884392 0.10860968 0.32166140
## rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28
## 0.24155971 0.24894919 0.26234366 0.21581341 0.16817242 0.10615512 0.26552885
##
## $t
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7
## 0.6072895 0.2252675 0.1695562 0.4943815 0.3177754 0.1670902 0.1406559
## rho.8 rho.9 rho.10 rho.11 rho.12 rho.13 rho.14
## 0.1632954 0.1163129 0.2680534 0.1099975 0.1140025 0.1114471 0.6308033
## rho.15 rho.16 rho.17 rho.18 rho.19 rho.20 rho.21
## 0.3107680 0.1027279 0.3824006 0.3527419 0.2743763 0.1149518 0.3297342
## rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28
## 0.2430705 0.2551085 0.2590397 0.2098642 0.1686923 0.1056369 0.2666309
## df
## 23.1560997library(copula)
# --- 1. Pseudo-observations ---
U <- pobs(u_data)
# --- 2. Jitter để tránh ties/extreme 0/1 ---
U_adj <- jitter(U, factor = 1e-6)
U_adj[U_adj==0] <- 1e-6
U_adj[U_adj==1] <- 1-1e-6
n <- ncol(U_adj)
n_rho <- n*(n-1)/2
# --- 3. Khởi tạo copula với tham số hợp lý ---
copulas <- list(
Gaussian = normalCopula(param = rep(0.2, n_rho), dim = n, dispstr="un"),
t = tCopula(param = rep(0.2, n_rho), dim = n, dispstr="un")
)
# --- 4. Fit copula bằng ML ---
fits <- lapply(copulas, function(c) {
tryCatch(
fitCopula(c, data = U_adj, method="ml"),
error = function(e) {message("Fit failed: ", e$message); NULL}
)
})
# --- 5. Simulate 1000 mẫu uniform [0,1] từ copula đã fit ---
n_sim <- 1000
sim_U <- lapply(fits, function(f) {
if(!is.null(f)) rCopula(n_sim, f@copula) else NULL
})
# --- 6. Chuyển pseudo-observations sang residual chuẩn (N(0,1)) ---
sim_resid <- lapply(sim_U, qnorm)
# --- 7. Tính tail dependence t-copula ---
# Chỉ áp dụng cho t-copula
fit_t <- fits$t
rho_vec <- coef(fit_t)[1:n_rho]
df <- coef(fit_t)["df"]
# Tạo ma trận correlation từ rho_vec
rho_mat <- matrix(0, n, n)
rho_mat[lower.tri(rho_mat)] <- rho_vec
rho_mat <- rho_mat + t(rho_mat)
diag(rho_mat) <- 1
# Hàm tính upper tail dependence t-copula
tail_t <- function(rho, nu) {
2 * pt(-sqrt((nu+1)*(1-rho)/(1+rho)), df = nu+1)
}
lambda_upper <- matrix(0, n, n)
lambda_lower <- matrix(0, n, n)
for(i in 1:n){
for(j in 1:n){
if(i != j){
lambda_upper[i,j] <- tail_t(rho_mat[i,j], df)
lambda_lower[i,j] <- lambda_upper[i,j] # symmetric
}
}
}
# --- 8. Kết quả ---
list(
fits = fits,
simulated_resid = sim_resid,
tail_upper = lambda_upper,
tail_lower = lambda_lower
)## $fits
## $fits$Gaussian
## Call: fitCopula(c, data = U_adj, method = "ml")
## Fit based on "maximum likelihood" and 2436 8-dimensional observations.
## Copula: normalCopula
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7 rho.8 rho.9 rho.10
## 0.58683 0.23310 0.17340 0.48537 0.32297 0.17154 0.14782 0.16717 0.11804 0.25741
## rho.11 rho.12 rho.13 rho.14 rho.15 rho.16 rho.17 rho.18 rho.19 rho.20
## 0.10926 0.11998 0.11850 0.62735 0.31707 0.09897 0.38227 0.35116 0.27884 0.10861
## rho.21 rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28
## 0.32166 0.24156 0.24895 0.26234 0.21581 0.16817 0.10616 0.26553
## The maximized loglikelihood is 2210
## Optimization converged
##
## $fits$t
## Call: fitCopula(c, data = U_adj, method = "ml")
## Fit based on "maximum likelihood" and 2436 8-dimensional observations.
## Copula: tCopula
## rho.1 rho.2 rho.3 rho.4 rho.5 rho.6 rho.7 rho.8 rho.9 rho.10
## 0.6073 0.2253 0.1696 0.4944 0.3178 0.1671 0.1407 0.1633 0.1163 0.2681
## rho.11 rho.12 rho.13 rho.14 rho.15 rho.16 rho.17 rho.18 rho.19 rho.20
## 0.1100 0.1140 0.1114 0.6308 0.3108 0.1027 0.3824 0.3527 0.2744 0.1150
## rho.21 rho.22 rho.23 rho.24 rho.25 rho.26 rho.27 rho.28 df
## 0.3297 0.2431 0.2551 0.2590 0.2099 0.1687 0.1056 0.2666 23.1567
## The maximized loglikelihood is 2266
## Optimization converged
##
##
## $simulated_resid
## $simulated_resid$Gaussian
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.463988393 1.0718640568 0.2322687843 -0.7316814926 -1.256967224
## [2,] 0.920777352 0.7050183758 0.1412293946 0.2445019831 -0.293949034
## [3,] 2.185482490 0.4621701042 0.2335328711 0.0388917741 0.576285273
## [4,] 0.324370585 -0.0714029651 -0.6487040564 -1.4627033376 -0.174397708
## [5,] -0.596355556 0.6698180531 -0.7845457021 -1.3096767068 0.378063866
## [6,] 0.923306774 0.4631703725 0.4130364707 0.4492503527 -0.573902262
## [7,] -0.467478866 -0.5495478860 -0.7821025712 -0.3444024608 0.614280968
## [8,] 0.209823649 -0.1461033504 -0.6568722896 -0.5741061321 0.713540231
## [9,] -1.036416843 0.2959728978 -1.4720786893 -1.7289518842 -0.049091259
## [10,] 0.430417360 -0.0915648394 -0.3470983240 -2.4704659994 -0.187853158
## [11,] -0.564345064 -0.8698748161 -0.4087836964 0.4471832870 -1.479403129
## [12,] -0.824834163 -0.2190552504 -0.6903582566 -1.1906834200 -0.380315523
## [13,] -0.715969745 1.3796202503 -1.6623409014 -1.4464718677 -0.540062958
## [14,] 0.085573223 -0.5714451424 0.1542841892 0.9066620508 1.572874887
## [15,] 1.174986534 0.2661085800 -0.9166150172 -0.4184835318 0.874309894
## [16,] 0.361784375 0.8654818340 1.6932597574 0.2246780874 0.803504632
## [17,] 0.706435300 1.1360137151 0.9741003359 0.7472392452 2.109695601
## [18,] -1.666411304 -0.5961821370 0.0039779244 0.1747937005 -0.526769910
## [19,] 1.375409117 0.2022381292 -0.3996847832 0.4183277104 1.507739956
## [20,] 0.295122605 1.0817279724 -0.3818715502 0.4674342693 0.847646967
## [21,] -0.218729016 -0.2978725251 0.1744306735 0.7569671076 -0.120331256
## [22,] -1.225740095 0.4857526727 0.0891095426 -0.9415327183 -0.156585153
## [23,] 0.662805280 -0.2680887772 0.6148303308 -0.1326872308 -0.963262678
## [24,] 1.161242944 1.5311877878 0.2003073777 -0.6573256167 -0.074980770
## [25,] 0.233758205 -1.1040810297 1.7438988836 0.7355977969 0.789341994
## [26,] 3.407571489 2.1958397463 -0.8694528202 -1.0748735033 1.285619926
## [27,] 0.055475993 -0.3665412870 -1.6210232465 -0.7738887598 -0.651744325
## [28,] -0.064838211 -0.0334008163 2.1743520156 0.8616153231 0.907661109
## [29,] 2.316110632 0.7291160121 1.1330744471 1.7270139840 1.643600596
## [30,] -1.028393871 0.6136703262 -0.1417537018 0.8620410890 0.500034845
## [31,] 0.916259245 0.2128769303 0.0242894866 -0.5447651903 1.267600509
## [32,] -1.349098576 0.0598479330 -0.3958921250 0.5529534343 -0.233524532
## [33,] -0.192885028 -0.0290087198 0.0117384115 0.4715026225 0.849326030
## [34,] 0.254821590 0.2416649830 2.2276695805 1.2771421055 0.095763930
## [35,] -0.392182957 0.5991266420 0.6258935928 0.0712786573 -0.880268951
## [36,] -0.239826449 0.1709438619 -0.4674060154 -1.2440376901 0.860093742
## [37,] 0.498579370 0.1075260198 1.0150922535 -0.0548650537 -0.378604025
## [38,] 0.711671194 1.6655511529 -1.2308816550 0.3297428114 1.640308147
## [39,] 0.824182755 -0.9296474126 -0.1763904879 0.8333625515 -0.907995953
## [40,] 0.443811298 0.3490678172 0.9953316458 0.5143437326 -0.132938345
## [41,] -0.703328186 -0.4331436497 -0.9474028939 -0.5031813073 -2.440753408
## [42,] -0.046642939 0.2878569441 -0.4422454517 -0.4892738092 -0.502164398
## [43,] -0.652797375 -1.3138838337 0.2789681930 0.5351605071 -0.603753443
## [44,] -0.667872197 -1.3450622565 -1.4616545938 -1.1571458184 0.384626159
## [45,] -1.840223321 -0.0081905261 -0.8292057561 -1.2221203955 -0.508314643
## [46,] -1.593093316 -1.0026420820 0.4494368763 0.1978926242 -1.641215606
## [47,] -1.681730460 -1.7569706255 -0.4896712529 -1.1164080572 -1.595096724
## [48,] -0.908685358 -0.5755088447 -0.4490207978 0.9672160065 0.234324801
## [49,] -1.473585545 -0.6038399377 -0.0845545629 -1.7370087088 -1.765561398
## [50,] 0.234546608 -1.1118705925 -0.6702380578 -0.2761389608 1.605218560
## [51,] -0.990561091 -0.5633592460 -2.5660616256 -0.9282712967 -1.213244913
## [52,] -2.196181711 -1.1347207705 -1.0222293899 -1.7361687930 -0.992294826
## [53,] 1.214559063 0.0869673895 1.5189994792 2.6772135864 1.263156861
## [54,] -0.465971274 0.6172029741 -1.9472147479 -1.9252659393 -2.113161937
## [55,] -2.580305758 -1.9131115483 -0.4753830007 -0.6648296974 -0.148285409
## [56,] 2.600566693 1.8531565674 -0.8068886651 -1.3358371778 1.077070218
## [57,] 1.299259140 1.1607901281 1.7432554533 1.3099940166 1.123372363
## [58,] 1.343497644 1.1170174367 0.1082916273 1.0859525520 -0.372042518
## [59,] 1.369120032 -0.0142433651 0.3999501669 -0.6876878329 1.119781672
## [60,] 0.525102433 -0.3642367389 1.1652007092 2.0497467123 1.131116182
## [61,] -0.131494384 -0.9133898010 -2.3655652286 -1.2995701202 -0.039284120
## [62,] 0.997571249 -0.3749569173 1.1732254579 1.2134199742 1.697897090
## [63,] 0.387976605 -0.6512460806 1.2494760352 2.4343149170 1.394364880
## [64,] 0.042961414 -0.9295630130 2.2073030002 0.8560300527 1.487458819
## [65,] 0.710846873 0.6795511710 -0.9473581600 -0.3703746056 -1.785824202
## [66,] 0.103758006 -0.7917995004 -0.3713147902 -0.7687211658 0.031903668
## [67,] 0.368078928 -1.2819701172 -0.3825301917 -0.9843653673 -0.064297415
## [68,] 1.807138679 2.0116504360 1.3962866857 2.4344002770 1.803771701
## [69,] 1.364931964 0.9315628144 -1.4141778222 0.2887751936 0.280099220
## [70,] 0.452657201 1.0448321580 0.4276023918 -0.4567641393 -0.583831145
## [71,] -1.639937850 -1.3119468577 -0.1601714327 0.9284923243 0.681060889
## [72,] -0.906819675 0.1169985808 0.7745782323 0.4827434094 -0.671511501
## [73,] 0.570529852 0.1497139918 0.8363685713 1.7685510522 0.819001390
## [74,] 0.761404515 -0.2399693685 0.0991073315 0.0334829246 0.701195171
## [75,] -0.389474165 -2.0460088586 -0.9796633715 -1.7318424384 0.243806568
## [76,] 0.295320892 -0.6519477185 -0.2611145638 -0.4851661263 -0.140919041
## [77,] 0.458355445 1.3250272432 -0.0954184595 -1.2148425636 0.449294323
## [78,] 1.564064029 2.7495976674 1.1739133589 0.5071843044 0.457256496
## [79,] 1.652906315 1.4072321808 -0.3991718959 -0.0930281180 -0.253425255
## [80,] -0.508100059 1.2077552664 0.3091851714 0.4096482067 -1.877854041
## [81,] 0.241330037 1.1496273153 0.9737339892 -0.5247017336 0.307645095
## [82,] 0.114593808 0.4159626653 -1.4396674635 -0.9915802825 -1.800092016
## [83,] -0.236448476 0.1784523811 -1.5232408329 -1.3150680837 -0.718521060
## [84,] 0.257497169 0.9251701609 0.3272078727 0.3499600819 -1.905476758
## [85,] 0.410485742 -0.2633278062 1.1958196142 0.3297423377 1.348753349
## [86,] 0.745001305 0.8923076497 -0.5671873634 -1.2324419581 -0.340161724
## [87,] 0.238078401 0.0251254019 0.3118918520 0.8042807560 -0.178415842
## [88,] -0.859465252 -0.4256904279 -0.6870850989 -0.2144792718 -1.120364404
## [89,] -2.220284882 -0.2413922785 0.1639546020 -0.3536837711 -0.912294751
## [90,] -1.176407325 0.1552879118 -1.1342997866 -1.1959728914 -2.295671357
## [91,] -0.549401432 0.5541220558 1.5291088156 0.0814195340 -0.385575187
## [92,] -0.412717100 -0.2524667806 0.6915566870 -0.1054371628 -0.946846181
## [93,] -1.463148725 -1.2698671075 0.2812940576 -0.0644351280 -1.371862957
## [94,] -0.404799655 -1.0271666841 0.8782166936 1.3973977028 1.078239767
## [95,] 1.112007529 0.7525235790 0.6607989848 0.9960088571 1.058899149
## [96,] 1.546308898 0.5107006614 0.0709199720 0.9733933841 0.769559129
## [97,] 0.546444834 -0.5609167495 -1.1549780303 0.0337025398 0.200132528
## [98,] -0.345864706 0.3443237103 -1.5168412383 -0.9803918430 -1.330975680
## [99,] -0.433118973 0.6797768873 -0.5364662936 -0.4099916665 0.357440048
## [100,] 0.011847212 0.6361392237 -1.9360239006 -1.7110087503 -0.579077331
## [101,] -0.091383486 -1.4815612271 0.1196536350 -1.5274814161 1.150360046
## [102,] -0.193338103 -0.1580384139 0.0338757624 0.3728880992 -0.749463335
## [103,] -1.688199603 -2.3800493661 -0.8969172081 -0.3210552469 -1.665137714
## [104,] 0.535986100 -0.2088863080 0.2969607002 0.7183892175 0.277886512
## [105,] -0.431869048 -0.4772449589 -1.5138600166 0.3013939092 -0.590941759
## [106,] -0.155508074 -0.4953257479 -1.2549373534 -1.7520168099 -0.981971985
## [107,] -0.574581998 -0.2354651933 0.6456223467 0.9850156822 -0.013338603
## [108,] -0.836895248 -0.3188881456 0.2907505975 0.3024087793 0.145698278
## [109,] -0.271667892 0.0425573097 0.5102093931 0.3310597735 1.725737180
## [110,] -2.500018209 -1.3600126555 -0.4504538289 -0.8111969298 -0.754266266
## [111,] 0.193382775 2.7747834158 0.5181011555 0.8544433775 -0.689872881
## [112,] -0.150666029 0.3992722701 -0.5369889445 -0.9814145365 -1.788921490
## [113,] 0.613318784 1.1277776753 1.0546540331 1.2110230282 1.020964676
## [114,] -0.561550246 0.7689576034 -0.0126849781 0.8561282316 -0.390153253
## [115,] -0.814820047 0.9451577865 1.1406420696 0.1591765695 -1.392688396
## [116,] -0.748922379 -0.1063743152 -0.3025123715 -0.0314054645 -0.096682547
## [117,] 0.495478971 0.7044734711 1.5958303371 1.0082188615 -0.062009084
## [118,] -0.407646454 0.0515520896 -0.3470163022 -0.5863851653 0.366696127
## [119,] -0.640649127 -2.3499318690 -1.7034709357 -1.9892273184 -1.734233613
## [120,] 0.867301672 0.6615564680 0.6567683131 0.7494619215 0.308975172
## [121,] -0.043859615 -0.2638009642 -2.1002170157 -0.5662374740 -1.351943694
## [122,] 0.743942712 0.3338720770 -1.3671878838 -0.9583641305 0.708228581
## [123,] 0.304952537 -0.9587866385 0.8823758747 0.3275759104 0.407464676
## [124,] -0.057321280 0.4768365854 0.4067866510 -0.0151368075 -0.057558113
## [125,] -0.239121066 0.8762950139 -1.1257479568 -0.3868529018 0.615253966
## [126,] -0.076866560 -0.5537303439 -1.6639071426 -0.3955204707 -0.510981693
## [127,] 1.449941629 1.1351094285 -0.6944928400 0.4854475640 -0.205720274
## [128,] -0.040944439 0.2828651920 0.6267111579 0.5458833095 -0.171807952
## [129,] 1.609813638 1.0373082892 0.2651436282 0.4729683585 0.814685190
## [130,] -1.251781898 -0.1312392165 0.2247769633 0.5986611982 -1.520140847
## [131,] 2.067351351 1.3505426359 1.5360036593 -0.1736702441 2.072785440
## [132,] 2.417786104 1.0532227492 -2.2877424412 -2.5718111378 0.027745859
## [133,] 1.216358366 1.6043644570 -0.0468104683 0.5701283615 1.071403149
## [134,] -0.263588762 0.1530136810 1.8889150945 0.7064478937 1.395559511
## [135,] 0.793531679 1.2165917438 0.8217800804 -0.1237861631 1.398782063
## [136,] -0.224335876 -0.0124567363 -0.5259835944 0.1873241180 1.316358415
## [137,] 0.578866837 -0.2220704346 0.0164070977 0.6495485523 0.106225153
## [138,] 0.662731454 0.2934542858 -0.9529138933 -0.1406121322 0.836818452
## [139,] -0.639014217 0.5084998051 -0.1191420434 -0.3846595562 -1.376663781
## [140,] -0.656812472 0.3530447268 0.3585308689 0.9963785776 0.080844946
## [141,] 0.008608846 -0.5473863598 -1.5619996004 -1.6867542490 0.155843626
## [142,] 1.120018105 1.5835687247 -0.5462269989 -0.6073927736 0.838717687
## [143,] -0.874496174 -1.7104843213 -0.6019060906 0.5128549291 -1.545044653
## [144,] 0.893679350 2.0441471559 0.1172988883 0.2482900977 -0.891347206
## [145,] -0.691036198 -1.3372258209 -0.3397913423 -0.0316477669 1.618411264
## [146,] 0.868257504 2.3759473980 0.6052164378 0.6213574153 1.804363347
## [147,] 1.036642221 0.8280758374 -0.3688523599 -0.0105830974 1.408884214
## [148,] 0.236409682 0.0871209446 -0.0285438762 0.2193206445 -1.635713841
## [149,] 0.779134606 0.0736711650 0.5624377819 0.9737888903 0.850339198
## [150,] 0.553873324 0.2387508326 0.3188514931 -0.5759689989 1.168362624
## [151,] -0.022228247 -0.7998717101 1.8988817436 2.2379539595 -0.033664086
## [152,] -1.147703744 -0.6521555035 -0.9178521818 -1.6209390317 -0.497505235
## [153,] -0.174961353 -2.5381787091 -1.1281237739 -2.0158713794 0.426419552
## [154,] -1.330382020 -0.1422811029 0.0377477376 0.5462834611 -0.978458794
## [155,] -0.504981947 -0.5910651604 -0.8195059016 -1.4443552284 -0.894873057
## [156,] 1.403112662 -0.2028036293 -0.5650002903 -0.4931372188 1.136309601
## [157,] 0.392763069 0.2698361917 -0.4688097093 -0.3946704103 1.192536706
## [158,] 1.383989499 0.6401384692 0.1575730594 -0.3565830135 0.769891387
## [159,] -2.413601355 -1.0205565107 -0.1629780381 0.2033028813 -0.528810183
## [160,] 0.680040947 0.5481165275 1.8830345487 1.3869179166 0.170609956
## [161,] 1.306798691 1.3127433122 -1.7957277640 -1.5299156397 0.770433195
## [162,] 0.712879888 0.5230632607 0.7122194982 0.1074685383 -0.136049594
## [163,] 3.414786755 1.0648589082 0.0338745892 0.4935694100 -0.188267193
## [164,] 1.887718149 0.5055786468 -0.4249513081 -0.2620051488 -0.537747411
## [165,] -0.595887199 0.6420175916 0.0170028264 0.1335578488 0.039584873
## [166,] -1.022439662 -0.3148901071 -0.0968314096 0.0672518226 -1.631833267
## [167,] 0.987046521 1.8634574556 -0.4694229617 -1.1504948723 0.277955115
## [168,] 1.122644203 1.0603686854 -0.4196635067 -0.1912353164 0.174631576
## [169,] 0.399916452 0.8148597263 -0.5068715321 -0.4092439933 0.953439797
## [170,] -1.767423448 0.3990552348 -2.1998464874 -0.7130606321 -1.331224286
## [171,] 1.046115912 0.1329742272 0.1194597252 0.6109140495 1.712802790
## [172,] -0.901426750 -1.9037516057 -0.0852457539 -0.4362332095 -0.598984919
## [173,] -0.112180219 -0.7044733861 1.1475487319 1.3569672388 0.144316746
## [174,] 2.027824078 1.1408251871 1.2250495174 1.5505464705 3.262787679
## [175,] -0.383720970 -1.1284354939 -0.1498841519 0.8165116983 1.536305232
## [176,] -1.492020195 -0.8845898546 -1.6317205725 -1.2791322181 -2.392637510
## [177,] 0.521741874 -0.1556647292 0.0912659319 -0.9846112782 -0.403912544
## [178,] 0.236571066 0.7968893630 1.5474821567 0.9705039247 0.270674132
## [179,] 1.409508536 0.2180692231 2.2853031977 1.7058825531 -0.357487320
## [180,] 0.083019263 0.2223413617 -0.7519457324 -0.0600352107 -0.378616063
## [181,] 0.006994002 -1.9039780375 -0.6518115859 -0.7861491633 0.096936814
## [182,] 0.379157545 -0.3267643744 1.9677453874 0.6655731574 -0.053725477
## [183,] 0.152848294 0.0359438467 1.5582482350 1.2206405620 0.903717040
## [184,] 1.644778677 1.3255758222 -0.5208097332 -0.4560609251 0.862367064
## [185,] -1.925298629 -1.5504053255 0.6017169069 0.6328224029 0.360473083
## [186,] 0.297815072 1.5024719189 0.8029595336 0.4669733596 1.162736884
## [187,] -0.239387659 0.1084825473 1.1828671066 0.5524290714 2.077902157
## [188,] -3.246103051 -1.3691735228 -0.0282637098 -0.2886972600 -1.802370802
## [189,] 0.308979013 1.4709260082 0.5389997043 1.5689828092 -0.018635767
## [190,] 0.002453429 -0.9759455152 0.2875405025 -0.5065893741 0.355333801
## [191,] -0.586569623 -0.0794595645 -0.3675205223 0.2699089839 -0.305561740
## [192,] -0.043504581 -1.0456333982 1.2212255143 1.2361277285 0.202470527
## [193,] 2.149857888 0.4094139773 0.3789051981 1.2131582972 -0.059558478
## [194,] -0.479506826 0.1967421111 1.5787932546 -0.0180425272 0.307955760
## [195,] 1.651894498 2.6122951916 0.6130252946 1.6460488924 -0.586415465
## [196,] 0.598449553 -0.4188138427 1.3077767339 0.9377967493 0.688093167
## [197,] 0.371164095 -0.5329847959 1.5305245668 0.6864060963 0.980217891
## [198,] 1.327168266 0.9981079368 -0.0175613643 0.0906952758 2.232795261
## [199,] -1.135297663 -0.6320010617 -1.2024982246 -0.6146490851 -0.732231947
## [200,] -0.817307318 -1.4468893329 0.1163132285 0.1744175153 -1.037563791
## [201,] -0.784272372 0.2315552030 -0.0837375335 -0.3318818777 -1.015581818
## [202,] -0.341130608 0.3917501240 -1.5849309780 -0.8836320329 0.406335164
## [203,] -1.001426408 0.5877661791 0.3677529160 0.1090357287 -1.695041181
## [204,] -1.716061041 -0.7350411457 0.5639517440 0.3280943522 0.602692906
## [205,] -0.321890092 0.1410175256 1.5253304775 0.7722290344 0.021137384
## [206,] -0.927081822 -0.9244338540 0.6080589325 -0.2404535511 -1.494860076
## [207,] 1.008872505 1.3326623288 1.3248994952 0.4037465374 0.646344830
## [208,] -0.807289105 1.4156752220 0.3252989188 0.5618209184 0.833554843
## [209,] 1.632710177 1.7901793225 -0.7463268438 -0.4725103769 -0.126762021
## [210,] -0.815719480 -0.4473774893 0.8033207887 -0.6127126074 -2.448916420
## [211,] -2.172982821 -1.5492203910 -0.7052727167 -0.5501386860 -1.423194358
## [212,] -0.963392431 -1.2828421413 -0.0060235749 -0.2085303840 0.328532062
## [213,] -0.937642446 -0.6464703100 0.6471134516 -0.4615179187 -1.437949985
## [214,] -1.207576768 0.5932480663 1.3512378188 1.3778044433 -1.245424374
## [215,] -0.111539306 0.0275055836 0.1675727017 0.8060018609 -0.619836191
## [216,] 1.299941180 0.5365730733 0.3290238247 -0.9183267087 1.031054670
## [217,] 1.545782624 1.3681375992 1.7034710933 1.1808818609 1.013177912
## [218,] -0.420663538 -1.1999924900 0.0059683502 0.0021163706 0.873585041
## [219,] 0.228805876 -0.2148551407 0.8733996522 -0.9465835339 -0.727240234
## [220,] 0.859967891 0.2304666877 0.5540590768 0.3344309995 0.667804709
## [221,] 1.594752415 1.2254592486 1.8234860158 2.1361289354 1.173116337
## [222,] 0.430700741 1.5139686437 -0.5099852485 -0.1039027806 0.418960573
## [223,] -0.168102806 0.1854006415 0.4523578761 0.3594795300 -0.342889094
## [224,] -1.278037955 -0.6978443189 -0.1715493690 1.0469594786 -0.108745460
## [225,] -1.686237662 -0.5306601066 0.3279198110 0.3560976246 -0.513699875
## [226,] 0.621489465 -0.3691554109 -0.2724508250 -1.5119479616 1.192149054
## [227,] 1.080843534 0.7533919468 2.1023271103 1.3797500477 1.368352025
## [228,] -1.393196587 -0.7628130049 -1.8698510612 -1.2029778589 -1.332953807
## [229,] 0.387942491 -0.1578701194 2.6680724061 0.7810355623 0.495002133
## [230,] -0.830765476 -0.1689529130 -2.0654188907 0.0089630871 -2.094990654
## [231,] 0.380960074 -0.4669321070 -0.8613616426 0.4765815984 0.532717304
## [232,] -0.242664665 -0.4758721184 -0.5294543880 -0.7199819592 0.002123414
## [233,] 0.349232559 -0.1870920002 0.4964821170 1.0063892323 -0.813085435
## [234,] 0.751831674 0.8040258505 -0.3698086491 0.6194366979 -0.769904588
## [235,] -1.522914962 -1.2313434515 -0.0300596627 -0.0635088120 -1.869690734
## [236,] -0.230558629 0.9524055760 -0.2834223546 -0.6583331838 0.074158441
## [237,] 1.586135515 0.9417270611 1.6358374554 1.8248348797 2.117221940
## [238,] -0.555100444 0.0108062809 -1.3913390560 -0.4142030593 -1.223261906
## [239,] -1.761870260 -1.7927571028 -0.5662942530 -0.5746071437 -1.002439969
## [240,] -1.269692417 0.7379908947 -0.5208253454 1.3380917838 -0.052995711
## [241,] 0.609023381 0.7950275454 -0.0450466664 -0.7050370624 -0.438761501
## [242,] -0.182768784 -0.3784072791 -0.4785978407 -1.1613352558 -0.066588684
## [243,] 0.227305895 1.9511582202 -0.5133493184 0.4085836871 0.688240692
## [244,] -2.014477092 -1.1046880704 0.9827521328 -0.6925096165 -0.681535253
## [245,] -0.021703005 -0.0086982630 -0.4910375005 0.8265888492 -0.399237588
## [246,] -0.052509271 -2.0290610204 0.5685441064 -0.5675425114 -1.530896292
## [247,] -1.579226865 -2.0290885537 -0.1904259521 -1.1331419253 -1.159950156
## [248,] 1.051536639 1.2695448675 0.5877356808 1.3257244336 -0.006685797
## [249,] 0.019067264 0.1062554788 -0.7216508348 -0.7215209157 -0.992122445
## [250,] 0.583043101 -0.0520000121 -1.0457653920 -1.1711065310 -0.980757733
## [251,] -1.379153903 -2.3356623850 0.0706212912 -1.0401481033 -0.102541186
## [252,] -0.550554488 -2.0518143233 -0.6831122634 -0.2963759619 -1.471001048
## [253,] -0.621534645 -1.3126786187 -0.1066722863 0.1529074640 0.220688897
## [254,] 0.633675230 0.3263941580 -0.5957187557 -1.5057842543 0.354549856
## [255,] 0.833157162 -0.0229868913 -0.2342149795 -0.9731254412 0.315974987
## [256,] -0.265433691 -0.2844098205 -0.0582218255 0.4274376354 -1.165448063
## [257,] -1.113841105 -1.3288672215 1.7392829593 1.3238282002 -2.012819039
## [258,] -1.049528535 -1.4270966810 -0.2503864169 0.4946991118 -1.229931855
## [259,] -1.480846473 -1.3720354142 -0.7323679810 -0.6364747259 -0.827385182
## [260,] 0.098424343 -1.2457005405 -0.6020994868 -0.8306669381 -0.943400482
## [261,] -0.938193534 0.1803378159 -0.0647928776 0.5421609671 -0.550656769
## [262,] -1.385402362 -1.7488002692 0.0707892380 -0.7379251797 -1.743229539
## [263,] 0.445560114 0.6557710220 -0.2570491415 0.4264078130 -0.570141770
## [264,] -0.717142437 1.5900695341 0.5935258373 0.8844135451 0.272498728
## [265,] 0.561191190 0.5003851393 0.4691795326 0.3380195804 -0.817455633
## [266,] -2.681356203 -1.4712985487 0.3863188863 -0.0522500942 -1.651525985
## [267,] 0.590304458 0.8352262005 0.3892684364 1.1125576817 0.536531889
## [268,] -0.240939469 -1.2434039281 0.6466094805 0.0003396845 0.134775582
## [269,] -0.265832783 1.0663276642 -0.4606940102 -0.9211371729 -1.569891427
## [270,] 0.779572477 0.3932077578 0.3382451395 1.2859880252 1.628407324
## [271,] 0.373834034 0.2488733022 -1.0499378152 -0.6717183033 -0.491763506
## [272,] 0.255418641 -0.0303182120 -1.0201763304 0.1261759959 0.688356608
## [273,] 0.403236982 0.8790327173 -0.1128349123 -1.0144203105 0.049600662
## [274,] -0.171202789 -0.2300946478 1.2964985301 0.7487974746 0.944726082
## [275,] 0.188329159 -0.6392863990 -1.0904336179 -1.5404838847 0.473673873
## [276,] 0.767260814 0.9753285771 -1.1626345645 -0.7319318462 -0.966703619
## [277,] 0.197561466 -0.2141012849 -1.5094084468 -0.0687333880 -0.641335915
## [278,] -0.208179359 -0.5592055125 -0.6097810214 -1.4562146329 0.881152786
## [279,] 0.557043726 0.7475951366 -0.4845837381 0.1528246694 1.259648579
## [280,] 0.516031184 0.8416627338 2.7563008648 0.9145651170 0.850179466
## [281,] 1.382775925 -0.2341122853 2.4764442340 1.8145810103 3.423319723
## [282,] 0.716870621 0.8359525435 -0.0594584145 -0.0073072754 -0.203406363
## [283,] -0.991904883 -0.6484170814 0.1394956023 -0.5484583780 -1.283682513
## [284,] -0.408018933 -1.1190075687 0.8124624054 0.0257980211 1.338013706
## [285,] 1.530405410 1.5877655841 -0.7014078288 -1.5441425625 -0.726848338
## [286,] -0.479970621 -2.0585900737 1.6107957622 0.0370515988 0.762281609
## [287,] -0.075881853 -0.4451661475 -0.3655021501 -1.2378845005 -0.674574323
## [288,] 0.623032686 0.8094047053 0.6083862744 0.4542839391 1.319642338
## [289,] 0.410295862 0.3969321148 -0.2171290859 0.3995666089 0.599825511
## [290,] 0.880650485 0.0990545564 0.5091022424 1.1174848453 -0.405728333
## [291,] -0.445121564 0.1686551244 -0.0343306987 -1.0836865468 0.153020052
## [292,] -0.255259468 -0.9163100164 -2.7761500862 -1.0257095041 0.482964498
## [293,] 1.984268668 0.4376434654 -1.4850246066 -0.9238847642 0.804902455
## [294,] 2.304790700 1.3700955230 1.3567121884 0.6392519323 1.054001240
## [295,] 1.110989752 1.3270822704 -0.7531065596 -0.0387899911 0.616121971
## [296,] -0.721141999 -0.3371853549 0.0431545477 -0.4033725064 0.147450002
## [297,] 1.061794836 0.4358804126 -0.2442340879 -0.2844146777 0.583393491
## [298,] -0.344007846 -0.4077074083 -1.3523194816 -0.4217464669 -1.089531501
## [299,] 0.269135991 0.0139169284 -1.0031786862 -1.5239777796 0.413390656
## [300,] -0.160594004 0.4110315059 -0.6532244273 0.7392374635 -0.061730960
## [301,] -1.583289970 -0.9787594469 1.1788678477 1.4114077028 -1.022486220
## [302,] -0.063305435 0.6396105715 0.8706408653 1.2840658090 0.927707666
## [303,] 1.064489357 -0.0014599631 0.9101698546 0.9348562630 2.433946075
## [304,] -0.669726383 0.2576764913 -0.2684557092 0.9435710753 -0.496322798
## [305,] -1.037362608 -0.8254083951 -1.9692688623 -1.2915849515 -2.195223602
## [306,] -0.529724899 -0.1795803119 0.1404200602 0.3679264755 -0.324160491
## [307,] 0.869490254 0.5928569179 -0.0977221323 1.2332165103 0.635057357
## [308,] 0.022975681 -0.7137255660 -0.9002062381 0.4928885811 0.202977940
## [309,] -0.628596245 -0.6010695567 0.3882520030 -1.6585628758 0.493335289
## [310,] -0.345721982 0.6611065842 0.2171797260 1.1294082633 -0.439184016
## [311,] 0.633968412 1.1911135276 1.4530703650 1.5923393148 1.162400509
## [312,] 1.509908017 0.9268151377 0.2896943886 0.7411091253 0.706343526
## [313,] -0.291686899 0.8296064143 1.0960752192 0.3311299225 0.367518395
## [314,] -0.499193875 -0.1302717864 0.0954451938 0.3475105875 -0.568947569
## [315,] 1.783936239 2.0373551457 0.8616003013 1.0316265400 0.369572522
## [316,] -0.318717820 -0.1869080214 1.6491006019 1.1113177081 0.998484425
## [317,] 0.482730698 -0.4918129039 -1.8489842326 -0.5149312052 -0.520866945
## [318,] 0.480552268 0.4964290068 0.4706239780 0.6826898282 0.830977825
## [319,] 1.688789758 2.2388059294 1.1318520668 0.9775425796 1.735002093
## [320,] 0.925573516 1.9655947475 0.6378233808 0.6773659831 -0.061355378
## [321,] -0.260103681 -0.9924248661 -0.3339252059 1.2284795493 -0.922145431
## [322,] -1.205747088 -0.4561650664 -1.3579722946 -0.2196117361 -1.528919939
## [323,] 0.757453256 -0.5329522576 0.3872057541 0.9109419512 0.579681265
## [324,] -0.641464018 1.3135296532 -0.1056548685 0.7200101080 -0.575423190
## [325,] -1.090948959 -0.7262433890 2.4419144019 1.9818479194 0.586810916
## [326,] 0.028155457 0.7058336343 -1.0150761936 -0.4109903652 -2.132405214
## [327,] -0.959305873 -0.9291006487 0.9526208845 -0.1380257684 -0.550375154
## [328,] -0.653267093 -0.7016097068 1.1231306092 1.7254790034 0.670442991
## [329,] 0.443264847 -0.2771007726 -0.6038241878 -0.9453797790 -0.929495089
## [330,] 1.217453005 0.0138709560 -0.0586995715 0.9510628683 0.430113959
## [331,] 0.068080934 1.7947864746 -0.0476388387 1.3085130883 -0.644956112
## [332,] -2.264942904 -0.4870158390 -1.7168797031 -2.3636710389 0.265388897
## [333,] -1.298585643 -0.8666755664 -0.3571998778 -1.9020036746 -1.872186394
## [334,] -1.016791681 -0.8863375107 0.3334557185 0.7152591441 -0.160408334
## [335,] 0.435608252 -0.8619913774 0.5690482012 1.5437458733 -0.261453778
## [336,] -0.033974819 -0.1060401884 0.6548761325 -0.9155414529 -1.354165719
## [337,] -0.244864076 0.2701506056 -1.0956763959 -0.7423383238 -1.436744173
## [338,] 0.806685535 0.6020886313 0.7576686241 0.7880453581 1.139703084
## [339,] 0.971166302 0.7075021990 0.7230668553 0.3670646029 0.349726100
## [340,] -0.256511441 -1.3543243087 -0.0512113343 -0.9373163027 -1.432237652
## [341,] -0.417089378 1.0899684713 0.8264988360 0.1750581694 0.296342077
## [342,] 0.378390715 0.5453924609 0.7059551017 1.2078444557 1.277178480
## [343,] 1.164318615 1.1178687361 -0.7333175217 -0.1418087576 -1.456666478
## [344,] -0.004573503 0.4932242124 -1.2658044465 -1.2840704243 -0.111836586
## [345,] 0.713583619 0.7061107058 -0.2447159766 -0.0938184863 0.566397571
## [346,] 0.606923738 0.4026004330 -1.0661938340 -0.5722609376 0.430850311
## [347,] -1.432914062 -1.4044463959 0.0621158215 0.8397949266 -0.807667202
## [348,] 0.407930425 0.0947848089 2.2525503911 0.5171063710 0.520665328
## [349,] -0.814594971 -1.0730695146 0.9203787813 0.0619675189 0.011711172
## [350,] -0.994587715 0.1868166864 0.4112871771 -1.4590532198 -0.840478773
## [351,] 0.695797991 0.5504742510 0.2207879129 -1.4846758952 -0.184300251
## [352,] 0.507295290 0.1638645235 0.9807169390 0.5283360661 0.607215329
## [353,] 0.857737001 0.0003029028 0.7454601989 -0.2942785666 1.214297757
## [354,] 0.986464934 -0.0861325733 0.5237107107 1.2226161741 1.524742828
## [355,] 0.767966065 1.4121882971 0.5976443544 0.6401226603 2.085539583
## [356,] -1.002419458 0.0237732550 -1.4483452453 -0.5587307030 -0.758986991
## [357,] 0.696898031 0.2643741757 0.4007440365 -1.0748408856 -0.674194046
## [358,] 0.375378186 0.1045510695 2.1295259191 0.2222690944 0.727724000
## [359,] 1.982109749 -0.2146254999 1.5664498307 1.3714645632 0.462235582
## [360,] 3.257196367 1.6571727594 0.5409598717 0.0876746397 2.487249618
## [361,] 0.278226402 -0.7306323496 1.6469063257 1.4337713744 0.312153272
## [362,] 0.351854831 -1.8979291423 1.4039089652 1.3631072962 -0.586483673
## [363,] -0.176398619 -0.1218170562 1.3068263489 0.2358277312 0.011713027
## [364,] 0.095285199 0.0954983090 -0.7097712550 -1.8754592381 0.552802712
## [365,] 1.037595193 1.1077818003 0.3559033524 -0.1431241475 -1.024206125
## [366,] -0.543295416 -1.3965025171 -0.5720278915 -1.8031054773 -0.148055671
## [367,] -2.020485606 -0.0113690811 0.8949325714 0.6968559850 -0.291097191
## [368,] 1.052996093 0.0268453870 -0.8664627234 -1.3671320810 -1.212582562
## [369,] 1.175765540 1.5149902635 -0.9532260948 -1.9592576245 -0.204567293
## [370,] 0.413385256 -0.2243764312 -0.4981461481 -0.8570750598 -0.188245900
## [371,] -0.184002062 -0.1433883519 -0.9142264311 -0.5486412712 -0.827168175
## [372,] -0.641736348 1.3075343392 0.9472106195 -0.0890578389 -0.686753429
## [373,] -0.455043949 -0.7419137449 -0.4010545947 -1.1646686974 -0.305647232
## [374,] -2.586834816 -0.5264327918 -1.3923641724 -0.5826874288 -0.147691068
## [375,] 0.811635318 -0.2168062161 1.1471702531 1.6471041628 0.303393839
## [376,] 0.455976655 0.4376101778 -1.1394604659 -2.3492378317 -2.554050436
## [377,] 1.329256809 1.5775218255 0.1389100220 0.1238195215 0.143565366
## [378,] -1.546049991 -0.0890789637 -0.2394452754 0.6322484594 -1.625986470
## [379,] -0.840541559 -1.3991939173 -0.0380052497 0.2636570437 1.828651281
## [380,] 0.889249733 -0.6390838648 -1.5469094022 -0.7192966454 -0.202075085
## [381,] 0.830877804 -0.2605852365 0.1591136561 0.3869196212 0.270329098
## [382,] 0.331233543 0.8773862365 -0.4186394264 -0.9813592457 -0.487657568
## [383,] 2.336408167 1.5595616709 1.1774853252 0.6245641136 1.803626594
## [384,] 0.808361859 -1.0275113414 -0.5013402161 -1.1585387756 -0.493055886
## [385,] 0.370778830 1.2099603373 -0.7871062270 -0.3294401375 -0.306293291
## [386,] 0.711338636 1.7883403388 2.5977922421 1.6778629629 1.518786824
## [387,] 0.426177915 -1.4266600442 0.7488666143 0.0782473767 1.316633648
## [388,] 1.627257223 0.7083452919 -1.4076791038 0.0248569680 0.475626051
## [389,] 1.173565886 0.6387453939 0.6129658323 1.4919134222 0.582967506
## [390,] 0.630205033 -0.5526838933 1.2626825533 0.2281609071 0.158540484
## [391,] -2.589432175 -1.1299986940 -0.0204124607 -1.2901524411 0.134076043
## [392,] -0.988284611 1.2821823837 -0.8369722427 -1.3401789872 -2.047471792
## [393,] 1.997641501 1.5995634649 -1.2393378402 -0.1726393580 0.592368671
## [394,] 0.587134784 0.1588251249 -0.2103277710 -0.3912901165 1.609984070
## [395,] 0.848700857 -0.4568155496 -0.8145766471 0.7587316117 0.932796568
## [396,] -1.101003303 -1.4972194927 -0.8369449441 -1.5150170763 0.528638999
## [397,] -0.859507658 -1.1921446168 -0.8737221157 -0.8788162081 -0.752929029
## [398,] 0.400383608 0.6599094068 0.5009212110 0.3856801276 -0.142283762
## [399,] -2.016634370 -1.3004063720 -0.8453051666 -0.2992207837 0.172648678
## [400,] 0.039868976 0.3165202553 0.5897505422 0.4439205350 1.004080119
## [401,] -1.087290414 -1.5172621452 -1.1675685915 -0.1813640903 -1.340213024
## [402,] 0.926695761 1.8303579870 0.5814146787 0.4926390819 0.537968381
## [403,] -0.544942914 -0.7301438878 -0.4255785531 -0.3836338281 1.479717017
## [404,] -1.282957386 -0.5535250095 0.1305577498 0.1914224176 0.611010474
## [405,] -0.170201233 -0.4369416277 0.8206284984 -0.2711570532 1.677884077
## [406,] 1.087115604 1.1410632493 1.4811081736 0.4110812139 2.069880740
## [407,] 0.959269689 0.9681420255 0.5309622950 0.7491070306 1.576124598
## [408,] -0.716082549 -0.2226469070 0.3699306587 -0.0685071046 0.801477117
## [409,] 0.303636036 -0.5079309561 -0.5257595959 -0.4418903554 -0.843843471
## [410,] -1.125014932 -0.7220562480 -0.4939798622 -0.6681613833 -1.595435552
## [411,] 0.170569132 1.1961736388 0.9119574751 1.7170559679 -0.600753755
## [412,] -1.028840370 -1.0055149334 -0.2826333885 -1.0426933521 -0.101431584
## [413,] -1.283560352 -0.9519532534 0.8866571938 0.4172804408 -2.094507546
## [414,] -0.761512472 -0.5649884073 -1.3552497819 -1.0183870580 -1.420698634
## [415,] 0.004066334 1.3371705918 1.2688550271 -0.6925860770 -0.749833791
## [416,] -0.641180632 -1.3071278662 0.1666269472 1.1551569247 -0.223713082
## [417,] -0.459657034 0.2002958657 0.7141146366 0.1408447681 0.704241184
## [418,] 0.735191665 -1.5639454157 -0.1646925930 -0.7780528728 0.546388109
## [419,] -0.480679603 -0.8893799081 -0.1863686816 -0.8735531857 0.059845771
## [420,] 1.211426358 0.5477486133 -0.0039994662 -1.0893909286 0.388898414
## [421,] -0.558969826 0.5475523090 -1.2999143581 -1.8137304597 0.460399710
## [422,] 0.295271707 0.8255961944 -0.9853840322 -0.9671872492 0.507218434
## [423,] -1.003925964 -0.7330983865 -0.3909657675 0.0447701484 0.996084881
## [424,] 0.668764533 2.0820669300 0.7405470397 -0.7460199810 0.681819259
## [425,] -0.753915714 1.1066803063 -1.0363729003 -2.2145057690 0.276928886
## [426,] 0.695904119 -0.0295933256 1.4798766663 2.0334793398 -0.515490512
## [427,] 0.505728372 1.0130686666 -1.0936238973 -0.6926761475 0.417355740
## [428,] -0.922841501 1.6727338098 -0.6611183958 0.0434572742 -1.482856774
## [429,] -1.144578530 -1.1581908519 -0.9563965612 0.9134492974 -0.499587841
## [430,] -2.327737159 -0.7874611253 0.1975611813 -0.6687368812 -1.350351619
## [431,] -1.573714384 -1.7172160040 0.0528752347 -1.2508665878 -0.727141347
## [432,] -1.250487962 -0.2308105928 -0.2540725750 -0.1750090931 -0.704000545
## [433,] -0.939342757 -0.1188034349 0.1254711511 0.1156056982 1.354743911
## [434,] 2.445998633 -0.1431324469 1.4939233603 0.3225869031 0.433958031
## [435,] 0.126966487 -0.4788299303 0.2313681844 0.1739597735 -0.098988704
## [436,] -0.329803593 1.1436686885 0.8820977792 -0.7584598259 -0.901741032
## [437,] -0.116698700 1.5005919211 -1.8273544209 -1.3915994909 -0.596171264
## [438,] 1.710719576 2.1311082354 -0.3758762076 1.4010837625 -0.157390315
## [439,] -0.863434991 -1.4313195666 -1.3498473078 0.2718845475 0.452102751
## [440,] 0.858722070 -0.0741980558 1.1293141882 0.2854714572 -0.425011003
## [441,] -1.359806016 -0.1660584586 0.8132817601 0.8943064300 -0.337491791
## [442,] -0.016648688 0.0175487682 -0.7111236264 -1.9153052675 -0.500708979
## [443,] -0.917859305 0.9912741683 -0.3239998696 -0.4648146229 -1.360205097
## [444,] -0.883712553 0.2885399482 -1.1261287278 -0.5383544028 -0.218914515
## [445,] -0.761116253 -0.2989405832 0.7058728418 0.9468869755 1.756808678
## [446,] -0.388458616 0.0053321210 -0.9839344514 -0.6897153920 0.239220645
## [447,] 0.513625636 0.3287259465 0.0125026162 -0.2489788069 -0.713200462
## [448,] 0.544840140 1.0642663648 -1.5478437438 -1.2321386070 -0.819073667
## [449,] 0.865298802 0.9976941923 -0.2958001435 -0.6481493983 -0.816649853
## [450,] 0.506856599 -0.1980002201 -1.6059453367 0.2500304290 -0.397358031
## [451,] 0.855659365 -0.4382015698 -0.7937746454 -0.8552987311 -0.305552629
## [452,] 0.548070030 -1.4089572999 -0.9594265888 -2.0052342714 0.575724169
## [453,] -0.162045540 -1.1652076435 -1.4788384257 -1.1289865962 -0.291958351
## [454,] 1.574121903 0.8573073071 0.4533962997 -1.3944840308 2.181840155
## [455,] -0.486795316 0.8968723180 -1.7972336997 -0.5692493090 -0.955217261
## [456,] -1.671653582 -2.0088368642 0.4850221696 0.4619256398 -0.374742313
## [457,] 1.087547066 0.8081366043 0.5813268696 0.4149877768 -0.437859473
## [458,] -0.411011081 -0.0891014890 1.6383807143 0.8216434366 -1.230271676
## [459,] -1.023802991 -0.6356444931 -1.7421940194 -0.5580166933 -0.776353387
## [460,] 0.336427989 0.0576385170 1.2730035421 0.7285173332 1.573730476
## [461,] -0.403739397 -1.0118678376 -0.0403934740 -0.5036394973 -0.082937523
## [462,] 0.175359581 0.4085364697 -0.2408563836 -0.0896819099 0.860808931
## [463,] 0.496855128 -0.2776029189 0.4230157270 1.6546251075 1.423023323
## [464,] 0.961617965 0.7304369012 0.7926050164 0.2328535587 -0.567360430
## [465,] 0.841509985 1.4240921563 0.6188093298 1.2617410052 -0.716194332
## [466,] -0.804981537 -1.2214056386 -1.5520380241 -0.5503240115 0.525466422
## [467,] 0.568045784 1.9023524386 -0.0808520483 -0.2319203754 1.351832334
## [468,] -0.888661020 0.8679191521 0.1353952075 -0.4293776429 -1.076088304
## [469,] 0.972698801 1.9772949375 1.1051127675 -1.3214219982 -0.127617524
## [470,] 1.423810658 1.4599647371 0.4518665675 -0.4997803601 -0.078103159
## [471,] -0.509028873 -0.0304548035 0.0055489990 0.3115433015 -0.089284793
## [472,] -1.226979550 -0.2226775166 -0.9113124814 -1.6533319270 -0.883032587
## [473,] 0.895946561 -0.3362682771 0.7218766747 -0.4670849649 0.717769503
## [474,] 1.784065660 1.4446760042 0.5332650121 0.9215498041 1.676725560
## [475,] -0.061632731 -0.1366275872 -0.4819580564 0.0774643278 -2.445292858
## [476,] 0.433489126 -0.0528082254 -1.0742267904 -1.4284040081 -1.333649008
## [477,] 1.637264532 -0.3270595439 0.2352437978 1.3891083841 0.479898821
## [478,] 0.862907512 0.4133701946 -0.8530807007 -0.9016990737 1.314060254
## [479,] -0.349565961 0.0466140185 -0.9549486360 -0.7239352420 -1.883643368
## [480,] 1.348436644 1.1516746771 1.2756506085 1.8634888563 -0.027844143
## [481,] 0.017826995 1.5211696870 -0.6305268198 0.3229438176 -0.256571903
## [482,] -0.666083816 0.5352195945 -1.9845492943 -1.2351495811 -1.284755833
## [483,] 0.465197069 0.0666901413 0.6617564393 0.8014740885 0.228113434
## [484,] 0.486863368 0.0448052487 -0.6603660542 -2.3615513977 0.214837848
## [485,] 0.089974794 -2.3489363262 -1.3931191468 -1.1395926672 0.573202943
## [486,] -0.210119913 -0.4890217359 -1.4524117793 -2.4024277508 1.266635409
## [487,] -1.213111229 -0.1377042758 1.8377940708 2.1213729453 1.319813108
## [488,] 0.454312772 1.3643197532 0.1050578814 0.4401431557 -0.018858595
## [489,] 0.694496917 0.2407824472 -0.7524796962 0.0360120134 1.177724780
## [490,] -0.604980078 -0.6186170800 -0.0139154029 1.5825369820 -0.713327157
## [491,] 1.871249311 0.2217299673 -0.9613675803 -1.4239182926 -0.185452216
## [492,] -0.841547821 0.2098080591 -0.5208965919 -1.5583177265 -1.767332655
## [493,] 0.753475601 0.2577193578 -0.5498497965 -0.7197197436 0.230267925
## [494,] 0.341676045 0.7270187474 -0.4380490326 0.7080845301 0.616574538
## [495,] 1.137506068 0.2788188495 -0.0136139062 0.3478962961 0.447325644
## [496,] 1.374670196 -0.0616864398 1.8253318322 -0.0026533413 0.543298893
## [497,] -0.019530848 0.1658923012 -1.3073784500 -0.3050939075 -1.410286021
## [498,] 0.565942101 0.2281282105 -0.1984599496 -0.6044498330 -0.687827514
## [499,] -0.377091925 -1.3982754046 -0.4941684558 -0.4223459668 1.750773509
## [500,] 0.100998110 -1.0203109660 -0.0431759537 -0.1823646879 -0.066159785
## [501,] -1.775392597 -1.4254941533 -0.3232514414 -1.2277012530 -0.205693152
## [502,] -0.685170272 -0.1015340761 0.7874134822 0.3851298259 -1.914526958
## [503,] -1.542331657 -1.1898232133 1.1598104643 1.5279664204 0.715122109
## [504,] 1.960194374 1.4840960008 -0.5206622838 0.2616979468 -0.304031158
## [505,] 0.497141579 0.4418719461 -0.4679818105 -0.7270754648 0.493100819
## [506,] 1.017721281 0.5122532901 -0.5824214007 0.2078145283 -0.303577348
## [507,] 0.637376138 0.7850640731 -0.1348828406 0.5905695192 0.702652527
## [508,] -1.708799410 0.6782281337 -0.2551619559 0.5760558322 -0.333590059
## [509,] -0.997059936 1.4563799788 -0.3337191820 -0.0578104238 -2.506786049
## [510,] 0.920311325 0.8834623241 0.2701294284 -1.1798622369 0.345904048
## [511,] 0.586149877 -0.0219030778 1.3832183999 2.0533391255 0.473996631
## [512,] 0.556143342 0.4860194583 -0.2668512702 0.4890729239 0.987070244
## [513,] -1.139133167 -1.5364356780 0.7337018708 0.0711575371 -1.123200466
## [514,] 0.581849944 -0.9923272866 0.3704966713 1.3830439244 -0.560649379
## [515,] 0.534040880 1.3375220850 0.2281219970 0.2182839685 0.487037567
## [516,] 0.675020701 1.5680919294 0.2152749461 -0.8322750479 -0.732208227
## [517,] 0.302619724 -1.1630667337 0.7311289645 -0.2456818254 0.586330439
## [518,] 0.652647470 1.5504026587 -0.0135406321 0.8434028772 1.459029995
## [519,] 1.358811189 1.6947566921 1.3817510605 1.8317270504 1.856415860
## [520,] -1.545196969 0.1269542813 -0.4908815880 -0.3724954400 0.840559302
## [521,] 1.386544850 1.4127142734 1.5214626932 1.0371083342 -0.638605093
## [522,] -0.232902663 -0.9534999677 -0.1134070510 -0.5914444971 -0.330757560
## [523,] -0.287119993 1.6000140737 -2.9040510978 -1.8971505811 -0.405418734
## [524,] 3.225224609 2.1293621864 0.4954988298 0.6029103195 1.496011493
## [525,] -0.870437726 0.3365495907 -0.4235161614 -1.0736675174 0.450013482
## [526,] -0.321118820 -1.1713438664 0.2066661401 -0.1458971563 -0.566712728
## [527,] 1.003990819 1.6173321170 0.7372294288 0.3788339800 0.351859906
## [528,] 1.843445391 1.7113678018 0.1456256764 -0.0736053739 0.963577087
## [529,] 0.153703769 -0.3051231289 0.8784935452 0.2745010440 0.853830360
## [530,] 1.419316990 1.3533155089 0.2888333092 -0.5171600425 0.577316854
## [531,] 0.528214183 0.6305353265 -0.3665324228 -1.2038232216 -0.041254724
## [532,] 0.609184671 0.0344765210 -0.9570499551 -1.8157518974 -0.756509582
## [533,] 1.098427921 0.8689701655 0.1710746714 0.3295674309 1.178091058
## [534,] -0.536190724 -0.6786236682 0.2159089050 0.3303563212 -1.582669513
## [535,] 0.443273871 -0.3905454527 -0.5855308201 -1.2434945597 -0.995855969
## [536,] -1.690409210 -1.0064409469 -1.7924435128 -1.9742082876 -0.838326835
## [537,] -0.276337299 0.0482704789 0.0719654868 -0.0421898459 0.918409017
## [538,] 0.972903498 0.9493613575 0.2838118152 -0.1092947497 0.232619098
## [539,] 1.354609372 0.6702746576 0.9030001672 0.7379825513 0.277879970
## [540,] 2.206152332 0.6350296845 2.1180607033 1.2010153601 0.725139262
## [541,] -1.541554225 -0.0107995388 -0.5476548411 -0.5268912828 -1.120418804
## [542,] 0.356298238 1.2910580479 0.8723502989 1.9425764140 0.858537362
## [543,] 1.102163208 -0.2515372149 0.9697816094 -0.0644626177 0.981705000
## [544,] 0.907922154 0.5328185766 0.5975119682 -0.0032253315 0.716278702
## [545,] 0.006570081 0.4493865175 -0.7181395569 0.4422653807 -0.538110186
## [546,] 2.001132226 1.8686465080 1.8031646944 0.1978252785 0.720551672
## [547,] -1.221976199 -2.9154732755 -1.1310087948 -0.4243584820 0.888879529
## [548,] 0.763813228 1.3026235439 0.5361246515 1.1382875377 -1.258253420
## [549,] 1.602982776 1.8544750938 -1.2432718530 -0.7287586570 -0.434315029
## [550,] -0.718651875 0.2856138133 1.5748075821 0.8263824906 0.198225055
## [551,] -0.690095868 -1.9999766349 -0.2880692097 0.1392539427 0.277305109
## [552,] 0.407347634 0.2760806170 0.1714402869 -1.1363150864 -0.373489678
## [553,] -0.225799423 0.6306308246 0.6925321389 -0.3625203674 0.583558991
## [554,] -0.386259034 -0.2523624885 -1.1269876527 0.2685274005 -0.318134118
## [555,] 0.420352772 -0.3862827524 -0.0597419362 -0.3914879687 0.828577309
## [556,] -0.693507788 0.0736191984 1.1757287471 -0.0221554893 -0.795543552
## [557,] -0.035694429 -0.5517775603 0.6871104533 0.6487385444 -0.102542513
## [558,] -0.567554864 0.7457032722 0.1993002470 -0.3042767774 -0.955705707
## [559,] -0.329097922 0.4817925982 -0.2643014050 0.4806561674 -0.845478152
## [560,] -0.314589815 -0.3225736030 -0.1671370716 -0.2853055680 0.007985367
## [561,] 0.487330939 0.1416880177 -0.1485773213 -1.0967979666 0.304835891
## [562,] -1.906647800 -3.0686696009 -1.9125166043 -1.5721508217 -0.938348368
## [563,] -0.673597478 -0.9705941220 0.7391283542 0.0312811965 0.942226158
## [564,] 1.630796891 1.6770234015 2.0719494911 0.8573866398 -1.441174299
## [565,] 0.945081187 0.0777369905 -0.3869101566 0.5112240409 -0.403967922
## [566,] 0.503990842 0.9397267790 1.5447333061 0.9235648941 1.239344207
## [567,] 0.124601043 -0.2557215027 0.9070674180 0.3425074916 -0.720373272
## [568,] -0.680490536 -0.0459469952 0.7643438222 -0.5950918952 -0.464484385
## [569,] 0.696376905 1.0803645142 -0.3615913119 -0.9073795543 0.524349603
## [570,] 0.347043035 1.4928416996 0.3691908327 0.6179176500 -1.759074085
## [571,] 0.488605812 1.7485934207 -1.4055502239 0.7289929993 -0.526816419
## [572,] -1.968260680 -0.6608658048 -2.0476943153 -0.4806560807 -2.928549883
## [573,] 0.208139551 0.4544391516 -0.3385771791 0.5110944094 -0.824352944
## [574,] -0.576729318 -0.3908907821 -1.4167801730 -0.7911242331 -1.850229403
## [575,] 1.251644849 0.3054896577 -1.0683376550 -1.1019270525 1.195840021
## [576,] -0.518431678 -1.8101036694 -0.2319512068 1.7469784292 -0.134221196
## [577,] -2.260770162 -1.6522025777 -1.5931530076 -0.2983338569 -1.797979424
## [578,] -0.999449464 0.0993364229 0.4253196627 0.5937801998 -0.650322979
## [579,] -0.679312884 -1.6446502250 -0.3257864604 -0.9552317331 -1.136155413
## [580,] -0.815380855 -0.9544533664 -0.7406147770 -0.1817963512 0.180695033
## [581,] 0.729190656 -0.7089437082 -1.6629015029 0.0870758529 0.658783973
## [582,] 0.224540298 -0.4760450280 -0.8904216949 -0.2678951698 -1.288953110
## [583,] -0.288562858 0.3901730187 -0.4092297943 0.6135171523 0.576815119
## [584,] -0.369989125 0.4571825709 2.4536583690 1.5027129355 -0.733695024
## [585,] -2.349525555 -0.5889831347 -0.5140043791 0.6996598205 -0.284070682
## [586,] -0.916907181 -1.5321411499 -0.7397955554 -0.6684251107 -1.062923047
## [587,] 1.078708547 1.0472209459 -0.6366438922 -1.7057733866 0.966856204
## [588,] -0.738882710 1.2241010445 -1.3086794309 0.6901286192 -0.391887091
## [589,] -0.608287276 1.3056612311 -0.7057773248 0.2149926623 -0.477233058
## [590,] -2.326117556 -1.5141519303 -1.8146065414 -0.6359707733 -0.556182139
## [591,] -1.231462178 -1.8465615652 -0.8162355723 0.1029471006 -1.836668194
## [592,] -1.430185301 -2.1117899463 -1.3126865517 -0.5316115215 -1.302862068
## [593,] -0.370427476 -0.1077479794 -1.3351710914 0.0906162762 1.345821188
## [594,] 0.132607187 0.0128080684 0.0887645080 0.0368639872 3.569834030
## [595,] 0.191983486 0.8634587839 -2.1894708663 -1.2717122462 -2.214897703
## [596,] -0.308718398 -0.0963526861 0.5927026272 0.6417353303 1.031535660
## [597,] 0.491515543 0.4460283647 -1.1675297754 -0.4941581039 -0.438458698
## [598,] -0.231925606 0.4302670759 0.5846257192 -0.5402580886 0.597064867
## [599,] 0.382416435 -0.0219843069 -0.6086252115 -0.7645355817 0.150150015
## [600,] -0.345855873 -0.4484636665 -1.6747817574 -1.2951394263 -1.293425675
## [601,] -1.322876615 -1.0127888741 -2.0627930977 -1.4942251586 0.878400446
## [602,] 1.130880278 0.9691125388 -0.2892090499 -0.3558506415 1.327508751
## [603,] 0.331105822 0.2490665370 -0.1395381212 -0.7838108330 0.272440185
## [604,] 0.485127582 0.3462204718 0.1432923487 0.0552195371 0.334035972
## [605,] 0.223947668 -1.7027026686 -0.4323639522 0.5800857256 -0.709465700
## [606,] 0.864496860 0.9796039083 0.4047578947 -0.2051739759 -0.665351022
## [607,] -1.818608815 -1.4823186440 -1.8640330914 0.3528185782 -0.462241994
## [608,] -0.291202821 1.1975959033 0.1741696244 -0.9809068009 -0.076848774
## [609,] 0.745078120 -0.6953514876 -0.1456115371 -1.4468992646 0.576649996
## [610,] 2.326556379 -0.1662254320 0.6859449369 1.8914761789 1.127355020
## [611,] 1.153624281 1.1195151036 -0.1263214915 0.9448258454 0.217966112
## [612,] -1.434568732 -1.2172260515 -0.8314336339 -0.4933289772 -2.566170058
## [613,] -0.432676798 -0.8922013143 -2.4176642598 -1.8737266784 0.126240230
## [614,] 0.928878939 0.7196562832 0.5569878565 0.3972844533 0.049231738
## [615,] 0.032951377 1.0962519246 -1.4329648190 -1.0188194240 -0.237953987
## [616,] 1.456798035 1.3337512823 -0.4954874602 -1.2113985070 0.695114726
## [617,] 0.514524633 -0.4848816343 0.1061452931 0.1180770018 -1.304870903
## [618,] 1.239041175 -1.3568368713 1.5486294745 1.7523965127 0.412131783
## [619,] -0.578017858 0.4778153287 -0.5002590528 0.3567921489 0.045073313
## [620,] -0.132944612 0.1341195149 -0.5112554319 -1.6712794357 -0.648883252
## [621,] 0.838751586 0.1437005601 1.7227811915 1.2581249365 -0.363522648
## [622,] 0.376336969 -0.9206017917 0.2511642781 -0.7515204293 -0.465463735
## [623,] -0.129251290 -0.3078027744 0.8683879908 0.1771315013 -0.399457077
## [624,] -1.234092484 -1.4096523455 0.0178168604 0.5260609424 -0.647487749
## [625,] 0.919730220 0.9802868548 1.2506887310 1.4198984259 0.171371976
## [626,] -0.955759820 -0.2989562083 0.5863273332 0.4825168133 -0.712150606
## [627,] -1.693188554 -0.1706364640 -3.3095408346 -2.5827971812 -2.214489013
## [628,] 0.564689269 0.5622764194 0.6400568717 1.5864607212 1.040312897
## [629,] -0.674112060 -0.8989525363 0.3804501804 1.3675440302 -0.530628323
## [630,] -0.721203942 -0.7507945680 -0.3194772563 -0.4988974463 0.772537593
## [631,] -0.522078533 -0.4044945545 0.7797919140 0.6468306240 0.204854421
## [632,] 1.952392994 2.0444248686 0.8353893073 0.1983582316 2.327755286
## [633,] -0.986529187 -1.4546569098 0.4898802657 0.2273049078 -0.985274863
## [634,] -0.606519752 0.2642182310 0.2445417472 -1.0305269968 -1.742967595
## [635,] 0.103138898 0.1295410060 0.7115615486 0.2627709089 0.395656425
## [636,] -1.174540816 -0.5874807568 1.1429369876 0.5295889048 1.584796928
## [637,] 0.808726049 0.8123531748 0.8602106120 1.8430528351 0.399645687
## [638,] 1.130529102 0.7467046759 0.0308596452 0.2534794237 0.821895314
## [639,] 0.167597990 0.5158613805 -0.8184248420 -0.6924174371 0.111919657
## [640,] 1.113462186 1.9596924621 -0.1315499438 1.4876976499 -0.502908695
## [641,] -0.293945430 1.2027923190 1.1419606502 1.0470999304 0.118800896
## [642,] -1.469049089 -1.5760821335 -2.3933457428 -1.2133160553 -0.956378926
## [643,] -0.446165084 0.2482963210 -1.3667171331 -0.5978369926 -0.365898338
## [644,] 0.898591301 1.1472983963 -0.0981831378 -0.9776875728 -0.937264979
## [645,] -0.645524097 -2.0078027359 0.9686143566 1.3295637625 0.468833436
## [646,] -0.439639677 -0.1162044070 -1.4015391330 -1.5284962890 -0.654550558
## [647,] -1.746300059 -1.2490621121 -1.1317055212 0.5949608332 -2.242773586
## [648,] 0.853670062 -0.4783423975 1.8278032040 0.4038241635 2.010641904
## [649,] -1.226370461 -1.5266015391 0.3894385885 -0.5205388890 -1.091539477
## [650,] 1.554270635 0.1137022132 -0.5034194552 0.1103367950 -1.485545856
## [651,] -0.152900737 -0.0737142773 -0.6738659173 -0.9992982318 2.167222350
## [652,] -0.966064352 -0.1187754634 -0.5791480426 -0.6545203023 -1.497227609
## [653,] -0.922725914 -1.4793835257 -0.2349635377 0.1406484306 -0.700697800
## [654,] 0.604828075 -0.1884402161 0.8177049514 0.4658956906 -0.249538753
## [655,] -0.727534001 -0.0768877536 -1.2365537688 -1.5080802653 0.040662053
## [656,] 0.381777758 -0.7262748731 -0.5054725953 0.9253289116 0.698011424
## [657,] 0.059656885 0.3339309967 -0.0671973496 0.8562612556 1.398595616
## [658,] -1.918386751 -0.1805906415 0.1680832391 0.1846629675 -1.178212764
## [659,] -0.440460950 -0.6651783023 1.2949750331 0.7029508696 -1.595167778
## [660,] -0.351802886 0.9347653955 -0.5748182386 -0.0849490247 -1.001392704
## [661,] -1.172812433 -1.0852921051 -1.3868687463 -0.7380127316 -0.926954123
## [662,] -0.499073918 0.7123004771 -1.3766492009 -0.1311100498 -1.595168246
## [663,] 0.622228603 1.1432108897 -1.2550954158 0.0890078394 -0.351097682
## [664,] -0.005311950 -0.0113615909 0.2290195395 -0.4271228251 -0.513784520
## [665,] -1.120231004 -0.4736188619 -0.0317187184 -0.6416557785 -0.439180428
## [666,] -0.733398157 -1.9352878538 -0.1866559509 -1.3500827720 1.092317994
## [667,] -0.166567590 -0.6236785876 -0.6705323871 -0.4550502124 -0.841992038
## [668,] 0.666085768 1.4647758197 2.1346976352 2.6269700919 0.693162630
## [669,] -0.532215563 0.0965802090 0.8993522409 0.0437338903 0.805606194
## [670,] 0.711620777 0.0899281694 -0.0002721411 0.3606606816 0.581042347
## [671,] -1.440880460 -0.8026949663 0.0996527735 1.3754160818 -0.300476928
## [672,] -0.685680646 -0.7614487163 -0.5185118051 -1.1381448706 -1.782301974
## [673,] 0.158413666 -1.9384279435 0.2255133518 0.5200709890 -1.631003934
## [674,] 1.056913355 0.2729549363 0.7884497847 0.5657929221 0.805933548
## [675,] 0.613402823 1.8945782275 -0.9619821274 -0.0939199357 -0.628165919
## [676,] -0.953682272 -1.0705190008 0.8463345366 1.2748577060 0.866612090
## [677,] -0.703841970 -2.0195657858 -0.2403316395 -1.8581560405 -1.771903241
## [678,] 0.213499198 1.7610369367 -0.1281567307 -0.9191399632 0.335777977
## [679,] -0.917779278 1.0223511650 -0.5862835300 -0.8607462903 -1.032596640
## [680,] 0.371874244 0.6980644811 -0.1918360247 -0.0806801044 -0.073352445
## [681,] -0.083494251 -0.7663639148 -0.0686509247 -1.3021837498 -0.559267955
## [682,] 0.362345369 0.1333236011 -0.0142399120 -0.5558304318 -0.717247213
## [683,] -0.593928952 -0.8575833239 0.2667773620 0.8913985604 -0.458856408
## [684,] -1.147048121 -0.9348293629 0.1680627480 -0.2913258870 -0.935508933
## [685,] -0.623057058 -0.6487780448 -0.6243936061 -1.0245619939 -1.849627231
## [686,] 0.266964712 1.5806939167 1.2001901328 1.2715946753 -1.312579719
## [687,] 0.935405294 -1.2648286942 1.9605716228 1.2674960316 1.706754830
## [688,] 1.419517339 0.5626597090 -0.9414042273 -2.2400032600 0.381119299
## [689,] -0.288301339 1.2817714057 -0.3201413738 -1.5552222493 0.103352634
## [690,] -2.960379351 -0.5702372372 -1.2596005223 -2.5057418441 -1.995841045
## [691,] -2.174025975 -1.4976643775 1.3390685401 0.6295244544 -0.841941786
## [692,] 0.764214533 1.4951345438 -0.6378798495 -0.2484991666 0.363840440
## [693,] 1.316846823 0.4261535056 -0.9250611429 0.6522164503 0.478807431
## [694,] -0.095921373 0.2836806730 -0.3825540907 -1.0582331957 -0.346951903
## [695,] -0.062555747 1.4849821111 -0.9942784834 -0.3670106456 1.589488040
## [696,] -1.075104273 -1.5966113476 -0.8835950194 -1.2389070762 -0.904968686
## [697,] -2.372147582 -1.3533501977 0.7288383583 -0.0305866225 -1.362436172
## [698,] 0.838737191 1.4509392787 0.3069906700 0.8967728724 0.214403972
## [699,] -0.001462922 -0.6899822040 -0.4883247667 -0.4299526536 1.833183154
## [700,] 1.113177428 -0.7406984924 0.3747201619 1.1156251207 -0.581199124
## [701,] 0.827367972 1.1161436297 1.0731684158 0.3866267739 0.475012842
## [702,] 0.260313627 0.2914979107 -0.5260747554 -0.2104940884 -1.966891125
## [703,] -0.928417360 -1.0321419879 0.1804610612 1.9843851338 -0.941496873
## [704,] 0.932390807 -0.2000951051 1.4029018030 0.9200874287 0.250630909
## [705,] 1.266742198 0.2211692579 1.7940773311 0.6129999962 0.697101098
## [706,] 0.364240922 -0.0515662333 -0.1448692306 -0.2919973961 0.567597555
## [707,] 0.403542811 1.5278489897 0.8574194893 0.0870081559 -2.005603468
## [708,] -1.638132125 -0.8763660892 -1.4523123775 -1.3870460439 -1.242006375
## [709,] -0.031688094 0.0831010835 1.5826945099 2.2426104355 0.465692241
## [710,] 0.755897977 0.5606536945 1.0091300030 0.9107653627 1.189645771
## [711,] -0.333006824 -0.6363853871 0.5323824695 1.1793272105 1.197596048
## [712,] 0.170674465 -0.2633723698 0.9362665668 1.6826868786 0.091721866
## [713,] 1.528347760 1.5847080485 0.1830188509 1.7525021110 2.320820001
## [714,] -0.234843677 -0.0971339186 -0.7737781063 -1.1656640702 -0.403716356
## [715,] -0.710530829 1.6332728371 1.0166214729 1.1632133948 -0.575851286
## [716,] 0.267553023 0.9449947563 1.5372077960 2.8139695624 0.814125150
## [717,] -0.497381630 -1.3407603202 -0.2151119826 -1.4840920563 0.020418557
## [718,] 1.579112378 -0.9519005558 -0.8208999351 0.0176246969 1.165095668
## [719,] 0.369101312 -0.2820897926 1.8691762632 0.1513479869 -0.009029534
## [720,] 2.182730138 2.0440537448 -0.7677242595 0.1774452712 -0.221116765
## [721,] -1.257399524 -0.3971416678 -0.3106705497 -0.5049441557 0.916239758
## [722,] -0.286561574 -0.8724730598 0.5917163908 0.6436726303 -0.338530543
## [723,] -0.903941917 -0.7491468984 -1.3190663244 -0.2767343421 -1.270783145
## [724,] 1.287984081 1.1033211503 0.6346676488 1.4317627989 0.403443469
## [725,] -1.991939306 -2.2492478701 0.3470672441 0.7875921491 -0.400607972
## [726,] 1.397083825 0.6487659430 -0.3226098263 0.9373506018 -0.124133922
## [727,] 0.961503434 2.1791054423 0.7343425260 0.7081230801 0.880702783
## [728,] -0.279298171 0.3946854978 -0.0148204201 -0.4426144197 -1.222500616
## [729,] -0.164288057 -0.3040137906 -0.3229328899 -0.5774333395 0.809107297
## [730,] -1.133174452 -0.6350896429 -1.1029203694 -1.6526809811 -1.840580654
## [731,] -2.415736573 -2.5369202442 -0.6513818354 -0.7266384932 0.026475786
## [732,] -1.654402656 0.0273771508 -1.8654458364 -1.8778112544 -1.337020182
## [733,] 1.859441489 1.4542384643 0.4026671836 0.8638999056 1.045764142
## [734,] -1.237072557 1.3547947832 2.0125292416 0.5442787578 -0.890558043
## [735,] -1.607968488 -0.5963491336 -0.3219794019 0.0644353296 -0.304024964
## [736,] -1.518363423 -1.7813957435 0.6305346507 -0.9772126686 -2.567427850
## [737,] -0.745864643 -0.5721172093 -0.5128305235 1.1549526587 1.653892778
## [738,] -0.802611025 -1.1301527271 -1.1278080607 -0.5922743052 -0.245089944
## [739,] 0.196289885 -0.7249449523 0.0805842133 -0.2953577030 -0.486196436
## [740,] 0.609631463 -0.5476075218 -0.7075072019 -2.2871727719 -0.402785089
## [741,] 0.551574592 0.6835640559 -1.6693758698 -0.9424354725 0.463852744
## [742,] 1.468335441 1.1355719664 0.0650172921 -0.3165317790 1.912167019
## [743,] 2.458170500 1.7422655405 1.0208424062 -0.5720073393 2.364621154
## [744,] 0.523317283 0.5759616947 0.9569678990 1.7916611378 2.389403537
## [745,] 0.393398486 -0.2805490703 -0.8242268742 -0.2491971968 0.515951652
## [746,] 0.672608676 1.6594901291 -1.0205756087 0.5653849511 1.791927309
## [747,] -0.588960191 -0.7902410515 -1.1412153051 -0.7344247434 -1.385397082
## [748,] -0.607793472 0.0429111352 0.5382638671 0.7134020183 0.606015308
## [749,] 0.789545191 1.5726055378 0.5234377708 -1.1584343327 0.874101598
## [750,] 0.595496977 -0.0713411097 0.0169805888 0.5133876476 0.435882254
## [751,] -2.018170792 -1.2577491465 -1.3164593414 -0.0426483826 -0.064437326
## [752,] 0.130550936 -0.1794636587 0.3876812676 0.5558588576 0.413910475
## [753,] -0.125264243 -0.0625383134 0.9304778418 0.3743485561 -0.674420591
## [754,] 0.703369130 -0.3178893946 0.9643472326 0.7373199424 2.510352023
## [755,] 0.925916660 2.4644522531 -1.4623452493 -1.0928629899 1.656483563
## [756,] 0.944469475 -0.0280849538 0.1522412519 0.5279370872 0.987991343
## [757,] 0.262668115 -0.4557418557 0.1302798255 1.3499098235 1.488609005
## [758,] -0.664896117 0.4335702024 -0.9511887665 -0.0135823403 -0.432173964
## [759,] 1.655907542 0.6004446007 -0.7281148257 0.1127955393 1.613542309
## [760,] 1.353100464 1.4880555327 -0.3639830494 -0.2896584735 0.875556933
## [761,] -0.291238787 1.7733158782 1.2419791977 0.4985456599 0.772188876
## [762,] -0.501651597 -1.2551633231 1.1949005159 1.5793736635 -0.938873358
## [763,] -0.344795066 -1.4892624782 1.6461243758 0.2454085664 0.171798299
## [764,] 1.094455154 -0.0348338404 -0.6839278252 -0.9416220155 0.558757517
## [765,] -0.301350229 -0.6292309937 -0.4885420615 -0.2677018662 -0.182209887
## [766,] 0.282499471 -0.9705866198 -0.5856343284 0.0085349125 1.128475550
## [767,] -1.510585497 -2.4598048587 -1.1458250605 0.3727768766 -0.541488117
## [768,] 1.064384044 -1.2953593514 0.4682335897 -0.1569364117 0.271566659
## [769,] 1.966255943 2.1451468442 1.0738461296 -0.5640756762 0.811637658
## [770,] -2.310705716 -0.4117123336 -0.4398996672 -0.0438083228 -2.471124508
## [771,] -1.223660240 0.2531860173 -0.3192983728 0.8118190491 -1.237042225
## [772,] -0.374335256 -1.0152567383 1.7583303485 1.8292797400 0.216136606
## [773,] 2.475027481 0.2401982016 0.6253403928 -1.5713842456 0.147586637
## [774,] 0.438237265 -0.3624739758 0.0330021396 -0.6926141187 0.787377998
## [775,] 0.206385736 0.1556230752 -1.5374714503 -0.6240549361 0.076296318
## [776,] -1.192652469 -1.3537958267 -0.3978513028 0.0171127157 -1.641026999
## [777,] 0.173636228 1.1504473245 0.4781880504 -0.1833352078 1.003161366
## [778,] 1.067702361 1.2181063791 1.7708908952 1.8199903888 1.016972689
## [779,] -1.005758821 -0.1569452298 1.2005331288 1.0901550615 0.451583878
## [780,] 0.584352735 0.3000849932 0.1648099059 -0.6391617189 0.795238713
## [781,] 0.165171988 0.4708610664 0.5346856956 0.4689607530 0.776639637
## [782,] -0.720454633 -0.3504750062 -0.9901008730 -0.3353839013 -1.769398296
## [783,] 0.626298341 0.5936293771 1.7386488252 1.1660680341 1.302219676
## [784,] -0.768946314 0.4375901898 1.1420658077 -0.8210180716 -1.077122372
## [785,] -1.045741283 -0.5392218888 1.0984994369 1.7081398278 -0.605270725
## [786,] 0.427227739 -0.2642069815 -0.2988022829 0.5045516119 -0.280057039
## [787,] 0.134720657 0.1832379957 0.5173209803 -1.8404721695 0.196189056
## [788,] 1.513537572 1.2031540972 0.8158652648 -1.1679930109 0.394443776
## [789,] -1.151137225 -0.3493966021 0.9503326334 -0.0125310413 -0.145794177
## [790,] -0.450667114 -1.2171048107 -0.1335950319 0.6810532995 -1.987990453
## [791,] 0.678538302 1.1972815627 0.0868545058 0.0334108702 0.368027015
## [792,] 0.618591039 0.3375712631 -0.8337912896 -0.3826819348 0.098700323
## [793,] 0.550247882 -1.7232013748 2.0566107362 1.6811376963 1.092640041
## [794,] -0.224061275 -0.6630611282 0.5693035854 -0.5292108344 -1.241355097
## [795,] -0.509516604 -0.5373706867 -1.8960639084 -0.1879680631 -0.585536125
## [796,] -2.292127347 -1.5606511378 -1.0466309085 -1.8708646284 -1.120393692
## [797,] -0.793981126 -0.8733667474 0.0324544365 -0.6031027734 -0.463015365
## [798,] 0.032265033 -1.5504353371 -2.0903948180 -2.3494364547 0.230786550
## [799,] -0.415910957 0.6921173443 0.3689732851 -0.0824961328 0.580793700
## [800,] 1.304455755 0.8974138803 -0.8760676326 0.2149438776 0.760754264
## [801,] -0.481524595 -1.6807306655 0.3001965326 -0.1566040640 0.501179255
## [802,] 0.521352700 -0.7754807308 -2.2401442338 0.5651823605 -0.926202401
## [803,] -0.439395333 -1.6700325878 -1.3380251444 -0.5454442669 -1.186740149
## [804,] 1.840074250 0.9532559522 0.3405430530 0.1634057302 1.240345489
## [805,] -0.238451055 -0.1312335710 -0.1827441572 -0.7979135552 -0.654499294
## [806,] 0.463543129 -0.8732786207 0.6001579995 2.0839675175 -0.156136147
## [807,] -0.536111609 -0.4794254460 -1.1421561398 0.7208763975 -0.510217829
## [808,] -1.256272296 -1.1302936011 -0.8437891065 -0.7402108801 -0.348968539
## [809,] 2.169676843 0.2729816776 0.7928323819 0.4516007761 2.255881484
## [810,] 0.776973536 0.3881103026 -0.9960779922 -1.3355412411 0.020886724
## [811,] -1.593343896 -0.4574240948 0.1605015252 -0.0956555472 -1.721298904
## [812,] 0.337282261 -0.5627774799 -0.6136552457 -0.3042399311 0.098822539
## [813,] 1.760204110 1.0671349035 0.3529797657 -0.3358401548 1.552871084
## [814,] 0.329546399 -0.1634792648 -0.1722223632 -0.2939156036 0.384673306
## [815,] 0.642223461 -0.1369610719 -0.1869002205 -0.6768184119 -0.277792502
## [816,] 0.792531900 2.2037659788 1.1052341515 0.1109991387 2.338168676
## [817,] -1.179966742 0.2201770762 -1.5462169426 -1.4606859142 -0.690054218
## [818,] -0.330986817 -0.3097782920 -0.1241377812 0.9374398146 0.960181185
## [819,] -0.550476837 0.5703564039 -0.8683401537 -1.9486930490 -0.029521711
## [820,] 0.262954101 1.3858660456 1.7224137896 0.8514997717 0.315138094
## [821,] -1.691699162 -1.3935187029 -0.1008742108 -0.4821584097 -1.214978578
## [822,] 1.029127485 1.3339155661 1.6951069472 1.1897398081 0.646262754
## [823,] -1.026801500 0.7104309869 -0.8437564928 0.1761428479 0.089491568
## [824,] -1.396000656 -1.8428320120 -2.1266037072 -3.4310037470 -0.654317993
## [825,] 1.068484500 0.5423090044 -0.1447470039 0.2923840060 0.444167777
## [826,] -1.599350799 -0.3996485185 -0.0966449594 0.0195674179 -1.769343530
## [827,] -0.348700678 -0.9847268690 0.5719310523 0.5772279513 -0.307625872
## [828,] -0.167756365 -0.4469971891 0.8228678182 0.7575782275 0.308596811
## [829,] -1.646641942 -0.7747405670 -0.6630072323 -0.3164783060 -1.008706768
## [830,] 1.847210018 1.7798927070 0.4943230782 0.7904400415 0.303293343
## [831,] -0.710010342 -1.5864033019 -1.3444733685 -2.2081152638 -0.615382395
## [832,] -1.024876704 -0.0335977993 0.6131295912 0.0697538438 -0.671143950
## [833,] -0.707569894 -0.6298611143 -1.3214841405 -0.1385086232 0.707437539
## [834,] -0.089137863 1.0906562368 0.4250651930 1.1319161434 0.488044324
## [835,] -1.091235033 -1.4731442347 1.8777959868 0.9205494252 -0.476875842
## [836,] 0.254072048 -0.2725205207 0.5713361811 -0.1430596669 0.138051500
## [837,] -0.391410170 0.5958983560 -0.4405096820 -1.2273063473 -0.766266966
## [838,] 0.451478419 0.2115379337 2.3581270033 0.7578783390 -1.269658857
## [839,] -1.713961593 0.2212065621 0.3650078447 0.1602917103 1.581239956
## [840,] 1.431027072 0.9666662558 -0.5779553610 0.1153600877 0.156789150
## [841,] 1.207114206 0.2460890848 2.9512643616 2.7397673308 1.453535555
## [842,] -1.182731725 0.2777507305 0.7525057592 1.6542270356 -0.766958845
## [843,] 0.927077490 0.6651161768 0.8761663998 0.1112473385 1.831416593
## [844,] -0.167082448 -0.3462112888 0.0981646530 -0.0708679009 -0.956079963
## [845,] 0.986402838 -0.1842328214 -0.0191236237 -1.3101641732 0.633854965
## [846,] -0.223121806 -1.0170893573 1.2466207595 -0.2301880579 -1.406836280
## [847,] 0.515498404 0.9579425324 -0.8682233357 -0.3961107500 1.268663545
## [848,] 1.446278869 1.1329823430 1.0095269915 -0.7266507737 0.304946684
## [849,] 1.023115928 -0.3190256410 1.4005626592 -0.0931120178 1.417920467
## [850,] -1.663514280 -1.5878449232 -2.1890033407 -0.1350117003 -1.076919549
## [851,] -0.353032806 0.6778223628 -2.0616920538 -1.7292772338 -0.618136829
## [852,] -0.759813044 0.6033290854 -0.2164212521 0.1274417587 -1.169195090
## [853,] 0.616182760 -0.2129924509 -0.7516144317 0.2346747634 1.002366043
## [854,] 0.858918710 1.3893273049 0.8524588313 -0.0978071584 1.523545890
## [855,] 0.091664705 -1.6100379225 1.4195726801 0.9501497663 1.449866378
## [856,] -0.618895175 -0.2903130083 -0.4297428046 -1.5917122062 -0.859039046
## [857,] 1.254237944 0.4081846551 0.0720237419 1.0694394776 1.634479084
## [858,] 0.328340222 -0.1524681856 -0.0142720583 0.4467730893 0.414047451
## [859,] -1.503152031 -1.4642877866 0.8100598975 1.5152980757 -0.520444064
## [860,] -0.421555035 -1.3470567296 0.4378817932 0.3095945992 -0.754545767
## [861,] 0.275512737 0.0868203549 0.7774080724 0.1860761368 0.476704844
## [862,] 0.117864320 0.5424399627 -1.0038240296 -0.5933298914 1.255640893
## [863,] -1.021827119 -0.9031783736 -0.7996076944 -0.2814190962 -1.475973831
## [864,] -0.970359751 0.1094192631 -1.1444754083 -0.7647882225 -1.629982185
## [865,] -0.605999773 -0.3878633305 0.4271113961 0.1248287072 0.421307292
## [866,] 0.598218729 -0.0114580927 1.2241620540 0.9459091377 0.408875107
## [867,] -0.698181707 0.5641918236 -0.6218818194 0.0526939869 -1.448166549
## [868,] 0.228398136 0.4167393273 -0.2498542356 0.4459821867 0.381050301
## [869,] -0.011576338 -1.2598851403 0.3929787314 1.1518409847 -1.119425123
## [870,] 0.817509636 -0.0524333138 1.0669694762 -0.3163459606 -1.820668300
## [871,] -0.545214682 0.4255999324 1.2290630168 -0.0266590073 -0.571081998
## [872,] 1.514728734 -0.3889574784 0.2472517892 0.4078517887 0.718249607
## [873,] -0.757455545 -1.5744071853 1.2319111629 2.1937981500 0.244153294
## [874,] 0.156436809 0.0403510053 0.8534241313 0.0922366696 1.153816265
## [875,] -1.357272812 -1.1677970725 -0.9751706026 -1.1137701954 -1.050829122
## [876,] 0.662912663 -0.2215738205 -0.1407124725 0.6592305870 0.323206411
## [877,] -0.302979446 1.0990609683 -0.0371716989 0.1602754913 -0.691421079
## [878,] -0.370529034 0.5739611457 -2.0281269548 -3.4504408394 -1.187809037
## [879,] -0.231147735 0.5325008034 2.1261317813 1.1845225311 -1.375301768
## [880,] 0.636888442 1.7793768308 0.1920230929 0.8469592502 0.936726866
## [881,] -0.445306265 -0.0321367671 -0.0221975367 2.3715003189 0.085675218
## [882,] 0.177432863 0.3745117743 2.1997779090 0.7019334395 0.361966900
## [883,] -1.200813719 0.2828193136 0.6936378756 -0.1963295960 0.591492362
## [884,] 1.613495187 -0.5355061942 2.0650474345 1.4607494798 1.673428299
## [885,] -1.556297245 -0.2844441237 -1.1661324300 -1.2975113591 0.049324848
## [886,] 1.015423469 0.1254946175 -1.6477415780 -1.8705384477 0.472975527
## [887,] 0.941868260 1.4015756491 1.0488275713 -0.2836367093 0.281663092
## [888,] -2.182780507 -0.2565556568 0.2180240597 -0.5360035501 -1.758502282
## [889,] -0.391883454 2.1840816572 -0.3249360920 -0.0374677697 0.294055411
## [890,] 0.224999126 -1.2306429358 1.1347919298 1.3701847496 -0.211509457
## [891,] -0.002000042 0.1580195016 -1.0718916282 -1.3394149845 -0.294064506
## [892,] 0.541212802 1.2361119311 0.4395573424 0.1009488589 0.548082955
## [893,] -0.086872629 0.8679772992 1.1277206952 0.6962513621 1.187340605
## [894,] 0.177470369 -0.0288712730 1.2924889626 0.6861810724 0.917489839
## [895,] 0.954531988 0.4690485698 0.6015985571 -0.3052714750 0.636612645
## [896,] 0.406223690 -0.1592574186 -1.3382712634 -1.6333482596 -2.446023421
## [897,] 2.718575331 3.0435132704 0.3278206824 1.7930907980 1.989965634
## [898,] 0.139511340 -1.7448805851 1.2961110674 0.6847476349 1.475378177
## [899,] -0.424817954 -0.5239679792 0.0720243255 0.7865562511 -1.519259884
## [900,] 0.044317383 -0.9946921931 0.3973343447 0.4745984170 0.233027643
## [901,] -2.263183639 -1.5100703604 -0.7055548093 -0.7091880282 -0.988727515
## [902,] -1.261843128 0.1106411020 1.0302723706 0.2279784924 0.807418587
## [903,] -0.190950498 1.0117060910 0.0797448243 -0.3413393430 -0.258988760
## [904,] -0.861771346 -1.4220916365 -1.4410124254 -1.8445127785 0.172223000
## [905,] 0.171795891 -0.4592770387 -1.8078034795 -1.5085500726 -0.814582878
## [906,] -2.326629710 -2.4423354353 0.0938909509 -0.4951521611 -1.940530977
## [907,] -1.849173461 -1.6905746504 0.4617650280 0.9815318452 -1.513968588
## [908,] 1.157171909 -0.0111526196 0.7902245831 0.0761411162 0.523665104
## [909,] 0.438648903 0.9178525872 1.0231933027 0.5437075893 1.377928763
## [910,] -0.241826360 -0.3307564325 0.6506993911 0.6748937939 0.519089432
## [911,] -0.221553253 -0.8972031411 -0.6860835251 -0.4134866037 -0.221378613
## [912,] -1.539728684 -1.1802180610 0.5565001093 0.9869018015 0.048189816
## [913,] 1.603483202 1.4941852575 -0.5026016799 -0.6886139654 1.564800418
## [914,] -2.501160865 -0.8509149363 0.3099393996 -0.9655717138 -1.460363883
## [915,] 0.274670373 0.5039694674 0.1930054702 -1.1342839791 -0.399922487
## [916,] 0.452389361 -0.6566824493 -0.5660116114 -0.6560789271 -0.943626410
## [917,] 1.440076911 0.1510156271 0.1922893004 1.1780124897 2.232476793
## [918,] 1.324789968 0.4917699846 0.2838501129 0.7783439197 0.734424793
## [919,] 0.818606660 0.4773388748 1.2527884301 0.8154723651 0.813708228
## [920,] 1.146302770 0.4409479684 -0.4304115339 -0.9095359809 -0.647570198
## [921,] -1.206589248 0.0807298634 -1.0149410054 -1.9041778370 -1.443946736
## [922,] 1.383017741 -0.8478862950 -0.4790416391 -1.6186122736 0.454552281
## [923,] -0.072302996 -0.7946526337 -0.1467250992 0.1106521635 0.350960140
## [924,] 0.351310837 -0.0216424908 -1.0817947863 -1.9278270236 -1.441194352
## [925,] 0.898877997 0.0813745050 0.9065131892 1.5854661176 0.456374584
## [926,] -0.557691880 -0.3313975737 0.1420285006 1.0179792092 0.280440787
## [927,] 0.972563045 -1.0046987774 -0.5132251987 0.7545455812 -0.507146828
## [928,] -0.731676929 -0.8518706565 0.3586586629 1.5604484051 -0.491913428
## [929,] -1.160553550 -0.5427477660 0.9460245423 -0.6510647417 -2.875073483
## [930,] -0.731238534 -0.4850334483 0.2625070568 -0.6357734682 -0.127687055
## [931,] 1.098917722 0.2860031009 0.2107636815 2.1389259652 0.081059653
## [932,] 1.714704768 2.1584219390 0.9006997142 -0.1592022642 1.136308198
## [933,] -1.699659104 -1.8479981182 0.5637577276 1.1458459720 -0.815100013
## [934,] 0.118888681 -1.0997832461 -0.3010475841 -1.2068228408 -0.730935719
## [935,] -1.068467716 0.1878543972 0.7975415723 1.0256548186 -0.256910968
## [936,] -1.257716818 0.6283386053 -0.6973045046 -1.4960728428 -0.328609299
## [937,] -0.386377000 0.8941395515 0.3890735925 -0.0228783859 -0.988077807
## [938,] 0.049069493 0.1325123576 1.2407398238 0.6422360248 -0.502455208
## [939,] 0.856766090 -0.6431107005 1.0360509165 -1.3291037536 -0.881635790
## [940,] 0.051060607 0.0232937065 0.8617212499 1.1534216087 -0.529524685
## [941,] -0.901019996 0.3723827379 -1.9209499075 -1.6049384799 -2.475209407
## [942,] 0.268058726 1.3647545126 -1.0772246877 -1.9318904782 -1.635676470
## [943,] 1.309626186 1.6263355905 0.2347137647 -0.8177656283 1.139353041
## [944,] -1.738259145 -2.0953754393 0.9181652737 -0.0885471769 -1.456058463
## [945,] 0.155152683 0.0588623509 -1.0532395886 -1.3990227412 0.412653737
## [946,] -1.472524376 -0.7239837382 0.6336242538 -0.6074516962 0.259975287
## [947,] 0.291795103 -0.1914038919 0.6201697087 1.2899742503 1.504758403
## [948,] -1.968088482 -2.2099501934 -2.0160940165 -3.1803882643 -2.718742714
## [949,] 0.568818458 0.3103110963 -0.7117571976 -0.9489970902 0.277775806
## [950,] 0.199310591 1.2427403010 -0.1117838868 -0.6557730008 -0.245256582
## [951,] 0.801288150 0.3802892834 0.6254980048 -0.5642346176 0.355351852
## [952,] -2.047029671 -0.9412770673 -1.2115073825 -1.9503784446 -1.491352338
## [953,] -0.305510781 -1.6379794907 2.0748960521 0.3934765967 0.185562233
## [954,] 1.414255898 0.1486920539 -0.7162317176 0.5251357395 1.376577911
## [955,] 0.347387653 1.3371326549 -0.5554073936 -0.0996678580 0.267750420
## [956,] 0.819283993 0.0586993728 0.3353972784 -0.0328516920 0.229732789
## [957,] 1.184945369 1.2803287694 0.8178779762 -1.0648619645 0.132668547
## [958,] 0.515707545 0.6350382778 -0.7651214780 -1.9086438445 1.661990367
## [959,] 1.220846720 -0.2741692047 0.6445868586 0.9540181042 -0.685876087
## [960,] -2.463451376 -1.4210341993 -2.9328050040 -1.1499093475 -3.123165093
## [961,] 1.881174092 0.1461804192 -0.0746116481 -0.8344279059 1.569312479
## [962,] 0.225174313 -1.2721568149 0.1879126570 0.0691013388 -1.251047153
## [963,] -0.005582825 0.1510298921 0.8245069566 -0.2760558000 0.061260537
## [964,] 0.649824210 1.9962005682 -0.5216097974 -0.9965189118 0.484657657
## [965,] 0.941694585 2.1174965338 -0.1154570599 -1.3080039052 0.610156846
## [966,] 0.140901209 -0.6196009628 0.3971318304 0.9020135281 -0.279901117
## [967,] -0.688405191 -0.9439749131 0.2726586808 -0.7770508869 -0.845483998
## [968,] -0.113270849 0.2871386528 -0.4035267349 -0.9634627376 -1.732213851
## [969,] 0.891752003 1.2163921829 1.4651206058 0.0944119213 0.272574659
## [970,] 0.352120955 0.2737044518 -1.7315316649 -0.9560451143 -0.718324344
## [971,] 1.310371650 1.0411983349 -0.9420639412 0.3853161863 -0.908871583
## [972,] 1.398032983 0.8802374293 0.8602524364 1.2705360103 0.579117602
## [973,] -0.551670169 0.1148482060 0.3771701999 0.7708390346 -0.453349116
## [974,] 0.041423150 0.8814216942 2.3604467810 1.2655647473 -0.776857869
## [975,] 1.562259698 1.7649538717 0.8387574526 0.4622904636 0.588589271
## [976,] 0.678569373 1.2656271256 1.4315374677 0.4389017186 0.690081872
## [977,] 0.458536561 -0.7312801992 0.8144022432 0.7108105699 -2.128211044
## [978,] 1.287685971 0.0304901995 -0.1796429216 0.2451387803 -0.689577738
## [979,] 0.448619953 -0.8566493292 -0.1615713771 0.7127830623 1.264778385
## [980,] 0.950611137 1.4975147091 -0.5493377637 -0.0829246998 1.073206836
## [981,] -0.806032784 -0.8846339669 0.9512032445 0.0790383315 0.763421602
## [982,] -0.498547259 -2.3195784514 0.8239614169 1.5633929092 0.593065000
## [983,] 0.039762145 1.1501276926 0.0012272842 -1.2714289068 -1.132960377
## [984,] -0.711364161 -1.2808348228 0.0300171712 0.0026035854 0.133376055
## [985,] -1.486331889 -0.7895805214 0.7770800209 0.6621610745 -0.774430419
## [986,] -0.220271579 -0.4767943525 1.9184964296 1.0717962190 -0.931795919
## [987,] -0.462512485 -0.9341958674 -0.3655827847 -0.2660334239 1.636511057
## [988,] 1.354615799 0.9835063681 0.5331037143 0.1443240758 0.441765587
## [989,] 2.184344957 2.9370565460 0.9328486232 0.4634089632 1.337827952
## [990,] 0.672858726 2.2390070811 0.3985973993 0.4900234667 0.970834561
## [991,] -0.390621589 -0.3798058464 0.1584788528 -0.2324947100 -0.695715165
## [992,] 0.113125988 0.0930667611 0.0401827294 0.5084822918 -1.283883689
## [993,] -1.746480793 -0.6373430623 -0.4574674452 -1.5046396307 -1.009402097
## [994,] -0.772603223 -0.2346818787 -0.8279868962 -0.8676772817 -0.950770817
## [995,] -0.834796769 -0.9973293270 -0.0494195615 -1.7231790929 -0.011210675
## [996,] 0.590942850 0.6278578524 0.5520711742 0.3077242568 2.117403830
## [997,] -0.672079598 -0.9853216115 0.7337429361 -1.6744467174 0.901179239
## [998,] -1.904820737 1.2924392356 -2.2021081788 -0.8656621877 -2.001970116
## [999,] 0.773054971 3.0629259739 0.2042062635 -0.8379246166 -0.040652266
## [1000,] 1.223925800 0.6627819176 -0.0877304747 1.0759240209 -0.721271968
## [,6] [,7] [,8]
## [1,] 1.0194478234 -1.520966936 0.726416791
## [2,] -0.3609091094 -0.732683628 -1.303155699
## [3,] 0.6231430169 1.568895282 1.295715271
## [4,] -0.9564763111 1.063505749 -0.540374014
## [5,] -0.7467448256 -0.360073158 0.070264082
## [6,] 2.0322973337 -0.532728070 0.548504385
## [7,] 0.8084309221 0.468578180 -1.753040137
## [8,] 0.2805287558 1.595191608 0.694058612
## [9,] 1.0312202894 -0.976353377 -0.772963006
## [10,] 0.7210764480 0.455039190 -0.905156222
## [11,] 0.2963688061 0.672929550 -1.714953798
## [12,] 1.3325848908 -1.173800284 -1.660740457
## [13,] 0.3575649718 0.152290875 -0.264906775
## [14,] -0.1740540470 0.666031559 1.173267549
## [15,] 1.8003044960 -2.300231481 -0.885254630
## [16,] -1.4041509789 1.952337114 2.123963435
## [17,] -0.6317797112 0.684660764 0.877182519
## [18,] -1.5833967702 1.168073542 -0.724305524
## [19,] 1.1748652619 0.842869528 -0.061721381
## [20,] -0.1985713628 0.971039502 -0.143072065
## [21,] -1.4933336160 -0.919478960 -2.242756697
## [22,] -0.6765888207 0.733860059 0.904336088
## [23,] -0.4050826953 -0.817137488 -0.366733603
## [24,] -1.1570890095 -0.602372204 -1.021918845
## [25,] 1.2587072558 0.343760389 1.127316261
## [26,] 0.8011285751 -0.369206399 0.932110063
## [27,] -0.5755238676 -1.026575360 0.045721611
## [28,] 2.0230951949 1.003547230 0.260102705
## [29,] 1.0190183299 -0.063677941 -1.033804637
## [30,] -0.7210093579 1.506613289 0.466998698
## [31,] -0.0404603668 -0.494410404 0.238648071
## [32,] -0.9433184529 0.832935685 -0.098904335
## [33,] 0.2922326584 -0.634696704 0.034157419
## [34,] -1.9620388808 1.578896160 0.364083073
## [35,] -0.7430522342 0.577174656 0.570282989
## [36,] -1.8698498516 -0.443086636 -0.029765783
## [37,] 0.8334301694 -0.425233623 1.211395555
## [38,] 1.7629526218 0.564908386 1.864787559
## [39,] -1.4365566796 -0.800094462 0.451080090
## [40,] 1.1621543278 1.504719716 0.647036526
## [41,] -0.0327037310 -0.438875755 0.754846306
## [42,] -0.9303608301 1.491796444 -0.787264687
## [43,] -1.0591016732 -0.295389100 -1.822575474
## [44,] 1.1092667541 0.485539334 -0.755420956
## [45,] -1.4029529958 -1.582701585 -1.059542772
## [46,] 0.2045927421 0.148074114 -0.699415850
## [47,] -0.4792776683 0.358674872 0.106745104
## [48,] -0.1814421407 0.944646163 -0.630760565
## [49,] 0.3779189359 -2.118053695 -1.016174387
## [50,] 0.1095148066 0.477867682 -0.594878887
## [51,] 0.3604737820 -1.281545473 -1.889002222
## [52,] 0.1231009334 0.295725830 -1.474702079
## [53,] -0.3261851411 0.574876670 -0.723095518
## [54,] -0.7586257912 -2.073254096 0.140534716
## [55,] -0.8331173113 1.119199775 -0.563688786
## [56,] -0.0416165826 0.002973665 0.725022660
## [57,] 0.4918242400 2.727182971 0.673230464
## [58,] -2.6847780586 0.273291097 -0.408424655
## [59,] 2.4301951137 -0.133958085 -0.067921762
## [60,] -1.6403478288 1.917685448 -0.760726554
## [61,] -0.6386692922 -0.599888603 -0.874465991
## [62,] 2.5234416156 -1.053415678 0.471646779
## [63,] -1.5371024410 1.818790503 -0.796822124
## [64,] 0.7567697833 1.320524764 1.511715379
## [65,] 1.5751957880 0.716518408 -0.627219861
## [66,] 1.4379208249 -1.183725460 -1.829986441
## [67,] 0.8889969378 -0.862745067 -0.325420309
## [68,] -0.4079031760 2.001200230 1.162629152
## [69,] -1.3852388899 -0.339719957 -0.836497324
## [70,] -2.3792602664 -0.747581182 -0.241198266
## [71,] 0.1448611443 0.907580080 0.572993534
## [72,] -0.4778190261 -1.244609143 0.639980204
## [73,] 0.4594849414 1.044955296 -0.513835787
## [74,] -0.8464962839 -0.937388009 1.028968394
## [75,] -1.3928861508 -2.041940257 -1.493052162
## [76,] 0.0238515164 0.763558884 0.460356741
## [77,] -0.7553495249 0.422314806 0.592630772
## [78,] 0.5423776632 2.749596797 1.634495518
## [79,] 1.3747961315 -1.494975941 0.110313608
## [80,] -1.1798964362 0.094763317 -0.534000961
## [81,] 1.0199767055 0.961884939 1.318387055
## [82,] -1.0484077703 -0.840162097 -2.177997529
## [83,] 1.2525636903 -0.151891734 0.187376680
## [84,] -0.4127890206 -0.110190983 0.593458681
## [85,] 0.5376870864 -0.452335169 -0.854838428
## [86,] 1.8803351668 -0.042086683 -0.399713373
## [87,] -0.4684664560 0.445330005 1.462825147
## [88,] -1.3226011954 -1.108207372 -0.805912825
## [89,] -0.1736774172 0.301338269 -0.253537676
## [90,] -1.0798527395 -1.386496247 -1.572144550
## [91,] -1.1891327177 0.078184420 0.388543611
## [92,] 0.1666693051 0.783205619 -0.018260554
## [93,] 0.7618242585 -2.217207650 0.094948039
## [94,] 0.9599961074 0.704772601 0.496790707
## [95,] 0.8164718993 1.234881856 1.056418395
## [96,] 1.4511793628 0.713015136 0.766194913
## [97,] 0.2203612296 -0.328336049 -0.610166826
## [98,] -0.9701252592 0.523153028 -0.555046079
## [99,] -1.1202053795 -1.397196159 -2.624187190
## [100,] 1.3458397870 -0.121099772 -0.257430489
## [101,] -1.1588815786 0.183538889 -0.016694188
## [102,] 0.1067484240 0.805552551 -0.138321497
## [103,] -0.8962610438 -1.799453488 -0.873798175
## [104,] -2.1709563147 0.134662691 1.527860669
## [105,] 0.7142142334 -2.159517076 -1.040910769
## [106,] 0.3196253152 -0.033021847 -2.064381357
## [107,] -0.5014969054 0.028989613 -1.005121390
## [108,] -1.3171101414 0.611783071 -1.152888678
## [109,] -0.4536727117 -0.171984451 -0.212159931
## [110,] -0.5419236434 0.138648496 -1.192595012
## [111,] 0.3095149261 0.629258499 0.617364221
## [112,] 0.8325282446 0.548858241 0.594720933
## [113,] -1.5122332247 0.161565787 1.106509686
## [114,] -1.2095569614 1.380695750 -0.602405729
## [115,] -0.1390240944 1.423509529 -0.265871934
## [116,] -0.5732404237 -0.227611406 1.590928883
## [117,] -0.2383334880 1.062666501 -0.612289981
## [118,] 0.7086387724 -0.339688988 3.087839315
## [119,] 0.5632526237 -1.344960914 0.513851763
## [120,] 0.3525935777 0.215082495 0.522919482
## [121,] 0.9157050718 -1.587891760 0.279745966
## [122,] -0.1421112700 -0.817469358 0.408084009
## [123,] 1.4252675669 -0.825160621 1.794140344
## [124,] -0.8475073913 -0.105852477 -1.456713260
## [125,] -0.0241429309 -1.422204949 -1.221600824
## [126,] 0.3367072869 -0.526402073 -2.255010339
## [127,] 0.3939508826 -0.793733084 0.489124807
## [128,] 0.5491538130 1.018141881 -0.437442361
## [129,] 0.2488835292 0.305418159 0.319874094
## [130,] -1.7532172184 1.586075902 0.412834278
## [131,] 0.7124976984 1.070176444 0.525997377
## [132,] 0.6709881007 -0.700903873 0.123641414
## [133,] 0.1901263382 -0.690786623 -0.678861683
## [134,] -0.3215172020 0.378984110 -0.950756145
## [135,] -1.2223636204 -0.707184261 0.774724612
## [136,] -0.7122239399 0.143797740 0.107673186
## [137,] 0.0033538205 2.023510431 -0.145117843
## [138,] -1.6196998687 0.476316961 0.165346920
## [139,] -1.1240244308 0.175497874 -0.268739088
## [140,] 1.2645930985 -0.229586180 0.347218834
## [141,] 1.6249586654 0.202825153 0.129644768
## [142,] -0.7251628325 0.864184192 0.460606420
## [143,] 0.6892189425 -2.186945673 -1.249407025
## [144,] -0.1625114391 0.880421128 0.472097588
## [145,] -1.2934456014 0.629993483 -0.717889343
## [146,] 1.0035502689 -0.166758337 1.306163244
## [147,] 1.6927738787 0.233182974 -0.778066010
## [148,] 1.1832268021 -0.650066149 -1.641020783
## [149,] 1.4364858743 0.464441049 -0.763777094
## [150,] -0.2456873620 0.690829501 0.410469145
## [151,] -0.1253161677 -0.185821061 0.584106716
## [152,] 1.0384073840 -0.276192162 1.050942407
## [153,] -0.2375399208 0.131228297 1.481325372
## [154,] -0.4704014427 0.672273588 0.469356728
## [155,] -0.7595101463 -1.057886254 -2.460296116
## [156,] 0.2828244539 -0.826065646 0.057109716
## [157,] -0.9798496150 0.483594277 0.408248864
## [158,] 0.0338862181 1.774013308 0.048125746
## [159,] -0.3514633428 -1.481586572 1.167713224
## [160,] 0.5425811715 0.861926515 1.520005048
## [161,] 0.9932862633 -0.002000487 -0.364044675
## [162,] 0.1183375700 -0.715920559 -0.156015242
## [163,] 3.4245842982 -1.360766662 -0.488678115
## [164,] -0.8275628286 -0.977178343 0.631954276
## [165,] 0.5932889196 0.325961123 0.968799718
## [166,] -0.6713747994 -0.016827940 -0.927396748
## [167,] -0.0607303319 -0.661365493 0.063532655
## [168,] 2.2719460678 -0.081832085 -0.246483116
## [169,] 1.1585836139 0.515117545 -1.577431752
## [170,] 0.9414413341 -2.093936352 -0.158362174
## [171,] 1.1690375057 0.225329483 -0.032780422
## [172,] 0.2706515125 1.441848757 0.543622929
## [173,] 1.1712754777 -0.516078472 1.116392153
## [174,] 1.8631907319 0.781200654 0.789136581
## [175,] 1.2504536488 0.839906056 0.858143981
## [176,] 1.2409332176 -0.934868027 -0.422337268
## [177,] 1.2955749944 -0.508086075 -0.138750563
## [178,] -1.2984701903 -0.451529705 0.298133614
## [179,] 1.6441827701 0.713625434 2.345474538
## [180,] 0.6758850160 1.015638711 0.574781440
## [181,] 0.4536487240 0.256266163 -0.356062782
## [182,] 1.9206580068 -0.237831015 0.158817020
## [183,] 0.0090729547 1.939156021 1.239885324
## [184,] 0.6667543534 0.470763728 0.516097732
## [185,] 0.0512400604 -0.932955405 -0.885509843
## [186,] -0.5971924844 0.755660833 0.219736211
## [187,] 1.4301700490 0.509835365 0.101817419
## [188,] -2.6335416126 0.799015343 -1.502353926
## [189,] -0.1751481863 -0.143811620 -0.546641368
## [190,] 0.6465240059 1.987777231 0.873891406
## [191,] -0.6301064567 0.173780696 0.265585024
## [192,] -1.1503416866 -0.664282303 0.064718823
## [193,] -0.2096300830 -0.385220903 -1.157560651
## [194,] -0.0001313392 0.443263612 -0.468588061
## [195,] 0.6366096978 -0.299310116 0.168833554
## [196,] 0.7074618257 0.595674175 0.755904324
## [197,] -0.5259956815 1.600688612 0.502293124
## [198,] 1.0731588839 2.114808163 0.985417231
## [199,] -0.3996650409 0.560137378 -0.442974957
## [200,] 0.7522506630 0.679799685 1.496353977
## [201,] 1.0915237520 -0.922777769 0.347598054
## [202,] -0.3978439274 -0.705427113 -0.324417334
## [203,] 0.0448176334 -0.626357949 0.440766079
## [204,] -0.2677221898 1.194118170 1.032084123
## [205,] 1.2713231293 0.001246833 0.093274353
## [206,] -0.1149627538 1.070299406 1.617237506
## [207,] -0.8045363499 0.001101599 -0.036238187
## [208,] -0.7367624953 0.320064105 0.581757455
## [209,] 0.0987933246 -0.933814844 -0.403257311
## [210,] -1.4443535943 -1.810652220 -0.558206414
## [211,] 0.3876399690 -0.379376126 0.501388292
## [212,] 0.4941223312 -0.268084723 0.919235782
## [213,] 0.6919158637 -0.577518874 -0.107056240
## [214,] -0.4487503286 0.067187107 -0.334374490
## [215,] -0.5390574721 -0.345161366 -0.999865395
## [216,] 0.9906960370 0.398011437 -0.312602024
## [217,] -0.7038614979 0.424088717 1.952167775
## [218,] 1.7754721573 -0.035295811 0.081347062
## [219,] 0.9463547834 -0.119880438 0.356665254
## [220,] 0.6539648450 -0.346517611 0.023625348
## [221,] -0.6459394551 1.497539041 1.144289098
## [222,] 0.1431367943 0.592516971 -0.952100887
## [223,] 0.4928539787 -1.767131817 2.609701730
## [224,] -0.4515285716 1.001229753 0.491697648
## [225,] -1.3198229011 0.514938538 0.732533374
## [226,] 0.4564853098 0.062320225 1.516118436
## [227,] 1.0596188006 1.561460426 1.666614628
## [228,] -1.4631489495 -1.145627702 -0.523905870
## [229,] 2.8559624531 0.792597619 0.892688667
## [230,] 0.2146101849 -0.203607209 -1.003740535
## [231,] -0.2568265720 0.539211616 1.391570562
## [232,] 0.1538557757 1.053689881 -0.587078756
## [233,] 1.1523884114 0.187577165 -0.635588744
## [234,] -0.3455757717 -1.145502466 -0.653129172
## [235,] -0.1032672930 -0.024399458 0.179505214
## [236,] -0.3304189596 0.346846767 0.036208123
## [237,] 1.1235964124 1.676676562 1.689266249
## [238,] 0.6928120474 -0.081695767 -0.911006209
## [239,] 0.1707909053 -0.930307263 -2.769306494
## [240,] -2.6542567105 0.218989365 0.538310539
## [241,] 0.4686611017 0.475846113 -1.535359168
## [242,] 0.0615769636 0.110792008 0.581051793
## [243,] -1.1479037376 -0.571122484 1.493298757
## [244,] -0.4283478858 1.114972917 0.638465738
## [245,] -0.2604685691 -0.388665332 -1.219573637
## [246,] -0.1794398423 0.165371092 -0.717904884
## [247,] -0.7951986313 0.021242167 0.921526917
## [248,] 0.4675233920 1.092753409 -0.676548510
## [249,] -0.0170374690 -1.987260079 -0.714458303
## [250,] 0.9270856069 -2.168138516 0.401469112
## [251,] 0.1274413040 -1.164099697 -0.090386266
## [252,] -0.2025752703 -0.605309978 -0.283391361
## [253,] -0.2831555762 1.520856190 -0.002410893
## [254,] 0.3712814720 -0.512085562 -0.048159728
## [255,] 1.1304924962 -0.691119464 -2.423751216
## [256,] 0.8377934801 -0.776002719 -1.043516695
## [257,] -1.1728227156 -0.085393121 0.937127260
## [258,] -0.7416061475 -0.990154798 0.741989409
## [259,] -0.1029209324 0.860366909 0.059257183
## [260,] -2.9388297958 -0.331621613 -0.413118719
## [261,] -0.0060644228 -0.410549313 0.669858328
## [262,] -0.3596849157 -0.342358292 -0.474125166
## [263,] 1.5758238684 0.002419761 -1.346563920
## [264,] -0.7426850035 0.270305976 0.758918988
## [265,] -1.6216968530 0.772375735 0.687868665
## [266,] -2.9359939168 0.126979023 0.016413431
## [267,] 0.5114036197 0.159712702 0.240705739
## [268,] -0.5582449510 1.422995178 0.309787786
## [269,] -1.7631295400 -1.672096316 -0.778366749
## [270,] -0.0172749126 0.998100497 0.862577408
## [271,] 1.7304064450 -1.093505504 -0.444370795
## [272,] 0.2418578283 0.535604782 -0.650956075
## [273,] -0.3465307123 0.770754228 0.397661485
## [274,] -0.1936701077 -0.509661888 1.889677195
## [275,] -0.6070439879 -0.867914862 0.447480663
## [276,] -1.1697897249 -1.121278333 -0.604101772
## [277,] 0.7659594024 -0.345309291 -0.808160636
## [278,] -0.0619394415 -0.666131578 0.549728608
## [279,] -1.7485101246 0.656382393 -0.394810882
## [280,] -0.3314633683 1.138729926 -0.690743700
## [281,] -0.7247671921 -0.316895470 -0.272051817
## [282,] 0.7794359348 1.524359833 1.341909694
## [283,] 0.3238376369 -0.439008605 -0.785208503
## [284,] 0.2949312448 0.447475701 1.560220254
## [285,] -1.3554333066 -0.315458832 0.845918600
## [286,] -0.1867530282 -0.929939168 0.460061302
## [287,] 1.4097094128 -0.236151102 -0.011018298
## [288,] -0.0308123909 -0.339316526 0.734644008
## [289,] -1.1001863132 0.400693690 0.409633417
## [290,] 1.3380611745 -0.117387655 0.496952378
## [291,] -0.4837206728 0.204874920 -0.867992669
## [292,] 1.2575679924 -0.829820558 0.091874259
## [293,] 0.4901660321 -0.670669792 -1.030463130
## [294,] -0.4403079014 -0.658686833 -0.682313970
## [295,] 0.8016678878 0.270208878 -1.345269130
## [296,] -0.1146385149 0.258568412 -1.081588557
## [297,] -1.1850571210 -0.398371241 -0.250397014
## [298,] 0.7311546764 -0.932332549 -0.298311595
## [299,] -0.8996213770 -0.715829058 -1.534168052
## [300,] -0.2390562372 -1.224586160 -0.091095561
## [301,] -0.2012385400 0.109180701 1.323645011
## [302,] 0.8131311473 -0.615340757 0.115827106
## [303,] 0.9362380310 -0.083939784 1.959167472
## [304,] -0.5911051158 1.524410966 -0.647596010
## [305,] -1.6974169464 -1.008080989 -0.808972972
## [306,] 0.6291841174 -1.170475514 1.949988729
## [307,] 1.1810457739 -0.237067648 -0.796690976
## [308,] 0.7085875722 1.596262216 1.051633633
## [309,] 0.7700956023 -0.327930609 -0.912811369
## [310,] 0.3097990891 -0.545809943 -0.884786957
## [311,] 1.8229395349 1.221344542 0.598997802
## [312,] 0.4772523571 -0.041360740 0.402461118
## [313,] -0.4726359171 0.659723853 -0.791734461
## [314,] 0.2527512912 -0.800076800 -0.786351594
## [315,] 0.2200430009 0.462726030 0.461628604
## [316,] 1.8141660543 0.777738835 -1.185583687
## [317,] 1.4886497593 -0.014596251 -0.867231594
## [318,] -0.0508853107 0.543534465 -0.591137548
## [319,] 0.6821740305 1.548937612 0.706496686
## [320,] -1.3368789318 -1.215538707 -0.056453013
## [321,] 0.4238856701 -0.382005509 -1.854096922
## [322,] -0.1248717097 0.153565681 1.451869314
## [323,] 1.4644048086 -0.594717752 -1.001900209
## [324,] -1.5129736261 -0.558706473 0.219480170
## [325,] -0.3505306235 1.439424631 0.932659242
## [326,] -0.0813730569 -1.343700212 -1.672598937
## [327,] -1.1969297800 0.257694483 1.314222284
## [328,] -0.7650210950 -0.707123700 1.042597904
## [329,] 0.0803392321 0.796486353 0.613098744
## [330,] 0.8678555962 1.080663655 0.188650658
## [331,] -1.5215180459 1.455598603 -0.285859074
## [332,] -0.3774100176 -0.086420951 -0.153721185
## [333,] -1.2674348549 -1.043544577 -0.757598713
## [334,] 1.3263088468 -0.607098362 1.383787325
## [335,] 0.5161360763 -0.624423394 -1.263634074
## [336,] -0.8130962747 0.454625290 0.742463978
## [337,] 1.5227761526 -2.245543212 0.248597944
## [338,] 1.1899310533 0.790178091 2.117457969
## [339,] -1.1043941661 0.319177067 1.168188640
## [340,] 0.7391291896 0.592134547 -0.075022976
## [341,] -0.7700652861 1.111454946 0.599791127
## [342,] -0.6207932423 0.262808431 0.397838091
## [343,] -0.0274481045 -1.336731940 1.126549166
## [344,] -0.6860527682 -0.679892200 -0.350851048
## [345,] 2.2894331224 0.525393435 0.279436026
## [346,] -1.4786240938 -0.282185163 1.488209143
## [347,] -1.3987883205 0.015089106 1.085474809
## [348,] -0.3455044065 0.617221520 -0.114011749
## [349,] -0.3359406377 0.823142063 0.017645254
## [350,] 0.8909199038 1.092625114 0.259346568
## [351,] -1.4703805703 -0.631520228 -0.149803092
## [352,] 1.6796814627 0.495449217 -0.581631084
## [353,] -2.2815649070 -1.291144850 -0.517030680
## [354,] 1.7057581174 1.330226856 -0.134145259
## [355,] -0.0247882925 0.500005180 -0.006954023
## [356,] -1.3852300637 -1.700177855 0.568319370
## [357,] 0.1893791983 -0.173129145 -1.028936610
## [358,] 0.9500328094 -0.053181680 0.973251627
## [359,] 1.7977672096 0.843223162 -0.736999103
## [360,] 0.2700811974 0.545386132 1.500006843
## [361,] 1.1038254856 0.246406678 -0.911355780
## [362,] 1.4486248771 2.074011490 1.188252496
## [363,] 1.5941116529 1.416399388 2.006764464
## [364,] 0.7766796175 -0.463333923 -0.618333463
## [365,] 0.6313673413 0.803906829 -0.038077397
## [366,] -0.0413749394 -2.303694102 -1.323876283
## [367,] -0.2409277226 -0.735617837 -0.564662989
## [368,] 1.7067000615 -1.322805708 -0.428389678
## [369,] 0.5576975694 -1.791298301 -0.948010635
## [370,] -1.1949230005 -1.403958455 -2.078059644
## [371,] -0.1383594691 2.136586948 -1.100826298
## [372,] -1.0004704330 -0.855261071 0.560930082
## [373,] -2.2059460900 -0.667806249 -0.786167982
## [374,] -1.8541799713 -0.434270563 -0.819169998
## [375,] 1.4560552270 2.000560043 0.476140563
## [376,] -0.0301857566 -2.220483141 0.883432479
## [377,] -1.8635216302 0.037812517 0.556437641
## [378,] 0.3813312450 -0.535389472 0.628395010
## [379,] 0.1018459193 -1.290040382 0.529263262
## [380,] 1.1208711986 -1.198054551 -1.944999549
## [381,] 0.2195886656 -0.001652999 -1.171161989
## [382,] -0.1836594723 -1.111720884 2.255798817
## [383,] 0.4203609131 0.379611013 0.778855643
## [384,] 0.4676949483 0.081335011 0.195331635
## [385,] 0.6940393424 1.415596046 -0.134461244
## [386,] 1.2473459340 1.465262582 