Skrmdb Manual
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Package ‘skrmdb’ August 3, 2018 Type Package Title Package to estimate ED50 Version 4.2.5 Date 2018-08-03 Author David Siev Maintainer Marie VendettuoliDescription Package to estimate ED50 by the methods of Spearman-Karber, Reed-Muench, Dragstedt-Behrens. No endorsement, claim, or warranty is implied for this package. It is made available for investigational or pedagogical use only License MIT + file LICENSE LazyLoad yes Depends R (>= 3.4.4) Imports methods, stats Collate 'class-skrmdb.r' 'skrmdb-package.r' 'aaa.r' 'bbb.r' 'dragbehr.r' 'reedmuench.r' 'skrmdb.R' RoxygenNote 6.0.1 NeedsCompilation no R topics documented: skrmdb-package DragBehr . . . getED . . . . . ReedMuench . sk-class . . . . SKRMDB-class SpearKarb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 4 5 7 7 8 11 1 2 DragBehr skrmdb-package skrmdb package. Description Includes functions to estimate ED50 by the methods of Spearman-Karber, Reed-Muench, DragstedtBehrens. For internal use only at the USDA Center for Veterinary Biologics Details Package: Type: Version: Date: License: LazyLoad: LazyData: skrmdb-package Package 4.2.5 2018-08-03 MIT yes yes Resources • QUICK START: https://github.com/ABS-dev/skrmdb/blob/master/README.md • PACKAGE VIGNETTE: https://www.aphis.usda.gov/animal_health/vet_biologics/ publications/STATWI0001.pdf • BUG REPORTS: https://github.com/ABS-dev/skrmdb/issues Author(s) David Siev DragBehr Dragstedt-Behrens estimator Description Gives the Dragstedt-Behrens estimate of median effective dose (ED50) Usage DragBehr(formula = NULL, data = NULL, y, n, x, warn.me = T, show = F) DragBehr 3 Arguments formula a formula of the form cbind(y,n) ~ x data a data frame y the number responding (response should be monotone increasing) n the group size x log dilution or dose warn.me boolean to give warning message show boolean to display extended summary Details Data input may either be a formula and data frame, or variables may be input directly (see example). The Dragstedt-Behrens method estimates the median effective dose by interpolating between the two doses that bracket the dose producing median response. It accumulates sums in both directions by assuming that those that responded at a lower dose would respond at a higher dose, and those that did not respond at a higher dose would not respond at a lower dose. The hypothetical fraction that would have responded at a particular dose is estimated from the cumulative sums at that dose. The ED50 is estimated by interpolation on the line that connects the hypothetical fractions of the bracketing doses. Value object of SKRMDB class with slots: ed Estimated median effective dose (ED50) eval Evaluation method: ’DragBehr’ Note Many microbiology texts mistakenly present the Dragstedt-Behrens method as the Reed-Muench method. And yes, it is absurd to have an R function for an archaic method developed to avoid complex calculations. Input data is expected to be sorted by x (either increasing or decreasing). Use of unsorted data will result in error. Author(s) David Siev References Miller, Rupert G. (1973). Nonparametric estimateors of the mean tolerance in bioassay. Biometrika. 60: 535 - 542. Behrens, B. (1929) Zur Auswertung der Digitalisblatter im Froschversuch. Arkiv fur Experimentelle 4 getED Pathologie und Pharmakologie. 140: 237-256. Dragstedt, CA., Lang, VF. (1928). Respiratory Stimulants in acute poisoning in rabbits. J. of Pharmacology 32: 215–222. See Also The function ReedMuench gives the Reed-Muench estimate of ED50, SKRMDB-class Examples X <- data.frame(dead=c(0,3,5,8,10,10),total=rep(10,6),dil=1:6) DragBehr(cbind(dead,total) ~ dil, X) # or DragBehr(y=c(0,3,5,8,10,10), n=rep(10,6), x=1:6) # # db 2.906593 ## Not run: ## unordered data X2 <- data.frame(dead = c(10,8,5,3,0), total = rep(10, 5), dil = c(1, 3, 2, 4, 5)) DragBehr(cbind(dead,total) ~ dil, X2) DragBehr(y = X2$dead, n = X2$total, x = X2$dil) ## monotone