## ----setup, include=F, error=T, message=F, warning=F---------------------
knitr::opts_chunk$set(echo=T, comment=NA, error=T, warning=F, message = F, fig.align = 'center', results="hold")
## ------------------------------------------------------------------------
# get a z value using DPR correlation
library(zFactor)
# library(rJava)
z.Ann10(pres.pr = 1.5, temp.pr = 2.0)
# HY = 0.9580002
## ------------------------------------------------------------------------
# get a z value from the SK chart at the same Ppr and Tpr
library(zFactor)
tpr_vec <- c(2.0)
getStandingKatzMatrix(tpr_vector = tpr_vec,
pprRange = "lp")[1, "1.5"]
## ------------------------------------------------------------------------
library(zFactor)
z.Ann10(pres.pr = 1.5, temp.pr = 1.1)
## ------------------------------------------------------------------------
library(zFactor)
tpr_vec <- c(1.1)
getStandingKatzMatrix(tpr_vector = tpr_vec,
pprRange = "lp")[1, "1.5"]
## ------------------------------------------------------------------------
# test HY with 1st-derivative using the values from paper
ppr <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5)
tpr <- c(1.3, 1.5, 1.7, 2)
dpr <- z.Ann10(ppr, tpr)
print(dpr)
# From Hall-Yarborough
# 0.5 1.5 2.5 3.5 4.5 5.5 6.5
# 1.3 0.9176300 0.7534433 0.6399020 0.6323003 0.6881127 0.7651710 0.8493794
# 1.5 0.9496855 0.8581232 0.7924067 0.7687902 0.7868071 0.8316848 0.8906351
# 1.7 0.9682547 0.9134862 0.8756412 0.8605668 0.8694525 0.8978885 0.9396353
# 2 0.9838234 0.9580002 0.9426939 0.9396286 0.9490995 0.9697839 0.9994317
# From Dranchuk-AbouKassem
# 0.5 1.5 2.5 3.5 4.5 5.5 6.5
# 1.3 0.9203019 0.7543694 0.6377871 0.6339357 0.6898314 0.7663247 0.8499523
# 1.5 0.9509373 0.8593144 0.7929993 0.7710525 0.7896224 0.8331893 0.8904317
# 1.7 0.9681353 0.9128087 0.8753784 0.8619509 0.8721085 0.9003962 0.9409634
# 2 0.9824731 0.9551087 0.9400752 0.9385273 0.9497137 0.9715388 1.0015560
## ------------------------------------------------------------------------
library(zFactor)
sk <- getStandingKatzMatrix(ppr_vector = ppr, tpr_vector = tpr)
print(sk)
## ------------------------------------------------------------------------
err <- round((sk - dpr) / sk * 100, 2)
err
# DAK
# 0.5 1.5 2.5 3.5 4.5 5.5 6.5
# 1.30 -0.47 0.22 0.03 -0.15 -0.85 -0.97 -0.71
# 1.50 -0.31 -0.04 0.13 -0.14 0.05 0.34 0.18
# 1.70 -0.01 0.13 0.07 -0.58 -0.94 -0.38 0.11
# 2.00 -0.05 0.09 0.10 -0.16 -0.50 -0.26 0.14
## ------------------------------------------------------------------------
print(colSums(err))
## ------------------------------------------------------------------------
print(rowSums(err))
## ------------------------------------------------------------------------
library(zFactor)
tpr2 <- c(1.05, 1.1)
ppr2 <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5)
sk2 <- getStandingKatzMatrix(ppr_vector = ppr2, tpr_vector = tpr2, pprRange = "lp")
sk2
## ------------------------------------------------------------------------
# calculate z values at lower values of Tpr
library(zFactor)
dpr2 <- z.Ann10(ppr2, tpr2)
print(dpr2)
## ------------------------------------------------------------------------
err2 <- round((sk2 - dpr2) / sk2 * 100, 2)
err2
# DAK
# 0.5 1.5 2.5 3.5 4.5 5.5
# 1.05 -0.13 -12.15 -12.78 -7.49 -4.34 -1.68
# 1.10 -0.36 -4.79 -4.97 -3.56 -2.14 -1.21
## ------------------------------------------------------------------------
t_err2 <- t(err2)
t_err2
## ------------------------------------------------------------------------
sum_t_err2 <- summary(t_err2)
sum_t_err2
## ------------------------------------------------------------------------
# We can see that the errors in `z` with `ANN10` are less than `HY` with a `r sum_t_err2[1,1]`% and `r sum_t_err2[6,1]`% for `Tpr = 1.05`, and a `r sum_t_err2[1,2]`% and `r sum_t_err2[6,2]`% for `Tpr = 1.10`.