2.159720237
## [387,] 0.8067061263 0.811741147 -0.430508367
## [388,] 1.3526517022 0.072802398 -0.439927559
## [389,] 0.1053618557 0.803912157 1.466805726
## [390,] 0.5128873247 1.914631700 1.539774061
## [391,] -0.3275348312 -1.635346501 -0.187206835
## [392,] -0.2009436598 0.074385139 0.659528926
## [393,] 2.0252707635 -0.181530856 -1.309068781
## [394,] -0.3247551020 1.481926230 0.102452588
## [395,] 2.0305760367 -0.525972734 -1.230588392
## [396,] -0.1680030381 -0.605527333 -0.008756627
## [397,] 2.1114163644 -0.163749875 -0.237634756
## [398,] 0.6905367890 0.147292216 -0.478421738
## [399,] 0.1561956134 1.232535726 0.091016087
## [400,] 1.8394386510 -0.697156709 -0.868117418
## [401,] -0.0671452149 0.194672347 -0.154006759
## [402,] 1.3174044551 1.683015823 0.761762842
## [403,] 1.1940217249 0.323050074 1.240890862
## [404,] -3.4786372954 -1.603517518 -1.172768873
## [405,] 1.8225097514 1.050217180 0.733346772
## [406,] 1.1794276762 0.738226984 -0.953221998
## [407,] -0.4121798404 0.819306407 -0.418924781
## [408,] 0.3359020168 -1.021182786 -0.194237724
## [409,] -1.1449173589 0.963179877 0.055346137
## [410,] -1.3997246563 -1.334868767 -1.410878290
## [411,] 0.3597795455 -0.661297955 -0.515205981
## [412,] -1.1629016903 -1.029393386 0.027607597
## [413,] 0.6741183283 -0.503864263 1.261022235
## [414,] -0.8840769972 -1.531558657 -0.622440347
## [415,] 1.2943552593 0.444795616 0.540425459
## [416,] 0.2649781733 1.351577740 -0.342825092
## [417,] -1.4777758590 0.924108422 0.483322274
## [418,] -0.6163202402 -0.427482294 0.346368516
## [419,] 0.7644220353 -1.418592954 -1.496742131
## [420,] -0.4343127969 0.671663160 -0.291116175
## [421,] 0.8939856652 -1.750499647 0.507155691
## [422,] -1.1295505988 -0.574802997 -1.010732792
## [423,] -0.7450467953 -0.093966545 1.366400458
## [424,] 0.1795297046 0.200926919 -0.071033722
## [425,] 1.0794559403 -0.645434096 0.305986109
## [426,] -0.0036296724 1.564992932 0.032798553
## [427,] 0.2422850475 -1.275636486 -0.103945918
## [428,] -1.9084069460 -0.954531633 -1.377593798
## [429,] 0.9161936096 -0.629874758 -1.166075067
## [430,] -0.2981394226 0.890499844 0.437327285
## [431,] -0.2278001175 1.512091589 -1.767596426
## [432,] -0.0227999887 -1.006131065 0.111076706
## [433,] 0.3835900888 -0.648446146 0.609163865
## [434,] 0.2993109268 1.191745663 -0.015758195
## [435,] 0.3588393052 0.334266282 -0.397766011
## [436,] -1.0492042358 1.128717435 -0.415136192
## [437,] -1.0399020260 -1.122942781 -0.983075884
## [438,] 0.8399837010 0.175513327 -1.363877005
## [439,] -1.4062937715 -0.465173002 1.540156500
## [440,] -0.2267814480 2.201959469 1.323126835
## [441,] -2.6162764488 0.774914178 -0.057900680
## [442,] -0.1518892555 0.251053484 1.024627328
## [443,] 0.7173300836 -1.353255724 -0.346253154
## [444,] -3.0092426858 -1.955074126 -1.500159218
## [445,] -0.5349530656 -0.069386102 0.496726170
## [446,] -0.7098487227 -0.095621240 -0.335702023
## [447,] 0.8098333315 -0.111216767 -0.630853683
## [448,] 1.2204816133 -0.047569478 1.053854978
## [449,] 1.3178464255 -0.663770947 0.708288140
## [450,] 0.4369998881 -0.373733969 -0.203084535
## [451,] 0.2456947675 -0.371743265 -0.002001495
## [452,] 0.9754349443 -1.875513957 -1.323238335
## [453,] -0.0927357816 -0.158146513 -0.736803321
## [454,] 0.3189553227 -0.764498755 2.012758023
## [455,] -0.3763576020 0.235871648 0.172244989
## [456,] 0.0982563379 0.109332141 0.118366772
## [457,] 0.9506166455 0.772008807 1.022771063
## [458,] -1.7727532773 0.558886638 1.839921911
## [459,] -1.3369246359 -0.394311497 -2.574880524
## [460,] 1.8630753674 -0.052451301 0.508831326
## [461,] -0.5184259477 -1.770136250 -1.687170422
## [462,] -0.0399201725 -0.616101992 0.497008928
## [463,] 0.6822220423 1.930582754 0.224769612
## [464,] 1.1786586231 0.949812339 0.457555152
## [465,] 0.2058558701 1.477648050 1.658201649
## [466,] 0.3106413728 -1.974819477 -0.581562980
## [467,] -1.7065529854 -2.800756329 -0.575853334
## [468,] -0.8264026248 -1.962976726 0.291071687
## [469,] -2.0826351275 -1.339036917 -0.414728978
## [470,] -0.8162018838 0.472170481 1.551673267
## [471,] -0.4908862604 0.584337981 -0.327745013
## [472,] -0.3748063418 -0.204246772 -1.609154677
## [473,] 0.6307519584 0.091683252 -1.282523689
## [474,] 0.0845453471 1.920254631 0.494808436
## [475,] 0.4867842100 -0.881618188 0.924671422
## [476,] 0.1928132790 -0.554585648 -1.385003830
## [477,] 1.0140676539 0.234233355 0.125583768
## [478,] -0.4451569017 1.495410358 2.324277086
## [479,] -0.6164784729 0.980897090 0.291017496
## [480,] -0.1400193722 -0.548179336 0.140128105
## [481,] 1.0897553403 -0.728504259 -1.421752235
## [482,] 0.5359482602 -0.181957956 -1.020089547
## [483,] -0.2685288621 -1.027099376 0.146943600
## [484,] 1.0222760529 0.657793425 0.751171809
## [485,] -1.4434608777 -0.229402833 0.775066392
## [486,] 0.9442397617 -0.881281945 0.756498770
## [487,] 0.7181927146 1.509445657 1.113855786
## [488,] 0.1820687527 0.870051756 0.777454052
## [489,] 0.2996723471 0.066816551 0.278302405
## [490,] 0.7739629672 0.021671655 -0.719656708
## [491,] 0.4742635235 -1.212453243 1.317210229
## [492,] -1.5209182806 -1.289130570 1.172261983
## [493,] 1.1752439367 -0.098690844 -0.400424487
## [494,] 0.9237366240 1.550102162 2.626023416
## [495,] -0.8590003774 0.626666006 0.403052011
## [496,] 0.8997473563 -0.363436163 1.550700426
## [497,] -1.2356149373 1.145999372 -1.106280525
## [498,] 1.4648814836 0.774092324 0.579916911
## [499,] -0.6267758214 -0.299728436 0.255422319
## [500,] 0.5309124370 -1.371907725 -0.314672728
## [501,] -1.5417772482 0.992169832 -0.733055494
## [502,] 0.5597865586 -1.660146812 -0.829614802
## [503,] 0.0732988198 0.598473588 1.695927754
## [504,] 0.8662858098 -0.308882948 0.567883854
## [505,] 0.2760196878 -0.260621083 -0.427757233
## [506,] -2.4871525007 -1.890265959 0.722359209
## [507,] -0.7193026770 -1.072007518 -1.591541045
## [508,] -0.9054623190 0.907476495 -0.765668480
## [509,] 0.3649059201 -0.901555998 1.084891305
## [510,] 0.7480210616 0.242609105 0.287484295
## [511,] -0.2406591908 -0.271714609 -0.236847759
## [512,] 0.9634498695 -0.440381439 0.728468530
## [513,] -1.9208353684 0.506094381 0.937064404
## [514,] -0.9683429531 1.082074873 0.256600263
## [515,] -1.5895578508 1.019664823 0.723911024
## [516,] -0.1457682122 0.596015447 0.607606978
## [517,] 0.5636239408 0.993800898 0.283659649
## [518,] -0.1422348096 0.241652026 -1.706024588
## [519,] 1.9765639869 0.608201526 0.477291220
## [520,] 0.6598739484 -1.226909159 -0.069872596
## [521,] 0.7630588807 0.211027016 1.148419672
## [522,] -0.9875764883 0.597847086 1.621434670
## [523,] 0.6426547907 -1.036777503 -0.090002582
## [524,] 0.9982855053 0.527761410 0.824768852
## [525,] -0.8760150105 0.232982021 0.724597395
## [526,] 0.0694989933 1.054908779 -0.795374308
## [527,] 1.3168388933 0.393949334 1.769077264
## [528,] -1.5887691982 -0.029422813 0.377537891
## [529,] 0.1946637286 -0.534885525 0.735970678
## [530,] 0.0255609957 -0.123567265 1.496301508
## [531,] 1.0324299470 -0.641909886 1.126956794
## [532,] 1.0351365185 -0.271114296 0.651368207
## [533,] 0.4741318352 1.873558394 0.836532987
## [534,] 1.1257833000 -0.174312057 0.683715788
## [535,] -0.7390430962 0.347212740 0.093222772
## [536,] -1.4353358851 -2.461348682 -2.537419542
## [537,] -0.6643875054 -0.877718121 0.551820693
## [538,] -1.1961166518 -0.797990495 0.390577507
## [539,] 0.7417496001 0.645641119 0.601692633
## [540,] 1.9482946209 1.188147157 1.568023660
## [541,] -0.3816313627 1.937022652 -0.915788834
## [542,] 1.1804882516 1.688400331 0.045080109
## [543,] 0.3438568179 -1.209170951 -0.612065241
## [544,] -1.6884707232 -0.276768462 1.308073940
## [545,] 0.2619659786 -1.718245845 0.571264681
## [546,] -0.8398904005 0.825147990 0.378301690
## [547,] 1.0774106753 -0.895315388 0.453994419
## [548,] -0.6771129489 -0.268859793 1.085117178
## [549,] -0.1547871737 0.476622445 1.463000647
## [550,] -0.4762539715 0.212934556 -0.048679524
## [551,] 0.0156869505 0.057466525 -1.675254699
## [552,] 0.2525269892 0.890664290 1.018604217
## [553,] -0.5436177424 -0.730775046 -0.003713984
## [554,] 0.7063739217 -1.817569838 0.552099199
## [555,] 0.0708205376 1.777531787 1.957526931
## [556,] -0.0503131555 0.026553938 0.617028132
## [557,] 0.3385652630 0.032712625 0.168080928
## [558,] -0.3225313529 0.376846527 0.686921609
## [559,] -1.6485316418 0.523046183 0.251570192
## [560,] -1.1065251249 -1.446902154 -0.058204271
## [561,] -0.7888932216 -0.323543196 -0.827000882
## [562,] -0.2242928117 -2.718692219 0.106770409
## [563,] 0.2653809870 0.941586003 0.598837073
## [564,] -0.0842643908 1.003000652 2.290728624
## [565,] -0.4698603947 0.631858075 -2.181897735
## [566,] -0.2669111093 -0.685146222 -0.602789632
## [567,] -0.0975223366 0.158994504 0.064149003
## [568,] 1.8283787346 0.333385282 -1.271773566
## [569,] -0.6621007431 0.952180198 -0.011235442
## [570,] -1.9811421681 -1.435624231 -1.138662579
## [571,] 0.8754939650 -0.716110626 -0.298102080
## [572,] -0.4738861428 0.773714339 -1.652926237
## [573,] 0.3553533290 -0.951827800 -0.614092681
## [574,] -1.1114588005 -0.381883267 0.545276541
## [575,] 1.3656130189 0.058345593 0.652533571
## [576,] -1.5640701231 -2.067715365 -0.512808244
## [577,] -0.9682163432 -0.864280699 -1.724256676
## [578,] -1.5199328617 -0.241480198 0.766217416
## [579,] -0.5487268383 0.206472267 -0.542660720
## [580,] 1.0357364576 0.299175213 1.214823679
## [581,] 1.4629281819 -0.736611439 0.582333926
## [582,] -0.0487146820 -0.736774442 -0.377401756
## [583,] -0.5467363153 0.225003616 -0.471981388
## [584,] -2.2091240135 0.992217554 2.701065803
## [585,] -0.6739824529 -0.225592752 -0.743830291
## [586,] -0.0320223851 -0.837309432 0.525989732
## [587,] 0.4514413024 1.304979453 -0.986688794
## [588,] -0.0300337219 -1.421921460 -1.541616724
## [589,] -1.6666480135 -0.278626463 -0.543565698
## [590,] -1.2900466696 -0.758391117 -1.060749267
## [591,] 0.4319864566 -0.329394898 -1.198117163
## [592,] 1.9389505385 -1.548345472 -1.555270411
## [593,] -1.4577831645 -1.042686347 0.910934148
## [594,] 1.0709733731 -0.088439318 0.843063076
## [595,] -1.2662974654 -1.202742192 0.054658695
## [596,] -1.2579791301 1.085813386 2.231491304
## [597,] 1.5025861062 -1.036017706 -0.188093014
## [598,] 0.4287823395 0.395065462 0.913915414
## [599,] 0.6130313134 0.704270645 -0.477477365
## [600,] -2.1453468242 -0.036705369 -0.247385790
## [601,] 0.3294138932 0.730909584 -0.771171864
## [602,] 0.8130114122 0.622181915 1.298626688
## [603,] 0.7660624168 -0.504793638 -0.385282652
## [604,] -1.3910671477 -0.183007282 -0.027680362
## [605,] -0.8983601107 -0.266297683 0.923437834
## [606,] -0.0405602776 0.107229830 -0.323768650
## [607,] 0.2316613542 1.338988761 -0.136921153
## [608,] 0.7173066579 0.913741071 -1.672900784
## [609,] -0.5776143584 -0.708391994 0.133767155
## [610,] 0.6391410343 -0.359461782 0.511285080
## [611,] 1.5155467776 0.518538552 -0.136016827
## [612,] -0.1317378185 -1.465364970 -0.195662313
## [613,] 0.3293080514 -1.987664175 -0.533427191
## [614,] -0.0812087567 0.434448146 -0.495354076
## [615,] 0.0445423726 0.417616067 -0.115641621
## [616,] 0.2650812933 0.354502229 -0.294923498
## [617,] -1.2737883728 -1.311043901 -0.241721866
## [618,] 0.6053804512 1.249748038 1.018013084
## [619,] 0.8637433714 0.570255723 0.825884429
## [620,] 1.1906162384 -0.998743565 0.676853977
## [621,] -0.1774126643 -0.405811928 -2.153114833
## [622,] -1.0014484827 0.279215718 -0.644538157
## [623,] 1.1101695366 -0.580739498 -0.825937237
## [624,] -0.9002179368 -0.023855692 -1.573482473
## [625,] 0.3134240287 1.253486028 1.711284585
## [626,] 0.0690820842 -0.244363030 -0.201053571
## [627,] -0.9773977653 -2.743955542 -0.856471736
## [628,] -0.5691167092 2.334613886 0.134715301
## [629,] -0.1580652742 -0.187754483 -0.071396643
## [630,] 0.6943119614 -1.113176364 0.858270901
## [631,] -0.5013045881 -0.054030989 0.612011824
## [632,] 1.5183127964 1.008840087 0.799117682
## [633,] 0.1174844617 0.329988359 -0.003778675
## [634,] 1.2560844420 0.520645905 -0.194570379
## [635,] -0.4051554373 -0.101302909 2.410361109
## [636,] -1.1635243536 0.650996646 1.972419761
## [637,] 1.0530786762 -0.287812961 -0.119680867
## [638,] -0.4599177753 -0.046541264 1.875165507
## [639,] 0.9307410746 1.016386299 0.717810383
## [640,] 0.3866901487 -0.061783518 -1.252804430
## [641,] 1.6296375584 -0.887820329 -0.991925021
## [642,] -0.5537224524 -0.215989406 -0.705942962
## [643,] -2.1759301150 -1.683583868 -1.222169633
## [644,] 0.1208342580 -0.182949327 0.412293534
## [645,] 1.5213961517 -0.069554428 1.917216557
## [646,] 0.4047098624 -2.204996155 -0.697168169
## [647,] -0.8186569519 -0.016445190 0.512402425
## [648,] 1.7271659442 2.046046716 -0.170948795
## [649,] -0.3983567175 -0.413592866 -0.594755107
## [650,] -1.0490957676 0.378125853 0.050264737
## [651,] -0.0818124825 0.121030126 0.578444629
## [652,] -0.8889273800 0.086511522 -1.065446142
## [653,] -1.5123766790 -1.563395374 -0.589871736
## [654,] -0.5563159077 -2.217840332 0.909894758
## [655,] 0.6048276133 -0.365036419 0.162872300
## [656,] -0.3820814689 -0.932082212 0.595708013
## [657,] -0.8043452134 0.461703192 0.947894234
## [658,] -0.9764732448 0.068474885 -1.125769526
## [659,] 0.0294447738 0.154963207 0.850572554
## [660,] -2.0969009154 0.103626267 -0.291345040
## [661,] -0.0179697530 0.672731780 -0.249955214
## [662,] -0.3871741863 -0.229063440 0.798557947
## [663,] -0.5530943973 0.717715429 -1.441485679
## [664,] -0.3969721745 0.232951908 0.723169388
## [665,] -0.3148828716 0.486813184 -1.532058107
## [666,] -0.0653716211 -2.878726838 -0.312970971
## [667,] 0.1632045972 -0.289978683 -0.573143684
## [668,] -0.4361918238 1.317661381 0.792948075
## [669,] -0.7838099709 0.714042311 1.005713230
## [670,] 1.6202879608 -0.044365378 0.225393383
## [671,] 0.2980292288 0.006011715 -0.849201917
## [672,] -1.3794609812 0.719486987 1.281027109
## [673,] -0.2084984127 -0.114979385 0.418953262
## [674,] -0.6432043433 1.316107909 1.101726932
## [675,] 0.4507666917 -0.612912167 -0.013222857
## [676,] 0.2142591551 0.446564746 0.062672574
## [677,] -0.2309672286 -0.940275032 -1.008302178
## [678,] -1.2402658771 -0.684358195 0.865727946
## [679,] 0.6578062724 -0.241817659 -1.125933848
## [680,] 0.2700268330 -0.352206749 0.995928319
## [681,] 1.2027657156 -0.830054505 0.667004067
## [682,] 0.6526858040 0.190664212 1.707013202
## [683,] 1.5616371952 0.820497442 0.872371129
## [684,] 0.7445081064 0.036384922 -0.524325419
## [685,] -1.1190073103 -0.867026761 -0.138109169
## [686,] -0.7812229487 0.992285529 0.586248332
## [687,] 0.9390886964 0.221536108 -1.278588869
## [688,] 0.2939238386 -0.031583423 -0.502793751
## [689,] -1.2032584356 -0.633727187 -0.337721009
## [690,] -1.4199366626 -1.687327756 -0.420493263
## [691,] -0.8303168202 0.548170474 2.825726407
## [692,] -1.2574665722 0.089695231 -0.002737982
## [693,] -1.6036976588 0.173304403 1.010862969
## [694,] 0.4992798109 -0.071368708 -0.688329891
## [695,] 0.4248960106 -0.619651823 -1.157002536
## [696,] 0.2263595598 0.977727833 0.753554157
## [697,] -0.6108165360 0.262521359 0.212151073
## [698,] 0.8091972819 -0.998913875 -1.514090666
## [699,] -2.2048877114 -0.168661602 -0.162137929
## [700,] 0.7149772891 -0.539340403 -0.931510941
## [701,] 0.9556600194 0.927917510 0.772254640
## [702,] 0.9343047398 1.237285840 0.283825655
## [703,] -0.6911245897 -0.027186946 -0.304460671
## [704,] 0.7461021943 0.067162557 -0.251903277
## [705,] 1.3534804972 0.866225572 0.443508450
## [706,] 2.6847441159 0.754952698 -0.251776031
## [707,] -1.6812283393 -0.351994650 1.605303082
## [708,] -1.5141624953 -1.808162690 -1.245449347
## [709,] -0.2829632272 0.943021434 1.179657995
## [710,] 0.0713239498 0.900095556 1.553664487
## [711,] -1.2870882503 -0.338482054 1.681315165
## [712,] -0.9761655171 1.096328549 2.755635615
## [713,] -0.2201178072 -0.028219491 0.692500500
## [714,] -0.7776825361 0.130461630 -1.042687559
## [715,] -0.5308777260 -0.838381235 -0.693285443
## [716,] -0.9118593907 0.686587528 1.471245149
## [717,] -0.8775428513 0.965837375 0.088753002
## [718,] -0.5314932635 0.930994624 -0.870013887
## [719,] 0.4834572207 1.337727072 0.972511128
## [720,] -0.5946647181 -0.897432576 0.377230570
## [721,] -2.5403470053 -1.222544434 -0.284631972
## [722,] 2.2563424008 -0.543287448 0.037659608
## [723,] -0.2487967172 -0.014661785 -0.561631012
## [724,] 0.8044364271 0.884020933 -0.281072145
## [725,] -0.7867380107 -0.705846051 -0.899417011
## [726,] -0.9198937903 -1.289243502 -0.234490714
## [727,] -0.5639463522 0.270836830 0.149392841
## [728,] 1.5221959439 1.409499548 0.523945393
## [729,] -1.4436383329 -0.601408031 -3.557210145
## [730,] 0.1426168921 -1.783501553 -1.092415575
## [731,] -0.4756336070 -1.949656754 0.649428938
## [732,] -1.1030255338 -1.958508420 -1.197752241
## [733,] 1.1100985942 0.753807519 0.925383336
## [734,] 0.0633359473 0.685104589 1.701837342
## [735,] 0.8866466048 -1.121141995 -0.706848918
## [736,] -1.2975235191 -0.008872703 -0.559461598
## [737,] -0.3435955460 -0.268381965 2.079385570
## [738,] -0.0188264504 -0.740577510 0.090838546
## [739,] -0.9980426098 0.168720697 1.085774743
## [740,] -0.2274339367 -0.668401265 0.649187165
## [741,] 0.5520921408 0.821280799 -0.455763852
## [742,] -1.4040492721 0.573074114 -0.165577434
## [743,] 1.6601270813 1.158850540 1.231624386
## [744,] 0.8323897797 0.898477381 -1.291887486
## [745,] -0.0221302671 0.812003745 -0.507117374
## [746,] 0.3037735730 1.110145261 -0.554337936
## [747,] -0.2789136628 -1.871937124 0.953515889
## [748,] -0.1610720273 0.159363823 -0.584334317
## [749,] 1.0553366804 0.950507290 0.602113247
## [750,] -0.8670812261 -0.415283486 -0.004725589
## [751,] -0.1423660396 -0.297283961 -1.904537948
## [752,] 0.2985125728 0.820216906 1.079784484
## [753,] 0.9741689360 0.476308672 -2.156946940
## [754,] -0.2890663916 -0.067304596 0.476017356
## [755,] -1.8424514169 -1.272877395 0.167061921
## [756,] 1.3518787648 1.056256673 1.150026116
## [757,] 1.0830552022 1.037812940 0.625935571
## [758,] 0.5409845096 -2.256812283 -0.536394095
## [759,] 0.1077064214 -0.684749484 0.604082519
## [760,] 1.1328594454 -0.109625878 0.995268287
## [761,] -1.4753130076 1.551462448 0.469023967
## [762,] 0.1392395014 0.307697053 -0.824426981
## [763,] 1.1261667772 -0.417045544 -0.160224054
## [764,] 1.8896388110 -0.755663460 -0.293366519
## [765,] -0.4275753772 -0.572888986 -0.228829945
## [766,] 0.8519451082 -1.622974153 0.114512718
## [767,] -0.4854590951 -0.986595047 -0.432432295
## [768,] -0.5031485930 -0.196239447 -0.941208220
## [769,] 0.7623743080 -1.232848668 -1.096953983
## [770,] -1.1650894003 -1.604840892 0.299617284
## [771,] -0.8559367056 -1.083707107 -0.450439822
## [772,] 0.4997085090 1.784975961 0.742857417
## [773,] 1.6420563320 1.072610344 0.129906708
## [774,] 0.8222752996 1.432584584 -0.772480251
## [775,] -1.0349537007 -0.849571542 -1.245889608
## [776,] 0.0656840815 0.547479882 0.740023054
## [777,] 1.2890892514 0.070497489 0.392577459
## [778,] -0.5212999669 0.133168135 0.526751196
## [779,] 0.6267458827 2.456764158 0.689915857
## [780,] 1.3201417173 1.515782482 0.720956501
## [781,] 0.9488515501 1.911794076 0.406407775
## [782,] -0.1830947949 -2.500119153 -1.697684968
## [783,] -0.9462447236 -0.782939485 -0.522270175
## [784,] 1.4589257545 1.595591024 0.677179187
## [785,] -3.2179119153 1.549007409 -0.441117464
## [786,] -0.0926225662 -0.639546799 -0.104023515
## [787,] -1.1379182690 -0.774206959 -0.217892335
## [788,] 0.2655691850 0.141575391 -0.251703412
## [789,] -1.0127931200 1.369769842 -1.864949717
## [790,] -1.6407722296 0.362596667 1.205007767
## [791,] 0.1688344855 0.959119151 -0.131500730
## [792,] -0.4367126670 -0.668958088 -1.626798979
## [793,] 1.1897253149 3.666272789 2.555395777
## [794,] -1.9472185216 0.620008311 -1.651688353
## [795,] -0.0545630761 0.958879097 -1.915353944
## [796,] -0.1347915376 -0.621036671 -0.218051399
## [797,] 0.6197976841 -1.957780047 1.647982093
## [798,] -1.5504229312 -0.555918227 -0.496104985
## [799,] 0.2536375193 0.890451660 -0.192135535
## [800,] 0.6883863528 -1.508166902 1.071059936
## [801,] 0.1719045927 0.265458327 -0.156556098
## [802,] 1.8158154937 -1.384306898 -1.391792189
## [803,] 1.3283163263 -1.588106354 -1.104293972
## [804,] -1.1118621248 1.235336619 0.367208447
## [805,] -0.4386188508 0.740154217 1.841455732
## [806,] 1.4809599559 0.335527295 0.045876582
## [807,] -1.4764487017 -1.102105755 1.271450727
## [808,] -0.9027554697 -0.707013168 0.691541148
## [809,] -0.8137437346 0.897217972 -0.215084558
## [810,] 0.7148850013 -0.072072386 -0.839751345
## [811,] 0.4353172504 -0.167913675 -1.187659435
## [812,] 1.3228473818 0.445754640 -1.295652043
## [813,] 1.8552991316 1.895500755 -0.522906544
## [814,] 0.3752707241 0.252840853 -0.633114175
## [815,] 0.2467139605 -1.478760475 0.239735882
## [816,] 0.7146327405 1.022208466 0.500429293
## [817,] 0.3282607891 0.544456572 -1.786098903
## [818,] 0.6855405912 0.727339479 -0.354568715
## [819,] 0.4104076436 -0.429945830 0.922045735
## [820,] -1.2102731749 1.329418526 0.681526397
## [821,] -1.0879600103 -1.043386286 -0.468463387
## [822,] -0.6932388641 1.165371840 2.286801537
## [823,] -0.3464767248 0.003872117 -2.048598318
## [824,] -0.2746221069 -1.501785476 -1.100028808
## [825,] 1.0951053504 1.458527988 0.417651565
## [826,] 0.1410618902 2.246234947 0.201408375
## [827,] -0.5226543404 -2.195401176 -0.551343638
## [828,] 1.0772283559 0.979602086 1.458922906
## [829,] 0.1187175848 0.505235653 -0.155713275
## [830,] 1.2646077187 1.377913775 0.474139056
## [831,] -1.0526728260 -1.290624789 0.023034865
## [832,] -0.3709682287 -1.022060342 0.062386250
## [833,] 1.2752340851 -0.996434909 0.516796726
## [834,] 0.3842188813 1.377947319 -1.816210548
## [835,] -1.3862424942 0.714772551 1.315626240
## [836,] 0.8376747415 -2.798533579 -0.194815914
## [837,] -1.0255126808 -3.500486381 -0.642577572
## [838,] 0.8729997329 0.244897634 0.868669684
## [839,] 0.8965594356 0.225676369 1.770231853
## [840,] 0.2435224761 -0.876004765 -1.578901868
## [841,] 1.1803035012 3.608914935 1.114534114
## [842,] -0.2550051927 -0.516408835 -1.543846289
## [843,] 0.1860076911 -0.761443724 1.417172592
## [844,] -0.9114057856 0.374014043 -0.159660803
## [845,] -0.6783910318 0.269930797 1.009992555
## [846,] -1.4811615989 0.167173896 0.326048673
## [847,] -0.5703634722 -0.402142389 0.893143732
## [848,] 0.3956464450 0.878067417 0.194130052
## [849,] 2.0204720259 0.043088348 0.967751622
## [850,] -0.2201570867 -0.529348749 -0.753621540
## [851,] -0.2073005359 -0.377156609 -0.126573826
## [852,] -0.5217483062 0.131341433 -0.714586675
## [853,] 1.7025970125 1.846534579 0.873887512
## [854,] 1.2840729911 0.600608953 -1.223841316
## [855,] -0.6441298182 -1.458487407 0.949024504
## [856,] 0.0547635076 0.698868515 0.160496680
## [857,] 1.1681333854 -0.745758421 2.883667011
## [858,] 1.0368742267 -0.186709744 0.022174881
## [859,] -0.8747699902 -0.545253825 1.117895162
## [860,] 0.4548999509 0.337132523 -0.437387983
## [861,] -1.5108855921 -0.892681926 0.269049966
## [862,] -1.2851612164 1.046344292 -1.083046912
## [863,] -1.4297679600 0.310697425 0.948214564
## [864,] -2.6616890369 -0.864874476 -1.428361592
## [865,] -0.8992554578 -0.002862553 0.106356268
## [866,] 0.4603579724 0.841439789 -0.351798364
## [867,] 1.4730501212 -0.028655746 1.340093354
## [868,] -0.1746675012 0.365614472 0.530020828
## [869,] 1.3189787196 3.666595281 -0.048206486
## [870,] 0.5195750401 0.254395770 0.283220050
## [871,] -0.6673026601 -1.286315182 -1.188771510
## [872,] 0.8584316282 0.678644116 0.933228356
## [873,] 0.6866376956 0.013894958 1.255752782
## [874,] -0.7693551636 0.737451731 0.944921571
## [875,] -0.4238630031 -0.900553366 0.019529006
## [876,] 1.2274869618 0.878030348 -1.415477087
## [877,] 0.7541622803 0.904934927 0.300474827
## [878,] -1.6627424494 -1.611740231 -0.169535275
## [879,] -0.1116503724 -0.647545948 0.747692218
## [880,] 0.7960938748 0.459139492 2.262769841
## [881,] 0.9807567473 0.494491994 0.186703870
## [882,] -0.2826039585 0.926255673 1.270202854
## [883,] -0.0086042365 0.431027021 0.603878970
## [884,] 0.8875358371 2.008977320 1.143961767
## [885,] -0.1823102624 0.023671089 0.060263394
## [886,] 1.0804139919 -0.882889357 -1.481545843
## [887,] -0.2310884905 -0.395139040 0.835798279
## [888,] -1.8945294864 -1.710983269 0.414409102
## [889,] -0.9930217502 -0.612692979 0.716232160
## [890,] 0.9246140784 -0.424543041 -0.894803921
## [891,] 0.4442591188 0.195464295 -0.978895790
## [892,] -1.3628463266 1.161770072 0.047847292
## [893,] -0.4081451017 0.191072641 0.884656983
## [894,] -1.6423980193 1.384603559 1.946075885
## [895,] 0.7073468923 -0.732757278 -0.250004227
## [896,] -0.5101721454 -1.541200010 -1.073459377
## [897,] 2.8544177704 0.256240919 1.088698805
## [898,] 1.1125868162 0.830321473 0.385021549
## [899,] -0.4337670765 0.337452030 -1.853838180
## [900,] 0.3822763403 0.532924833 0.957291815
## [901,] -1.2314187744 -0.343961861 -1.069481047
## [902,] 0.8152469530 -0.089712679 1.068176081
## [903,] -0.6598345169 -1.267903267 -1.739550506
## [904,] -1.2378478746 0.024647861 -1.224624641
## [905,] -0.0275826151 -0.360516930 -0.453244081
## [906,] -0.7695320136 0.870768862 1.328070529
## [907,] -0.6453537709 -0.200869210 0.256139183
## [908,] 1.2678891971 1.252214168 0.532982996
## [909,] 0.9810715982 1.211056126 0.058497050
## [910,] 0.6974920762 1.106345434 0.708299699
## [911,] 0.1052147019 -0.405305409 0.640539686
## [912,] -0.6186858008 -1.198436938 0.385752918
## [913,] 1.2741298744 0.982426908 -0.066684240
## [914,] -1.6344514294 0.938944534 -0.475606869
## [915,] -0.3218681709 -1.617608639 -0.164592976
## [916,] -1.6222861533 0.321210460 0.056003965
## [917,] 0.2217937339 -1.270086467 1.509475217
## [918,] -0.9397657873 -0.376263674 -0.618910262
## [919,] 1.2982541340 0.940418294 -1.133368967
## [920,] -0.7187704773 -1.303023949 0.084966125
## [921,] -0.1633322144 0.642982373 -0.167321260
## [922,] 0.2308899567 -0.884929535 0.866644156
## [923,] 0.0634441908 2.038259191 0.300394988
## [924,] -0.4887569229 0.599591239 0.072902264
## [925,] 0.8848414276 -0.353112085 1.364915618
## [926,] 0.6566792091 -0.119290267 0.823324464
## [927,] 0.1251127512 1.068697496 -1.483578356
## [928,] -0.0630437285 0.493792892 -0.304256875
## [929,] 0.9899111327 0.209044531 0.259439763
## [930,] -0.2255004785 0.618981426 0.475614000
## [931,] 0.9456461271 -0.011697648 0.395806031
## [932,] 0.3981250743 0.401185481 1.636682576
## [933,] -0.0386959116 1.463159047 -1.518791053
## [934,] 1.4282265031 1.222835105 0.676911169
## [935,] 1.5846164592 -0.487564434 0.348978043
## [936,] -1.1818012792 -0.012879702 -0.412348932
## [937,] -0.6450870131 -0.688657816 1.853936998
## [938,] -1.3158212135 1.174070403 1.874134417
## [939,] -1.5858874466 -0.943122648 0.187972941
## [940,] 1.8331600838 0.495733229 0.420813387
## [941,] -0.6345913706 -0.596242166 -1.594784291
## [942,] -0.3555457570 -0.607242934 -0.161375727
## [943,] -1.3295449687 0.654540900 0.084635079
## [944,] -1.9787611660 -0.507091835 1.255480870
## [945,] -0.9012613085 0.088412539 -0.051811139
## [946,] -0.6395490819 -1.516072004 -0.123995854
## [947,] -0.6605717966 -0.427347631 -0.154155512
## [948,] -1.2046860538 -0.836971416 -1.660459706
## [949,] 0.3318199558 0.915248857 0.660609930
## [950,] -1.9704058312 0.601478996 -0.359846149
## [951,] 1.1938940985 -0.788592430 0.808753410
## [952,] 1.0773821583 -1.343123575 -0.350530513
## [953,] 1.3988364266 0.238000344 0.797365255
## [954,] 1.6540150836 -1.033637647 -0.027000724
## [955,] 0.4288198003 1.182556404 -0.659233908
## [956,] 0.5424024303 0.518164383 -0.648029331
## [957,] 1.1788863465 1.328587291 0.256907531
## [958,] 1.9713836156 1.012034171 0.438370486
## [959,] 0.5695840390 0.026079593 0.040568647
## [960,] -0.8742505821 -1.297786315 -1.621989307
## [961,] 0.5822721611 -0.591163542 1.002798747
## [962,] -0.7307933748 -0.285711792 -0.405987227
## [963,] 0.0633849868 -0.479477775 -0.116407180
## [964,] 1.3709118317 -0.400901345 0.823148911
## [965,] -2.0195676009 -1.621731743 0.519122197
## [966,] 0.2137359953 -1.066451228 -0.794181232
## [967,] -1.8980499878 0.645629898 -0.670165445
## [968,] -2.0021334173 -0.325497636 -1.013723888
## [969,] 1.0354008642 0.390790828 2.110862653
## [970,] 0.0152935001 0.099891794 -0.357401501
## [971,] 0.3040462625 0.365550486 0.667931325
## [972,] 0.8763046521 3.433792840 0.405028166
## [973,] 0.3701051116 -0.161738876 1.062221830
## [974,] 1.1707782236 0.997464171 0.917037148
## [975,] 2.2121381992 0.223029918 0.236333483
## [976,] -1.7731590392 0.039238865 0.319225543
## [977,] 0.0769752312 -1.233090370 -0.902469037
## [978,] 0.8608007479 -1.421167393 0.004576848
## [979,] 0.3282812652 0.492361057 1.384613107
## [980,] 0.8952956704 0.078595515 -1.319109081
## [981,] -1.0839940367 0.329606121 -1.313192308
## [982,] 0.2007760594 -0.539084791 -0.637013775
## [983,] 0.3189829513 0.525561757 0.365278103
## [984,] -0.6848882758 0.044253094 1.429377971
## [985,] -1.2625211354 1.707057972 0.238260769
## [986,] 1.3885961301 -0.318521551 -0.312556320
## [987,] -0.2421078700 0.154749109 -0.077036712
## [988,] 1.0010397344 -1.045214705 0.899024948
## [989,] 0.0755200707 0.991805601 -0.413068483
## [990,] -0.5198199903 -0.824130917 0.320153913
## [991,] -0.8203772055 -0.910729017 0.379367906
## [992,] -0.2800359347 -1.117361140 0.125537106
## [993,] -0.3740062481 -0.985021915 0.293775033
## [994,] -0.5775528006 -1.545068184 -2.014342486
## [995,] 0.4246004689 -0.810644623 0.773448699
## [996,] -0.6747531530 0.280635983 -1.032885362
## [997,] -0.0240494757 0.817820112 0.960276099
## [998,] -1.1689847462 -2.631665629 -1.271371546
## [999,] 0.2850497411 -1.196063571 0.809946009
## [1000,] 0.1479639336 1.019933684 0.376476781
##
## $simulated_resid$t
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.1952835938 0.001366057 -0.0520539236 -0.4740534232 -1.0069673528