decreasing (note reverse <- data.frame(dead = DragBehr(cbind(dead,total) ~ DragBehr(y = reverse$dead, n that x variable direction is ignored!!) c(10, 8, 5, 3, 0), total = rep(10, 5), dil = 5:1) dil, reverse) = reverse$total, x = reverse$dil) ## not monotone X3 <- data.frame(dead = c(1:3, 5, 4), total = rep(10, 5), dil = 1:5 ) DragBehr(cbind(dead, total) ~ dil, X3) DragBehr(y = X3$dead, n = X3$total, x = X3$dil) ## End(Not run) getED Accessor to retrieve the numeric median effective dose from a SKRMDB or sk object Description This is an accessor function for retrieving the numeric value of effective from a SKRMDB or sk data object object generated by ReedMuench, SpearKarb or DragBehr Usage getED(x) ## S4 method for signature 'SKRMDB' getED(object) ReedMuench 5 Arguments object class SKRMDB or sk Author(s) Marie Vendettuol See Also SKRMDB-class, sk-class Examples ## with an object of class SKRMDB temp1 <- DragBehr(y=c(0,3,5,8,10,10), n=rep(10,6), x=1:6) getED(temp1) ## with an object of class sk X <- data.frame(dead=c(0,3,5,8,10), total=rep(10,5), dil=1:5) temp2 <- SpearKarb(cbind(dead,total) ~ dil, X) getED(temp2) ReedMuench Reed-Muench estimator Description Gives the Reed-Muench estimate of median effective dose (ED50) Usage ReedMuench(formula = NULL, data = NULL, y, n, x, warn.me = T, show = F) Arguments formula a formula of the form cbind(y,n) ~ x data a data frame y the number responding (response should be monotone increasing) n the group size x log dilution or dose warn.me boolean to give warning message show boolean to display extended summary Details Data input may either be a formula and data frame, or variables may be input directly (see example). The Reed-Muench method estimates the median effective dose by interpolating between the two doses that bracket the dose producing median response. It accumulates sums in both directions that represent the hypothetical number that would have responded or not at each dose. It does so by assuming that those that responded at a lower dose would respond at a higher dose, and those that did not respond at a higher dose would not respond at a lower dose. The ED50 is the intersection of the lines connecting the two sets of cumulative sums between the bracketing doses. 6 ReedMuench Value object of class SKRMDB with slots: ed Estimated median effective dose (ED50) eval Evaluation method: ’ReedMuench’ Note Many microbiology texts mistakenly present the Dragstedt-Behrens method as the Reed-Muench method. And yes, it is absurd to have an R function for an archaic method developed to avoid complex calculations. Input data is expected to be sorted by X variable (either increasing or decreasing). Use of unsorted X variables will result in error. Y variables are evaluated for monotone, increasing or decreasing; however the estimate will be calculated in the original order regardless. Author(s) David Siev References Miller, Rupert G. (1973). Nonparametric estimateors of the mean tolerance in bioassay. Biometrika. 60: 535 – 542. Reed LJ, Muench H (1938). A simple method of estimating fifty percent endpoints. American Journal of Hygiene. 27:493–497. See Also The function DragBehr gives the Dragstedt-Behrens estimate of ED50 SKRMDB-class Examples X <- data.frame(dead=c(0,3,5,8,10,10),total=rep(10,6),dil=1:6) ReedMuench(cbind(dead,total) ~ dil, X) # or ReedMuench(y=c(0,3,5,8,10,10), n=rep(10,6), x=1:6) # # rm 2.916667 ## Not run: ## unordered data X2 <- data.frame(dead = c(10,8,5,3,0), total = rep(10, 5), dil = c(1, 3, 2, 4, 5)) ReedMuench(cbind(dead,total) ~ dil, X2) ReedMuench(y = X2$dead, n = X2$total, x = X2$dil) sk-class 7 ## monotone decreasing (note reverse <- data.frame(dead = ReedMuench(cbind(dead,total) ReedMuench(y = reverse$dead, that x variable direction is ignored!!) c(10, 8, 5, 3, 0), total = rep(10, 5), dil = 5:1) ~ dil, reverse) n = reverse$total, x = reverse$dil) ## not monotone X3 <- data.frame(dead = c(1:3, 5, 4), total = rep(10, 5), dil = 1:5 ) ReedMuench(cbind(dead, total) ~ dil, X3) ReedMuench(y = X3$dead, n = X3$total, x = X3$dil) ## End(Not run) sk-class Class definition for sk object Description The sk object holds values for the Spear Karb estimator for median estimated dose. It extends the SKRMDB data object with value for variance. Details eval Evaluation method. "SpearKarb". Character string. ed Median effective dose by eval method. Numeric. sk.var variance. Numeric. Author(s) Marie Vendettuoli See Also SKRMDB-class Examples new('sk', sk.var = 0.06888889, ed = 2.9, eval = "SpearKarb") SKRMDB-class Class definition for SKRMDB object Description The SKRMDB object holds output from functions in the SKRMDB package. Details eval Evaluation method. One of ’ReedMeunch’, ’SpearKarb’, or ’DragBehr’. Character string. ed Median effective dose by eval method. Numeric. 8 SpearKarb Author(s) Marie Vendettuoli See Also sk-class Examples new("SKRMDB", ed = 2.906593, eval = "DragBehr") SpearKarb Spearman-Karber estimator Description Gives the Spearman-Karber estimate of the mean effective dose Usage SpearKarb(formula = NULL, data = NULL, y, n, x) Arguments formula a formula of the form cbind(y,n) ~ x data a data frame y the number responding (response should be monotone increasing) n the group size x log dilution or dose Details Data input may either be a formula and data frame, or variables may be input directly (see example). The Spearman-Karber method gives a non-parametric estimate of the mean of an tolerance distribution from its empirical distribution (EDF). The empirical PMF is derived from the EDF by P differencing and the estimator is xf (x). If the EDF does not cover the entire support of x, SpearKarb() extends it by assuming the next lower dilution would produce zero response and the next higher dilution would produce complete response. Value object of class sk ed estimator of mean response sk.var variance eval evaluation method: ’SpearKarb’ SpearKarb 9 Note Input data is expected to be sorted by X variable (either increasing or decreasing). Use of unsorted X variables will result in error. Y variables are evaluated for monotone, increasing or decreasing; however the estimate will be calculated in the original order regardless of direction. Author(s) David Siev References Miller, Rupert G. (1973). Nonparametric estimateors of the mean tolerance in bioassay. Biometrika. 60: 535 – 542. Karber, G. (1931). Beitrag zur kollektiven Behandlung Parmakogischer Reihenversuche. Archiv fur Experimentelle Pathologie und Pharmakologie. 162: 480–487. Spearman, C. (1908). The method of "right and wrong cases" ("constant stimuli") without Gauss’s formulae. Brit J. of Psychology. 2: 227–242. Examples X <- data.frame(dead=c(0,3,5,8,10), total=rep(10,5), dil=1:5) SpearKarb(cbind(dead,total) ~ dil, X) # sk sk.var #2.90000000 0.06888889 # without zero and complete response X <- data.frame(dead=c(3,5,8),total=rep(10,3),dil=2:4) SpearKarb(cbind(dead,total) ~ dil, X) # or SpearKarb(y=c(3,5,8), n=rep(10,3), x=2:4) # sk sk.var #2.90000000 0.06888889 ## Not run: ## unordered X2 <- data.frame(dead = c(10,8,5,3,0), total = rep(10, 5), dil = c(1, 3, 2, 4, 5)) SpearKarb(cbind(dead,total) ~ dil, X2) SpearKarb(y = X2$dead, n = X2$total, x = X2$dil) ## monotone decreasing (note that x variable direction is ignored!!) reverse <- data.frame(dead = c(10, 8, 5, 3, 0), total = rep(10, 5), dil = 5:1) SpearKarb(cbind(dead,total) ~ dil, reverse) SpearKarb(y = reverse$dead, n = reverse$total, x = reverse$dil) ## not monotone X3 <- data.frame(dead = c(1:3, 5, 4), total = rep(10, 5), dil = 1:5 ) SpearKarb(cbind(dead, total) ~ dil, X3) SpearKarb(y = X3$dead, n = X3$total, x = X3$dil) 10 SpearKarb ## End(Not run) Index DragBehr, 2, 6 getED, 4 getED,SKRMDB-method (getED), 4 ReedMuench, 4, 5 sk (sk-class), 7 sk-class, 7 SKRMDB (SKRMDB-class), 7 skrmdb (skrmdb-package), 2 SKRMDB-class, 7 skrmdb-package, 2 SpearKarb, 8 11
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