[Genabel-commits] r1397 - in branches/ProbABEL-pvals/ProbABEL: . checks checks/R-tests checks/inputfiles checks/verified_results doc src
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Nov 19 00:33:21 CET 2013
Author: lckarssen
Date: 2013-11-19 00:33:21 +0100 (Tue, 19 Nov 2013)
New Revision: 1397
Added:
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_coxph.R
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_linear.R
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_logist.R
branches/ProbABEL-pvals/ProbABEL/doc/Doxyfile
branches/ProbABEL-pvals/ProbABEL/doc/README.dox
branches/ProbABEL-pvals/ProbABEL/test-driver
Modified:
branches/ProbABEL-pvals/ProbABEL/checks/Makefile.am
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_R_test_pacox.sh
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_pacox.R
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palinear.R
branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palogist.R
branches/ProbABEL-pvals/ProbABEL/checks/check_dose_input.sh
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.dose.fvd
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.dose.fvi
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.map
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mldose
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mlinfo
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mlprob
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.prob.fvd
branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.prob.fvi
branches/ProbABEL-pvals/ProbABEL/checks/verified_results/height_base_add.out.txt
branches/ProbABEL-pvals/ProbABEL/configure.ac
branches/ProbABEL-pvals/ProbABEL/doc/
branches/ProbABEL-pvals/ProbABEL/doc/ChangeLog
branches/ProbABEL-pvals/ProbABEL/doc/INSTALL
branches/ProbABEL-pvals/ProbABEL/missing
branches/ProbABEL-pvals/ProbABEL/src/command_line_settings.cpp
branches/ProbABEL-pvals/ProbABEL/src/command_line_settings.h
branches/ProbABEL-pvals/ProbABEL/src/coxfit2.c
branches/ProbABEL-pvals/ProbABEL/src/coxph_data.cpp
branches/ProbABEL-pvals/ProbABEL/src/coxph_data.h
branches/ProbABEL-pvals/ProbABEL/src/data.cpp
branches/ProbABEL-pvals/ProbABEL/src/data.h
branches/ProbABEL-pvals/ProbABEL/src/eigen_mematrix.h
branches/ProbABEL-pvals/ProbABEL/src/extract-snp.cpp
branches/ProbABEL-pvals/ProbABEL/src/gendata.cpp
branches/ProbABEL-pvals/ProbABEL/src/gendata.h
branches/ProbABEL-pvals/ProbABEL/src/main.cpp
branches/ProbABEL-pvals/ProbABEL/src/maskedmatrix.h
branches/ProbABEL-pvals/ProbABEL/src/mematrix.h
branches/ProbABEL-pvals/ProbABEL/src/phedata.cpp
branches/ProbABEL-pvals/ProbABEL/src/phedata.h
branches/ProbABEL-pvals/ProbABEL/src/reg1.cpp
branches/ProbABEL-pvals/ProbABEL/src/reg1.h
branches/ProbABEL-pvals/ProbABEL/src/regdata.cpp
branches/ProbABEL-pvals/ProbABEL/src/regdata.h
branches/ProbABEL-pvals/ProbABEL/src/usage.cpp
Log:
Merged changes from ProbABEL trunk into the p-values branch.
Modified: branches/ProbABEL-pvals/ProbABEL/checks/Makefile.am
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/Makefile.am 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/Makefile.am 2013-11-18 23:33:21 UTC (rev 1397)
@@ -41,7 +41,6 @@
check_SCRIPTS =
-TESTS_ENVIRONMENT = bash
if BUILD_palinear
check_SCRIPTS += check_probabel.pl_chunk.sh check_dose_input.sh
check_SCRIPTS += test_qt.sh test_mms.sh
Modified: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_R_test_pacox.sh
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_R_test_pacox.sh 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_R_test_pacox.sh 2013-11-18 23:33:21 UTC (rev 1397)
@@ -2,7 +2,7 @@
#
# This script runs the R-based tests for Cox PH regression
-Rcommand="R --vanilla --slave"
+Rcommand="R --vanilla --slave --quiet"
if [ -z ${srcdir} ]; then
srcdir="."
Copied: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_coxph.R (from rev 1396, pkg/ProbABEL/checks/R-tests/run_model_coxph.R)
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_coxph.R (rev 0)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_coxph.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -0,0 +1,98 @@
+##' This function runs the actual models for Cox regression. It is
+##' called by run_models_in_R_pacox.R
+##'
+##'
+##' @title run.model
+##' @param model0.txt String containing the null model (without SNP term)
+##' @param model.txt String containing the alternative model (with SNP
+##' term)
+##' @param snpcomponent1 String telling how the SNP term is defined
+##' @param snpcomponent2 String telling how the second SNP term is
+##' defined (only used in the 2 df model). By default this term is
+##' constant ("1")
+##' @return A data frame containing the coefficients from the
+##' regression analysis and some other variables, such that this
+##' output can be compared to the ProbABEL output.