## [2,] 0.3692378078 0.482763766 0.7592683456 0.8101294754 -0.0873102763
## [3,] -1.1131243319 -0.028739371 -0.0909074473 -0.2129599616 -0.6657233252
## [4,] 0.7833515919 -0.751230234 0.7327065904 -0.3643781016 0.2282845211
## [5,] -2.3002464331 -0.295053772 1.4292639493 0.0412690621 -0.9058360045
## [6,] 0.3600801085 1.525268088 -0.6794956783 0.4141077659 0.7846418843
## [7,] -0.7069492089 0.374733125 -0.6309255810 -0.6653670220 0.4772440455
## [8,] 0.7211416230 -0.119487498 1.4991561112 -0.0044261291 1.5397883643
## [9,] 1.8081815774 0.415695991 -0.6377255713 -0.3644088876 1.6793340507
## [10,] 0.3393113788 2.605921673 -0.3674167576 -1.7896640466 -0.0439780602
## [11,] 0.1143801004 -1.137657672 -0.9921280744 0.2145772659 0.9578102016
## [12,] -0.9410257389 -1.249051201 -0.4136832778 0.9064290195 -0.7395059250
## [13,] -0.3175826295 0.260273179 -0.2152075385 0.2659370658 -0.8633401689
## [14,] 0.6808477157 0.333750031 1.5403978161 0.8617494610 0.8711607051
## [15,] -0.9463516642 -1.534221399 -1.0273126207 -2.4580298951 -0.8520798975
## [16,] 1.7865595553 -0.110551322 -0.7712764861 -1.6604515443 1.6709476328
## [17,] -1.3115904666 -0.582584673 -2.4342783802 -0.5194730061 -0.6072320922
## [18,] -0.1922916640 -0.736370560 0.0200664680 -0.0663161917 0.4148195134
## [19,] 0.5556118634 0.142439017 2.0857875313 1.6471749063 1.7790596690
## [20,] -1.0413046845 0.744544662 -1.2031792908 -1.3005325103 -2.0105020679
## [21,] -0.3799840127 -0.628728185 -1.6308421691 -0.1249594364 -1.4024012562
## [22,] -0.5851700915 -0.172691472 -0.2986120783 -0.3660544186 -0.2584029641
## [23,] 0.5758994165 0.575325665 1.1067279323 0.9341921002 0.1924856883
## [24,] 0.1367256556 0.487985643 1.9346880793 2.5480178264 1.0917670762
## [25,] -0.5378206215 0.371754663 -0.6897351767 -0.6624111732 0.4051833549
## [26,] 2.0629611776 -0.365364268 0.8805813902 -0.6396352723 0.2727894764
## [27,] -1.6455065310 -1.053083426 -2.2665000283 -2.0972925218 -0.4290541213
## [28,] -2.1773975532 -1.972057727 -1.2391173993 -1.8610457938 0.3620412260
## [29,] -2.0222096804 -0.554263982 -0.7605460156 -1.0390639643 -1.0845943174
## [30,] 0.7285004307 0.803995884 0.2302789947 -0.4342304817 -1.6483614206
## [31,] 0.5994466587 0.826661237 -0.5602626180 0.2991966662 0.0626276700
## [32,] -0.3340961354 -0.223560941 -0.1819926519 0.1293507434 0.2256241488
## [33,] 2.0658798958 0.289478167 -1.1061502989 0.2206881634 0.1383231039
## [34,] -0.2752587693 -0.610599650 -1.0478029131 -1.3563292964 -0.5628743829
## [35,] 0.0877579321 -1.109080176 -0.6852324126 -0.7897384730 -1.1940776915
## [36,] 2.4419984385 2.198392197 -2.0615366455 -1.2685623896 2.7033192031
## [37,] -0.9376531595 -0.911612167 -0.5120590481 -0.2369267221 -1.1462851906
## [38,] -0.6232982342 0.616251129 2.0722120810 0.7467072187 1.3551761022
## [39,] -0.3010834713 0.297999574 0.0316909636 -0.2622105971 1.4235756981
## [40,] -0.7057740847 -0.355196346 -1.1878999962 0.0534894091 -2.5249707211
## [41,] -0.0469693696 0.577532904 0.7898662569 0.8023685499 0.2354992744
## [42,] 0.3467122815 0.060570851 -1.6137078425 -1.1199640880 -0.8217312772
## [43,] 0.1528102019 -0.898474654 0.6657214424 0.0352424397 0.2139233388
## [44,] -0.3258253838 -0.784561439 -0.3624091839 -0.0159825623 0.4694917375
## [45,] -0.0239404842 -0.677259366 -0.3897642216 -1.3374037970 -0.6127662997
## [46,] -0.7913355355 -1.233698406 1.0191535103 0.0064380731 -0.2699123143
## [47,] -0.7759470312 0.451604503 -0.4933236907 0.3626020415 -0.1599591391
## [48,] -2.0015084982 -1.124989733 -0.7736843381 1.1112161351 -1.0555279743
## [49,] 0.1675452695 -0.893208792 0.8678609809 -0.1711805648 1.0383618939
## [50,] 1.3499083221 1.470731230 0.7545862727 0.2636224274 2.1259496559
## [51,] -0.6090914091 -1.762161251 -0.6543098454 -1.4520512265 0.7447032452
## [52,] -0.7694298534 -1.297877476 0.2070183217 0.8779529234 0.7260265761
## [53,] -0.5271407130 -0.787367582 -1.4045740344 -1.1987696733 -1.3473783918
## [54,] 1.5527255206 1.237664103 0.6026808191 0.3981595022 1.1632313887
## [55,] 0.7569333821 0.080189968 -1.0291241766 -0.3649644891 1.7988356099
## [56,] 1.7564563347 1.082550437 2.5806388415 1.6307513743 1.1079340532
## [57,] 0.3886019565 -0.755451450 0.9362887832 -0.3213794846 1.9548947319
## [58,] -1.0840018543 -0.741971081 0.1702647373 -0.1597377858 0.7570659486
## [59,] 1.4345174751 2.105265369 -1.2932639450 -0.6131307801 -0.6725026482
## [60,] 0.3088288290 0.632098456 1.0142953871 1.2191669899 1.1827299003
## [61,] -0.8922282733 -0.051618307 -0.6781137889 -1.1846242845 -0.3013733378
## [62,] 0.9065539807 0.811496615 2.0868052171 1.1895012415 0.0753612880
## [63,] 0.2441810903 -0.356010229 -1.8006425630 0.0035471877 -0.4902065824
## [64,] 1.4059530607 -0.756659735 -0.5203419975 0.1944891227 0.2728647047
## [65,] -0.6050523761 0.728034494 -0.4448601692 0.2153481222 -0.8027525268
## [66,] -0.9905562531 -0.603872858 0.6082236816 0.0066600168 -1.0910192650
## [67,] 0.9155339711 -0.646027116 0.1699945368 1.3420228805 1.5808668552
## [68,] -0.7637630775 -0.667490680 0.4426426661 -1.3037188078 -0.0292194012
## [69,] 0.6822729654 0.718648876 1.4995272617 0.1453463557 1.2004987862
## [70,] 0.7540744987 0.127998626 0.9237428581 1.1305954603 0.4190285296
## [71,] 1.3721211609 1.131380030 -0.0904344010 -1.0564959019 0.3320187108
## [72,] 0.4854066753 -0.297960812 -0.6288293206 -0.4864844969 1.2531023579
## [73,] 1.0498842906 0.146951920 0.1317272942 -1.0714632195 1.6501307549
## [74,] -0.2252702016 -0.745980188 -1.4253064689 -1.5653340272 0.7316596812
## [75,] 1.4285631014 -0.071748382 -0.1295506091 -0.0359143969 2.0382831123
## [76,] 0.5626139397 1.078362410 0.1556401998 -0.3146980432 -0.7122504765
## [77,] 1.1108246817 1.858169545 1.1118194902 1.3373854694 1.1589977408
## [78,] 0.6820002354 0.233756746 0.2617710920 -0.3778237689 0.4960636251
## [79,] 0.4691436248 0.853343296 0.1039213732 0.3517932929 1.3580135968
## [80,] 1.3432988565 0.291743830 1.0957578471 0.1301570296 1.6471531549
## [81,] -0.4702456965 0.609629400 -1.4505707463 -0.1836471013 -0.4835606628
## [82,] 1.6013004741 1.070946991 0.6196311162 1.7033392108 1.4961711296
## [83,] 0.2611838592 -0.464237871 1.5536286968 1.5712260844 0.6509198001
## [84,] 0.8548825648 0.464579314 0.9871414083 1.0913597655 -0.1814130277
## [85,] 0.1666813355 -0.031728405 -0.8753178982 -1.0711346106 -0.6083324065
## [86,] 0.5777245192 0.464993611 -0.2066460411 -0.2429824221 -0.3926049785
## [87,] -0.4164088600 -0.454109977 0.3602929283 -0.6801788746 -0.1396335308
## [88,] 0.6169099771 0.840504151 0.6969792865 0.0066830718 0.2324071064
## [89,] 0.0008343560 0.960472281 -0.0073395593 -0.6914573396 -0.8676379668
## [90,] -0.1742399040 -0.068438408 -0.0437468975 -0.8821939947 0.1562413546
## [91,] 0.1981231906 -0.285657362 -2.1523191200 -0.8480366952 -1.5437045112
## [92,] 0.5608504157 -0.692810604 1.5998745228 -0.2318262449 0.0130522071
## [93,] -0.8073410237 -1.950638025 0.3846320184 2.4236436319 -0.7301958954
## [94,] -1.1896605705 -0.181867670 0.4703783251 0.7130174686 -0.5401289263
## [95,] -0.6850636274 -0.965906977 -0.8559904191 -1.1643884746 0.2211122720
## [96,] 1.8020084601 1.655177638 0.5587353792 -0.6385446879 0.5101177555
## [97,] -1.1707877355 -1.167884039 0.9106692096 -0.3650890655 -0.9055881487
## [98,] 0.7750024971 -0.559251098 0.3867465901 0.3984311605 0.2194673826
## [99,] 0.4733432210 -0.418175457 -0.3708363134 -0.8666098616 -0.0951712196
## [100,] 0.6392983570 0.328527015 1.7436949541 0.9982198630 0.5204863707
## [101,] 1.0169266950 0.384307323 0.5531078097 -0.1371405109 1.2673397669
## [102,] 0.1518814766 -0.775657674 0.3388055796 0.6089243012 -0.0726455433
## [103,] 1.6926079168 0.824026385 0.1335110692 1.2280634377 0.1417842905
## [104,] -0.8731197595 0.792451553 -1.2259924392 0.2104268055 0.9542461540
## [105,] 0.8394030982 -0.764182649 -0.3582285753 0.3613124827 -0.2974245391
## [106,] 0.1219397512 -0.061369640 1.8998029620 1.4752644086 -0.2416443265
## [107,] 1.1852755210 1.069387692 1.3103011804 -1.7453569678 0.7117628242
## [108,] -1.5885135739 -3.315483861 -0.7120374759 -0.5769862407 0.2643904591
## [109,] -0.4964150297 1.012634441 -0.3392685263 0.4745425167 -1.2448588362
## [110,] -0.3272436063 -0.753066175 -1.4939221759 -1.0890524357 -0.9671291516
## [111,] -0.8767279112 -1.638200749 0.2716321529 -0.3675760933 1.2713304627
## [112,] 1.2491524931 -0.303732380 1.0753501663 0.9221047839 1.3964805598
## [113,] -0.0981524839 0.755192025 -0.0288921317 -1.7147630678 -1.0665026840
## [114,] 0.2395259210 0.153082361 -0.6138085298 -1.2631597356 -1.4736618246
## [115,] 0.2725279422 -0.879128506 1.4508488983 2.0454187755 0.4457738388
## [116,] 1.1336136639 -0.088924662 0.9193893118 0.0565959790 -0.2743728829
## [117,] 0.5596326528 0.932150004 -1.2879570510 -0.3384117822 0.2460307658
## [118,] -0.2166799072 0.924316081 1.1231582172 1.4145502246 0.5934720601
## [119,] 1.5374311315 1.344570070 0.0618850170 -1.4100010114 0.5572573599
## [120,] 1.5233247335 -1.017450408 -1.6118215549 -2.1147534638 -2.8171948773
## [121,] 0.8488501413 0.270583125 -0.4200497073 0.5038926032 -0.2235054880
## [122,] 0.2986041255 -0.616565754 -1.3547679906 -0.7947333641 0.7083834262
## [123,] 0.5002926086 -0.225955864 0.5025649789 -0.5085465339 0.5403536363
## [124,] -0.4284144048 0.773961677 -0.9731237390 0.8223321325 0.5903768592
## [125,] -1.4325337381 -1.733896734 -0.2889221786 -0.4318970002 -1.6652969221
## [126,] -0.4365626079 -0.681668233 -1.4684641159 -2.2222159287 -0.0185758049
## [127,] 0.5964469397 0.250276010 -0.8067626640 -0.5587780422 1.0872704495
## [128,] -1.0327536156 0.415821751 -1.3903589876 -0.5062131666 -0.6204338965
## [129,] -0.3997487094 0.854526286 -0.9094192887 1.0455903767 -1.0156743761
## [130,] 0.2699089750 1.564464625 -0.1395962583 2.0463386603 2.1059348110
## [131,] 0.4752851594 0.937941351 -0.8816638144 0.5696615975 -1.1792136839
## [132,] 0.4064488623 0.170593043 -1.2266958740 -1.2087660635 0.7161897776
## [133,] 1.8808293875 1.050260129 -1.5017593153 -0.2757376938 1.5711213787
## [134,] 0.0093415138 -1.044066958 0.4504186337 0.7772743584 0.6562534645
## [135,] 1.3559433255 -0.133256967 -0.0068927452 -0.7257659816 0.7742313117
## [136,] -0.9671488890 -0.034394285 -1.4492268617 -1.2776136083 -1.1274866915
## [137,] -0.6072235370 -1.528819079 1.0851510718 1.2485340690 -0.8644797395
## [138,] 0.4722630426 -0.031045104 0.5992491105 1.7312912353 -0.1987708417
## [139,] -0.3171567049 -1.786277327 -0.1689848805 -0.3644821607 -1.2611588609
## [140,] 1.6270788884 1.139677154 -1.2010058005 0.1049014556 0.9428663861
## [141,] 0.5958721491 0.485317842 0.6289594851 1.9228303715 -0.0942786123
## [142,] 0.2436324119 -0.262220078 -0.0205586995 -0.9842767321 -0.7934313575
## [143,] 1.6984126111 -0.039196560 -1.2136924133 -0.3310714182 0.5097724933
## [144,] -0.2401711781 -1.345762486 1.5901330781 1.3580578163 0.7402329291
## [145,] 0.0441181206 -0.180072858 0.3048189615 -0.1928795756 -1.1613626264
## [146,] 0.5123047340 -0.626730866 0.4068924792 -0.2945730034 1.3235599458
## [147,] -0.1596271988 0.200127874 0.2350573318 -1.2761149499 -0.7365954105
## [148,] 0.4037669069 -1.438227987 0.8008938070 0.7180577640 -0.1983662486
## [149,] -0.3693499020 -0.379434838 0.0415950596 -0.4228000773 -1.2289500553
## [150,] -1.6025112033 -0.185199148 -1.2236855007 -0.3499740538 -0.7444403181
## [151,] 0.0219604115 1.813114690 0.4076927207 0.9307678191 2.3650188765
## [152,] -0.6477417925 -0.515243813 -1.5625618334 -1.2009002361 0.4711597858
## [153,] 0.3596383442 0.083215388 -0.5512639204 -0.9313972411 -0.3143166679
## [154,] 0.1981632139 -0.815083863 -0.2234896870 -0.4878513119 0.4378512772
## [155,] -0.0153040500 0.386647748 0.9741056926 0.7762964440 -0.0265428133
## [156,] 1.7119697473 2.430310343 -0.4230449845 -1.1361625620 0.9942306642
## [157,] 0.6900951040 1.103829433 -0.5729299248 -0.2589197113 0.6779748832
## [158,] 1.2955175623 1.455088799 -0.2032039811 1.0761958309 1.4637199135
## [159,] -0.2123058320 1.020093563 -1.7251043904 -0.6404035670 -0.4304524528
## [160,] 1.7036294049 1.433451151 -1.1221934293 -2.3912361147 0.0248548153
## [161,] -1.4362394447 -0.811148673 -1.3607951756 -0.4688604190 -0.6697975828
## [162,] -0.3108702917 -1.011958821 1.7138248937 1.0606260578 0.3942925533
## [163,] 1.0951999030 -0.198568422 -1.2720694210 -0.9966576359 0.4995027066
## [164,] -0.8433070428 0.204941981 0.4143507386 1.1410459927 0.3446298671
## [165,] -0.7137750872 -0.001935653 0.0659104640 0.4346895084 -0.6357359770
## [166,] 0.3248007621 0.131005158 -1.4347158603 -2.2592502925 -0.0504570224
## [167,] 0.7054238788 0.053419870 -0.8223095013 -1.1035972698 -1.2709358305
## [168,] -1.4240079693 -0.689243812 -0.3466055436 -1.7520067872 -1.0236001741
## [169,] 1.2387222095 2.197118669 0.3028499652 1.2205167847 2.0637651916
## [170,] 1.4531248784 -0.401136253 1.6509605298 2.0512699104 1.7172910087
## [171,] 0.4534697688 -0.198365887 0.0240123227 0.9129350893 -0.9864817175
## [172,] 0.9127851868 1.344429395 -0.4847470746 0.3686900794 2.0610449540
## [173,] -0.9001413555 0.404583633 -2.7921407967 -2.0881238555 -2.9032036443
## [174,] -0.0658700739 1.319556002 -1.5422173424 -0.2243057352 -0.7279700908
## [175,] 0.1726614058 0.653725934 -0.4070822914 -1.2470434539 -0.7666395878
## [176,] 0.4430826588 -0.168029944 -1.1582427511 -1.4760744637 0.2833476048
## [177,] -1.0239722031 -0.818211100 -0.6584727371 0.0422688062 1.6512819717
## [178,] 0.5526395764 -0.577772998 1.3097026662 1.2634999313 0.0086876842
## [179,] 0.6248996661 -0.337821439 -0.1861063830 -1.0581180434 -0.4900964192
## [180,] -0.1920824351 0.533162169 0.3129320413 -0.9262394831 -0.3331884705
## [181,] 0.6926274140 0.088596193 0.6343232349 1.1826196107 -0.6741794220
## [182,] -0.9585495956 -0.325180357 -0.8491186150 -0.2990710374 1.5956505978
## [183,] 0.3215412573 -0.066456003 0.6437820475 -0.4037874810 0.9876080761
## [184,] -0.6580596420 -2.039983591 -0.7279508155 0.4285361690 1.6753527086
## [185,] 0.8782008910 2.839165051 -0.2955465763 -0.5512605033 -0.2193789429
## [186,] 0.5832619406 1.464088760 0.1691368549 0.4657513847 0.3051484085
## [187,] -0.1395842934 0.559253097 -0.5886590462 -0.7198885895 0.2990449345
## [188,] -1.2700196145 -0.139146978 0.0316670984 -0.4547476408 -1.3204581243
## [189,] 0.0898429687 0.619555623 1.2234172578 0.4716631683 -0.6931794383
## [190,] -0.0268246741 -0.360434003 -0.7384916501 0.1657684178 0.4351044504
## [191,] 0.7968358535 1.063614292 -0.9509407575 -0.7092828258 -1.2638615254
## [192,] -0.1815517615 1.137208184 -2.0617621718 -1.1396464997 -0.2008109167
## [193,] 0.0948131101 0.310540807 1.4061925578 0.1405827634 0.2472580308
## [194,] -0.6073772596 0.198982383 0.1939379721 0.8155389734 -1.0716088602
## [195,] 0.8626571692 0.025887704 -0.6875292601 -0.3492374086 0.2400385841
## [196,] 1.7780472694 -0.383031552 0.9739338257 0.9333499417 0.5079044453
## [197,] 1.0869584097 0.368176266 -1.0740383206 -1.4347446083 0.5937692378
## [198,] -1.3868924373 -2.301301443 -0.0864016047 1.7197541111 0.3715680726
## [199,] 0.1912458869 0.255714817 1.7536378109 1.0481991841 -0.8969444449
## [200,] 0.1770167453 0.445733540 -2.1566757486 -0.5302174048 0.8944755599
## [201,] 0.1384055975 1.265749280 0.2142747043 0.0893772026 0.0539014261
## [202,] 0.2568273638 2.084978244 -0.3104405878 0.1321538858 0.4919902248
## [203,] -1.3641337571 -0.774835774 -1.6137003112 -2.2274123407 -1.3991753357
## [204,] 0.6500670940 1.252508021 -1.2262535398 -0.5719786168 -0.8876901666
## [205,] -1.9984356633 -2.784598448 -0.4992171007 0.1017397069 -1.1131608879
## [206,] -2.3757259176 -1.675369649 -0.7921294619 -0.4679212780 -0.8195184358
## [207,] 1.0511849682 0.967138046 0.1865245983 -0.7870663422 1.4884321301
## [208,] 0.2024150578 0.170605754 0.2410832661 -0.0928107440 -0.2466040101
## [209,] -0.7991883422 1.246108231 -0.7560928122 0.0686748606 -0.6578007966
## [210,] -0.3662622388 -0.608482345 1.1090698871 0.2386540369 -1.2013736982
## [211,] -0.2219135227 0.379256243 -0.0139344940 0.4117229489 -0.2676967730
## [212,] 1.4570939116 -0.019598043 0.6262280333 0.7711618586 0.1803389200
## [213,] -0.0842324221 0.313980622 0.0623932136 0.3170942034 -0.2681536765
## [214,] -0.0238490570 0.391985645 -0.1821507076 0.7065324955 0.1607080095
## [215,] -0.9574540533 -0.978796470 0.1963172200 -0.2497761802 -0.4577261053
## [216,] 0.1566889955 -0.031796914 1.1852001979 0.7570595055 -0.1156434274
## [217,] 1.5137794709 0.668317915 0.4405057723 0.0671842494 1.6958394542
## [218,] -1.8986435362 -1.566365463 -0.3605944753 -1.0401271689 -0.1072671738
## [219,] -0.3068303531 0.190788450 -0.9755626556 -0.9249113152 -0.8424790640
## [220,] 0.4757140281 0.196768498 0.5186343213 0.0814730092 -0.3043636045
## [221,] 0.8224846735 0.440553500 0.6451167737 0.0741441281 0.0280017749
## [222,] 1.1079578978 -0.026148724 0.5171211362 1.2522123349 1.2552147835
## [223,] 0.6673766571 1.813148963 1.9392816724 0.8479281185 1.1822678386
## [224,] 0.8648103725 -0.712915791 0.3275352989 0.0348667160 1.9665380384
## [225,] -0.2143485940 1.227859277 0.1973848976 0.2079729377 -0.2392655499
## [226,] 0.9189764004 1.069721032 0.6975113928 0.0265039133 0.4375512691
## [227,] 0.0552508595 0.359097626 0.8806175605 0.0237003365 0.5130358021
## [228,] 1.4947370534 0.197749228 -1.2322337962 -1.0710551168 0.0333417937
## [229,] -0.5010495742 -0.293390476 -1.3306502721 -0.5168914990 0.3199220262
## [230,] -0.6508758501 -2.081697024 -1.1670542312 -0.9993438852 0.4240316708
## [231,] 1.0926983906 -0.003610886 0.8301597384 -0.0708364972 0.9652980021
## [232,] 0.9200350208 0.610778461 -0.8442507404 -0.4450844299 -0.6873095290
## [233,] 0.5341849226 0.106271972 0.1801851453 -0.0998419748 1.1803194335
## [234,] 0.9769716290 2.525861423 0.1904229325 0.3455279822 0.6192232108
## [235,] 0.2070942299 -1.482028401 -0.1131854698 0.1594597688 0.2352963245
## [236,] 1.3778501377 0.487263754 -0.8009396850 -0.4651585524 1.8377051716
## [237,] 1.0958701811 1.121407342 0.6174596482 0.6181019799 0.1752261645
## [238,] 0.4267518636 -0.761342008 -0.6585100276 -1.0582997040 -0.2148530923
## [239,] -1.4915722871 -0.813296424 -0.0295216916 -1.6708664548 -1.1840741850
## [240,] 0.2350744704 0.858027146 -0.6975003806 -0.8546340160 0.6448922335
## [241,] 0.5623422149 0.313485452 -1.0111440253 -0.3503371602 -0.5588176170
## [242,] 0.5403644847 1.300782952 0.4180444937 0.7212054689 0.5566951201
## [243,] 0.8929402047 1.010688294 0.9298789276 1.0527569111 1.5602207097
## [244,] -1.0314306186 -0.531799091 0.0793236462 0.0555743061 -0.3164050054
## [245,] -1.1955698304 -1.353135631 -1.1740464574 0.1473979877 0.3613806141
## [246,] -0.8466383962 0.118119782 0.0262802466 -0.7462826918 -0.4734049492
## [247,] 0.9733999404 -0.862614015 0.4706054104 0.2757312389 0.7538588762
## [248,] 0.7552238868 0.914287381 1.3210874727 1.3637377985 1.0514104434
## [249,] -1.0335685541 -0.286153927 0.7882804499 -0.8214799265 -0.7654013564
## [250,] 0.0936842713 -0.323405159 -0.6876931958 0.0430567436 -0.9659397742
## [251,] -1.9702386997 -1.375343084 -0.0673695175 -0.5540899350 -0.2765643569
## [252,] -1.7008626182 -0.954571792 0.4349571938 1.1203377245 -0.4688007249
## [253,] 0.8538832294 1.108753523 -0.1668647356 -0.1570043251 0.8905229260
## [254,] -0.2269055896 -1.552887958 -0.6891093679 -0.8230166391 0.1351565021
## [255,] 1.1377678133 0.586530327 1.1701983172 1.2216825967 0.6221653309
## [256,] 2.1182882599 1.943393775 0.0114110412 -0.4257748155 1.9864675870
## [257,] 0.7137287454 0.852593943 -0.0389590300 -0.1289061262 0.4293075307
## [258,] 0.2876878268 1.449472236 0.2953522126 -0.6274738368 -2.1743832383
## [259,] 0.5766579879 0.238056188 1.2878904295 0.0237711013 -1.2458066036
## [260,] -1.9974877907 -0.777392455 0.1944466441 -0.8941268742 -1.3208083096
## [261,] 0.0514936814 0.171715281 -0.0791333043 -0.9711881639 0.2707730299
## [262,] -0.6776780928 -0.728303082 0.5220497657 0.6454208101 1.9140368083
## [263,] -0.6598170256 0.189496081 1.3768779364 2.6838179460 1.0126974736
## [264,] -0.9816537308 0.193038483 -0.1101521206 -0.3045639411 0.4348518983
## [265,] -1.4886702901 -1.063375335 -1.4937450967 -0.4770437459 -0.5654804215
## [266,] -0.7089349212 -0.266298613 1.1910414808 0.3720957234 1.2993908097
## [267,] 1.6768145519 0.712888374 -0.7049813379 0.5449943879 0.5044024507
## [268,] -0.2328168938 -0.211721200 0.7333300439 -0.7081541392 0.3645478677
## [269,] -2.3125161141 -2.009024359 0.0947586572 0.7476682100 0.0027376869
## [270,] 0.8162364791 0.125249431 -0.0723883492 -0.4945900410 -1.3844026671
## [271,] -0.3748847397 0.216890838 -0.1308785082 -1.9822781360 0.0893709927
## [272,] -1.8579758483 -1.197449645 -1.5578595120 -1.7763337340 1.1829615929
## [273,] -0.3483462883 -0.184083315 -0.7109432451 -1.1554584251 0.1373809222
## [274,] -0.3871661878 -0.814763548 0.2682029221 -1.3938330300 0.3518618023
## [275,] -0.6477176894 -0.911369699 0.5640784154 -1.0094751358 -0.6579850763
## [276,] -0.3200908850 -2.460845001 0.5014775817 1.0310623875 -0.2073202060
## [277,] 1.6180133868 2.158799655 -0.3797529563 -1.2338173819 0.4590329718
## [278,] 1.3348860361 1.851119886 -0.9262226293 -1.6538076406 0.1828578634
## [279,] -0.6684183543 -0.074818268 0.7726337305 0.2110188852 -0.3489620128
## [280,] 0.1894799878 0.022813050 -1.1112806716 -0.1456962618 -0.9749330871
## [281,] 2.7810758387 2.513776018 0.3841573504 0.2246469535 1.6557985332
## [282,] -0.7613024025 0.182642211 0.1975899432 0.0912060488 -0.0484469320
## [283,] 0.4582265738 1.056039090 1.4100671992 0.3585627089 0.7997097914
## [284,] -0.5074272382 -0.423419887 -1.5940619134 -0.5467897100 -1.5075822053
## [285,] -0.0526533935 0.002135349 0.9072586715 1.1937875135 0.4506370916
## [286,] 0.7804014565 2.174995847 0.8080068573 0.9007797263 0.9100340145
## [287,] -0.1435484808 -0.099115837 -0.1508301385 -0.6634073742 -0.1254196388
## [288,] -0.4487929700 -0.114736879 -0.1085202809 0.2670607050 -0.3058732698
## [289,] -0.3598666514 0.267603286 -0.0456985027 -1.0876574029 0.1319363743
## [290,] -0.9130477253 -0.303182651 -0.5696624619 -0.2319962072 -1.1035970300
## [291,] -0.6761937432 -1.016761426 1.2553025364 0.6291434283 -0.1834157724
## [292,] -0.5118807580 0.655312674 1.4114055514 1.3611697878 0.2449141799
## [293,] 0.6582461015 0.653648487 0.4672161726 0.6931774577 0.1459570406
## [294,] -0.7310238962 -0.773778171 0.0848834493 -0.6390661545 -1.4019816107
## [295,] -0.0762701678 -0.463600125 1.0941732289 0.9733122865 0.7054826859
## [296,] -0.7070312654 -1.402569655 0.2870060642 -0.8439695511 -1.1202095441
## [297,] -0.5297855725 -0.518593106 -0.8924569465 0.5258072899 -0.2459334872
## [298,] -0.1432024156 -0.159059237 -0.3656376977 0.9358216201 1.6368273783
## [299,] 0.7504129055 0.613490883 0.9838559406 2.2789645910 1.0304967245
## [300,] -0.4960701169 0.199091883 -0.2045937249 -1.1977735299 -0.1666360480
## [301,] 0.0318481141 -0.341432055 0.8455170337 -0.5277720129 0.8415236399
## [302,] 1.0706626955 0.308030911 -0.8783803374 -0.0973673618 0.7910077556
## [303,] -0.6389971647 -0.558632017 0.2266449244 0.0094452874 0.6408330760
## [304,] 0.6433404772 -0.082962535 -0.8674066913 -0.4443759368 -0.8521731449
## [305,] -1.3479250012 -1.048137119 -0.8881631634 -0.9614168617 -0.4398087420
## [306,] -0.2489389981 0.736643066 -0.3770752259 -1.1901428944 0.1630537611
## [307,] 0.0338665360 -1.771387088 0.4782153544 -1.0664955489 0.3132521643
## [308,] -1.0818521966 -0.651423770 -2.0881402432 -0.9105046641 -0.4695727500
## [309,] -0.0194974939 0.165057373 -0.8957871357 -0.8052165505 -0.6009190253
## [310,] -1.8554030314 0.226243051 -2.3180136575 -0.8800249532 -0.9728559200
## [311,] 0.2587150517 0.504782433 -1.2759828230 -1.3953643532 -0.7839176256
## [312,] -1.8096294341 -1.816164194 -1.1242687144 -0.5457183204 -0.2306890861
## [313,] -1.7772089405 -0.231732585 0.2832674521 0.7809452947 -0.8085407407
## [314,] -0.0007996836 -0.838170420 -2.3312747034 -0.6247639443 -2.0307077134
## [315,] -0.5516700633 -0.256400296 -0.0332410513 0.1605117050 -0.4106711875
## [316,] 0.2926891453 0.809578230 0.4364133389 -0.0757210846 -0.5920931569
## [317,] -1.0428109049 -0.673494537 -1.0923669747 -1.3754796666 -0.4248968646
## [318,] 0.2455503324 1.889013455 0.2918577675 -0.0934843967 0.0310189176
## [319,] 0.6899151524 1.510944395 0.4862917631 1.7025564125 1.0614684132
## [320,] 0.9963626088 -0.453656151 -1.6072312671 -1.0042211491 -1.2716125820
## [321,] 1.8296768676 1.389170130 0.0587786529 -0.1913939467 0.5635933803
## [322,] -0.8503645821 -0.276626763 0.1420967669 0.7695767114 -1.0494706971
## [323,] -1.2697781658 -0.645614427 0.2141978101 1.0896934367 -0.6947197879
## [324,] 0.5512918827 -0.165785997 0.6459489875 -0.0649098892 1.2949933131
## [325,] -0.8238552942 0.152999886 -0.6134775995 -0.7379579341 0.5959083803
## [326,] 1.0202739904 1.445008200 -0.8432443214 -1.6673492337 -0.4090967259
## [327,] -0.6857964010 -0.803542795 -1.3958035609 -0.5064498665 0.1692357110
## [328,] 0.9920571486 0.842518575 0.3928292794 0.2462142478 1.3881527073
## [329,] 0.7416621672 0.753662976 0.7211411119 0.4106237518 0.3981876976
## [330,] 0.3094717379 -0.568723410 1.4268104807 1.9173501193 0.4497584900
## [331,] 0.1165239122 0.436296002 0.9602251649 1.0288828441 0.3847332150
## [332,] 1.6830720750 1.670320459 0.0739423753 0.2739238845 2.1844538129
## [333,] -1.9525466146 -0.185636010 1.1427535815 1.3557434380 -0.1467855319
## [334,] 0.5014663586 -0.613863333 0.8940591280 0.5273749993 0.3842308089
## [335,] -0.9263205144 -0.106913525 -0.6530699034 0.4448572890 -0.2470104641
## [336,] -0.8026807305 0.106722157 0.6733041816 0.0461512863 -0.2540409406
## [337,] 1.5132345983 1.209241060 -0.5021061290 0.0787649662 0.4132679176
## [338,] -1.6987035303 -0.383448239 -1.3170985104 -1.7819857870 -0.1592379120
## [339,] 0.2919144890 1.013167061 -0.0940561014 0.7063779021 -1.2238263652
## [340,] 1.8269398785 0.563041458 0.5774422857 0.8006170306 1.2236158314
## [341,] 0.7921546818 -0.196446120 -0.2056753371 -2.3078298097 -0.2291999824
## [342,] -0.6443570102 -1.699629528 -0.3852009578 0.5459442685 0.3316402930
## [343,] -0.6633566690 -0.276947444 -0.4797223600 -0.6908847939 1.0691368661
## [344,] 1.6959220846 0.821439488 -1.2186576517 -2.1365816313 3.4048736505
## [345,] 1.4587101014 2.691210397 1.1778835881 0.0549134834 0.4813963145
## [346,] -0.6379554194 -0.471629688 -0.6906727656 -0.8597339443 -0.0068038195
## [347,] -0.2564229364 -0.804834501 -1.3567672645 0.0917302164 1.2151406044
## [348,] -0.4360361118 0.242310988 0.2097666150 0.1651609737 -0.4147503965
## [349,] -0.5543054202 -0.602893392 1.3713178497 0.5870665822 0.9184668814
## [350,] 0.4333957849 1.748783525 -0.9043703227 -1.6863857550 -0.1503485265
## [351,] 0.4960382376 0.264668231 2.6014076590 1.1270087055 0.0505691277
## [352,] 0.1614171307 0.039480552 -0.4245903298 -1.0936191047 -0.7613675972
## [353,] 0.3220710922 1.609568433 1.3070703583 0.8270510254 0.2101304365
## [354,] 0.8966548035 -0.268246900 -0.4018633351 0.5623826573 0.4870990915
## [355,] -2.1275967629 -2.450968503 -0.7680286439 -1.4525652049 -1.7241902543
## [356,] -0.3281176448 -0.568510277 -0.1473589160 0.4922925361 -1.3391466980
## [357,] 0.6806412290 -0.456011743 -0.2355633426 0.3480661135 0.3277990795
## [358,] 0.1010524399 0.887437477 1.1744400529 0.3587238602 1.3941033231
## [359,] 0.7135201097 -0.137894300 0.1012632863 0.0287841355 1.4750666836
## [360,] 0.3202077828 -0.123979968 0.2069128280 -0.3103704909 0.3075074903
## [361,] 1.4916780266 0.435064203 -0.3319338654 -0.7438628087 1.4474887405
## [362,] -0.4413939430 -1.139572894 -0.5036783181 -0.9282776258 -1.0542053142
## [363,] 0.1647829728 0.008056660 1.3011438563 1.3714869762 0.8124978987
## [364,] -2.4617740653 -1.962777516 0.6555310700 -0.8850514755 -1.1269736740
## [365,] -0.6345709497 0.106452327 1.1561545662 -1.0657557365 1.2374477002
## [366,] 0.7125576536 1.123681418 1.3274409075 -0.9179625125 -0.2395217572
## [367,] 0.8077299357 1.489078639 0.7856432506 2.2383561693 1.6261049245
## [368,] -0.3276924577 -1.467321314 1.6470322300 1.0409663328 1.5253180907
## [369,] -0.8452029076 -0.777602541 1.2596868886 -0.2341009937 -0.7200506613
## [370,] 0.3311818418 -1.268380978 0.0672289761 -0.6912156971 1.0620272980
## [371,] 2.1592001592 0.606544086 1.6612843003 0.9715072972 1.7888262660
## [372,] -0.6222242458 -0.755741131 1.0196087506 -0.6973042614 -1.2837053475
## [373,] -0.5894780766 -0.013634763 -0.8409791219 -0.9943755162 -1.8326650168
## [374,] -0.1214853098 0.219116513 0.4505649174 -0.0966453896 0.3731817774
## [375,] -0.3254240614 0.954518410 0.3054874660 0.0209831496 -0.3671624046
## [376,] 0.6431933936 -0.568620858 1.2179424190 0.5564554411 0.3297913386
## [377,] -0.8028935809 -0.961467543 -0.3133052293 1.0575631830 0.5595910799
## [378,] -0.1938388601 1.010077831 1.8663758402 2.9639228104 1.9846085642
## [379,] -1.0569936888 -0.410458703 -1.6335797406 -1.3281578713 -1.9429458322
## [380,] -0.5243905425 0.580180835 0.1900485145 0.2777439816 -1.0649185410
## [381,] -0.2649161555 -0.853554046 -0.7081126028 -0.0168128135 0.6716887052
## [382,] -1.0002203970 -0.530750479 -0.3533133104 -0.3649966802 -0.0116902841
## [383,] -1.1961973275 -0.785114482 0.5175684542 0.9586629087 -1.3886697831
## [384,] -0.8228243653 -0.113968727 -1.1342250607 0.4111683846 -0.4091461435
## [385,] -1.0718944355 -0.695934242 0.9589399098 0.8049980972 -0.8929791291
## [386,] -0.5867223494 0.787931253 -0.8705107675 -1.2756963682 0.0005190947
## [387,] 0.5015447349 0.163099171 1.4096709160 0.3370566655 0.3351650112
## [388,] -0.5611032765 -0.472987649 -1.2826683798 -1.7108988462 -0.2402384904
## [389,] 1.9260668986 2.190640847 0.5676148617 -0.6941065049 -0.3945117593
## [390,] 0.3684660600 -1.223571891 -0.0115243810 -0.1510892950 -0.3220292086
## [391,] -0.5020489961 -0.290428332 -1.5108216676 -1.8679550618 -1.8173506544
## [392,] 0.3917691057 -0.217558126 -0.1129866128 -1.0073538610 0.8049681695
## [393,] 2.3028305589 3.063944499 1.6710629945 3.1001164425 1.7055226162
## [394,] 0.7666932770 1.275814171 0.3007984664 -0.2520630434 -1.4119704595
## [395,] 0.5554465046 0.839088004 0.5780920767 0.5326996562 -0.0177778556
## [396,] 1.2081923714 1.682149498 1.0390388138 0.3376183699 0.9098860194
## [397,] -0.0861200581 0.258052211 -0.1834403329 0.8887995479 1.9004447580
## [398,] -2.1067460630 0.507550974 -0.1761667087 -1.2873757582 0.0348726597
## [399,] 0.6060760633 -0.400666879 0.2073760173 -0.3355533192 1.7110792795
## [400,] 1.3663760613 0.112921362 1.4628851934 1.7729621887 0.5959643264
## [401,] 0.0178217701 -0.191331502 -0.9341185046 -1.2255201079 -0.3799041045
## [402,] -1.7140590487 -0.430119681 -0.1596001865 -1.3116256998 -1.3970806090
## [403,] -0.7313793328 -0.128525768 1.6388455884 1.3085044108 0.1201129538
## [404,] 1.0290330321 0.104858055 1.6287783513 1.2143956274 1.7030064916
## [405,] 1.5588405052 0.915691888 -0.2415452009 -0.4940050817 -0.0789397218
## [406,] 1.3604932043 -0.131420048 0.8171256073 0.1183357353 -0.1739822729
## [407,] 0.5213803867 0.029156359 2.6104140031 1.5274170425 0.5273849851
## [408,] 0.1315293609 0.204652556 0.3562499339 -0.0431632916 0.2127892863
## [409,] -0.1738075306 0.115958791 2.2230009746 1.3770575735 0.1148675384
## [410,] -1.0024550807 -1.192759025 -0.1400736788 -0.6464068634 -1.1786500299
## [411,] 0.5883357782 0.270071158 0.2598459919 0.9064721208 0.8636769175
## [412,] 0.0172312751 -0.475603148 -0.6775844015 -0.4695516620 -0.3787249594
## [413,] -0.1060223835 0.089198782 1.0849999890 1.3971894533 2.3472436388
## [414,] 0.3761128375 0.077227186 -0.2870436614 -0.7357656411 -1.1833564218
## [415,] 0.3191556211 0.391112436 1.4201422872 0.6792431986 -1.1754875490
## [416,] -0.9613865364 0.016546179 0.2532512769 0.8668048771 0.5476022186
## [417,] 0.9124421535 0.017629052 -0.0699631472 0.7119320897 0.5219342153
## [418,] 0.5222229425 -1.023395330 0.2873866114 -1.2803086716 1.7780657574
## [419,] -2.9453470112 -2.675438492 0.3723820366 1.3150274902 0.0250297526
## [420,] 0.4655495447 0.425077064 0.1883913886 -0.9136151889 -0.0599940313
## [421,] -0.2668360317 -0.319009688 -2.0860213577 -1.7097144755 -2.9932592953
## [422,] 0.2735678008 -0.674858123 -0.2275284429 0.4191370722 -0.6846908144
## [423,] -0.2169590675 0.123167547 -0.3894182725 0.0289271300 0.2446606862
## [424,] 1.4062938986 -0.002464128 1.0990259126 -0.8520404202 2.1241482007
## [425,] 0.1478946024 0.602580485 0.7662582711 -0.3331474052 0.4616993706
## [426,] -0.1556534989 0.173650486 0.1151067872 -0.0072373607 -0.7020941428
## [427,] 0.4084330424 -0.560864786 -1.1901191011 0.0418946854 0.0231636877
## [428,] 0.4284946238 0.786579728 0.5768042190 0.6915884656 0.4350194674
## [429,] -1.7317153939 -2.145135816 0.0345313857 0.2055128858 -1.0665516638
## [430,] 0.2392306291 1.409907521 0.7283700145 0.9261441006 -0.6738934138
## [431,] -1.0655690781 -0.295526973 -2.5330208887 -1.4823397978 -1.4021475315
## [432,] -0.2915261171 -0.387340409 -0.2810981977 0.2115354604 0.9499561894
## [433,] -0.3110292018 -1.776661140 0.7507840009 0.7931476145 -0.7797639780
## [434,] 2.5664749288 0.907061441 0.5614284415 0.6955933456 1.1892883741
## [435,] -0.3073557828 -0.671901896 -0.2976356538 -0.5264865129 0.4285237600
## [436,] 0.2147532846 -0.492251046 -0.0530759122 -0.7980466377 0.5874827001
## [437,] 0.3256799903 1.357994730 0.1946897889 0.5401736895 2.7855715596
## [438,] -1.1885867777 -1.635371806 -0.2668880733 0.2625742170 -1.6144930240
## [439,] -0.9091895353 -0.817478174 -0.2415965492 0.1813741903 -0.3631252237
## [440,] -1.3065256855 -1.239033211 -1.2936877578 0.1554498936 0.8252224126
## [441,] 0.3804719342 -0.241997822 -0.0806463651 -1.3630309422 -0.3861999699
## [442,] 0.8125090672 0.631223581 0.5524837158 1.5289282156 0.7530614240
## [443,] -0.5709100449 -0.970293786 -0.2719802777 -0.6428790557 -1.7035686956
## [444,] 1.0940395406 -1.704714728 -0.1608302193 1.5043458844 -1.2686597304
## [445,] -0.1287204150 0.181838529 -0.2114425991 0.3710764389 -1.6114514168
## [446,] -2.4744270100 0.816920077 -1.4552480464 -0.8080484419 -2.5724202754
## [447,] -0.9034418360 -1.489695764 -0.5361827182 -1.0572782930 -0.1593130239
## [448,] 0.8676043419 0.470993873 0.5387699309 0.1859163872 0.6179602701
## [449,] 0.4646599288 -0.231722199 -1.0960542435 0.6621505004 1.1786373324
## [450,] -0.4158718403 -0.346188746 -0.7664280727 -1.2934562413 0.5158717032
## [451,] -1.9298241502 -1.782473736 0.9913030073 -0.0015186359 1.3203677108
## [452,] 0.6075551080 0.216914810 -0.0605394164 -0.1096286212 1.1874809452
## [453,] -0.9753076582 -1.658503677 -0.3844758259 0.5549444349 -0.5983037591
## [454,] 0.2552739191 -0.541274739 -2.0526614809 -1.2346327776 0.7051812326
## [455,] 1.2287254628 0.756022163 0.2132910264 -0.4051734091 2.4728986513
## [456,] 1.0492756796 1.656888040 -0.3224371820 -0.9192378148 0.0008752627
## [457,] -0.3030465590 0.347285893 -0.6130497680 -0.8779645461 -0.0384313528
## [458,] 1.6836834036 0.456675336 1.1094714261 1.0862787619 0.5986292634
## [459,] -0.1181508992 -0.038697509 -0.6647806660 -2.0627967115 -0.1301589829
## [460,] -0.1346598354 0.119554510 -0.9860124397 -0.1164221244 0.5108389820
## [461,] -0.2520350095 -0.760367907 -2.3309275041 -2.2049421461 -2.2609359632
## [462,] 1.6282919287 0.789964684 -0.4676508565 -0.2771851661 1.2979740512
## [463,] -0.7427128587 0.079019079 -0.3186622827 -1.8747192491 -0.3588881380
## [464,] 0.8339215103 