## ------------------------------------------------------------------------
library(zFactor)
library(tibble)
tpr2 <- c(1.05, 1.1, 1.2, 1.3)
ppr2 <- c(0.5, 1.0, 1.5, 2, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5)
sk_dpr_2 <- createTidyFromMatrix(ppr2, tpr2, correlation = "N10")
as.tibble(sk_dpr_2)
## ------------------------------------------------------------------------
library(ggplot2)
p <- ggplot(sk_dpr_2, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
geom_line() +
geom_point() +
geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.4,
position=position_dodge(0.05))
print(p)
## ------------------------------------------------------------------------
sk_dpr_3 <- sk_dpr_2[sk_dpr_2$Tpr==1.05,]
sk_dpr_3
## ------------------------------------------------------------------------
p <- ggplot(sk_dpr_3, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
geom_line() +
geom_point() +
geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.4,
position=position_dodge(0.05))
print(p)
## ------------------------------------------------------------------------
summary(sk_dpr_3)
# dif ANN10
# Min. :-0.048404
# 1st Qu.:-0.035300
# Median :-0.025978
# Mean :-0.023178
# 3rd Qu.:-0.009960
# Max. : 0.002325
## ------------------------------------------------------------------------
library(ggplot2)
library(tibble)
# get all `lp` Tpr curves
tpr_all <- getCurvesDigitized(pprRange = "lp")
ppr <- c(0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5)
sk_corr_all <- createTidyFromMatrix(ppr, tpr_all, correlation = "N10")
as.tibble(sk_corr_all)
p <- ggplot(sk_corr_all, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
geom_line() +
geom_point() +
geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.4,
position=position_dodge(0.05))
print(p)
## ------------------------------------------------------------------------
# MSE: Mean Squared Error
# RMSE: Root Mean Sqyared Error
# RSS: residual sum of square
# ARE: Average Relative Error, %
# AARE: Average Absolute Relative Error, %
library(dplyr)
grouped <- group_by(sk_corr_all, Tpr, Ppr)
smry_tpr_ppr <- summarise(grouped,
#mean=mean(z.calc),
#sd=sd(z.calc),
RMSE= sqrt(mean((z.chart-z.calc)^2)),
MSE = sum((z.calc - z.chart)^2) / n(),
# rmse2 = sqrt(sum((z.chart-z.calc)^2)/n()),
RSS = sum((z.calc - z.chart)^2),
ARE = sum((z.calc - z.chart) / z.chart) * 100 / n(),
AARE = sum( abs((z.calc - z.chart) / z.chart)) * 100 / n()
)
ggplot(smry_tpr_ppr, aes(Ppr, Tpr)) +
geom_tile(data=smry_tpr_ppr, aes(fill=AARE), color="white") +
scale_fill_gradient2(low="blue", high="red", mid="yellow", na.value = "pink",
midpoint=12.5, limit=c(0, 25), name="AARE") +
theme(axis.text.x = element_text(angle=45, vjust=1, size=11, hjust=1))+
coord_equal()
## ----results="hold"------------------------------------------------------
# get all `lp` Tpr curves
tpr <- getCurvesDigitized(pprRange = "lp")
ppr <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5)
# calculate HY for the given Tpr
all_dak <- z.Ann10(pres.pr = ppr, temp.pr = tpr)
cat("Calculated from the correlation \n")
print(all_dak)
cat("\nStanding-Katz chart\n")
all_sk <- getStandingKatzMatrix(ppr_vector = ppr, tpr_vector = tpr)
all_sk
# find the error
cat("\n Errors in percentage \n")
all_err <- round((all_sk - all_dak) / all_sk * 100, 2) # in percentage
all_err
cat("\n Errors in Ppr\n")
summary(all_err)
# for the transposed matrix
cat("\n Errors for the transposed matrix: Tpr \n")
summary(t(all_err))
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