knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Given (Sokol eq 6): The example will be a single unit where we add a new one.
$$h_w = \frac{T_1 H_1}{T_1}$$ $$h_w + \Delta h_w = \frac{T_1 H_1 + T_2 H_2}{T_1 + T_2}$$
where:
Multiply denominators:
$$h_w T_1 = T_1 H_1$$ $$h_w T_1 + h_w T_2 + \Delta h_w T_1 + \Delta h_w T_2 = T_1 H_1 + T_2 H_2$$
Subtract first from second:
$$h_w T_2 + \Delta h_w T_1 + \Delta h_w T_2 = T_2 H_2$$
Put $T_2$ on one side: $$\Delta h_w T_1 = T_2 H_2 - h_w T_2 - \Delta h_w T_2$$
Rearrange: $$\frac{\Delta h_w T_1}{H_2 - h_w - \Delta h_w} = T_2$$
Can also solve for the transmissivity ratio: $$\frac{\Delta h_w}{H_2 - h_w - \Delta h_w} = \frac{T_2}{T_1}$$
Start from the previous proof: $$h_w T_2 + \Delta h_w T_1 + \Delta h_w T_2 = T_2 H_2$$
Rearrange: $$H_2 = \frac{h_w T_2 + \Delta h_w T_1 + \Delta h_w T_2}{T_2}$$
Can we recover missing values? In this example we test if we can recover the missing value for the $10^{th}$ interval.
library(sokol) set.seed(123) transmissivity <- abs(rnorm(10)) head <- sort(rnorm(10)) plot_blended(transmissivity, head) blended_1 <- estimate_blended_head(transmissivity[1:9], head[1:9]) blended_2 <- estimate_blended_head(transmissivity, head) # estimate interval head estimate_missing(blended_2, c(transmissivity), c(head[1:9], NA_real_)) head[10] # estimate interval transmissivity estimate_missing(blended_2, c(transmissivity[1:9], NA_real_), c(head)) transmissivity[10] # estimate blended head estimate_missing(NA_real_, c(transmissivity), c(head)) blended_2
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