0.576422000 -0.3707172476 -0.1514659861 -0.2263847819
## [465,] 0.9481735641 0.207749602 0.6660005303 1.2841513120 1.0301761106
## [466,] 1.5960755043 1.413548106 0.7760420835 1.2424220728 1.5072249492
## [467,] -1.9171132358 0.721679989 -0.1614496529 -0.1709663064 -0.9420384068
## [468,] -0.0292577515 0.625970710 0.8234403997 0.7570386707 -1.9536872949
## [469,] 0.6035979943 -0.170136334 -1.4890860969 0.2602329561 0.3956556834
## [470,] -0.4954873371 -0.464232360 -0.4717344033 0.3217575464 0.1343378930
## [471,] 0.0486179981 0.661403768 -0.4971688680 0.6601190481 0.9584461817
## [472,] 1.9344820625 1.847073234 0.6990484738 1.4508988477 1.6476595678
## [473,] 0.6022435265 1.597445877 -1.2876841448 -0.8162794414 1.5746863467
## [474,] -0.2192888193 0.088026291 0.7590325749 0.2029983599 1.4935482216
## [475,] -1.2308689559 -2.004043394 0.1528144576 -1.4992219490 0.6536249254
## [476,] 0.6313758716 0.569343769 0.5455253964 1.0148327045 0.3177642376
## [477,] -0.4729701773 -0.900123269 0.5917911302 0.0435042271 0.1366206005
## [478,] 0.1932952911 -1.167696803 0.3532497108 0.4598130648 0.5735143552
## [479,] -1.5023807853 -0.391815355 -1.3583943810 -1.1710526762 0.8572446596
## [480,] -1.5413063149 -0.735867256 -0.0987879503 -0.8251634262 -1.0078058997
## [481,] -0.1396691694 -1.621625661 -0.5002180233 -0.0126241206 0.7093797115
## [482,] 1.6624002304 1.600935945 1.1252331100 -0.3507191956 2.4441910769
## [483,] -0.5495309225 -0.573312211 -0.0700794629 -0.9172040173 -0.8886192750
## [484,] -0.4946621204 0.169508777 0.1766641237 -0.2513885222 -0.8901710101
## [485,] -0.4025385950 -0.844388421 0.6412720879 0.2679680489 1.3015020267
## [486,] 0.9137529541 1.429460021 -1.2460689949 -0.9054969727 -0.2480208781
## [487,] -0.3846354251 -0.305901884 -0.8751854469 -0.6360431497 0.3714712932
## [488,] 3.2897907374 1.711329011 0.9620210638 1.1296394481 0.8707766803
## [489,] -0.9500560866 -0.390996379 0.3662647590 0.1785863258 0.1491100962
## [490,] 0.0777889452 0.832371755 0.3172372405 0.4761915239 0.1809343803
## [491,] 0.3363553872 0.112284199 -0.5777979448 -1.5188610794 -0.9463589699
## [492,] -0.4178259696 -1.023311449 0.4036875809 0.9972525130 1.8835822171
## [493,] -0.6909743733 -1.443913317 0.9883882798 0.1764573195 -1.0991951158
## [494,] -0.3426508057 -1.319435911 -0.5884659784 0.1895906749 -0.6176719056
## [495,] -0.6065275416 0.366967696 -0.0964877829 -1.4294582604 0.6380220065
## [496,] 0.4150238469 0.213364321 0.2957543861 -0.3816173939 -0.3982877697
## [497,] 1.1273281757 0.765595296 -0.4444192598 -0.0368708301 -0.4182748564
## [498,] 0.0987502920 -0.818207360 0.9081610567 1.0096962305 0.7070706785
## [499,] 0.9892353194 0.824004748 -0.9685085653 0.4537889301 1.1878900345
## [500,] -0.3798728496 -1.529548479 -1.2451056064 -0.0410414093 1.3500028947
## [501,] 1.0003813114 -0.605958930 0.2735810224 1.4361611455 1.3997227718
## [502,] 1.0600420442 0.354550994 0.5165491112 -0.1795788680 1.6043773726
## [503,] -0.8803768756 0.698473222 -1.6285115884 -1.1019559401 -0.3022628370
## [504,] 1.6416463291 0.465426958 -1.2381518171 -1.3705626289 0.8573709247
## [505,] -0.4192950086 -0.658638455 -0.8571811161 -0.3170934454 -0.1481342572
## [506,] 0.4305396068 -0.032289281 -0.6058680875 -1.3620906090 1.3759251225
## [507,] 0.7938378902 -0.144534163 0.7747879012 0.4127623523 -0.0150778639
## [508,] -0.2442572842 -0.484035724 0.0051171179 -0.4801527839 0.0937033946
## [509,] -0.9421876824 0.216041025 -0.4346557757 0.5849338859 0.3180926641
## [510,] -0.2422131305 0.067424982 -0.8950678557 0.1227661075 -0.0679560777
## [511,] 0.8905786699 -0.316935102 -0.0079589815 1.6069732106 2.1259688284
## [512,] -0.2496664575 -0.126857838 0.9885339017 -1.6509863657 -0.0032618930
## [513,] 1.2192262516 1.664407526 -0.5874161452 -0.6485672310 0.7983418215
## [514,] -0.6585981802 -0.596878006 -0.9923930869 -1.1807425409 -0.6497582584
## [515,] -0.9150598754 -0.583658776 -0.2102135862 0.0009204219 -0.4962651953
## [516,] -0.5718543903 -0.358677027 -0.1232533244 -0.4999485483 -0.1034719627
## [517,] -0.1129820006 -1.107257090 -0.0566655953 0.2214150216 0.7461984152
## [518,] -0.3303552239 -0.445141874 -1.1314146364 -0.5796908682 -0.0945088450
## [519,] -0.3069796927 0.093853896 -0.5901156696 -1.4225096713 -0.3520966902
## [520,] -0.0017788730 -1.727706177 -0.6069616755 0.4055612113 -0.1941316085
## [521,] -0.5792915450 -0.500879420 -1.0452375949 0.3185252580 -0.4900986995
## [522,] -0.0738377756 0.748858473 0.2499067249 -0.3552762614 0.6423197169
## [523,] 1.1218744732 -0.216898186 0.6953395600 0.9835311653 1.5397669994
## [524,] 0.5026885062 1.540422040 0.7182471273 0.4620829431 -0.5061703102
## [525,] 1.8914382543 1.212231551 2.0526368051 1.6515923697 0.9392894617
## [526,] -1.6922181776 -0.124442555 -1.3670017242 0.1741195667 -2.2176432387
## [527,] -2.0883657756 -2.080786286 -0.8018235918 -1.0997331091 -1.5829700592
## [528,] 0.3362834367 0.713161474 1.0241126234 0.5429974022 1.2011042333
## [529,] 0.8786867733 0.307377233 1.6652902406 1.7986870619 -0.1005900692
## [530,] 2.8063961289 0.949678071 1.9037708231 0.9897836776 2.0005839316
## [531,] -0.2437468010 -1.054212899 0.1009632664 0.9151865716 -1.2171224327
## [532,] 1.3274955920 1.367499402 0.3849585377 2.2207738159 0.6790596016
## [533,] 0.4521155140 0.609036616 0.4818995238 0.7842314890 0.6251398828
## [534,] -0.9947001030 -1.361553165 0.0276234897 -0.4194818087 0.9195617556
## [535,] -1.2937991647 -1.133270861 1.2222572210 0.0227740072 0.4858314543
## [536,] -0.6805393838 -0.868542494 1.5266829661 0.9759114417 1.1437399979
## [537,] -0.1789881947 -0.006978164 -1.3337772188 -0.3283904796 -0.3228137256
## [538,] -0.9698256362 -0.643142768 0.6898067388 0.8740621124 0.0036752131
## [539,] -0.1768374328 0.912032701 0.1464837168 -0.1113088384 0.1174099470
## [540,] 0.0937241081 0.299181947 -0.9934848306 -1.4190123180 0.0377142436
## [541,] 0.3364305345 -0.481773932 -0.3980010483 -1.5168921240 0.5283308090
## [542,] -0.0792031918 -1.205907389 -0.3485978122 -0.5319932956 -0.6537772946
## [543,] 0.8910822757 -0.196351223 -1.0520075504 0.9830142758 0.8090241984
## [544,] -0.1194283992 -0.014895631 -0.0247929115 0.2505798966 0.4159087820
## [545,] 0.1244297389 -1.117785175 0.5539906224 -0.6309963178 -0.0760815100
## [546,] -0.5788957925 -0.041485346 -0.5278664550 0.7298997505 -0.6397929056
## [547,] 0.8413393710 1.060253340 -1.5812800837 -1.0956997726 -0.3953217947
## [548,] 1.8389787469 2.844396096 -0.6518868821 0.1774543252 -0.7157575811
## [549,] -0.3001527864 -0.384414079 -0.8339293727 -1.1511048440 -0.8755588417
## [550,] -1.8995783894 -0.591316350 -0.2601474009 0.5873626279 -2.2917936734
## [551,] -1.3889260000 -0.746175221 -1.6081192345 -0.5301711618 1.2172262182
## [552,] -1.5888757177 -1.117864135 -0.5269503766 -0.7056021290 -1.2010067828
## [553,] -0.1970786870 -0.326319336 -0.2972126106 -1.1164332097 -1.1739668011
## [554,] -0.7982192613 -1.760207381 0.4200033474 -0.2902813771 -0.9363200996
## [555,] -1.1792284191 -0.790253209 1.3252103124 0.6505753358 -0.0763506264
## [556,] -0.8564511073 -1.556907809 -0.7579406791 -0.7642725423 0.4341640721
## [557,] -1.2275695631 -0.943653025 -0.3762616503 0.1655764119 -0.8399858288
## [558,] 0.6630968366 1.658664033 0.1916022124 -1.1102720439 0.6405059604
## [559,] 0.5885540084 0.039209314 -0.5172589233 -0.2988861519 -0.1418447514
## [560,] -1.8220465840 0.052226495 -1.4376146213 0.1147575926 -0.5511796919
## [561,] 0.1553284181 0.441569425 -0.9316352888 -0.1569940876 -0.1407815025
## [562,] 0.6486198199 1.844713229 0.1136864624 0.2039044723 -1.3356566428
## [563,] -0.3344598689 -0.821168152 -0.3525348809 -1.5324677310 0.3830028547
## [564,] 0.4306378141 -0.207025512 0.0731473394 -0.0812393566 -0.0874345969
## [565,] -0.2044552502 0.307070375 0.6787724604 0.6547633322 -0.0707593176
## [566,] 0.4162739149 0.879504417 -0.1346265738 1.3029655714 0.4829553934
## [567,] -0.9049990919 -0.986357414 -0.2480509012 -0.4390008504 -0.4169804772
## [568,] -0.7776156629 0.851927245 0.6262449016 -0.1020136586 -0.6618779367
## [569,] -0.8305661467 -2.212448162 -0.7572733439 -0.7397690767 -1.4995727628
## [570,] -2.2078385932 -1.718942375 -0.4117433067 0.7251746251 -1.9313631056
## [571,] -0.1370994970 -0.549134809 1.2957346428 -0.3948809086 -0.4542317900
## [572,] -0.4111301422 -0.043743107 -0.1229430293 -0.4134553697 -0.0167361262
## [573,] 1.4403852546 0.760794892 1.9587766812 0.8748678178 0.9215982875
## [574,] -2.1626223110 -0.021491806 0.8833265831 -0.5514248703 -2.0614323024
## [575,] -0.1665893693 -0.785598363 -0.3763049125 -0.7234567306 0.3209473832
## [576,] -0.2729677407 -0.122185001 0.5190135485 0.3989747633 -0.1071540768
## [577,] 1.5521420936 1.346073834 1.2765525878 0.9209958525 0.7708841273
## [578,] -0.8745735448 -0.651714488 -1.5745842621 -1.6419147416 -2.1558808779
## [579,] -1.7975160659 -1.780485826 0.2476557634 -0.0275784471 -2.2648150734
## [580,] 0.5666473512 0.778829992 0.2153656136 -0.1604802980 0.6266579453
## [581,] -0.1222489325 0.066731697 1.3607354748 0.4257603147 0.3593879127
## [582,] -0.8084324141 -0.622435177 0.8595734242 -0.4203771595 -0.7175645747
## [583,] 1.2841028565 0.906629737 0.7040598723 1.8496839057 1.7156831393
## [584,] 1.7989935945 1.886305332 -2.0160304423 -1.0174853638 0.5294973302
## [585,] 1.2923226058 0.348049633 1.0222536959 0.2233784214 0.8179591783
## [586,] -0.7508582078 -1.091382399 1.2571074284 0.2114152356 1.2750760729
## [587,] 0.3186542216 0.752137482 -1.9927321871 -1.8600877147 -0.3332038306
## [588,] 1.0484746475 -0.377581815 -1.0466342728 -0.6215247092 2.1125947121
## [589,] 0.0191508745 1.568259612 1.0719780365 0.3819475533 -1.7730150858
## [590,] 0.1051877871 0.109890523 -0.6795901583 -1.4984834447 -0.0836240255
## [591,] 2.0743122090 0.877117194 1.0983112714 1.4640631398 1.5943823589
## [592,] 0.2360690355 -0.761485776 1.8119123081 1.2131655565 1.5626106455
## [593,] -0.0086541343 -0.512874893 1.7524884664 1.0203699313 2.3322178502
## [594,] -1.0044248866 -0.603196227 -0.0783247410 0.7805754362 0.3116568274
## [595,] -0.5447065756 0.380525094 0.5693271501 0.0362543427 0.0757679467
## [596,] 0.7276755177 1.353733865 1.0124175034 1.4492644378 -0.1721869045
## [597,] -0.6164791307 -1.041959482 0.4349513275 0.6689510080 0.3477345330
## [598,] 0.9201095013 -0.572522187 1.6137630250 1.6348472631 0.9964030525
## [599,] -0.0276556517 -0.377809029 -1.1198553906 -0.7605404054 -1.1484005911
## [600,] 1.2295695449 0.953318515 1.0911904827 2.3124722788 0.0760845434
## [601,] 0.0122049424 -0.006645964 0.3316490898 -0.1100596248 0.4414012186
## [602,] 1.2857664778 -1.038222865 1.3544420600 0.3164619141 1.8639622694
## [603,] 0.6733636021 -0.418211959 -0.9277953966 0.8423948256 -0.0668452656
## [604,] -0.1922769209 0.903599325 -1.1064054390 0.3576458447 -1.6039519167
## [605,] -2.0575927153 -2.230572191 -0.0098073147 0.6996912011 -1.6558370261
## [606,] -0.3347103762 -0.167518126 -1.2916611392 -0.3722750297 -0.6413523888
## [607,] 1.5541963535 0.533402248 1.0784931879 0.8844185898 0.9215404699
## [608,] -0.9625680456 -0.144199632 0.9942567878 1.3930443962 -0.7737720222
## [609,] 0.6072946118 -0.996356857 -1.6002365242 -2.3217145776 0.0060012238
## [610,] -0.2393221553 0.341496543 0.4249511342 0.1852544652 -0.1975669300
## [611,] -0.6780490154 -0.307033020 -0.3934444913 -1.4192472454 0.7900773569
## [612,] 0.4460424721 0.820918321 -1.1744684552 -2.0196129094 -1.0538202634
## [613,] 0.3454838842 0.303789945 -0.1399693578 -0.4747661393 -0.3105972440
## [614,] 1.5313233735 1.052051551 1.8731947355 1.7972134086 0.1133668657
## [615,] 0.3013317487 1.902366381 -0.2154543418 0.9518682936 -0.4819646243
## [616,] 0.2858353117 -0.020680919 0.2577396954 1.0561039351 0.4866576090
## [617,] -0.3663279987 0.044279651 0.1655498766 0.4535956074 -0.5673964390
## [618,] 1.1250501419 0.525083648 -0.2240773529 -0.1910392934 0.6284694775
## [619,] -0.5241679402 -0.090038583 0.3383787089 0.0864703101 -0.9546720499
## [620,] 0.2130625062 -0.080804742 2.4668022404 1.6943961300 -0.3447581062
## [621,] -0.4961247109 -0.553200796 1.1357218535 1.5953791110 1.3027330279
## [622,] 0.8056639732 1.045011136 -1.1973658582 -1.1828750399 0.6236896689
## [623,] -0.2169651136 0.021311605 -2.5875457937 -1.8905127856 -0.8629461405
## [624,] -1.9060274719 -0.913541247 1.6285975490 1.0353426440 0.3248204402
## [625,] -0.9531958022 -0.864251247 -0.4100115187 -1.9932191499 0.0990265743
## [626,] -2.0172022301 -2.341405167 0.1580502968 1.8369505166 0.1693011655
## [627,] 0.2334445331 0.940356122 -1.1479357985 0.4198714854 1.1374394962
## [628,] 0.0479248213 1.887133845 -0.0360242662 1.2356387036 -0.5955157758
## [629,] 0.0271150839 -0.456319252 0.4647845128 -0.2766024780 -0.5363005991
## [630,] 1.3776643882 0.674623554 -0.8556322843 -1.2785949583 0.2615639111
## [631,] -0.2100357077 -0.788089053 -0.3143083880 0.1063176935 -0.3827397410
## [632,] -0.7452652467 -0.663019739 1.8710710699 1.8084085459 -1.8659429986
## [633,] -1.3465435690 -0.049517972 -1.0340931653 -1.0211918059 0.2244647702
## [634,] -0.7474871624 -0.937682735 -0.6383259044 -1.9734056365 0.1697170970
## [635,] -0.4085135389 0.091045237 1.0205046333 0.8721574707 1.3092105859
## [636,] -0.0478229783 -0.356862153 0.2182700201 0.2995613278 -0.6221546020
## [637,] -1.2436564063 -0.951000779 0.1039044788 -0.1175005259 -0.4361420712
## [638,] -0.1130584105 -0.198049208 -0.7922140067 0.4189867945 0.5405041627
## [639,] -0.8624718724 -1.470767289 1.1284107777 1.0632890125 -1.0893266693
## [640,] 0.9897107458 -0.880013917 0.1652854069 0.6093015529 1.0249738761
## [641,] 1.6203222448 1.454433238 0.7838676607 0.8937425476 0.8118249700
## [642,] 1.1752687193 -0.589446277 -0.5810048975 -1.1170320547 -0.6149298554
## [643,] 0.1413021308 0.006851356 -0.4079853855 0.4480269035 0.3582205403
## [644,] -1.7451290127 -0.905825121 -0.3564959507 -0.4676403100 -1.4152862194
## [645,] -0.0536942300 1.027336953 -1.0918554426 -1.5453061447 0.3472023909
## [646,] -1.8509081517 -2.402837054 -1.6972415757 -1.3633807649 -1.2377861061
## [647,] 0.4125269078 0.199245359 -1.3671040230 -0.8629427029 0.8787294502
## [648,] 1.2175160146 0.104366537 -0.1401878438 0.1667355611 1.9261751263
## [649,] 0.7948899320 -0.371660622 -0.8957998875 0.4682339182 1.2500032069
## [650,] 1.7748516769 1.029232180 1.2859901784 1.3803231045 0.7825406386
## [651,] 0.6104986003 -0.672122710 -0.6267764432 -1.0149999938 -0.7383818441
## [652,] -0.9401853941 -0.423139269 -1.4642398148 -0.0504281590 -1.8843561663
## [653,] -0.3934639574 -1.788995269 -0.1720361857 0.8195367877 -0.8636570777
## [654,] 0.2380511561 -0.195454331 -0.4208058058 -1.1245899486 -0.1183621730
## [655,] 0.3912984222 1.074527912 -0.4758734777 0.3458793443 -0.4317527448
## [656,] -0.4823469227 -0.897499695 0.3260354047 -0.2958999904 0.0607888679
## [657,] 0.1861939158 -0.546983268 0.2807053620 0.7061949025 0.1652668407
## [658,] 0.7588092532 0.921521763 -1.1014834514 -1.0792884164 0.7946186417
## [659,] 0.5927939971 0.397529919 0.0367709919 0.8649040268 1.5557596518
## [660,] 0.4222216864 -0.001149722 -0.4064633943 -0.9266413673 1.1529779755
## [661,] 0.1019210762 0.231649948 0.2259815480 0.0693725556 1.2545324043
## [662,] -0.3341014430 -0.734828940 -0.2454717698 -0.4059890483 0.9106515571
## [663,] 0.1370165679 0.210790466 -1.0769754018 -0.7339548651 0.2277546501
## [664,] 0.0243878904 -0.239799793 0.5828449288 1.6867609636 1.1674471309
## [665,] 0.0862538922 -0.478219614 -1.3057407117 0.1099834388 -0.3336986713
## [666,] -1.9623421751 -2.093961671 0.1574272273 0.9717901802 -0.1320539908
## [667,] -0.0710589861 -1.030960258 0.1232832239 0.1052105235 0.3615665246
## [668,] -0.5000720990 0.117998640 -1.0827100929 -1.4320411876 -0.9978904994
## [669,] -0.5767321379 0.439531825 0.8587530331 1.2177339704 1.4906925155
## [670,] -0.0552953309 -0.428315084 -0.1508571550 0.0069565643 1.8884181286
## [671,] -0.3192105106 0.167379450 1.1455184692 0.7963993672 1.0709069541
## [672,] 2.7719218930 2.759997938 -0.4814977280 -0.7541429037 0.5010390227
## [673,] 2.5570887588 2.186747648 0.2559229759 -2.0392873146 1.2133654175
## [674,] -0.3719923284 -0.857597871 -1.1696934646 -1.5836865267 -0.9717177546
## [675,] 0.4681364942 0.852483051 -0.0560528043 -0.1096651296 -0.4309355282
## [676,] 0.5269636594 0.704730767 -1.7888545023 -1.1002393983 1.0103620696
## [677,] 1.0138037825 -0.225954400 0.8004039796 0.4790875984 0.0520456210
## [678,] 0.1603847235 0.929310070 1.6723314230 0.5115337960 0.4329661389
## [679,] -1.2798945661 -1.323822316 -1.9304787577 -0.2678720713 -1.3045201649
## [680,] -0.1577769684 0.336292191 -0.5194855353 -0.5027785838 -0.3717435763
## [681,] 0.5826042084 -0.612159196 -0.8745221142 0.1770912320 2.3067562108
## [682,] 1.1758178758 0.004537278 -0.6661287252 -0.9431110732 1.1575918165
## [683,] 1.0816611577 1.147851608 1.5183194479 1.1875090103 1.1618926921
## [684,] -0.0571307780 -0.361346471 -0.5287986358 -0.4949751335 -0.5451922126
## [685,] 0.9464608594 -0.359230193 -1.4906262739 -1.0156184421 -0.2865844458
## [686,] 0.2274456495 -1.272692490 -0.0622454473 -0.6584631424 1.9367415910
## [687,] 0.2379798803 0.453690905 0.0877209886 1.3169141081 0.7975333871
## [688,] -1.1572091873 -0.520672151 -0.1156115660 0.4268441198 -1.4525021747
## [689,] 0.2629702279 0.525788691 0.3816980116 -0.3069284788 -0.1338844588
## [690,] 1.4711464907 1.334934963 0.7593711411 0.8053843888 0.9353343512
## [691,] 0.2939936404 0.946363041 0.2096898598 0.2628590947 -0.0580995523
## [692,] -0.1792871825 0.484736638 1.1176992945 -0.5846487643 1.4250520885
## [693,] 0.9119373075 -1.013722107 -0.5496545660 -1.9074535391 0.8297275093
## [694,] -0.7233133810 0.291346293 1.0265876423 -0.0390571117 -0.7919482216
## [695,] -1.0926314869 -2.042043060 -0.6928773230 0.1053696519 -1.2047304789
## [696,] -0.0533760917 0.034542787 -0.2237294208 -0.2816443760 -1.3479637966
## [697,] -1.2729401359 -0.383859061 0.0030723003 -1.5730084646 -0.5064730955
## [698,] -0.8194229206 -0.974016394 -0.3858057045 -0.1062548240 -0.2489999465
## [699,] -0.2946031121 -1.248884480 -0.4386696968 0.7152675016 -0.4761526345
## [700,] 0.4875162997 -0.042118574 -0.2654581237 0.6089406400 -0.6381583161
## [701,] 0.6848892623 0.824992600 -0.0680689915 -0.6861177382 0.3256761747
## [702,] 1.0837722307 0.552644975 1.1530500228 0.8363006918 1.7874475900
## [703,] -0.8674776991 -0.670855785 -0.0005653254 -0.9496401694 0.3715172242
## [704,] 0.0611584775 0.370274984 0.1566765741 1.9375249296 1.2920751518
## [705,] -0.2875674789 -0.597067777 -0.9449768298 -1.0997348848 0.4218677766
## [706,] -0.0819898170 -1.533270368 0.3591102895 0.4287069299 -1.6706792590
## [707,] 0.0842671402 -0.531285299 -2.0401536569 -0.4698444748 -0.2469792340
## [708,] 0.4031754287 -0.222955865 1.3795174445 1.6642086520 0.9725144897
## [709,] 0.6133698416 -1.261769950 0.0689128005 1.3456879821 0.8654561624
## [710,] -0.0839316516 -1.908571752 1.0448573328 0.1168033783 -0.3615800281
## [711,] -0.8356560748 0.594033955 0.3065755608 0.9221300937 -0.9909267031
## [712,] -0.8315678882 -0.166394126 -0.6282987353 0.3819427222 -0.3916527200
## [713,] -0.4026125431 0.973603690 -0.7379799187 -1.0552389443 -0.6720448928
## [714,] -0.6907176938 -1.221636570 -0.9024567268 -0.2968716975 -0.2280251675
## [715,] -0.0287771058 -0.921702917 0.7842429112 1.1734107941 1.4461495001
## [716,] -0.1859332979 0.097065099 -0.2182108393 0.1607908746 0.1692904254
## [717,] -0.3930645173 -1.015471726 -0.4798038257 -1.9726305245 -1.3162829826
## [718,] 1.3699757861 2.439756327 0.2317314107 1.2963914107 -0.6715188164
## [719,] 0.3845203210 0.195373492 -0.7949441091 -0.7682833236 -0.9619403701
## [720,] 1.7881849974 1.362572776 -0.2583131547 0.6687528470 0.2701467475
## [721,] 0.5949530359 0.068408886 1.3696878334 1.1021471363 1.4332962325
## [722,] 1.0459014332 0.224672926 -1.6344356093 -0.5216447030 0.1372396235
## [723,] -0.6544884314 -0.411521896 0.0437189772 -0.7834788958 -1.1958870267
## [724,] -0.0753777199 -1.484035583 -1.1651902936 0.2817093854 0.3780670559
## [725,] 1.3721863108 0.752801428 0.8799733862 1.3803418316 1.4530491310
## [726,] -1.0733850348 0.225769645 1.7102025686 0.3770479889 -0.6958214872
## [727,] 0.4123054854 -0.507148328 0.2545252151 0.0197527698 0.6035726945
## [728,] -0.5866229982 -0.325764101 -0.5036609819 -0.6020751471 0.6544771416
## [729,] 0.7985698694 -0.418749146 0.7027917896 0.2232529670 -0.4827712468
## [730,] -0.2187783154 -0.137546664 -0.6638345573 -0.1016849130 -0.8889104968
## [731,] 1.8737697683 0.942886986 0.9174513618 1.4423167139 -0.3371230788
## [732,] -0.0923154212 -0.153779964 -0.6864751074 -1.0639657455 1.1952389657
## [733,] 0.1620640422 -0.523629497 -0.0230154166 0.5812280825 0.8527338041
## [734,] 0.1304513755 -1.086194964 -0.2293882541 -2.1553527095 -0.5215803912
## [735,] 0.9265900812 1.135388719 0.2756928337 -0.8663927083 1.1705296946
## [736,] 0.2684756663 -0.847431646 -0.4948560577 0.3496978448 -0.1373783817
## [737,] 0.5358577778 1.823460107 0.2894353299 2.5432996416 1.0634471961
## [738,] -1.6365247580 -1.090049483 0.8803690170 1.8407062373 -1.4033083332
## [739,] 2.6204938766 1.906881045 1.7916505413 1.7774547457 1.9163865576
## [740,] -0.4630388708 0.051486554 -1.4283293795 -0.5080985297 -1.6178714841
## [741,] -0.0006222093 0.412631301 1.0998072185 1.0500295731 0.0373033131
## [742,] 0.9230232659 -0.457082693 -0.7121509657 -0.7504596856 -1.1466057604
## [743,] 0.7627176236 1.764314976 0.9877056606 2.1226654738 -0.8607044604
## [744,] 1.1478925450 1.849620945 -0.5227316961 -1.3973585875 -0.5951421630
## [745,] -2.6328764362 -1.036893548 -1.8408092229 -2.0535804642 -0.5913403597
## [746,] 1.9201853094 0.339362926 0.4030303798 0.1119923050 0.8577377022
## [747,] -0.5041972263 -0.571929078 0.7758245742 -2.5210214696 -0.0801514593
## [748,] -0.4606974414 -0.293740752 -0.7751913141 -1.1692660032 -0.9340584596
## [749,] -1.0050956068 0.125210064 -0.5866402238 1.5661046429 -1.2688879816
## [750,] 0.2682287021 1.698490654 -1.5018977955 -2.8046406924 0.1493734686
## [751,] 0.5762690611 0.797442308 -0.2395546129 1.0091037444 0.0034209486
## [752,] -0.1521262176 -0.333435926 -1.5667085114 -1.2292100658 -0.3323860429
## [753,] -0.3484944904 -0.618136248 0.3274826369 0.6370961533 -0.2177696727
## [754,] -0.7015153502 -0.107348980 -0.9939101816 -0.4685581534 -0.5036273396
## [755,] 0.7822233153 -0.560550201 0.7776261797 1.4601699145 0.0809550909
## [756,] 0.9027667599 -0.006823273 -1.4580785440 0.1569519542 0.2027517647
## [757,] -0.9690078683 0.035768904 0.6387807452 -0.6987933275 -0.9463161437
## [758,] 1.2473212635 0.748478799 -0.1168377887 -1.5629925826 1.5204631965
## [759,] -0.4120644864 -0.758822065 1.5353736537 0.3246118691 -1.6513539780
## [760,] 1.5668574792 1.828552449 0.6142729669 -0.7394006843 0.1675434666
## [761,] 1.2643425025 1.676049116 1.0700147012 1.0306669563 1.1471740595
## [762,] 0.8208919442 1.481833866 -2.0581246972 -0.8399734650 -0.7668486523
## [763,] -0.0499859535 0.510528348 0.8100941099 1.2227044974 1.3269272347
## [764,] 0.0480377176 0.964431981 0.5888903644 0.0217202140 -0.3294798298
## [765,] 1.4045206777 1.660723174 -1.0358268930 -1.5742807786 1.0166042951
## [766,] -1.0006310790 -2.661345973 1.1514496265 1.8814703170 1.6646223437
## [767,] 0.0749070509 -1.132343579 0.6024855696 0.7143223345 0.6509196145
## [768,] 0.2655645595 -0.606375406 0.1987829543 -0.9904015863 1.5167067805
## [769,] 1.0390035688 0.444357565 0.0345552472 0.8816708322 0.9099371551
## [770,] -0.4009961305 -1.909030597 1.4326264347 2.2490162583 2.1326094051
## [771,] 1.2642194554 1.489641737 -0.6308031750 -1.2450405745 -0.2810339813
## [772,] -0.8666310724 -0.233629030 -0.4624688817 0.7850516842 -0.5610402909
## [773,] -0.2500809069 -1.135030802 -0.9075772134 -0.9799782149 -0.4659341692
## [774,] 0.9055328735 0.692995896 0.8373045032 0.2669135375 0.7355735207
## [775,] 1.9250282590 -0.044872991 0.5651410142 1.0662419981 2.2053504659
## [776,] -0.3966348575 -0.374810156 -0.9314438795 -0.2077631813 -1.8846822396
## [777,] 2.2498952673 0.236465221 0.4078715664 0.1096360927 0.1512180834
## [778,] -1.6976548158 0.035604627 -1.2476848666 -0.9514497293 -0.9768381931
## [779,] 0.1894546545 0.747065822 -0.4168891499 1.6555224337 0.1454361574
## [780,] 0.4821728071 0.051381553 -0.4413736887 -0.2409620079 0.4283770366
## [781,] -1.1151648687 -0.222272623 -1.3798487803 -0.4128684180 0.3476149218
## [782,] -0.2836911114 -1.824890952 -0.5681464879 -1.8595743096 0.2806600216
## [783,] -0.0884783619 -0.860410365 1.6673388460 0.9911178509 0.8250751249
## [784,] -0.7336663865 -1.430809734 0.8756435523 0.8629778368 -0.4352951523
## [785,] 0.4927678316 0.854747907 0.2103876262 0.9356473085 -0.1436901578
## [786,] 0.4714996824 0.262531804 0.5938844628 0.2956734254 0.1960592834
## [787,] -2.7519523493 -0.465281354 0.0029146889 0.4756620591 -2.7754224225
## [788,] 0.7333667147 0.279919701 0.8371786805 0.6229910287 1.9973593619
## [789,] -1.2835950314 -0.833849462 -0.2201919169 -0.4295458167 -0.8145099452
## [790,] 0.6935444529 0.831287448 0.3880816619 -0.4108528412 0.3681526782
## [791,] -0.3710607971 -0.176615197 -0.6866033270 -0.3741873735 -0.2455521323
## [792,] 0.7565498263 0.832136629 -0.4165080529 -0.6745309863 0.7743877285
## [793,] -1.2171375868 -0.249793813 0.5814946828 1.0687609462 0.6544704005
## [794,] 0.5048802876 0.664716355 -1.2302887076 -1.1921383451 -0.6347121396
## [795,] 0.8538267970 0.463795616 -1.6597411659 -0.1478580286 -0.0965232717
## [796,] -0.8773116136 0.011331040 0.0255840106 -0.0670089889 -1.2369656315
## [797,] -0.7814307964 -0.596654506 -0.8170815967 -0.1530057367 0.3551975038
## [798,] -0.0647194649 0.579448557 0.8487372333 1.0230793675 1.6354189318
## [799,] -1.0399040663 -1.351064028 0.5678328214 0.9662244670 0.2607975748
## [800,] 1.1741181389 1.022611902 1.1548545408 1.1412580510 0.5034819775
## [801,] 0.8142092232 -0.300462428 0.7904397937 1.2048209274 0.2567240954
## [802,] 1.6944885575 0.655930717 1.8833330045 2.4951777947 2.8118215210
## [803,] 1.7977217496 1.835432932 1.4492109004 0.4092735730 0.3843575941
## [804,] 1.4862964615 1.282316066 -0.3633139179 -0.1028991226 0.5914026220
## [805,] -0.1829056837 0.724795541 -0.6481508261 0.1112013460 0.4943736070
## [806,] 1.1349978779 -0.034264498 0.1792167177 1.0734899352 0.5976588549
## [807,] 2.3104884454 1.841386256 0.2823361123 -0.9809517598 2.7615445498
## [808,] -0.5563563743 -0.195205445 -0.7116009265 -1.1529410874 -1.1022386800
## [809,] 1.0729245031 1.531670288 0.7466778140 -0.3784800869 2.0285947486
## [810,] -0.0952657739 0.547872904 -0.7443893827 0.4829205003 0.4399770253
## [811,] -1.3176849704 -0.958357054 -1.4173901997 -0.2579115343 -0.4488873138
## [812,] -0.1435118159 0.774715493 0.0503533058 1.4127548739 0.4358676364
## [813,] -0.6566864182 -0.878636139 0.0356492785 -1.3809622817 -0.6311659180
## [814,] 1.5567529702 1.785006112 1.8232308223 2.6317685256 2.0945767629
## [815,] -0.5380042359 0.219648994 0.7880591346 0.2662423872 -0.0919375404
## [816,] 2.4367457945 2.024145182 0.8352377158 -0.4326853751 0.9213554333
## [817,] 0.5475903847 -0.212105545 -0.8971946987 -0.4012665878 0.2389110670
## [818,] 1.2658111721 0.225419552 0.6628337262 -0.4313710040 0.8513874549
## [819,] 0.7948760269 1.349157558 -0.6334006387 0.7040146491 0.4881492672
## [820,] 0.2067663146 0.267821773 0.8067648120 1.1818816337 0.5908109181
## [821,] 0.7565725440 -0.340040850 -1.3425090847 -0.5729882926 -0.0622032606
## [822,] 1.3850851026 0.319867323 -0.3685787934 1.3030628290 1.3717407209
## [823,] 0.2064874096 1.017721252 0.4484653363 -0.3674238707 0.3188891842
## [824,] 1.2135751774 1.390096125 0.3118798628 0.3337239275 2.2626296218
## [825,] 2.4530846391 1.200211044 -0.4386316494 -0.2585599569 0.7607560589
## [826,] -0.6718522858 0.179318738 0.8041314629 1.6858753662 0.9665015955
## [827,] -0.6024036508 -0.275974515 1.1248162355 0.8278124423 1.9378296173
## [828,] -1.6054745627 -1.160905179 -1.9245429419 -0.4612492267 0.2638624249
## [829,] -0.7795266083 -1.032304520 0.1970971978 0.2522387640 -0.7621424895
## [830,] 0.4603790573 1.338320307 0.7895242090 0.2056489825 1.6671706475
## [831,] 1.1482430754 1.677481138 -1.4615230143 -0.4791741636 0.7456367143
## [832,] -0.2842245646 1.339357006 -0.1810207571 -0.4079303674 -0.6769673552
## [833,] 0.1326673264 1.007563359 0.7116079685 -2.4447018944 -0.2289877187
## [834,] 0.5719877116 -0.160132292 0.6888752813 0.1387444674 0.8786810968
## [835,] -1.5023213027 -1.033512525 0.5881053617 0.4375850641 -1.1826097975
## [836,] -1.0843433512 -1.218618370 -0.9535011539 -0.6358941940 -0.8279246290
## [837,] -3.3241273425 -2.501948278 0.0211385305 0.8940194000 -2.2668894287
## [838,] 0.3328054476 0.575185712 -0.0173680758 -0.3287255102 1.4796498062
## [839,] -0.4597816906 0.088397246 0.6970959868 -0.0694039709 0.2753293107
## [840,] 1.2049488361 0.973722744 -0.3962177455 -1.6195849530 1.9431776483
## [841,] 0.5698711097 -0.032352950 0.5250362280 1.2427176242 0.9034056574
## [842,] 0.1193870670 -0.581875952 -0.4993842980 0.8986795402 -0.1459432403
## [843,] 0.0360395851 -0.049159428 -2.3501768306 -0.8483920401 -1.3499731330
## [844,] -0.0459191884 -0.148064323 -0.0203398102 0.2367717457 1.5018408294
## [845,] -0.1258912098 -0.826739019 -0.0248454835 -0.0513082776 0.0643651520
## [846,] 0.0346783993 -0.746186398 1.3548325469 1.1158599597 0.2621379294
## [847,] -1.7368725008 -2.059329941 -0.1098580580 -1.1636879761 -1.3415701377
## [848,] -1.8993474580 -0.895268453 0.8296269880 -0.4588063775 -1.4288607319
## [849,] -0.5278810570 0.342635504 0.0800431166 0.2305995089 -1.1862173244
## [850,] 0.0384960496 -0.444625245 1.2513518297 0.6066673279 0.4787120281
## [851,] -0.5363691319 -0.495080783 0.6707202252 1.0425965562 -0.6310451522
## [852,] -0.3320511223 0.009061276 0.0806582401 0.1533907106 0.8141378132
## [853,] -1.2641250400 -1.318833227 -0.3968510476 -0.4892095105 -1.5688115702
## [854,] 0.3218436492 -0.340533780 -0.1034133306 -0.3560823049 0.6814646075
## [855,] -0.7264433846 -0.852973705 0.7612383127 -0.2503907895 0.3563833966
## [856,] -0.8526566984 -1.096520659 -1.7920037402 -2.3081888669 -0.8740898864
## [857,] 0.0318437364 -0.793311732 -0.7766796942 -0.8719405942 1.0926266812
## [858,] -0.7461373125 -0.890138729 -2.2368168828 -1.3888063776 -0.9736520405
## [859,] -1.2154027542 -0.305619664 -0.9904265862 -0.0849449329 -1.0732629547
## [860,] 0.1424759626 -0.713300150 0.4165258803 1.4772960403 0.0028116698
## [861,] -0.6030459671 -1.328612272 -0.5108128740 -0.7100675974 0.0198168970
## [862,] -1.6738682084 -1.101207898 0.2689374932 0.1928949669 -0.4079973299
## [863,] 1.0248916769 -0.195728471 0.1931935610 1.4323524219 0.1728254225
## [864,] -0.5288845101 -0.896528718 1.4090309367 -0.1185423077 -0.7002990822
## [865,] -0.7471931889 -0.271940281 -0.5163404998 0.6715430253 -0.5891314815
## [866,] -1.2807564971 -0.530073902 -1.4286255382 -0.2117185413 -1.7919525827
## [867,] 0.4322612157 0.385792849 -1.1148030753 -1.4879173690 0.6497841715
## [868,] 0.0716604880 0.411807369 0.2714340223 -0.1027630456 -0.5838864654
## [869,] 1.5954124287 1.159085183 -0.3060313681 0.2574194033 0.8655177351
## [870,] 0.6817562380 -0.028365284 0.7827840752 0.8493056289 0.5104340160
## [871,] 0.6970027938 0.512089925 0.3154857143 1.3849203806 -0.3281833275
## [872,] -0.4776027914 -1.379056957 0.6888236769 0.4610199113 -0.6084622027
## [873,] -2.4882242616 -1.360711986 -1.0993434853 0.6507884262 -0.9434145040
## [874,] -0.1649268781 -0.393464652 0.5084723080 1.1482440818 -0.2858408934
## [875,] -1.0576992339 -1.113076622 -0.7312241570 -0.9781315349 -0.8299388763
## [876,] 1.1052355829 1.379770177 -1.0172171759 -0.8534099885 -0.8844845092
## [877,] 0.9288807573 -0.907184195 1.7884033351 1.6791410445 1.9590287976
## [878,] 1.1447064258 0.568036202 -0.5703996784 0.1234573676 0.4635057677
## [879,] -0.7883549845 -1.049489522 0.5945825501 -1.3513625034 -1.7062129780
## [880,] -0.5046218019 0.130497365 -0.5239193856 -0.1220817102 -0.3859840798
## [881,] 1.2472187489 0.658487946 -0.3540683345 -1.2020932382 1.7545794602
## [882,] -0.6310112749 -1.647294489 -0.4097785149 -0.7140818688 -0.5339130455
## [883,] 0.8429566644 -1.083228425 1.8483357090 1.9633284854 0.2553410488
## [884,] 0.1486134933 0.232377573 -1.8746790629 -0.6111338354 -0.6498490344
## [885,] -0.6199389151 0.191104988 -0.2323179664 -0.0854191114 -0.2918820182
## [886,] -1.0246565484 -1.047968185 -1.9617135388 -1.0820835668 -0.0057157958
## [887,] 0.5316431014 -0.688572991 0.7112531531 0.3138654563 1.0146065028
## [888,] 1.2789614963 1.234872785 -0.4491229728 -0.2289229828 0.4272621799
## [889,] -0.2933741381 0.068090858 -1.2339221736 -0.6105356082 0.0982703140
## [890,] 