From 5b3ffc4a1f4826d488a253e83eb96bffbeb1098b Mon Sep 17 00:00:00 2001 From: OVVO-Financial Date: Sun, 1 Sep 2024 10:40:53 -0400 Subject: [PATCH] NNS 10.9.2 Beta --- DESCRIPTION | 2 +- NNS_10.9.2.tar.gz | Bin 1452252 -> 1453070 bytes NNS_10.9.2.zip | Bin 866012 -> 868118 bytes R/ANOVA.R | 26 ++++++++++---- R/Binary_ANOVA.R | 85 +++++++++++++++++++++++++++++----------------- man/NNS.ANOVA.Rd | 6 ++++ 6 files changed, 81 insertions(+), 38 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 45e7ea5fb..2b0918124 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: NNS Type: Package Title: Nonlinear Nonparametric Statistics Version: 10.9.2 -Date: 2024-08-29 +Date: 2024-09-01 Authors@R: c( person("Fred", "Viole", role=c("aut","cre"), email="ovvo.financial.systems@gmail.com"), person("Roberto", "Spadim", role=c("ctb")) diff --git a/NNS_10.9.2.tar.gz b/NNS_10.9.2.tar.gz index 5a004121fd168325f86d48ee33326dd8f3fcde12..b0fe3a1fe230d85b1e4292d909d30c07b87454e7 100644 GIT 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zm;0a5BkMhb96*48>Y#vtp#O{QmJ0+(PEMIYQcgtl*Z;-;4f|g}0pYy=rm(g!bYl4b zC^Y^@*1t}0|G7Z~x)7@SzijU8tpCG`3IqZK1@wpz;fc%hvzEj`-b6ogu z{9o3TW1es9-`>!O(zoXo`vx%t3IqiCKji<$jx3)-zzc`=ACuwwC_?=Um8^-g!GCoA zW(AV_MrF(UZJk2JlnCDcF<0F`9g0dRQ@n7*|FZIr;5BxgZ}NW{@_!Wj$I77UDTsV< zg#V(7S~;Kh{r1H_4duV6Vl`3}_~3{L|A7hKp(Shx{+-(Y-Tp7kLVp3>lsG;(Vx4~+ zWWC0rEAUP6Peb@GS@TB!2a1w0y`!x6$BM!+Hk zksl7{U%^HHeZj78{cmYX|K*N<-qqwPf_qlqLWsZ7{C9{1TBiW`;V`uRp~}bR93$!k z1ETK&`_EASs9WfdZuh^Ezr*QUOT+&Sm2a6q|MfBOJ;48J`EOQ3nv@%UI06WE#((<% EADwcr7ytkO diff --git a/R/ANOVA.R b/R/ANOVA.R index b5dbd3814..0a0fdb394 100644 --- a/R/ANOVA.R +++ b/R/ANOVA.R @@ -5,6 +5,7 @@ #' @param control a numeric vector, matrix or data frame, or list if unequal vector lengths. #' @param treatment \code{NULL} (default) a numeric vector, matrix or data frame. #' @param means.only logical; \code{FALSE} (default) test whether difference in sample means only is zero. +#' @param medians logical; \code{FALSE} (default) test whether difference in sample medians only is zero. Requires \code{means.only = TRUE}. #' @param confidence.interval numeric [0, 1]; The confidence interval surrounding the \code{control} mean, defaults to \code{(confidence.interval = 0.95)}. #' @param tails options: ("Left", "Right", "Both"). \code{tails = "Both"}(Default) Selects the tail of the distribution to determine effect size. #' @param pairwise logical; \code{FALSE} (default) Returns pairwise certainty tests when set to \code{pairwise = TRUE}. @@ -37,6 +38,9 @@ #' ### Two variable analysis with no control variable #' A <- cbind(x, y) #' NNS.ANOVA(A) +#' +#' ### Medians test +#' NNS.ANOVA(A, means.only = TRUE, medians = TRUE) #' #' ### Multiple variable analysis with no control variable #' set.seed(123) @@ -56,6 +60,7 @@ NNS.ANOVA <- function( control, treatment, means.only = FALSE, + medians = FALSE, confidence.interval = 0.95, tails = "Both", pairwise = FALSE, @@ -82,7 +87,7 @@ NNS.ANOVA <- function( nns.certainties <- sapply( 1:ncol(control_matrix), - function(g) NNS.ANOVA.bin(control_matrix[,g], treatment_matrix[,g], means.only = means.only, plot = FALSE)$Certainty + function(g) NNS.ANOVA.bin(control_matrix[,g], treatment_matrix[,g], means.only = means.only, medians = medians, plot = FALSE)$Certainty ) cer_lower_CI <- LPM.VaR(.025, 1, nns.certainties[-1]) @@ -105,6 +110,7 @@ NNS.ANOVA <- function( control, treatment, means.only = means.only, + medians = medians, confidence.interval = confidence.interval, plot = plot, tails = tails, @@ -121,6 +127,7 @@ NNS.ANOVA <- function( control, treatment, means.only = means.only, + medians = medians, confidence.interval = confidence.interval, plot = plot, tails = tails @@ -146,15 +153,21 @@ NNS.ANOVA <- function( A <- control } - mean.of.means <- mean(colMeans(A, na.rm = T)) + if(medians) mean.of.means <- mean(apply(A, 2, function(i) median(i, na.rm = TRUE))) else mean.of.means <- mean(colMeans(A, na.rm = T)) if(!pairwise){ #Continuous CDF for each variable from Mean of Means - LPM_ratio <- sapply(1:n, function(b) LPM.ratio(1, mean.of.means, na.omit(unlist(A[ , b])))) + if(medians){ + LPM_ratio <- sapply(1:n, function(b) LPM.ratio(0, mean.of.means, na.omit(unlist(A[ , b])))) + } else { + LPM_ratio <- sapply(1:n, function(b) LPM.ratio(1, mean.of.means, na.omit(unlist(A[ , b])))) + } + lower.25.target <- mean(sapply(1:n, function(i) LPM.VaR(.25, 1, na.omit(unlist(A[,i]))))) upper.25.target <- mean(sapply(1:n, function(i) UPM.VaR(.25, 1, na.omit(unlist(A[,i]))))) lower.125.target <- mean(sapply(1:n, function(i) LPM.VaR(.125, 1, na.omit(unlist(A[,i]))))) upper.125.target <- mean(sapply(1:n, function(i) UPM.VaR(.125, 1, na.omit(unlist(A[,i]))))) + raw.certainties <- list(n - 1) for(i in 1:(n - 1)){ raw.certainties[[i]] <- sapply( @@ -163,6 +176,7 @@ NNS.ANOVA <- function( na.omit(unlist(A[ , i])), na.omit(unlist(A[ , b])), means.only = means.only, + medians = medians, mean.of.means = mean.of.means, upper.25.target = upper.25.target, lower.25.target = lower.25.target, @@ -188,7 +202,7 @@ NNS.ANOVA <- function( ) #For ANOVA Visualization abline(v = mean.of.means, col = "red", lwd = 4) - mtext("Grand Mean", side = 3,col = "red", at = mean.of.means) + if(medians) mtext("Grand Median", side = 3,col = "red", at = mean.of.means) else mtext("Grand Mean", side = 3,col = "red", at = mean.of.means) } return(c("Certainty" = NNS.ANOVA.rho)) } @@ -197,7 +211,7 @@ NNS.ANOVA <- function( for(i in 1:(n - 1)){ raw.certainties[[i]] <- sapply( (i + 1) : n, - function(b) NNS.ANOVA.bin(na.omit(unlist(A[ , i])), na.omit(unlist(A[ , b])), means.only = means.only, plot = FALSE)$Certainty + function(b) NNS.ANOVA.bin(na.omit(unlist(A[ , i])), na.omit(unlist(A[ , b])), means.only = means.only, medians = medians, plot = FALSE)$Certainty ) } @@ -217,7 +231,7 @@ NNS.ANOVA <- function( col = c('steelblue', rainbow(n - 1)) ) abline(v = mean.of.means, col = "red", lwd = 4) - mtext("Grand Mean", side = 3,col = "red", at = mean.of.means) + if(medians) mtext("Grand Median", side = 3,col = "red", at = mean.of.means) else mtext("Grand Mean", side = 3,col = "red", at = mean.of.means) } return(certainties) } diff --git a/R/Binary_ANOVA.R b/R/Binary_ANOVA.R index a951af44f..6b873b11e 100644 --- a/R/Binary_ANOVA.R +++ b/R/Binary_ANOVA.R @@ -1,5 +1,6 @@ NNS.ANOVA.bin <- function(control, treatment, means.only = FALSE, + medians = FALSE, mean.of.means = NULL, upper.25.target = NULL, lower.25.target = NULL, @@ -8,7 +9,7 @@ NNS.ANOVA.bin <- function(control, treatment, confidence.interval = NULL, tails = NULL, plot = TRUE, par = NULL){ if(is.null(upper.25.target) && is.null(lower.25.target)){ - mean.of.means <- mean(c(mean(control), mean(treatment))) + if(medians) mean.of.means <- mean(c(median(control), median(treatment))) else mean.of.means <- mean(c(mean(control), mean(treatment))) upper.25.target <- mean(c(UPM.VaR(.25, 1, control), UPM.VaR(.25, 1, treatment))) lower.25.target <- mean(c(LPM.VaR(.25, 