+##' @author L.C. Larsen
+run.model <- function(model0.txt, model.txt,
+ snpcomponent1, snpcomponent2="1") {
+
+ if (snpcomponent2 != "1") {
+ ## SNP component 2 is not constant: assume we run the 2df
+ ## model.
+ twoDF = TRUE
+ } else {
+ twoDF = FALSE
+ }
+
+ resultR <- data.frame()
+
+ for (i in 3:dim(dose)[2]) {
+ indexHom <- 3 + ( i - 3 ) * 2
+ indexHet <- indexHom + 1
+ snp1 <- eval(parse(text=snpcomponent1))
+ snp2 <- eval(parse(text=snpcomponent2))
+ snp <- snp1 + snp2
+
+ noNA <- which( !is.na(snp) )
+ model.0 <- eval(parse(text=model0.txt))
+
+ ## Evaluate the model. The whole tryCatch is needed to catch
+ ## problems with non-converging regression.
+ model = tryCatch({
+ list(
+ eval(parse(text=model.txt)),
+ list(message="no warnings")
+ )
+ }, warning = function(war) {
+ return(list(
+ eval(parse(text=model.txt)),
+ war)
+ )
+ })
+
+ if ( grepl("infinite", model[[2]]$message) |
+ grepl("singular", model[[2]]$message) ) {
+ ## The model did not converge, fill the coefficients with
+ ## NaNs
+ if (twoDF) {
+ smA1A2 <- c(NaN, NaN)
+ smA1A1 <- c(NaN, NaN)
+ } else {
+ sm <- c(NaN, NaN)
+ }
+ lrt <- NaN
+ } else {
+ ## No convergence problems, we can trust the
+ ## coefficients.
+ coeff <- summary(model[[1]])$coefficients
+ if (twoDF) {
+ smA1A2 <- coeff[4, c("coef", "se(coef)")]
+ smA1A1 <- coeff[5, c("coef", "se(coef)")]
+ } else {
+ sm <- coeff[4, c("coef", "se(coef)")]
+ }
+ lrt <- 2 * ( model[[1]]$loglik[2] - model.0$loglik[2] )
+ }
+
+ ## Check the imputation R^2, if below threshold ProbABEL will
+ ## set the coefficients to NaN.
+ rsq <- Rsq[i-2]
+ if (twoDF) {
+ if( rsq < rsq.thresh ) {
+ row <- c(rsq, NaN, NaN, NaN, NaN, NaN)
+ } else {
+ row <- c(rsq, smA1A2[1], smA1A2[2], smA1A1[1], smA1A1[2], lrt)
+ }
+ } else {
+ if( rsq < rsq.thresh ) {
+ row <- c(rsq, NaN, NaN, NaN)
+ } else {
+ row <- c(rsq, sm[1], sm[2], lrt)
+ }
+ }
+
+ resultR <- rbind(resultR, row)
+ }
+ return(resultR)
+}
Copied: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_linear.R (from rev 1396, pkg/ProbABEL/checks/R-tests/run_model_linear.R)
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_linear.R (rev 0)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_linear.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -0,0 +1,29 @@
+run.model <- function(model0.txt, model.txt, snpdata) {
+ resultR <- data.frame()
+ for (i in 3:dim(dose)[2]) {
+ indexHom <- 3 + ( i - 3 ) * 2
+ indexHet <- indexHom + 1
+ snp <- eval(parse(text=snpdata))
+
+ noNA <- which( !is.na(snp) )
+ model.0 <- eval(parse(text=model0.txt))
+ model <- eval(parse(text=model.txt))
+
+ coeff <- summary(model)$coefficients
+ if ( dim(coeff)[1] != 4 ) {
+ sm <- c(NaN, NaN)
+ } else {
+ sm <- coeff[4, c("Estimate", "Std. Error")]
+ }
+
+ lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
+ rsq <- Rsq[i-2]
+ if( rsq < rsq.thresh) {
+ row <- c(rsq, NaN, NaN, NaN)
+ } else {
+ row <- c(rsq, sm[1], sm[2], lrt)
+ }
+ resultR <- rbind(resultR, row)
+ }
+ return(resultR)
+}
Copied: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_logist.R (from rev 1396, pkg/ProbABEL/checks/R-tests/run_model_logist.R)
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_logist.R (rev 0)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_model_logist.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -0,0 +1,23 @@
+run.model <- function(model0.txt, model.txt, snpdata) {
+ resultR <- data.frame()
+ for (i in 3:dim(dose)[2]) {
+ indexHom <- 3 + ( i - 3 ) * 2
+ indexHet <- indexHom + 1
+ snp <- eval(parse(text=snpdata))
+
+ noNA <- which( !is.na(snp) )
+ model.0 <- eval(parse(text=model0.txt))
+ model <- eval(parse(text=model.txt))
+ sm <- summary(model)$coef[5, 1:2]
+ lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
+
+ rsq <- Rsq[i-2]
+ if( rsq < rsq.thresh) {
+ row <- c(rsq, NaN, NaN, NaN)
+ } else {
+ row <- c(rsq, sm[1], sm[2], lrt)
+ }
+ resultR <- rbind(resultR, row)
+ }
+ return(resultR)
+}
Modified: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_pacox.R
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_pacox.R 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_pacox.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -6,8 +6,15 @@
if (is.na(srcdir)) {
srcdir <- "./"
+} else {
+ ## Apparently we are running R from the command line. Disable
+ ## warnings so that they don't clutter the screen when running
+ ## this script.
