The R package funtimes contains the function beales that can be used to implement Beale’s (Beale 1962) ratio estimator for estimating total value. The function also calculates recommended sample size for desired confidence level and absolute or relative error.
The Beale’s estimator is often used in ecology to compute total pollutant load, \(\widehat{Y}\), given a sample of the loads \(y_i\) and corresponding river flow or discharges, \(x_i\) (\(i = 1,\ldots,n\)): \[ \widehat{Y} =X\frac{\bar{y}}{\bar{x}}\frac{\left( 1+ \theta\frac{s_{xy}}{\bar{x}\bar{y}}\right)}{\left( 1+\theta\frac{s^2_x}{\bar{x}^2} \right)}, \] where \(\theta=n^{-1} - N^{-1}\), \(s_{xy}=(n-1)^{-1}\sum_{i=1}^n(x_i-\bar{x})(y_i-\bar{y})\), and \(s^2_{x}=(n-1)^{-1}\sum_{i=1}^n(x_i-\bar{x})^2\). Total flow, \(X=\sum_{i=1}^Nx_i\), is assumed to be known. If the data set for flow contains only \(n'\) observations (\(n\leqslant n'< N\)), we use an estimate \(\widehat{X}=\frac{N}{n'}\sum_{i=1}^{n'}x_i\) following formula (2.8) in Thompson (2012).
Help file for the function can be opened from R with:
The function uses the following groups of arguments as its inputs.
x and y (both are required) for discharge and corresponding load measurements;level defines the confidence level (optional; if not specified, level = 0.95 is used, i.e., 95%);N (optional, see details in the section below).verbose (optional) is a logical value (TRUE or FALSE) defining whether text output should be shown. If not specified, its value is set to TRUE to show the text outputs.p relative error, ord margin of error.The ideal case is when all discharge data are know, and only some measurements of loads are missing.
The inputs should be organized in vectors of same length. Consider a toy example where ten measurements cover the whole period of interest (i.e., the population size N = 10):
discharge <- c(60, 50, 90, 100, 80, 90, 100, 90, 80, 70)
loads <- c(33, 22, 44, 48, NA, 44, 49, NA, NA, 36)NAs stand for missing values.
To estimate the total load for this period, use:
B10 <- beales(x = discharge, y = loads)
# [1] "Beale's estimate of the total (for population size 10) is 394.578 with 95% confidence interval from 386.921 to 402.235."By default (the setting verbose = TRUE), the function shows text output. All estimates have been saved in the object B10 and can be extracted from there. For example, see the list of elements saved in B10, then extract the standard error of the load estimate, and population size:
If a different level of confidence (default is 95%) is needed, set it using the argument level:
B11 <- beales(x = discharge, y = loads, level = 0.9)
# [1] "Beale's estimate of the total (for population size 10) is 394.578 with 90% confidence interval from 388.152 to 401.004."To suppress the text outputs, use verbose = FALSE:
It is common that some discharge data are missing. The function fills-in the missing discharge measurements with average estimates automatically. For example, now the first discharge value is missing:
discharge2 <- c(NA, 50, 90, 100, 80, 90, 100, 90, 80, 70)
loads2 <- c(33, 22, 44, 48, NA, 44, 49, NA, NA, 36)The NA in discharge will be replaced by an average value of the non-missing measurements, and the first pair of discharge and load (average discharge and the corresponding load of 33) will be still used in estimating covariance and other quantities. Simply use the function in the same way as above:
B20 <- beales(x = discharge2, y = loads2)
# [1] "Beale's estimate of the total (for population size 10) is 394.526 with 95% confidence interval from 381.745 to 407.306."In another case, both discharge and load data might be missing. If they are not represented at all in the data vectors (e.g., by NAs), a simple trick is to set the population size, N, which is one of the arguments in the function. For example, if the data above are ten monthly measurements, and an estimate for the whole year (12 months) is required, set N = 12:
B21 <- beales(x = discharge2, y = loads2, N = 12)
# [1] "Beale's estimate of the total (for population size 12) is 473.542 with 95% confidence interval from 455.453 to 491.632."which is equivalent to adding two missing values to each vector, like this:
The other two arguments of the function, p and d, allow the user to set the desired relative error or margin of error, respectively, for sample size calculations. (If both p and d are defined, the calculations will run for p.) The estimated sample size, \(\hat{n}\), is added to the output list as the element nhat, and an additional sentence is printed out at the output.
For example, using our simulated data for 10 months out of 12, estimate the sample size needed to estimate the total yearly load with the relative error up to 5%:
B30 <- beales(x = discharge2, y = loads2, N = 12, p = 0.05)
# [1] "Beale's estimate of the total (for population size 12) is 473.542 with 95% confidence interval from 455.453 to 491.632."
# [1] "To obtain a 95% confidence interval with a relative error of 5%, a sample of size 6 is required."What if increase the confidence of such interval (notice the differences in the last line of the output):
B31 <- beales(x = discharge2, y = loads2, N = 12, p = 0.05, level = 0.99)
# [1] "Beale's estimate of the total (for population size 12) is 473.542 with 99% confidence interval from 449.769 to 497.316."
# [1] "To obtain a 99% confidence interval with a relative error of 5%, a sample of size 8 is required."Similarly, when the margin of error is set:
x and y are of different lengths.n is the number of non-missing values in y (missing values in x are replaced with an average, \(\bar{x}\), automatically).N is set such that N < length(x) (more discharge samples than possible in a given period) or if N <= n (sample size is bigger than or equals the population size). In the case when N = n, no estimation is needed, because the total load can be calculated just by summing up all individual loads.n > 1 (for estimating the variances and covariance), and \(\bar{x}\neq 0\) and \(\bar{y}\neq 0\).Beale, E. M. L. 1962. “Some Uses of Computers in Operational Research.” Industrielle Organisation 31 (1): 27–28.
Thompson, S. K. 2012. Sampling. 3rd ed. Hoboken: Wiley.