1, control), LPM.VaR(.25, 1, treatment))) upper.125.target <- mean(c(UPM.VaR(.125, 1, control), UPM.VaR(.125, 1, treatment))) @@ -18,9 +19,14 @@ NNS.ANOVA.bin <- function(control, treatment, #Continuous CDF for each variable from Mean of Means - LPM_ratio.1 <- LPM.ratio(1, mean.of.means, control) - LPM_ratio.2 <- LPM.ratio(1, mean.of.means, treatment) - + if(medians){ + LPM_ratio.1 <- LPM.ratio(0, mean.of.means, control) + LPM_ratio.2 <- LPM.ratio(0, mean.of.means, treatment) + } else { + LPM_ratio.1 <- LPM.ratio(1, mean.of.means, control) + LPM_ratio.2 <- LPM.ratio(1, mean.of.means, treatment) + } + Upper_25_ratio.1 <- UPM.ratio(1, upper.25.target, control) Upper_25_ratio.2 <- UPM.ratio(1, upper.25.target, treatment) Upper_25_ratio <- mean(c(Upper_25_ratio.1, Upper_25_ratio.2)) @@ -59,13 +65,9 @@ NNS.ANOVA.bin <- function(control, treatment, } - - - pop.adjustment <- ((length(control) + length(treatment) - 2) / (length(control) + length(treatment) )) ^ 2 #Graphs - if(plot){ if(is.null(par)) original.par <- par(no.readonly = TRUE) else original.par <- par @@ -73,17 +75,26 @@ NNS.ANOVA.bin <- function(control, treatment, #For ANOVA Visualization abline(v = mean.of.means, col = "red", lwd = 4) - mtext("Grand Mean", side = 3, col = "red", at = mean.of.means) + if(medians) mtext("Grand Median", side = 3, col = "red", at = mean.of.means) else mtext("Grand Mean", side = 3, col = "red", at = mean.of.means) } if(is.null(confidence.interval)){ + if(medians){ + return(list("Control Median" = median(control), + "Treatment Median" = median(treatment), + "Grand Median" = mean.of.means, + "Control CDF" = LPM_ratio.1, + "Treatment CDF" = LPM_ratio.2, + "Certainty" = min(1, NNS.ANOVA.rho * pop.adjustment))) + } else { return(list("Control Mean" = mean(control), "Treatment Mean" = mean(treatment), "Grand Mean" = mean.of.means, "Control CDF" = LPM_ratio.1, "Treatment CDF" = LPM_ratio.2, "Certainty" = min(1, NNS.ANOVA.rho * pop.adjustment))) + } } else { #Upper end of CDF confidence interval for control mean @@ -93,15 +104,18 @@ NNS.ANOVA.bin <- function(control, treatment, # Resample control means y_p <- replicate(1000, sample.int(length(control), replace = TRUE)) - control_means <- Rfast::colmeans(matrix(control[y_p], ncol = ncol(y_p), byrow = T)) - + if(medians){ + control_means <- apply(matrix(control[y_p], ncol = ncol(y_p), byrow = T), 1, function(i) median(i, na.rm = T)) + } else { + control_means <- apply(matrix(control[y_p], ncol = ncol(y_p), byrow = T), 1, function(i) mean(i, na.rm = T)) + } a <- UPM.VaR(CI, 0, control_means) b <- mean(control_means) if(plot){ if(tails == "both" | tails == "right"){ abline(v = max(a, b), col = "green", lwd = 2, lty = 3) - text(max(a, b), pos = 4, 0.5, paste0("<--- ", "ctl mu+ ", CI * 100, "%" ), col = "green") + text(max(a, b), pos = 4, 0.5, paste0("<--- ", ifelse(medians, "ctl med+ " , "ctl mu+ "), CI * 100, "%" ), col = "green") } } @@ -112,39 +126,48 @@ NNS.ANOVA.bin <- function(control, treatment, if(plot){ if(tails == "both" | tails == "left"){ abline(v = min(c, d), col = "blue", lwd = 2, lty = 3) - text(min(c, d), pos = 2, 0.5, paste0("ctl mu- ", paste0(CI * 100, "% --->")) , col = "blue") + text(min(c, d), pos = 2, 0.5, paste0(ifelse(medians, "ctl med- ", "ctl mu- "), paste0(CI * 100, "% --->")) , col = "blue") } par(original.par) } #Effect Size Lower Bound - if(tails == "both") Lower.Bound.Effect <- mean(treatment) - max(a, b) - - if(tails == "left") Lower.Bound.Effect <- mean(treatment) - max(c, d) - - if(tails == "right") Lower.Bound.Effect <- mean(treatment) - max(a, b) + if(tails == "both") if(medians) Lower.Bound.Effect <- median(treatment) - max(a, b) else Lower.Bound.Effect <- mean(treatment) - max(a, b) + if(tails == "left") if(medians) Lower.Bound.Effect <- median(treatment) - max(c, d) else Lower.Bound.Effect <- mean(treatment) - max(c, d) + if(tails == "right") if(medians) Lower.Bound.Effect <- mean(treatment) - max(a, b) else Lower.Bound.Effect <- mean(treatment) - max(a, b) #Effect Size Upper Bound - if(tails == "both") Upper.Bound.Effect <- mean(treatment) - min(c, d) - - if(tails == "left") Upper.Bound.Effect <- mean(treatment) - min(c, d) - - if(tails == "right") Upper.Bound.Effect <- mean(treatment) - min(a, b) + if(tails == "both") if(medians) Upper.Bound.Effect <- median(treatment) - min(c, d) else Upper.Bound.Effect <- mean(treatment) - min(c, d) + if(tails == "left") if(medians) Upper.Bound.Effect <- median(treatment) - min(c, d) else Upper.Bound.Effect <- mean(treatment) - min(c, d) + if(tails == "right") if(medians) Upper.Bound.Effect <- median(treatment) - min(a, b) else Upper.Bound.Effect <- mean(treatment) - min(a, b) #Certainty Statistic and Effect Size Given Confidence Interval - return(list("Control Mean" = mean(control), - "Treatment Mean" = mean(treatment), - "Grand Mean" = mean.of.means, - "Control CDF" = LPM_ratio.1, - "Treatment CDF" = LPM_ratio.2, - "Certainty" = min(1, NNS.ANOVA.rho * pop.adjustment), - "Lower Bound Effect" = Lower.Bound.Effect, - "Upper Bound Effect" = Upper.Bound.Effect)) + if(medians){ + return(list("Control Median" = median(control), + "Treatment Median" = median(treatment), + "Grand Median" = mean.of.means, + "Control CDF" = LPM_ratio.1, + "Treatment CDF" = LPM_ratio.2, + "Certainty" = min(1, NNS.ANOVA.rho * pop.adjustment), + "Lower Bound Effect" = Lower.Bound.Effect, + "Upper Bound Effect" = Upper.Bound.Effect)) + } else { + return(list("Control Mean" = mean(control), + "Treatment Mean" = mean(treatment), + "Grand Mean" = mean.of.means, + "Control CDF" = LPM_ratio.1, + "Treatment CDF" = LPM_ratio.2, + "Certainty" = min(1, NNS.ANOVA.rho * pop.adjustment), + "Lower Bound Effect" = Lower.Bound.Effect, + "Upper Bound Effect" = Upper.Bound.Effect)) + + } + } } diff --git a/man/NNS.ANOVA.Rd b/man/NNS.ANOVA.Rd index 9dae0c637..0fa912581 100644 --- a/man/NNS.ANOVA.Rd +++ b/man/NNS.ANOVA.Rd @@ -8,6 +8,7 @@ NNS.ANOVA( control, treatment, means.only = FALSE, + medians = FALSE, confidence.interval = 0.95, tails = "Both", pairwise = FALSE, @@ -22,6 +23,8 @@ NNS.ANOVA( \item{means.only}{logical; \code{FALSE} (default) test whether difference in sample means only is zero.} +\item{medians}{logical; \code{FALSE} (default) test whether difference in sample medians only is zero. Requires \code{means.only = TRUE}.} + \item{confidence.interval}{numeric [0, 1]; The confidence interval surrounding the \code{control} mean, defaults to \code{(confidence.interval = 0.95)}.} \item{tails}{options: ("Left", "Right", "Both"). \code{tails = "Both"}(Default) Selects the tail of the distribution to determine effect size.} @@ -59,6 +62,9 @@ NNS.ANOVA(control = x, treatment = y) A <- cbind(x, y) NNS.ANOVA(A) +### Medians test +NNS.ANOVA(A, means.only = TRUE, medians = TRUE) + ### Multiple variable analysis with no control variable set.seed(123) x <- rnorm(100) ; y <- rnorm(100) ; z <- rnorm(100)

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