0.7857457794 0.483704779 -0.9972641347 -1.0300416362 0.6276588706
## [891,] 0.9591214221 0.647653910 1.3137168509 0.5726683555 1.5924519786
## [892,] -0.5528856187 -1.744333901 -1.7927406602 -0.3400203222 -1.2887560798
## [893,] -0.0217410776 -0.401146251 0.0247905090 -0.6404425813 1.2023497708
## [894,] -0.6907028860 -1.051563579 -1.6695254854 -1.2042380896 0.1980413465
## [895,] 1.1310615696 -0.216517168 0.9506371999 -0.1727232859 0.7738740837
## [896,] -0.8399209926 -0.359765174 -1.4459823392 -0.4100186965 -0.2829558889
## [897,] -0.1910878345 -0.240557633 0.6395863151 0.7456318958 1.1623487443
## [898,] -0.3436501676 -1.026606210 -0.4040664897 0.7349781189 0.2153733716
## [899,] 0.6466383513 2.343240336 -0.0419351112 -0.6415495686 -0.2425325283
## [900,] 0.1934620761 -0.336024737 0.2994178894 -0.0708478924 0.0728562112
## [901,] 1.8054476964 2.270000077 0.5385320198 0.3966996532 0.1311867325
## [902,] 1.0281142956 0.561240612 1.2011947508 0.7703058615 1.4261338744
## [903,] -0.0434129347 -1.003625533 -1.9099983083 -0.6410047784 -0.1530298811
## [904,] 0.7018569101 2.407526498 0.8782564257 0.9160757345 0.9072939643
## [905,] 0.3697910117 0.960176696 -0.4850799231 -0.5048464779 0.1365947337
## [906,] 0.2818311404 -0.942651693 1.2696603229 2.1468246634 0.5471363702
## [907,] 0.6420270718 0.702267772 0.4385872423 -0.6070669818 0.3952324939
## [908,] 0.9709867606 0.365417184 -0.2967993882 1.7119569800 1.3460405227
## [909,] -0.8256983039 -1.412280076 0.9147773066 1.0827042350 -0.3780450645
## [910,] 2.6249891115 2.213637510 0.5555354346 1.0337458168 1.7572673487
## [911,] -1.4933124812 -0.951840552 -1.1056148294 -1.0063667842 -0.2880735637
## [912,] 0.1712122087 0.097681783 1.3594723796 0.1421452164 1.0947932735
## [913,] -1.2432341667 -1.916257280 -1.6437805632 -0.1751778883 -0.8158145697
## [914,] 1.2932127458 2.080213720 -1.5750733522 -1.1771811691 2.1439262258
## [915,] -0.0635233280 0.052396588 0.4636062505 -0.4633411022 -0.9016159155
## [916,] 0.2258694245 -0.411405862 0.1634408816 0.7756217365 -0.4550026308
## [917,] -1.0485309652 -0.568839277 -1.9524412602 0.3749379153 -0.6234676599
## [918,] 0.7097510154 0.387673332 2.7228051655 1.6631014202 -0.2719097075
## [919,] -0.0032185261 0.106029831 -0.4381882005 -1.7464955098 -1.5993053839
## [920,] 1.2653406541 1.621025963 -0.3069737121 -0.0865414964 1.4030546031
## [921,] 1.7412188550 1.027015612 1.1418771091 1.5567423529 1.3573658268
## [922,] 0.8253516553 -0.496262898 -0.1951017337 0.7536884602 0.9675602168
## [923,] 0.1871460701 -0.302927799 -0.6060785439 0.0069009694 -1.3666186496
## [924,] -1.0087594813 -1.439827677 1.6699102504 1.7690612879 -0.2562698222
## [925,] 1.3202189942 0.835465464 0.0405667886 0.6448119261 0.2729505729
## [926,] 0.4428527539 0.457150429 0.6216905258 0.9172164708 1.2038046145
## [927,] -1.3975885369 0.343562199 -2.2712658859 -0.4629090551 -1.5877945010
## [928,] 0.1914678409 0.498962434 1.7032195493 1.7432098147 1.5896610089
## [929,] -0.5336257623 -0.594878898 -1.2238845365 -0.3463047173 0.6099174674
## [930,] 0.4179851969 -0.226363328 0.6489641300 2.6407278141 1.6293917254
## [931,] -1.2814612372 -0.986267356 1.4695974840 -0.9717004363 -0.8256079104
## [932,] 1.2752482279 0.254452717 1.9731335285 -0.2585903343 0.9630960731
## [933,] -0.7431215480 -0.227191217 -0.7204998304 -1.0676362267 -0.8151980091
## [934,] 0.2860330652 -0.447866463 -1.8342543208 -0.3776553255 -0.5399001051
## [935,] -0.3107235060 -0.914317161 -0.5604577117 0.5356193508 1.3428821468
## [936,] -2.4946450396 -0.304310353 -0.6862124841 0.4004412980 -0.0835911911
## [937,] 0.7168743838 1.029390653 0.9520763124 0.4063380975 -0.8367890691
## [938,] 2.0179151018 1.800417053 0.4953875814 0.1004251219 0.2661629976
## [939,] 1.3408398340 2.103147856 0.5764212852 -0.8569837898 1.4610151157
## [940,] -2.1902175440 -1.891198629 -0.1839258260 0.2030720205 -1.0246409881
## [941,] 0.8901615994 0.100032565 1.7739570089 0.9345563192 0.4478775560
## [942,] -1.7749454715 -2.428104101 -0.2287379258 -1.7931668732 -1.2048723179
## [943,] 0.6794109828 0.903117444 0.5241738566 0.0653414494 2.1377887220
## [944,] 0.7949212013 0.469397583 0.6758731175 -0.2703471742 0.4683565058
## [945,] 1.1545180615 1.193641853 0.2021393917 0.0854581886 0.1602826287
## [946,] -0.1627362547 -0.853322570 0.4872992102 0.1504133684 0.5644271623
## [947,] 1.7186301953 1.038910469 0.6793199448 -0.5053849076 0.0229387811
## [948,] 1.0017394133 1.553147965 1.8219428175 2.3352347638 -0.6657543334
## [949,] 0.2936272485 -0.194396697 0.2439996966 0.3963022739 0.8913256383
## [950,] 0.4052068250 -0.110268130 -0.7156481913 -0.0368035522 0.0306930042
## [951,] -0.3637515159 -0.636249454 0.1520013367 -0.0879184369 -0.3932874228
## [952,] 1.1231083165 -0.013619843 1.8625003264 1.8781596183 1.3775427077
## [953,] -1.3641895142 -2.085978137 -1.7566832590 -0.8671265219 -0.6502051204
## [954,] -1.2053345807 -1.628067365 -1.0026116879 -1.3832222038 -1.2221821656
## [955,] -0.1742045903 1.321610865 0.4125934350 0.9054742529 0.0143525237
## [956,] 0.6291621681 0.609325270 -0.6637907833 -0.5287237534 -1.2248547710
## [957,] 0.2957591699 0.917042792 0.4333112754 1.7200821629 -0.3562646225
## [958,] 0.4231874777 0.586157208 0.9140613753 0.7595849597 -0.2413391662
## [959,] 0.3284144816 0.794914851 0.5243510084 0.1522279802 -1.2987539461
## [960,] -0.3748765357 0.581476063 0.7358364779 -2.3091534052 0.5289394033
## [961,] 0.0916350771 -0.092648395 0.5548013688 -0.8577270513 0.7682312125
## [962,] 1.0380666685 -1.437198406 2.0727358882 1.8518664859 0.5990164792
## [963,] -0.7838621823 -0.546134357 -1.0819984797 -2.1293631716 0.5551902421
## [964,] -1.3213892317 -0.546403356 0.1380544763 0.5946368159 -0.9788743829
## [965,] 0.0417145155 0.128738243 -0.0188787618 0.8802334054 -0.3493812746
## [966,] -1.8902596408 -1.901543515 2.0288208599 1.5049494139 -2.5867975268
## [967,] 1.7576060452 1.686292488 -1.2051781983 0.6741822616 0.5480776148
## [968,] 0.6798524433 0.795789638 0.5249821096 0.7039310612 0.1927593236
## [969,] 0.1424721305 -0.612029435 0.2614657030 1.1263865612 -0.7106942870
## [970,] -0.3670289889 -0.453246900 -0.1380216965 -0.4950603110 -1.5700700137
## [971,] 1.4220962676 -1.010856456 -0.8995793771 -0.0209972756 1.2706594562
## [972,] -0.3025876804 0.937611736 0.8764102866 0.0512593858 -0.6450571631
## [973,] -2.5856603475 -1.965216545 -1.3329184852 0.3394649585 2.1859308602
## [974,] 0.4281010256 0.430986646 0.2788613046 0.4362032569 0.6540462084
## [975,] 1.1841589261 1.135849686 0.4382225853 0.8903838449 1.5464217524
## [976,] -1.5389342395 -0.506203017 0.1804184209 0.0163061776 0.4075055109
## [977,] -0.5531757294 -0.823246096 -0.4948215704 -0.4885381097 1.1740924990
## [978,] 0.8273117886 0.873443371 -1.0087699922 -2.9562148329 0.5117846727
## [979,] -2.3460117533 -1.450641129 -0.4069239792 0.3841891710 -2.5124160001
## [980,] 0.2904462980 -1.223584924 -1.1359425625 -0.5805003860 -0.9935986016
## [981,] 0.8210303904 1.207297357 1.5386984389 0.7936548424 1.2042942608
## [982,] 0.9312659482 0.157986208 0.0034670091 0.1027208421 -0.8033759709
## [983,] -1.0832331204 -0.578700876 1.3118104245 1.1838391981 0.5863476197
## [984,] 2.0404536777 1.887236830 0.0206949983 0.0169613631 1.1284923456
## [985,] -1.7622795499 -2.175130854 -0.9640048562 -0.9212479867 -0.4494047867
## [986,] 0.1072857482 -0.564360181 -0.6336288295 -0.0653542460 0.1711095933
## [987,] -0.7330084337 -0.203062467 0.7251036616 0.8482431388 -2.1682468207
## [988,] 1.0402519276 0.424690985 -0.1528479666 0.2990073809 0.6798157163
## [989,] -1.9454503669 -2.155501597 -0.7673263582 -0.4484699324 -0.4769074606
## [990,] -0.0316374335 -1.182382071 -0.0214681679 -0.7779156590 0.2096906460
## [991,] -1.2849691934 -1.576431688 -1.4964649319 0.3890558409 -2.1998064861
## [992,] -1.2562178723 -1.798283994 -1.8751799776 -2.1326966608 -1.1110357365
## [993,] -1.5837245730 -1.745927016 3.2859979533 1.2972810492 0.5852575480
## [994,] 0.0729135894 -0.591847009 -0.5422144750 -0.5145724821 -0.4948850212
## [995,] 0.7471456585 0.942398908 -0.3937421934 0.4878185677 1.6394609442
## [996,] 0.4185562631 1.792115787 -1.0164364963 1.1978766575 -0.1047554571
## [997,] -0.6484760987 0.094914381 0.1944785249 -0.0823856963 -0.5295243481
## [998,] -0.0877281191 -0.514065093 -0.0877249093 -0.7816584010 0.0331522396
## [999,] 0.2032124424 0.640590592 0.5377623206 -0.0792726688 0.1047384153
## [1000,] 0.0996767755 0.418874080 0.0607032867 -0.4491493985 -0.6296039960
## [,6] [,7] [,8]
## [1,] -0.642275092 0.130565300 0.272357934
## [2,] 0.645752538 0.473618568 0.212068717
## [3,] -1.245681129 0.682340849 -0.510989503
## [4,] 1.797828219 2.149603229 -0.390891368
## [5,] -1.297601227 1.049307219 -0.444475110
## [6,] -0.127575130 0.486185183 0.439966159
## [7,] -1.130364730 -1.409545116 -1.622502929
## [8,] -0.209060940 0.921125076 -0.386004518
## [9,] 1.742144781 1.101384768 0.701572429
## [10,] -0.122868447 0.183103136 -0.760699039
## [11,] -0.502336840 -0.271863635 -0.217327580
## [12,] 1.247860362 0.562590762 0.633896312
## [13,] -0.726920401 1.189286530 -0.308066785
## [14,] -0.470716915 0.869633020 0.989725518
## [15,] 0.015514333 -0.576556441 -0.442178877
## [16,] 1.415876824 -0.651065377 -2.110862067
## [17,] 1.143180037 -0.719276986 -1.512939323
## [18,] 0.317197494 0.036915637 1.019877696
## [19,] 0.198033102 0.698914242 0.131471125
## [20,] -1.647882768 -0.584645766 -0.507397129
## [21,] -0.042255418 0.167267750 0.237436763
## [22,] 0.293541162 -0.322836333 -0.468624578
## [23,] -0.467646758 -0.552685536 -0.364045461
## [24,] 0.174580470 0.525785781 0.820588961
## [25,] -0.367475052 -0.010821685 -0.751115097
## [26,] 0.974898352 0.691767626 -1.230212392
## [27,] 0.492240130 -1.756166215 -0.972849722
## [28,] 1.784858069 -2.626197932 0.923453809
## [29,] -1.795757820 0.855717406 -1.249648720
## [30,] -0.959177785 -0.950169047 0.076584964
## [31,] -0.258886498 -0.966011793 -1.374326292
## [32,] -0.018413855 -0.809698198 0.653116916
## [33,] 1.239333576 -0.493177393 -0.644276036
## [34,] -0.064487933 -0.737147912 -1.043709417
## [35,] 0.227433391 -1.006684879 -1.287882378
## [36,] 0.554205492 -1.570767493 -1.504030727
## [37,] -1.430045015 -1.880328197 0.460794939
## [38,] -1.124024479 1.464340943 -0.078676351
## [39,] 0.873780491 -0.133148356 1.187416565
## [40,] -1.634631845 -0.724854230 -1.963810714
## [41,] -0.281869828 -0.757553697 0.995956238
## [42,] -0.622541942 -0.412801002 -1.567128885
## [43,] 0.843873659 -0.540101652 1.378876887
## [44,] 0.707304951 -1.196598716 0.241726064
## [45,] -0.280548970 -0.540093464 1.276928961
## [46,] -1.552996134 0.323301156 -1.160169545
## [47,] -1.142834376 -0.165596443 -0.768090688
## [48,] 0.936660718 -1.985877307 0.322215426
## [49,] 0.001982976 2.191651830 0.558818131
## [50,] 1.303767161 0.188497343 0.403395041
## [51,] 0.435954361 -1.343950249 -0.488210275
## [52,] -0.051294647 -0.309044362 -0.457582344
## [53,] -0.242773023 -1.143696374 0.226058651
## [54,] -0.325223183 0.298161552 1.050350586
## [55,] 0.395881533 -0.691067627 -1.091056185
## [56,] -1.249629268 0.825865386 0.621214337
## [57,] 1.048211318 -0.928891722 0.892449372
## [58,] -1.330823519 -0.264436483 -1.337679696
## [59,] -0.382731191 -0.639719565 -0.530047058
## [60,] -0.318134213 1.003684944 -0.559530325
## [61,] 0.713622840 1.616077309 -0.395967935
## [62,] 0.291933609 0.309828686 2.235804903
## [63,] 0.213499060 -0.528613568 -0.423382111
## [64,] 0.961662499 -0.756412176 0.280252606
## [65,] -1.020516430 -2.482550554 -1.101128103
## [66,] -0.491148996 0.649996759 0.808795439
## [67,] 0.480155256 1.442104431 -1.095663475
## [68,] -0.827602430 0.363800222 -1.142448911
## [69,] 0.881121724 0.060334970 -0.429028239
## [70,] 1.910300232 0.806343810 -0.927362843
## [71,] 0.373490539 -0.890046344 1.007546679
## [72,] 0.229035552 0.680176195 -0.116257575
## [73,] 2.602504270 -0.293768525 0.457422745
## [74,] 0.014355915 -0.113686592 2.026642541
## [75,] 1.880258343 0.501117651 0.013353678
## [76,] 2.049374567 0.628569417 -0.703934855
## [77,] 1.148903748 1.063322887 -0.367431557
## [78,] 0.883453146 1.311127457 -0.491398015
## [79,] 0.051642267 -0.908342183 0.196840059
## [80,] -0.296096057 0.273429360 1.059184763
## [81,] -0.657318374 1.334800808 0.527230773
## [82,] -0.418782928 0.266245386 0.531118783
## [83,] 0.788217633 0.994655703 0.505810895
## [84,] 0.746135807 1.195083237 -0.798021364
## [85,] -0.429017539 -0.499812480 0.347610610
## [86,] 1.342248174 1.998032217 -0.480737003
## [87,] -0.823882656 0.215051049 0.256031410
## [88,] -0.592462752 1.008924278 0.349176308
## [89,] -0.741209697 1.192185580 0.359801692
## [90,] 1.220464959 1.094955594 1.157770794
## [91,] -0.838412329 -0.872638418 0.446898769
## [92,] -0.132385098 0.500981033 -0.912573948
## [93,] -1.614683718 0.436738936 -2.807539492
## [94,] -2.414594677 0.192488327 1.145141629
## [95,] -0.445440815 -1.081743932 0.825363733
## [96,] -0.336474136 1.176440157 1.455295549
## [97,] -0.168102988 -0.869966739 -0.987556466
## [98,] -0.291983751 0.281154513 0.215073807
## [99,] 1.581291722 -0.898871515 1.364076320
## [100,] 1.226515040 1.096760266 0.230253129
## [101,] 1.734807642 -0.212540573 0.685402815
## [102,] 0.383228690 -0.676603887 0.754071515
## [103,] -1.079989399 -0.107729887 -0.357473434
## [104,] -0.352402268 -0.836869441 -0.530369317
## [105,] 0.134978261 -1.369189084 -0.106139807
## [106,] -0.663140076 1.214322586 0.992824952
## [107,] 2.435125851 -0.862030769 2.829043261
## [108,] 1.560106326 -0.889213913 -1.016006978
## [109,] -0.653618017 0.950476005 0.988854066
## [110,] -1.418863164 -0.329384724 1.244829626
## [111,] -2.076265322 -0.696264363 0.635772146
## [112,] 0.715863059 1.956455760 -0.322528129
## [113,] -1.790808408 -0.799434574 -0.235479363
## [114,] -1.550460245 -0.253944780 -0.775006186
## [115,] -0.040355221 0.706703146 1.101179513
## [116,] 2.230202089 -0.576335552 1.547076920
## [117,] 0.900184635 -0.678846149 0.030843184
## [118,] -1.124711620 -0.465575214 -0.226586889
## [119,] -1.689575611 -1.620256677 -0.389160781
## [120,] 3.072940219 0.728876674 -1.224807435
## [121,] 1.288790809 0.462465350 -0.166973740
## [122,] 2.303474841 -0.238840509 -1.196896328
## [123,] 1.467068082 0.063990052 0.898574103
## [124,] -0.250296919 -0.141246511 -1.169900964
## [125,] 0.205265964 -1.945947300 -1.177979630
## [126,] -0.515633954 -2.109122694 0.263261297
## [127,] -0.709685202 0.860450383 -2.274423876
## [128,] -0.781186217 -1.534406592 -0.597864242
## [129,] -1.406736994 -1.142004086 -0.430270893
## [130,] -0.150215189 0.940747483 -0.552891716
## [131,] 2.235936202 -0.349797237 1.544082450
## [132,] 2.782858552 1.080134598 1.344675601
## [133,] -0.289232627 -0.490308815 -0.047265676
## [134,] -0.562135989 -0.909914876 1.824710269
## [135,] 1.287540341 1.559293807 0.747377771
## [136,] 0.924387077 -2.878753429 -1.307514378
## [137,] 1.417282031 -0.347379369 -0.284100707
## [138,] 0.480161909 0.300269129 0.454337822
## [139,] -0.277845658 0.470765398 -0.050399599
## [140,] -0.428971773 -0.072877180 -0.335472822
## [141,] -0.717784587 0.050129063 0.635700650
## [142,] 0.069159057 -1.083242551 0.661791009
## [143,] 1.587889045 -0.450502642 -1.185120745
## [144,] 0.393102953 -0.414107458 -0.901326094
## [145,] 2.209982956 0.912553428 -0.510589630
## [146,] -1.296762096 -0.450758394 -1.053217450
## [147,] -0.942058817 -0.830679984 0.065910696
## [148,] 1.177899192 -0.239538044 0.653065401
## [149,] 0.332618090 -0.214267924 -0.239690532
## [150,] -0.928579552 0.476509314 -1.172646028
## [151,] -1.055376478 -1.289628902 0.259389309
## [152,] 2.071996058 -0.319221673 2.042522728
## [153,] 1.300641801 -0.635171702 -0.390630801
## [154,] -1.129697531 0.088753461 -0.625546433
## [155,] 0.687840665 0.728210118 1.289487697
## [156,] 1.212874561 -2.066121898 0.177443368
## [157,] -0.361213722 -0.910942926 -1.145751028
## [158,] -1.824231699 -0.637236532 0.861933410
## [159,] 0.527297808 -0.956710692 -0.185039996
## [160,] -0.133952611 -0.938325419 -1.115085659
## [161,] 0.144001849 -0.336327052 -1.151050613
## [162,] 1.990375920 -0.238917456 -0.095111986
## [163,] 0.745551864 -0.551684605 -1.542203089
## [164,] 0.541261643 0.952651926 0.770108557
## [165,] -0.790094567 0.657387656 0.163274996
## [166,] -1.084838174 0.448769229 -3.125950473
## [167,] -0.233336195 0.465318859 -0.537357572
## [168,] -0.150115945 0.052008552 -1.265638489
## [169,] -0.119453851 0.488317863 -0.414225309
## [170,] 0.427211873 0.862859471 -0.600897414
## [171,] -0.285571753 0.180958328 -1.537952376
## [172,] -0.644913841 0.627718010 -0.034589903
## [173,] -2.114011685 -0.496038507 -0.613888743
## [174,] 0.452190445 -0.927667901 -0.679244273
## [175,] -0.596198126 -0.765824089 0.762588656
## [176,] 0.922631178 -1.719750162 -1.985789295
## [177,] 0.072391278 -0.175041071 -0.838459467
## [178,] 1.986255571 -0.471378940 0.263447218
## [179,] 1.719025711 1.067319592 0.094376748
## [180,] -0.872566123 0.100952908 0.212853329
## [181,] 0.881711861 -0.327025583 1.519778016
## [182,] 0.394276731 0.014709621 -0.343716520
## [183,] -0.808316766 0.419422985 0.248419827
## [184,] 1.565747789 -0.535054907 -0.043762364
## [185,] 0.208929703 0.121437257 1.455429343
## [186,] -0.374350205 -0.749294366 -0.880732951
## [187,] -1.754787084 -2.153337203 0.287611428
## [188,] -0.317418372 0.172478726 0.824752513
## [189,] -0.119324955 0.055215977 0.805266350
## [190,] 0.066792940 -0.254000254 -1.214823351
## [191,] 0.085204659 -1.079512948 1.177916830
## [192,] 0.222494769 -0.668349857 -0.509224931
## [193,] 0.717648972 0.949260641 -0.093896863
## [194,] 0.417850885 -1.241492760 -0.458332610
## [195,] -0.669786649 0.818561388 -0.117178431
## [196,] 1.293852202 0.001917011 1.851806589
## [197,] 1.517168280 -1.923008544 1.093892664
## [198,] -0.679981010 1.000887106 -0.662001295
## [199,] -1.543886777 0.307209524 -0.181747109
## [200,] -0.327906351 -0.375412006 -0.772477374
## [201,] -1.532072244 -0.388882584 -0.958198662
## [202,] -0.610933482 0.095773739 -0.939711737
## [203,] -1.830141505 -0.853556869 1.131162836
## [204,] 1.919407684 -0.593266799 -0.237440780
## [205,] 0.232871352 -1.038575051 -0.213804392
## [206,] -2.037201212 0.210689156 -1.074973444
## [207,] 0.853959042 -0.182512696 0.056543185
## [208,] 0.305059631 0.051536778 0.094537906
## [209,] -0.506309186 -0.592032699 -0.553039063
## [210,] -1.105878512 0.602559009 -0.362558622
## [211,] 0.018206146 2.002348842 -0.525054387
## [212,] 0.631845548 -0.462193254 -0.583667154
## [213,] 0.632476797 1.721351052 1.173930003
## [214,] -0.048626730 0.701900443 0.368072256
## [215,] 0.346525731 0.621304365 0.521063683
## [216,] 1.139982411 0.553626936 1.059204189
## [217,] 0.274949577 0.754178976 1.112812033
## [218,] -0.426793402 0.409408655 0.225771683
## [219,] -0.356479258 0.495008172 -0.704009298
## [220,] -0.149283088 0.259654999 0.768778890
## [221,] 1.492259182 -0.288789039 0.480840513
## [222,] -0.039761767 0.703929113 -0.373033697
## [223,] 0.010079466 1.221371572 2.261120159
## [224,] -0.121507357 0.881691121 -1.245092103
## [225,] -0.521299799 0.719888748 -0.723100351
## [226,] 0.254121961 0.215139144 1.558245583
## [227,] 0.620760475 0.446762061 0.614090086
## [228,] 0.363043859 -0.618238562 0.448088863
## [229,] 0.051468706 1.609411549 1.727352117
## [230,] -0.285930384 0.442595327 0.251930803
## [231,] -0.467790014 1.242150145 1.325849835
## [232,] -0.058920268 1.338359466 1.761908526
## [233,] 1.733419960 0.423852556 -0.165847325
## [234,] -0.493578477 -1.479006976 -0.241396652
## [235,] 0.504846270 -0.818180295 0.183327907
## [236,] 2.092540333 -1.807752560 1.857619238
## [237,] 1.142696825 -0.398559951 0.185232694
## [238,] 0.026755511 -0.881380694 -0.866777329
## [239,] -0.121247624 -0.477967682 -0.437647750
## [240,] 0.563696320 -0.827477340 0.075295855
## [241,] 0.719816781 -0.539953054 -1.393393169
## [242,] -0.247582111 -0.357468865 -0.164371873
## [243,] 0.712686061 1.397552961 1.379882683
## [244,] -1.942645066 -1.305694837 0.564531968
## [245,] -1.380697487 -1.532325985 -0.460404176
## [246,] -0.705383738 -0.628437127 0.083467578
## [247,] 0.097690524 0.283122530 -0.541468073
## [248,] 0.991984064 1.366848155 0.560919272
## [249,] -0.886021329 1.138240963 -0.070095714
## [250,] 0.852465130 -0.704094461 0.411497911
## [251,] -0.809992402 -0.187094258 0.314210493
## [252,] 0.388200371 2.087298039 -0.040421838
## [253,] -0.262355796 0.706396471 -1.547703408
## [254,] -1.670702485 -1.065037223 0.792987670
## [255,] -0.929160699 0.311042292 0.931783065
## [256,] -0.022085068 -0.123561929 0.834724396
## [257,] 1.121585636 0.782606230 -0.002929988
## [258,] 0.816463713 0.189725351 -1.497887227
## [259,] 0.162343739 0.910506786 -0.578872910
## [260,] -0.438895449 0.041113038 -1.597760163
## [261,] 0.485467130 -0.581622876 -0.053467052
## [262,] 0.306721963 0.566217987 2.860917960
## [263,] 0.493790574 1.251197862 1.037148639
## [264,] 0.466114998 0.564711184 -0.842463515
## [265,] -0.169674662 -0.436071529 -0.234071781
## [266,] 1.648013805 0.164205781 0.137926578
## [267,] 0.228056124 -0.533793382 -1.324065396
## [268,] -0.303157454 1.935041083 1.626003281
## [269,] -0.697466307 1.159723494 -0.008870419
## [270,] 0.088469136 2.111619071 -1.219045442
## [271,] -1.385832661 -2.137542197 -2.139286746
## [272,] -0.952409669 -0.338558107 -0.672072709
## [273,] -0.065081469 -0.681793166 -0.349921747
## [274,] 1.540318909 -1.077649964 0.954172461
## [275,] -2.770330332 -2.607445985 -0.758548075
## [276,] 0.802475092 1.041382813 -0.600864946
## [277,] 0.592683115 -1.521067532 0.393023187
## [278,] -1.066118724 -1.664238443 -0.247144370
## [279,] -0.526738989 -0.046777798 -0.052074869
## [280,] 0.610875598 -2.157776396 -0.462294491
## [281,] -0.570169834 1.146959043 0.498972561
## [282,] -2.068560518 -0.821971939 -1.015234820
## [283,] -0.841045380 1.452564302 0.356587017
## [284,] 0.439449445 -0.186047606 -0.455623071
## [285,] 1.336152595 -1.623755908 0.195541864
## [286,] -0.519148918 1.096808072 0.207219464
## [287,] -1.031329723 -1.312881062 -1.426196986
## [288,] -0.134816683 -0.552304404 0.274109437
## [289,] -0.654412877 -0.888099853 0.567680578
## [290,] -0.080146889 -1.041466372 0.806316108
## [291,] -0.604484182 -1.234802869 0.838667287
## [292,] 0.581166445 1.287382183 0.644300187
## [293,] 0.709438334 -0.175121442 -0.511160577
## [294,] 0.010057274 -0.143222321 -0.062335399
## [295,] 0.033973993 1.545784770 0.061501370
## [296,] -0.084986304 -1.014091585 1.056322039
## [297,] 0.075705602 -0.719209267 -0.087059443
## [298,] -1.043068750 1.207828841 1.143391603
## [299,] 0.447233192 0.494081329 -0.890640741
## [300,] -1.440588343 0.794179742 -0.951196427
## [301,] -1.981185023 -0.873924517 1.589670106
## [302,] -0.992365547 1.062201836 -0.138313826
## [303,] -0.002286346 0.396012306 -0.259775106
## [304,] 0.262566713 0.457189941 1.433306431
## [305,] 1.226864700 1.366349058 -1.365415543
## [306,] -0.587318191 -0.278313380 -0.512324622
## [307,] -0.409942967 -1.953777480 -0.470691255
## [308,] 0.336514874 -1.336545313 -1.694406630
## [309,] 0.694205834 -1.832606665 -0.715878270
## [310,] -0.378522948 0.560812103 0.986150399
## [311,] -0.877190793 -0.847150807 -0.296774835
## [312,] 0.341985399 -1.198095530 -1.534929147
## [313,] -0.468106824 0.819134566 -0.148042734
## [314,] -1.477051968 -0.456606567 -0.993900687
## [315,] -1.738418434 -1.205629087 -0.444106653
## [316,] -0.355376036 1.498245548 0.260888315
## [317,] 0.489917456 -0.659725341 0.242769043
## [318,] -0.115715262 -0.364768119 0.345155189
## [319,] -1.032672501 2.042401370 -0.978610343
## [320,] 0.832077337 -2.575856746 0.231401030
## [321,] 0.691587705 0.197592276 -0.658650241
## [322,] 0.330925446 1.090964012 0.719730467
## [323,] -0.868530045 0.651097251 -0.490418112
## [324,] -0.122435352 1.810982199 1.591043304
## [325,] -0.486404388 -0.117652405 0.075303359
## [326,] 0.465631527 -0.363743738 -0.235428705
## [327,] -0.961014922 -0.904312211 -0.023642917
## [328,] 0.516155341 -0.177023262 -1.652646017
## [329,] 1.934187472 -1.488338732 -0.886832522
## [330,] -0.654519459 0.160676434 0.205598901
## [331,] 0.910717187 -0.309722440 -0.746224269
## [332,] 0.676357817 0.498868258 0.166319227
## [333,] 0.067525579 0.368981088 1.289319077
## [334,] -0.510424989 0.969863413 -0.846669004
## [335,] -0.328576441 0.648211469 -1.572484402
## [336,] -1.215717814 0.544791368 0.412830879
## [337,] 0.986600445 -1.568521822 -0.106668975
## [338,] -1.779746061 -0.834130733 1.802718638
## [339,] -0.983498229 0.863441289 -1.002892965
## [340,] 0.836203802 -0.076323290 0.027460709
## [341,] -0.870463698 0.850849507 -0.221712178
## [342,] 0.804874986 -1.225652040 -0.111508559
## [343,] -0.543621364 0.626687669 -0.503939410
## [344,] 2.224558064 1.112778825 3.728275970
## [345,] -0.493451183 -1.701227810 1.551031615
## [346,] -0.391321978 -1.802361369 0.389801536
## [347,] 0.066126810 0.274682659 -0.737445769
## [348,] 0.480184151 -0.385515532 0.722517701
## [349,] -0.316559257 0.210173907 -1.157865734
## [350,] -0.501516129 -0.412642376 -0.781653278
## [351,] 0.156536583 2.224709022 2.446568770
## [352,] 1.001012207 -0.768100602 -0.196348683
## [353,] -0.918275396 0.816305557 -0.877412850
## [354,] 2.272228692 -0.142957301 0.651229348
## [355,] -0.134367076 -0.539001536 0.188089253
## [356,] -1.200738738 -1.271915849 -0.818734845
## [357,] -0.701623792 -1.397280062 -0.697052664
## [358,] 0.221883916 -0.614805089 -0.532288335
## [359,] -0.369281368 0.528709461 1.058190937
## [360,] -0.346939781 0.514104890 0.059484985
## [361,] -0.849359689 0.999007140 1.858234417
## [362,] -0.032336337 -1.197736205 -2.573147071
## [363,] 0.891457545 1.507664973 0.422107522
## [364,] -1.035087390 -0.280916276 -1.196970826
## [365,] 1.242646928 1.842427842 0.372382675
## [366,] 1.071916465 0.471858608 -0.559068055
## [367,] 0.448602644 -1.584054253 -0.198470053
## [368,] -0.039366154 0.670585094 2.482492558
## [369,] 0.105013567 -0.485207215 1.791575732
## [370,] 0.249988551 -1.306650029 1.635494849
## [371,] -0.268052990 0.511283741 0.255312064
## [372,] 0.097849607 0.569257070 -0.282788137
## [373,] -1.261253720 -1.640212185 -1.268484491
## [374,] 0.548457117 1.935348627 0.240105321
## [375,] -0.344306267 0.630317563 0.010667094
## [376,] -0.214701540 0.023268265 0.929548265
## [377,] 0.513016652 1.090807939 0.793143663
## [378,] 1.840405502 2.420920913 0.781040397
## [379,] -0.177075230 -1.319306194 0.116259618
## [380,] -1.129135404 0.074720766 1.789827001
## [381,] 0.222168297 0.505973177 -0.292843640
## [382,] 0.226681051 0.618548744 -0.442410888
## [383,] -1.530056897 0.959962094 -0.337305403
## [384,] -0.840298888 -0.368859295 -1.707110994
## [385,] -0.577900965 -1.149363949 -0.004424344
## [386,] -0.960599868 -1.590078895 0.003740977
## [387,] 0.645554969 0.433946042 2.403555603
## [388,] -1.979850364 -1.504965863 -2.040825999
## [389,] 0.172362390 0.388005255 0.291548732
## [390,] 0.609853201 -0.995414341 -0.375531472
## [391,] -1.481856754 0.227780609 0.305389399
## [392,] 0.166020628 -2.347062539 0.555038124
## [393,] 2.059995970 0.751579504 1.682792675
## [394,] 1.444641105 -0.371073925 -1.049280706
## [395,] 0.656131641 0.107018914 -0.436560405
## [396,] 0.056321927 0.976121169 -1.020623736
## [397,] 1.685204335 -1.109799760 -0.359752491
## [398,] -1.256329542 -1.215019595 0.070654494
## [399,] -1.426785075 -0.141770751 0.440299233
## [400,] 2.280138123 1.094464798 0.832663286
## [401,] -1.019851108 0.289728708 -1.133075538
## [402,] -1.564794349 0.279892649 0.937471893
## [403,] 0.761416155 0.816187935 1.013964608
## [404,] 0.990621856 -0.356918725 1.303670968
## [405,] -0.989519690 -1.948429781 0.186273604
## [406,] 0.453289533 0.971019291 -0.647310358
## [407,] 2.078525631 2.573658040 1.430702221
## [408,] 1.640193001 -0.032527944 -0.275261120
## [409,] -0.334332723 -0.247606306 1.469042159
## [410,] 0.356318096 -0.542002370 0.321315778
## [411,] 0.041525391 -0.887839716 2.329375918
## [412,] 0.488600015 0.364988591 0.763898697
## [413,] 0.031190460 0.838644903 1.863732724
## [414,] 0.013257058 -0.478424444 0.716426982
## [415,] 1.110176198 -0.111868321 1.036678958
## [416,] -0.491739061 1.617517708 -1.360710061
## [417,] 1.055319272 0.405448110 0.763433611
## [418,] 1.082511573 0.745965980 1.676290009
## [419,] 0.077609543 0.097825043 -1.183709058
## [420,] -0.172216026 -0.694545763 1.621383737
## [421,] 0.712798005 -3.137850045 -1.270566419
## [422,] 1.530831755 -0.072411546 -0.175080772
## [423,] -0.819467358 -0.803253141 0.170836143
## [424,] -0.401615541 0.311113933 0.708240989
## [425,] 0.054580163 -0.015954261 2.189080104
## [426,] -0.317553897 -1.326876226 -2.093209258
## [427,] 0.052080295 -1.123280447 0.963264938
## [428,] -0.487429529 -2.143379987 -0.477434815
## [429,] -0.213903056 0.870438983 -1.339886061
## [430,] 0.726912223 -0.206970832 1.341401819
## [431,] 0.604576062 0.363656752 -1.028071652
## [432,] 0.322576771 0.843239572 0.040099460
## [433,] -0.703545181 1.278642131 0.185936274
## [434,] -1.169668790 -0.490972226 0.806034132
## [435,] 0.470296071 0.135903856 -1.225106144
## [436,] -0.255114433 -0.904830500 0.036524249
## [437,] -1.113567360 -0.086854929 -1.311426073
## [438,] 0.617165075 2.004500358 0.703993537
## [439,] -0.491746921 0.820568998 -0.488708855
## [440,] 0.470815125 -1.957735045 -0.576277653
## [441,] 0.044706072 0.594537102 -0.433909663
## [442,] 0.032681082 -0.616975810 0.852148610
## [443,] -1.164441534 -1.332244567 -1.340536984
## [444,] -0.246664418 1.227243382 2.682327090
## [445,] -0.192169806 0.505830863 2.577869249
## [446,] -0.913184042 -0.438609406 -0.285787931
## [447,] 0.467240670 0.171597089 -0.762779679
## [448,] -0.450165207 0.641053737 -0.292518942
## [449,] -0.430977455 -0.154793161 0.811900210
## [450,] -1.079591894 1.218014797 -0.032294333
## [451,] 0.094525757 0.673674154 0.367902449
## [452,] -0.475595121 0.184796532 -0.877915883
## [453,] 1.011495006 -0.985393274 -0.024488212
## [454,] -0.197613012 -0.560112908 1.047874296
## [455,] -0.305294874 -0.124060795 0.294880177
## [456,] 0.074239212 -0.747963185 -1.112385092
## [457,] -0.291786903 1.304282575 0.731067018
## [458,] 0.607408208 -1.351164504 0.510298266
## [459,] -1.081895950 -0.236162533 0.171818676
## [460,] -1.183827545 0.755432667 0.626413669
## [461,] -0.949765511 -0.104472963 -2.058842674
## [462,] -0.341611584 1.179320438 0.925866788
## [463,] 0.162861091 -0.623816005 -0.882035419
## [464,] 0.083552411 0.341290068 -2.161127863
## [465,] -0.391508377 -1.110203561 0.352049101
## [466,] -1.042566454 0.838812028 -0.305760324
## [467,] -1.074704247 -0.166438100 0.030690218
## [468,] -0.494831471 0.743081792 1.177728722
## [469,] 0.681728403 0.111381583 -2.046201586
## [470,] -1.584792678 -0.401926755 -0.845136925
## [471,] -0.506601963 0.676450722 1.852197410
## [472,] 1.323159114 1.480135189 -0.066061360
## [473,] -0.006850566 -0.063536143 0.170013916
## [474,] 0.640300446 0.318814533 -0.177074400
## [475,] -0.003520900 -1.635233805 -0.460606737
## [476,] -0.750214406 -0.059007634 -0.122784474
## [477,] -1.101134271 -0.947344753 1.122482253
## [478,] -0.577756188 0.037345284 -1.108438099
## [479,] 0.154175323 -0.237605476 0.506762614
## [480,] 0.851483137 -0.756395211 -0.104241905
## [481,] 0.049290017 -0.901840905 -0.510670701
## [482,] 1.088882990 0.113323086 1.793545083
## [483,] 0.823530859 -0.013683117 -0.144841041
## [484,] -1.762731765 -2.485989934 -0.727354704
## [485,] -0.910944026 1.394161998 1.757766152
## [486,] 0.223184291 -1.041943527 -0.815605759
## [487,] 0.151255930 0.175162605 -1.627227079
## [488,] 2.863373174 0.142006646 -0.138054238
## [489,] -0.620993576 1.164015696 0.296552287
## [490,] 0.103090989 1.938986664 -0.140118724
## [491,] 1.700627869 -0.390356098 0.254283088
## [492,] -0.071109781 -0.255462516 -1.423330891
## [493,] -0.093199014 1.404115259 0.886817681
## [494,] -0.199756640 0.415272490 0.617505142
## [495,] 0.156681047 -2.555660292 -0.605550918
## [496,] -0.718461706 -1.562782558 1.576216220
## [497,] -0.135321369 0.695901715 -0.159423648
## [498,] -0.271135241 0.154969930 0.052533338
## [499,] 0.143553066 2.930335566 2.513558018
## [500,] 0.168560865 0.545731372 -1.382653090
## [501,] 0.673549250 0.793963531 2.025587849
## [502,] 1.510321517 0.933566128 0.349576409
## [503,] 1.348082974 -1.174822134 -1.567067140
## [504,] -0.190849013 0.203019701 0.441724205
## [505,] -0.038922321 -0.274002482 1.086503495
## [506,] -0.351892670 -0.462426636 0.556986285
## [507,] 0.746404438 0.891337315 -0.139526870
## [508,] 1.838206071 0.352348812 1.296448373
## [509,] -0.560154773 0.563050176 -0.444693049
## [510,] 0.099205581 0.687482077 -1.150252024
## [511,] 1.051855780 0.084979837 0.106770680
## [512,] -0.521750860 -1.806795857 1.413248874
## [513,] 0.388044547 -0.214324194 -0.478854073
## [514,] 0.720580074 -0.310487225 -1.228353762
## [515,] -0.372944236 0.362985327 -0.609939304
## [516,] -0.448570951 -0.578703316 -0.245976326
## [517,] 0.890728739 0.169315185 0.272941537
## [518,] 0.903501015 -1.546342001 0.021395611
## [519,] 1.279073779 -0.004134975 -0.228384419
## [520,] 0.600746689 -1.509354008 0.072587620
## [521,] 1.650682539 -0.083107683 -0.714687745
## [522,] 0.915806461 0.331069038 0.362503772
## [523,] 1.346856823 1.642586025 0.842091190
## [524,] 0.825696367 -1.235482923 -0.065673833
## [525,] 0.964728556 2.012940748 -0.525813344
## [526,] -0.483671520 -0.126325477 -0.781498671
## [527,] 0.170923151 0.090523558 -0.644349312
## [528,] 0.925973966 1.449411130 0.267682669
## [529,] 1.293491668 2.471053496 0.357861026
## [530,] 1.541352453 0.537381182 2.884251451
## [531,] 0.662489415 1.424711019 -1.186394482
## [532,] -0.080935472 -0.301176650 -0.891048097
## [533,] -0.041706329 -0.351864522 0.460365966
## [534,] 0.241852101 0.016471806 -0.206874162
## [535,] -1.179446238 -0.507842833 -0.739448507
## [536,] -1.312557637 1.576224929 0.871853858
## [537,] 0.299909114 -1.328978007 -1.221665015
## [538,] 0.007452727 0.962693825 0.510471243
## [539,] -0.009850033 0.779871552 -0.238855548
## [540,] 0.064826362 0.206973171 1.132084957
## [541,] 1.611896189 -1.921825762 -1.127904133
## [542,] 0.092809476 -1.311170484 -0.686920421
## [543,] -0.082936017 0.277701632 0.611144266
## [544,] -0.754015057 0.505491942 -0.550884603
## [545,] 1.423771269 0.010587326 -0.234567536
## [546,] -0.118672095 0.316775922 -0.346548034
## [547,] -0.183174542 -1.305495749 -1.078364030
## [548,] -0.519210555 -0.615750249 0.406569176
## [549,] -0.453574419 -0.605588862 0.192124665
## [550,] 0.294397157 -0.022925484 2.390680632
## [551,] 0.578921568 0.380497274 0.147523758
## [552,] 1.854406674 0.110913841 -0.977069146
## [553,] -0.199424215 -0.412453482 -1.458320771
## [554,] -0.095522464 -0.291014780 0.591935756
## [555,] -0.652624330 0.720725169 0.366106442
## [556,] -1.181746933 -1.660372174 0.106866342
## [557,] 0.164467677 0.330943733 -1.434814491
## [558,] 2.149923452 1.848883415 -0.088146521
## [559,] -0.872541558 -0.270192784 0.307419555
## [560,] 0.221773776 -0.860307028 -1.018819531
## [561,] 0.132500058 -1.072340724 -0.944365953
## [562,] 0.069035399 -1.013292597 -0.263941346
## [563,] -0.735719162 0.415254924 0.701877949
## [564,] -0.269723412 0.279094986 -0.104321601
## [565,] -1.914492088 1.022749973 1.749646350
## [566,] 1.218640029 1.211875701 -1.496025738
## [567,] -0.984371117 -1.039983954 0.663198049
## [568,] -0.012776822 0.213383402 0.032092585
## [569,] -0.151944604 -1.809320538 -2.068875742
## [570,] -1.994029441 -0.929809749 -1.378062991
## [571,] -0.679100046 0.389580559 0.492962541
## [572,] 0.105900839 0.517394455 0.338683871
## [573,] 0.891114960 -0.500224596 1.161082392
## [574,] 0.594859930 0.921991908 -1.143824425
## [575,] 0.535847514 -2.074925886 -2.021521742
## [576,] -0.423475382 2.102075370 1.424652802
## [577,] 1.117220623 1.516607539 1.019186231
## [578,] -0.260843316 -0.043351475 -0.435899961
## [579,] -0.249602690 0.505886969 -0.543328166
## [580,] 1.474027233 -0.218179075 1.060129747
## [581,] -0.644341129 -1.158559605 -0.659294696
## [582,] 0.720270803 -0.267900605 0.637524615
## [583,] 1.506678174 -0.555968051 -1.340501121
## [584,] -1.014096266 -0.683736921 -1.561543929
## [585,] 1.726358049 0.488705721 1.456331518
## [586,] -0.725070033 0.565884182 2.528063799
## [587,] 0.691724642 1.316841555 -0.391548970
## [588,] 1.439731411 1.142706315 0.083350777
## [589,] -0.064702540 0.715545570 -0.481643167
## [590,] -1.317253165 -0.260091553 -0.514211866
## [591,] 2.378141872 -0.118240417 0.461996263
## [592,] -0.470822879 0.544034512 0.633034597
## [593,] 0.254903048 -0.720785456 0.460839440
## [594,] -2.019789588 -0.663249278 -1.289904458
## [595,] 0.520326810 0.496251459 0.817506070
## [596,] 0.304119325 -0.228938554 0.679180068
## [597,] -0.379896057 -1.235094890 -1.065468918
## [598,] 0.673180877 0.888577870 0.071473377
## [599,] -0.438800471 -0.879086833 -1.779059484
## [600,] 1.805493130 -0.049061589 -0.924738709
## [601,] -0.497459902 -0.178059354 -0.287166116
## [602,] -0.780326786 0.532905526 1.500533108
## [603,] -0.031416494 -1.441143326 -1.080347270
## [604,] 0.245404479 -1.336374960 -0.652231602
## [605,] -0.385844988 -1.617781021 1.232638217
## [606,] -0.533829969 -0.703490958 -0.332492616
## [607,] 0.611080869 0.784422501 0.430891553
## [608,] -0.394916357 0.345048198 0.140146456
## [609,] -0.207794278 -2.374754746 -0.352068252
## [610,] 0.327810111 0.698236643 0.199245722
## [611,] -0.659495781 -1.039091245 1.883519701
## [612,] 0.730349924 0.274849675 -0.050980311
## [613,] 0.166968194 -1.402706890 0.791807965
## [614,] 1.767061647 0.268516497 0.656932770
## [615,] -2.057923052 -0.964182339 0.754558047
## [616,] 0.120078746 1.117932903 0.008176815
## [617,] -1.585978027 -1.139509029 0.960558591
## [618,] -0.625593514 -1.134348752 -0.132139573
## [619,] -0.793312009 -0.457216237 -0.471504390
## [620,] 0.156198117 2.572079092 0.898276373
## [621,] -0.707173473 0.408621747 2.755677279
## [622,] 0.419102948 1.343310706 -0.423655275
## [623,] -0.803567042 -1.581677785 -1.504732468
## [624,] 0.488419375 0.403033016 1.301973213
## [625,] 1.066328968 -2.169295675 -0.367353065
## [626,] 0.401217693 -0.806734942 -1.538556558
## [627,] -1.255469413 -0.552905361 1.093277391
## [628,] 1.416226932 0.440353028 0.586285945
## [629,] 0.129342587 -0.272023940 0.553914913
## [630,] 0.754229987 1.247380683 -0.359515533
## [631,] -0.187070961 0.123683835 -0.824793767
## [632,] 1.251868402 -0.404915826 -0.333251380
## [633,] 0.773097779 1.584793830 1.390962999
## [634,] -0.836458086 -1.423830295 -1.628634558
## [635,] 0.107578199 0.649764914 1.684583786
## [636,] -0.790090997 -0.014920051 1.042247063
## [637,] -0.166870716 -0.008786598 -0.167676200
## [638,] 0.042864416 -0.161429920 -0.075281104
## [639,] -0.203888991 -1.090938195 0.080013528
## [640,] -1.169518545 0.492887046 1.972402468
## [641,] 1.319737929 0.370396065 1.585828476
## [642,] 0.815631019 -0.966337631 1.648477143
## [643,] 0.751722945 1.232344251 1.012004911
## [644,] -0.291257853 -0.366136727 -0.441997328
## [645,] 0.020500337 0.270442038 -0.716403153
## [646,] 0.035802668 -0.550886380 -0.228675133
## [647,] -0.427158997 -1.370686271 -0.462375525
## [648,] 0.655652241 -0.657047028 -0.422227827
## [649,] 1.345800057 -0.447451489 -0.583049482
## [650,] 1.945396180 0.820964793 1.633273462
## [651,] 0.120873153 -1.603358036 -0.650560551
## [652,] -0.303686272 -0.275937445 -1.339001579
## [653,] -0.182380684 -0.511152960 -0.820980421
## [654,] -0.060577653 -1.385951433 -1.018996424
## [655,] 0.589367023 -0.495815798 0.509108396
## [656,] 1.549557142 0.533916087 0.013936817
## [657,] -0.437159581 0.267914145 0.072650391
## [658,] 0.012045590 1.041243515 -0.672788069
## [659,] 1.323884786 -0.889182746 -0.316598305
## [660,] 0.197580283 -0.247579629 1.251654367
## [661,] -0.819783253 0.457884758 0.588783358
## [662,] 1.087089640 1.159834180 0.220444051
## [663,] 0.948099060 0.428030947 0.824263315
## [664,] 1.131790263 0.149347245 2.150666294
## [665,] -1.074748700 0.506591354 0.212578254
## [666,] -0.478593358 0.043190310 0.518283697
## [667,] 0.690040639 0.880435924 -0.772941473
## [668,] -1.074131988 -0.730922502 -0.699895104
## [669,] -1.159114685 1.108994075 1.556181541
## [670,] 2.309113240 -0.069618315 2.057221259
## [671,] 0.307063546 0.004780379 0.664350082
## [672,] 1.452720463 1.025821001 -0.455678632
## [673,] 1.281598835 -0.346531789 0.879670975
## [674,] 0.400080839 0.046393990 1.666503399
## [675,] -0.931143600 1.659871111 -0.817787194
## [676,] -2.050884191 -2.158312344 -0.603398354
## [677,] 1.406965706 2.130433834 -0.320670604
## [678,] -1.147239828 0.208909734 -0.209038963
## [679,] 0.030137594 -0.140854477 -1.089274325
## [680,] 0.776404219 -0.004667117 1.506423785
## [681,] -0.440141894 1.455467687 0.186889213
## [682,] 0.069457554 0.616623916 -1.990736704
## [683,] 1.037732037 0.129583420 0.887444631
## [684,] 1.205934778 0.790975824 0.877432289
## [685,] 0.061064066 0.243761923 -0.449552407
## [686,] 1.198397648 -0.144095367 -1.544132218
## [687,] 0.271906002 0.512345719 -0.424832197
## [688,] -0.076824189 1.062841061 0.067166749
## [689,] 0.854583817 0.825629683 -0.263151662
## [690,] 0.231864649 -0.692546269 0.965137112
## [691,] -1.449057777 0.844451945 0.884373032
## [692,] -0.124961488 0.998439096 1.734286767
## [693,] 0.433177264 -1.071279582 -1.340224206
## [694,] 1.316789425 -0.857944381 -0.543612444
## [695,] 1.199420920 2.035482157 0.327098359
## [696,] -1.642272862 0.007114724 0.403888852
## [697,] -0.865630951 0.428251025 0.827952030
## [698,] -1.012594737 -0.395050921 0.271863257
## [699,] 1.120899184 0.427972186 0.057960502
## [700,] 1.767440913 0.736478040 2.193048176
## [701,] -1.220231135 -1.036386241 0.767277192
## [702,] 1.891288149 0.402100985 -0.758340092
## [703,] -0.014686663 0.941148399 0.512355813
## [704,] 0.339022340 1.915719441 0.026084106
## [705,] 1.187874331 -1.695229068 -1.760818626
## [706,] -1.146801381 -1.532021191 -1.216344729
## [707,] 1.105810214 -0.685743409 -1.479639337
## [708,] 1.373917767 0.077809375 0.955755684
## [709,] 1.235833365 0.324027162 1.861851902
## [710,] 1.866540907 -0.016809957 0.032275976
## [711,] -1.567612856 -0.325152060 1.654852093
## [712,] 0.415800720 -1.347926997 0.279905378
## [713,] 0.552765374 -1.012227066 -0.166307420
## [714,] -0.093732456 -1.465876026 -0.525799333
## [715,] -0.181115747 0.876056302 -0.970054900
## [716,] 0.985898549 -0.981923703 1.849351030
## [717,] 0.016261445 -0.378277445 -0.719073122
## [718,] -0.091845808 0.404137345 -0.481661340
## [719,] 0.724662208 -0.972308698 0.245136521
## [720,] 1.368172992 -0.044415783 -0.147105341
## [721,] 0.451994839 0.551222107 0.549000752
## [722,] 1.943603880 0.178369542 -0.055947102
## [723,] -0.199142719 0.431531802 -0.973244430
## [724,] -0.512216609 -0.439599273 -0.336338254
## [725,] 0.517075424 -0.992519774 0.198411184
## [726,] -1.590799528 -0.554594893 0.449464732
## [727,] 0.326486788 0.606687685 0.433248689
## [728,] 1.939438773 0.643602290 0.499661544
## [729,] 1.063235362 1.670137541 0.045228749
## [730,] -0.029387213 0.050140353 -1.113109873
## [731,] 0.114330612 1.446659428 0.779833161
## [732,] 1.141621851 0.101746711 -0.109988953
## [733,] -0.170083770 -0.344688340 -0.797694771
## [734,] 0.688559847 -0.124474824 -1.393387846
## [735,] 0.067381717 0.411921153 0.658042580
## [736,] -0.908690449 0.022496422 0.257219959
## [737,] -0.770336693 0.258304805 0.031934874
## [738,] 0.323944230 1.126746110 0.185198329
## [739,] 1.245685557 0.560795576 0.316127217
## [740,] 0.635616305 -0.353925080 0.848109227
## [741,] 0.249229677 0.555233553 -0.314866127
## [742,] -0.272293551 -1.130215165 -0.275439757
## [743,] 0.669899205 0.083508213 -0.367325380
## [744,] 1.317642070 1.552480174 -1.947277739
## [745,] -0.820888321 0.234740802 -1.921590283
## [746,] 0.200685231 0.018801077 0.585464031
## [747,] 0.687847415 1.325470669 0.447426074
## [748,] 0.678719574 -0.806235235 -0.771649934
## [749,] 0.848149275 0.784765346 0.668215851
## [750,] -0.500467110 -2.367802263 -1.653512807
## [751,] -1.275589067 -1.485563910 0.671524626
## [752,] 1.079488723 0.122082619 -0.261021612
## [753,] -0.855894054 -0.198014460 0.463728535
## [754,] -1.421101252 -0.979613480 -0.653796963
## [755,] 1.349731990 -0.455093858 0.032457556
## [756,] 0.548607259 -1.881956503 -0.018446739
## [757,] -0.238191132 1.297691015 -0.672151008
## [758,] 1.140298818 -1.118331700 -0.371910433
## [759,] -0.689059569 1.157820323 1.076592530
## [760,] -0.262489676 2.311106629 0.400135994
## [761,] 0.921271807 -0.327519121 0.836699883
## [762,] 1.055473162 -0.715184527 -1.223414468
## [763,] -0.278667045 -0.803284021 0.731493427
## [764,] -0.295053389 -0.498749136 -0.958572625
## [765,] 1.985073438 -0.337952164 1.449817127
## [766,] -1.040469902 -1.428722706 -0.359529496
## [767,] 0.485321047 0.558118957 -1.890446883
## [768,] 0.485745214 -2.500759695 0.809204591
## [769,] 0.554090948 0.187482034 -0.953976265
## [770,] 1.395699079 1.573274060 -1.628671188
## [771,] 1.180742298 -1.778666700 -0.397313779
## [772,] 0.301101595 -0.639847464 0.511331662
## [773,] 1.176805951 -1.248180926 -0.167417610
## [774,] -0.726634956 0.938105137 1.416364843
## [775,] 1.126457605 1.016163908 1.546661491
## [776,] -1.670786932 -0.505334188 -0.666468337
## [777,] 2.218794560 2.070972187 -0.895736388
## [778,] -0.794684221 -1.606583462 -1.480951112
## [779,] 0.351304182 1.870839315 -0.073943729
## [780,] 0.093294823 -1.013733198 0.444251477
## [781,] -0.496829215 -0.361732286 0.314771127
## [782,] -0.731258222 -0.953409929 -0.909703708
## [783,] 1.232055943 0.744319520 3.076027298
## [784,] 0.092860111 0.826179047 -0.610236864
## [785,] 0.471168901 0.076064058 0.279909632
## [786,] 1.744997321 0.868417240 1.221009271
## [787,] -1.098583684 -1.507841370 -0.947597844
## [788,] -0.103611985 0.853801995 0.979647144
## [789,] -0.750290947 0.347193333 -0.724541466
## [790,] 0.936985196 0.459008527 1.120389828
## [791,] -0.430473609 -1.527643542 1.158041284
## [792,] 0.052704710 0.314992664 1.099905591
## [793,] 0.374886913 -0.382574652 2.066715488
## [794,] 0.134228928 -1.998762512 0.242842393
## [795,] 0.428208564 -0.155482249 1.379902367
## [796,] 0.616863512 0.045715239 1.877434348
## [797,] 0.384835380 0.889220058 -0.693266911
## [798,] 1.541942570 1.179398012 -0.426243536
## [799,] -2.056087755 1.108703621 0.561853690
## [800,] -0.075749663 -0.187228327 0.233254991
## [801,] 1.669126525 0.115087412 1.596611873
## [802,] -0.538049512 1.957325144 -0.010910437
## [803,] 0.620968800 -0.785881472 0.051544659
## [804,] 0.981505524 0.539532154 0.905341621
## [805,] 0.966286525 -0.357147179 -0.900226035
## [806,] 0.974886589 1.058315673 0.652275987
## [807,] 0.187950834 -1.450643492 0.776237568
## [808,] -0.006212901 0.024857207 0.345336919
## [809,] 0.769020580 -1.867782802 1.094476347
## [810,] -0.690692366 -1.025245894 -0.841066119
## [811,] 1.085607405 -1.007431416 -1.883592332
## [812,] -0.651475141 -0.116703278 0.751568388
## [813,] -0.095757414 -2.633091959 -0.498514160
## [814,] 1.542309293 -0.445984584 -0.752424650
## [815,] -1.593069757 -0.054454806 -0.588059557
## [816,] 2.981472757 1.673194591 1.793022058
## [817,] 1.125162251 -1.003486989 -0.113937469
## [818,] -1.149940966 0.134604767 0.487203226
## [819,] -1.912447257 0.441136113 -0.145590660
## [820,] 1.362596201 1.635494894 -0.413423224
## [821,] 1.103809840 0.439660486 -1.070780089
## [822,] 0.559090300 1.030499530 -0.361512039
## [823,] 0.439859494 1.221475598 -1.005774267
## [824,] 1.211098396 1.670280887 -0.077643315
## [825,] -0.054818321 -0.982851455 0.504805607
## [826,] -0.191844398 1.552231495 1.748288254
## [827,] -1.347030281 -0.658549237 0.940985609
## [828,] -0.270514094 0.107218746 0.617482587
## [829,] -0.747187352 -0.938466312 0.057124840
## [830,] -0.138116893 0.567301893 0.317856498
## [831,] 0.881035693 -1.336319807 -0.650923616
## [832,] -1.538160754 0.267658548 -0.793214832
## [833,] 0.088054724 -0.054109226 -0.830933933
## [834,] 1.363983722 0.147141781 0.316046821
## [835,] -0.082141711 0.742959049 -1.134192187
## [836,] -0.260963457 -0.169249598 -0.010994507
## [837,] -0.300471736 -2.235085543 -3.368542856
## [838,] 0.232079129 0.920708320 -0.010809682
## [839,] 1.250489478 1.180066177 0.162447393
## [840,] 1.380226964 -0.533837294 -2.092448234
## [841,] 0.880819123 -1.116196881 -0.449312820
## [842,] -0.064390819 -0.682192754 -0.529621659
## [843,] -0.329877392 -0.307251329 -0.437844578
## [844,] -0.881246039 -0.949975539 1.024699941
## [845,] -1.290495947 -0.530624341 -0.611976227
## [846,] 0.351960116 0.904807236 -0.675575477
## [847,] -0.783503424 -0.546285921 1.045061972
## [848,] 0.633131943 -0.311838628 1.372945581
## [849,] -1.256838026 -0.113391050 0.077421266
## [850,] 0.181693092 -0.659554674 -0.177470791
## [851,] -0.924093116 -0.258829229 -0.827607574
## [852,] 1.847562026 0.082207853 -0.008690673
## [853,] -2.163469769 -0.017560201 -0.102098427
## [854,] -0.590135251 0.090627736 -1.107377655
## [855,] -0.865044954 -0.284240330 -0.598449287
## [856,] 0.150401720 -0.207294995 -2.843873164
## [857,] -0.513768587 -2.691949999 -2.750850737
## [858,] -0.401779444 0.220119833 -0.067419509
## [859,] -0.810403367 -0.789521407 -0.681244300
## [860,] -1.995703963 1.484694007 -0.918539306
## [861,] -1.093532030 0.784633253 0.298393695
## [862,] 0.220533123 1.119052165 -0.158403872
## [863,] 0.190579943 1.324516107 0.480218837
## [864,] -0.044382346 0.536004867 0.104869972
## [865,] -0.123581315 0.381581244 -0.004605238
## [866,] -1.693910177 -0.662479751 -1.579751663
## [867,] 1.319232420 -0.705264779 -1.536094931
## [868,] -0.868144885 -0.173036560 -0.173661021
## [869,] -0.647592221 -0.892087198 0.273116265
## [870,] 0.465905506 0.540820176 1.776686924
## [871,] -0.938822718 0.029522362 -0.144219287
## [872,] 1.049991143 -1.952381487 -0.666037954
## [873,] 0.788463258 0.529529901 -1.023819509
## [874,] 0.191090502 -0.513659200 1.511349191
## [875,] 0.956135872 0.337578858 -0.712842833
## [876,] -0.315054819 -1.079177119 1.412235780
## [877,] 1.152437292 1.028886982 1.632513689
## [878,] 0.906088687 0.280885782 -0.659424431
## [879,] 0.841627683 1.115994657 -0.241187945
## [880,] -1.135412182 1.482937425 -0.399680277
## [881,] 1.315873552 -0.487371932 0.054227588
## [882,] -0.975816043 0.239580076 -0.255186356
## [883,] 0.523354185 2.479025459 0.596012071
## [884,] 0.093961700 0.345935992 1.153908997
## [885,] -1.328332500 1.340938609 -1.384534045
## [886,] -1.250163474 -0.808484593 -0.947080656
## [887,] 0.156137509 -1.071334756 -0.194582233
## [888,] 1.236725035 -0.373933809 -0.099686611
## [889,] -0.606783909 0.544256822 1.807929751
## [890,] 0.249198464 0.493848377 -0.741970239
## [891,] 0.107290916 1.533354471 1.169773794
## [892,] 1.378336035 -0.680692162 0.218120482
## [893,] -0.930631619 0.023561081 2.950277414
## [894,] -0.997146631 -0.569367513 0.348438161
## [895,] 0.296033249 0.796048221 0.861776890
## [896,] -1.248634510 -0.670166775 -0.549014421
## [897,] 1.207066144 1.341955277 0.918889530
## [898,] -0.451998686 -0.300596231 -1.180232230
## [899,] -0.881851049 -1.792087575 0.968953793
## [900,] -0.727244135 -0.328755429 -0.387644721
## [901,] 0.170763221 1.107534283 1.014862541
## [902,] 1.389178906 1.926292554 1.564270265
## [903,] 0.059411732 -1.313038198 -1.184879534
## [904,] 0.204376930 0.232582853 2.016396306
## [905,] -1.586169567 -1.677517862 0.150163253
## [906,] 1.059696364 0.674083999 0.198883918
## [907,] -0.346008504 -0.598441972 0.918550532
## [908,] -0.016023039 -0.886951475 1.390857227
## [909,] 0.349838123 0.259581252 0.075867200
## [910,] -0.070381415 2.047720729 0.435316599
## [911,] -1.308016516 -1.191840808 -1.785754415
## [912,] -0.834941362 1.662685813 0.819380344
## [913,] -0.620953191 -0.182938529 -2.002970378
## [914,] 0.804804865 0.254074593 0.229413323
## [915,] 1.031452255 -0.582853563 0.270604742
## [916,] -0.525453216 0.204501725 -1.740164460
## [917,] 0.858280761 1.173594052 0.158605464
## [918,] 1.208237769 0.822243163 1.311340319
## [919,] -1.445342707 -1.121812303 -0.535593124
## [920,] 1.218541524 0.989100077 0.959258787
## [921,] 0.281633538 1.115959066 2.184406402
## [922,] 0.413104348 0.445290921 -1.134220253
## [923,] 1.252864844 -0.524380234 -1.152850829
## [924,] 1.711541348 0.473189385 1.056565112
## [925,] -0.016471801 0.289671233 0.974759833
## [926,] 0.857724605 0.712329450 1.149518698
## [927,] 0.081306398 -0.085198531 -0.293301639
## [928,] 1.139319788 1.481284674 0.523978435
## [929,] 0.261521552 0.835302648 -0.365138032
## [930,] -0.705759105 2.163791692 1.077690352
## [931,] -0.581363325 0.521245638 1.093654491
## [932,] -0.546280344 0.676951205 1.891348705
## [933,] 0.463087338 0.441787546 0.204614135
## [934,] -0.431474782 0.325958394 0.328646090
## [935,] 1.628336267 -0.234638475 1.293635439
## [936,] -1.541234905 1.097623176 0.692283445
## [937,] 0.996230612 0.405413113 0.595421040
## [938,] 0.411560987 -0.421471798 0.107218728
## [939,] 1.217378808 1.650252315 1.550322537
## [940,] -1.124837858 -0.330893973 1.851292728
## [941,] 2.555932480 1.185457135 0.343292069
## [942,] -0.831513991 -2.390168813 -2.005773345
## [943,] -0.691275338 1.359410163 -0.608882731
## [944,] -0.260231863 -0.055856552 -0.218150414
## [945,] -0.648565388 -1.180272753 0.537265457
## [946,] 1.282942627 0.672118581 0.053916747
## [947,] -0.320482002 0.452770210 0.506207657
## [948,] 1.963726199 0.331080496 -0.258007865
## [949,] 0.888462706 -0.048620989 0.866107426
## [950,] 0.467471600 -0.486228114 0.398477193
## [951,] 1.146789102 -2.487217923 -0.560164970
## [952,] 0.862642443 1.010843083 1.629081893
## [953,] -0.268641661 -1.031717151 0.039860500
## [954,] -1.942961678 -0.113445179 -1.029102785
## [955,] -0.158530043 -0.329096802 -0.356855473
## [956,] -0.328455357 -0.839690191 -0.319656101
## [957,] 1.374867778 1.473514625 0.738974216
## [958,] 0.459999711 2.109234712 0.005887259
## [959,] -1.933904789 -0.605042862 -0.356520559
## [960,] -0.004056031 -0.012808113 0.436029391
## [961,] 1.364516197 0.537394543 0.798061854
## [962,] 2.388726412 2.387612009 1.970981894
## [963,] -0.347269053 0.364388913 -0.233147209
## [964,] 0.412135592 -0.090140546 -0.508748252
## [965,] 0.085875952 0.304110191 0.549650731
## [966,] -0.750280031 0.521258487 1.155726961
## [967,] 0.283895021 1.357876130 0.743840498
## [968,] -0.326913513 -0.088993049 -0.699944425
## [969,] -1.346009595 1.336970332 0.673810059
## [970,] -0.730245217 -0.999808154 0.015611614
## [971,] -1.751403202 2.597854964 -0.114868721
## [972,] 1.196620593 -0.411514885 0.724964993
## [973,] 0.422977386 0.324986952 0.753482186
## [974,] 0.207510242 -0.439178493 -0.528158461
## [975,] 1.871916926 -1.735352070 0.743099721
## [976,] 0.187395496 0.337283059 0.974271879
## [977,] 0.745539813 -1.091718184 0.509578280
## [978,] 1.560688795 -1.612800323 -0.740663311
## [979,] -0.315498945 -2.361336722 -1.607962268
## [980,] -0.706753973 -1.857501153 -1.339971607
## [981,] 0.920286845 0.506672519 1.092067582
## [982,] -0.401704680 -2.327241945 0.515720536
## [983,] 0.461804365 0.908518240 -0.949001233
## [984,] 1.616314295 0.173046722 -0.498209327
## [985,] -1.176721738 0.209374026 -2.243311356
## [986,] 0.602637783 -0.087033717 0.218878709
## [987,] 0.828194545 0.292428100 0.481330642
## [988,] 1.238152226 -0.667355083 -0.622803795
## [989,] 1.823629690 0.765180074 -1.972939997
## [990,] -0.428460945 -0.832960123 0.139361256
## [991,] 0.103277517 -0.510242679 0.339368280
## [992,] -1.081362848 0.911187348 -1.092449417
## [993,] 0.121754416 1.172002343 1.800707429
## [994,] -0.145371457 -0.976883861 -0.942264886
## [995,] 0.898411510 -1.065629951 -1.281963761
## [996,] -0.816376433 -1.218431502 0.166117389
## [997,] -0.498411151 0.256071426 -0.765551862
## [998,] 0.154528020 -1.254242783 0.305043771
## [999,] 0.805116245 0.600213237 0.967449359
## [1000,] 0.022835701 1.091468271 -0.352146662
##
##
## $tail_upper
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0000000000 0.0229218236 0.0006580736 0.0003642573 0.0086222337
## [2,] 0.0229218236 0.0000000000 0.0003403118 0.0002020644 0.0010202037
## [3,] 0.0006580736 0.0003403118 0.0000000000 0.0279600531 0.0015607793
## [4,] 0.0003642573 0.0002020644 0.0279600531 0.0000000000 0.0010873192
## [5,] 0.0086222337 0.0010202037 0.0015607793 0.0010873192 0.0000000000
## [6,] 0.0016716968 0.0001881053 0.0001731534 0.0001989784 0.0008946779
## [7,] 0.0003546379 0.0001968458 0.0031051577 0.0018781417 0.0009311947
## [8,] 0.0002653675 0.0001912344 0.0023440362 0.0007910424 0.0005601702
## [,6] [,7] [,8]
## [1,] 0.0016716968 0.0003546379 0.0002653675
## [2,] 0.0001881053 0.0001968458 0.0001912344
## [3,] 0.0001731534 0.0031051577 0.0023440362
## [4,] 0.0001989784 0.0018781417 0.0007910424
## [5,] 0.0008946779 0.0009311947 0.0005601702
## [6,] 0.0000000000 0.0003608645 0.0001789964
## [7,] 0.0003608645 0.0000000000 0.0010056318
## [8,] 0.0001789964 0.0010056318 0.0000000000
##
## $tail_lower
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0000000000 0.0229218236 0.0006580736 0.0003642573 0.0086222337
## [2,] 0.0229218236 0.0000000000 0.0003403118 0.0002020644 0.0010202037
## [3,] 0.0006580736 0.0003403118 0.0000000000 0.0279600531 0.0015607793
## [4,] 0.0003642573 0.0002020644 0.0279600531 0.0000000000 0.0010873192
## [5,] 0.0086222337 0.0010202037 0.0015607793 0.0010873192 0.0000000000
## [6,] 0.0016716968 0.0001881053 0.0001731534 0.0001989784 0.0008946779
## [7,] 0.0003546379 0.0001968458 0.0031051577 0.0018781417 0.0009311947
## [8,] 0.0002653675 0.0001912344 0.0023440362 0.0007910424 0.0005601702
## [,6] [,7] [,8]
## [1,] 0.0016716968 0.0003546379 0.0002653675
## [2,] 0.0001881053 0.0001968458 0.0001912344
## [3,] 0.0001731534 0.0031051577 0.0023440362
## [4,] 0.0001989784 0.0018781417 0.0007910424
## [5,] 0.0008946779 0.0009311947 0.0005601702
## [6,] 0.0000000000 0.0003608645 0.0001789964
## [7,] 0.0003608645 0.0000000000 0.0010056318
## [8,] 0.0001789964 0.0010056318 0.0000000000# Ví dụ minh họa: tạo ma trận phụ thuộc Gaussian và t copula
# Kích thước 8x8 theo cổ phiếu của bạn
stocks <- c("MBB","VCB","HPG","NKG","SSI","VND","GEX","MSN")
# Nếu bạn đã tính ma trận copula, dùng trực tiếp:
# gaussian_rho <- ma_tran_gaussian
# t_rho <- ma_tran_t
# Nếu chưa có, tạo ma trận giả để test code:
set.seed(123)
gaussian_rho <- matrix(runif(64, 0, 1), nrow=8)
t_rho <- matrix(runif(64, 0, 1), nrow=8)
colnames(gaussian_rho) <- colnames(t_rho) <- stocks
rownames(gaussian_rho) <- rownames(t_rho) <- stockslibrary(ggplot2)
library(reshape2)
# Giả sử bạn đã có hai ma trận phụ thuộc (rho)
# gaussian_rho và t_rho (cùng kích thước)
# VD:
# gaussian_rho <- matrix(runif(64, 0, 1), nrow=8)
# t_rho <- matrix(runif(64, 0, 1), nrow=8)
colnames(gaussian_rho) <- colnames(t_rho) <- c("MBB","VCB","HPG","NKG","SSI","VND","GEX","MSN")
rownames(gaussian_rho) <- rownames(t_rho) <- colnames(gaussian_rho)
# Melt ma trận để ggplot
gaussian_melt <- melt(gaussian_rho)
t_melt <- melt(t_rho)
# Thêm cột loại copula
gaussian_melt$Copula <- "Gaussian"
t_melt$Copula <- "t"
# Kết hợp
df_plot <- rbind(gaussian_melt, t_melt)
# Vẽ heatmap
ggplot(df_plot, aes(Var1, Var2, fill=value)) +
geom_tile(color="white") +
geom_text(aes(label=round(value,2)), size=3) +
scale_fill_gradient2(low="blue", mid="white", high="red", midpoint=0.5, limit=c(0,1), name="Rho") +
facet_wrap(~Copula) +
theme_minimal() +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
labs(x="", y="", title="So sánh ma trận phụ thuộc giữa Gaussian và t Copula")
library(VineCopula)
# Gaussian copula
fit_gauss <- fitCopula(normalCopula(dim = ncol(U)), data = U, method = "ml")
# t-copula
fit_t <- fitCopula(tCopula(dim = ncol(U), df = 4), data = U, method = "ml")
# Joe copula (nếu muốn)
fit_joe <- fitCopula(joeCopula(dim = ncol(U)), data = U, method = "ml")library(copula)
# Giả sử bạn đã có dữ liệu chuẩn hóa U
# Nếu nhiều cổ phiếu, bạn có thể chọn 2-3 cổ phiếu chính để nhẹ máy
U_small <- U[, 1:3] # chọn 3 cổ phiếu đầu, ví dụ MBB, VCB, HPG
# Fit copula
fit_gauss <- fitCopula(normalCopula(dim = ncol(U_small)), data = U_small, method = "ml")
fit_t <- fitCopula(tCopula(dim = ncol(U_small)), data = U_small, method = "ml")
fit_joe <- fitCopula(joeCopula(dim = ncol(U_small)), data = U_small, method = "ml")
# So sánh AIC/BIC nhẹ nhàng
aic_values <- c(
Gaussian = AIC(fit_gauss),
t = AIC(fit_t),
Joe = AIC(fit_joe)
)
bic_values <- c(
Gaussian = BIC(fit_gauss),
t = BIC(fit_t),
Joe = BIC(fit_joe)
)
print(aic_values)## Gaussian t Joe
## -703.4235 -701.3398 -514.2937## Gaussian t Joe
## -697.6254 -689.7436 -508.4956# Nếu muốn kiểm định GoF nhẹ, giảm N
gof_gauss <- gofCopula(fit_gauss@copula, x = U_small, N = 200)
gof_joe <- gofCopula(fit_joe@copula, x = U_small, N = 200)## Warning in fitCopula.ml(copula, u = data, method = method, start = start, :
## possible convergence problem: optim() gave code=52
## Warning in fitCopula.ml(copula, u = data, method = method, start = start, :
## possible convergence problem: optim() gave code=52## [1] 0.002487562## [1] 0.002487562## [1] 1.337895## [1] 0.1606729# Giả sử muốn tạo 1000 quan sát
set.seed(123)
sim_joe <- rCopula(1000, fit_joe@copula)
# Kiểm tra dữ liệu mô phỏng
head(sim_joe)## [,1] [,2] [,3]
## [1,] 0.3646394 0.84737744 0.3839692
## [2,] 0.8352232 0.59288942 0.2940890
## [3,] 0.1446741 0.13831861 0.4325025
## [4,] 0.8493439 0.77143535 0.7485971
## [5,] 0.8686778 0.72985025 0.8488355
## [6,] 0.3318579 0.07823425 0.3693272## [1] 0.7497542## 5%
## 0.03814659library(ggplot2)
df <- as.data.frame(sim_joe)
ggplot(df, aes(x=V1, y=V2)) +
geom_point(alpha=0.5) +
labs(title="Simulated Joe copula", x="U1", y="U2")
library(copula)
library(ggplot2)
# Giả sử fit_joe đã có
# fit_joe@copula@parameters
# 1. Mô phỏng 1000 quan sát từ Joe copula
set.seed(123)
sim_joe <- rCopula(1000, fit_joe@copula)
# 2. Kiểm tra dữ liệu
head(sim_joe)## [,1] [,2] [,3]
## [1,] 0.3646394 0.84737744 0.3839692
## [2,] 0.8352232 0.59288942 0.2940890
## [3,] 0.1446741 0.13831861 0.4325025
## [4,] 0.8493439 0.77143535 0.7485971
## [5,] 0.8686778 0.72985025 0.8488355
## [6,] 0.3318579 0.07823425 0.3693272## [1] 0.5681873## [1] 0.03814659 0.03739132 0.04701377# 5. Tính VaR đa chiều (joint)
# Ví dụ xác suất 5% cho đồng thời cả 3 biến dưới VaR_5
pCopula(VaR_5, fit_joe@copula)## [1] 0.000117543# 6. Chuyển sang data frame để vẽ
df <- as.data.frame(sim_joe)
colnames(df) <- c("V1", "V2", "V3")
# 7. Vẽ scatter plot từng cặp
pairs(df, main="Simulated Joe Copula (3 variables)")
# Hoặc vẽ 2D scatter từng cặp riêng bằng ggplot
ggplot(df, aes(x=V1, y=V2)) +
geom_point(alpha=0.5) +
labs(title="V1 vs V2 - Joe Copula", x="V1", y="V2")
ggplot(df, aes(x=V1, y=V3)) +
geom_point(alpha=0.5) +
labs(title="V1 vs V3 - Joe Copula", x="V1", y="V3")
ggplot(df, aes(x=V2, y=V3)) +
geom_point(alpha=0.5) +
labs(title="V2 vs V3 - Joe Copula", x="V2", y="V3")
# Thư viện cần thiết
library(copula)
library(ggplot2)
library(GGally)
# 1. Tạo Joe copula 8 chiều
param_joe <- 1.337895 # tham số đã ước lượng từ Joe copula
joe8 <- joeCopula(param = param_joe, dim = 8)
# 2. Mô phỏng 1000 quan sát
set.seed(123)
sim_8 <- rCopula(1000, joe8)
# 3. Chuyển sang data frame
df8 <- as.data.frame(sim_8)
colnames(df8) <- paste0("V", 1:8)
# 4. Kiểm tra dữ liệu
head(df8)| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 |
|---|---|---|---|---|---|---|---|
| 0.3646394 | 0.8473775 | 0.3839692 | 0.0401600 | 0.5826657 | 0.1410338 | 0.5923582 | 0.2686753 |
| 0.8352232 | 0.5928894 | 0.2940890 | 0.7050778 | 0.2695275 | 0.2562489 | 0.4661646 | 0.2252080 |
| 0.1446741 | 0.1383186 | 0.4325025 | 0.1018113 | 0.1863881 | 0.6529285 | 0.0138164 | 0.1234449 |
| 0.8493440 | 0.7714354 | 0.7485971 | 0.3843760 | 0.6424492 | 0.6665342 | 0.6204969 | 0.7348230 |
| 0.8686778 | 0.7298503 | 0.8488355 | 0.7961926 | 0.8415360 | 0.9261760 | 0.9445370 | 0.9604127 |
| 0.3318579 | 0.0782343 | 0.3693272 | 0.9842828 | 0.9138341 | 0.0765513 | 0.5611360 | 0.6881926 |
library(copula)
library(GGally)
library(ggplot2)
set.seed(123)
# --- Tham số ước lượng từ trước ---
par_gauss <- 0.2 # ví dụ, điền đúng từ fit_gauss@copula@parameters
par_t <- 0.25
df_t <- 4
par_joe <- 1.337895
dim_cop <- 8
n_sim <- 1000
# --- 1. Gaussian Copula ---
gauss8 <- normalCopula(param = par_gauss, dim = dim_cop)
sim_gauss <- rCopula(n_sim, gauss8)
df_gauss <- as.data.frame(sim_gauss)
colnames(df_gauss) <- paste0("V", 1:dim_cop)
# --- 2. t Copula ---
t8 <- tCopula(param = par_t, dim = dim_cop, df = df_t)
sim_t <- rCopula(n_sim, t8)
df_tcop <- as.data.frame(sim_t)
colnames(df_tcop) <- paste0("V", 1:dim_cop)
# --- 3. Joe Copula ---
joe8 <- joeCopula(param = par_joe, dim = dim_cop)
sim_joe <- rCopula(n_sim, joe8)
df_joe <- as.data.frame(sim_joe)
colnames(df_joe) <- paste0("V", 1:dim_cop)
# --- Vẽ scatterplot matrix ---
ggpairs(df_gauss) + ggtitle("Gaussian Copula - 8 biến")
# --- Vẽ heatmap tương quan Spearman ---
library(reshape2)
cor_heatmap <- function(df, title=""){
cor_mat <- cor(df, method="spearman")
melted <- melt(cor_mat)
ggplot(melted, aes(Var1, Var2, fill=value)) +
geom_tile() +
scale_fill_gradient2(low="blue", mid="white", high="red", midpoint=0) +
geom_text(aes(label=round(value,2)), color="black") +
theme_minimal() +
ggtitle(title)
}
cor_heatmap(df_gauss, "Gaussian Copula Spearman")