+ old.warn <- options()$warn
+ options(warn=-1)
}
+
pheno.file <- "coxph_data.txt"
source(paste0(srcdir, "initial_checks.R"))
@@ -16,7 +23,8 @@
## Run ProbABEL to get the output data we want to compare/verify
####
cat("Running ProbABEL...\t\t\t\t")
-tmp <- system(paste0("cd ", tests.path, "; bash test_cox.sh; cd -"),
+tmp <- system(paste0("cd ", tests.path,
+ "; bash test_cox.sh 2> /dev/null; cd -"),
intern=TRUE)
cat("OK\n")
@@ -46,34 +54,11 @@
cat("Comparing R output with ProbABEL output\t\t")
-run.model <- function(model0.txt, model.txt, snpdata) {
- resultR <- data.frame()
- for (i in 3:dim(dose)[2]) {
- indexHom <- 3 + ( i - 3 ) * 2
- indexHet <- indexHom + 1
- snp <- eval(parse(text=snpdata))
+source("run_model_coxph.R")
- noNA <- which( !is.na(snp) )
- model.0 <- eval(parse(text=model0.txt))
- model <- eval(parse(text=model.txt))
- sm <- summary(model)$coef[4, c(1,3)]
- lrt <- 2 * ( model$loglik[2] - model.0$loglik[2] )
-
- rsq <- Rsq[i-2]
- if( rsq < rsq.thresh) {
- row <- c(rsq, NaN, NaN, NaN)
- } else {
- row <- c(rsq, sm[1], sm[2], lrt)
- }
- resultR <- rbind(resultR, row)
- }
- return(resultR)
-}
-
-
model.fn.0 <-
"coxph( Surv(fupt_chd, chd)[noNA] ~ sex[noNA] + age[noNA] + othercov[noNA] )"
-model.fn <- "coxph( Surv(fupt_chd, chd) ~ sex + age + othercov + snp )"
+model.fn <- "coxph( Surv(fupt_chd, chd) ~ sex + age + othercov + snp1 )"
## Additive model, dosages
snpdose <- "dose[, i]"
@@ -83,6 +68,7 @@
stopifnot( all.equal(resPaAddDose, dose.add.R, tol=tol) )
cat("additive ")
+
## Additive model, probabilities
snpprob <- "doseFromProb[, i]"
prob.add.R <- run.model(model.fn.0, model.fn, snpprob)
@@ -117,30 +103,11 @@
## 2df model
-prob.2df.R <- data.frame()
-for (i in 3:dim(dose)[2]) {
- indexHom <- 3 + ( i - 3 ) * 2
- indexHet <- indexHom + 1
- regProb <- prob[, indexHet]
-
- noNA <- which( !is.na(regProb) )
- model.0 <- coxph( Surv(fupt_chd, chd)[noNA] ~ sex[noNA] +
- age[noNA] + othercov[noNA])
- model <- coxph( Surv(fupt_chd, chd) ~ sex + age +
- othercov + prob[, indexHet] + prob[, indexHom] )
- smA1A2 <- summary(model)$coef[4, c(1,3)]
- smA1A1 <- summary(model)$coef[5, c(1,3)]
- lrt <- 2 * ( model$loglik[2] - model.0$loglik[2] )
-
- rsq <- resPa2df[i-2, "Rsq"]
- if( rsq < rsq.thresh) {
- row <- c(rsq, NaN, NaN, NaN, NaN, NaN)
- } else {
- row <- c(rsq, smA1A2[1], smA1A2[2], smA1A1[1], smA1A1[2], lrt)
-
- }
- prob.2df.R <- rbind(prob.2df.R, row)
-}
+model.fn <-
+ "coxph( Surv(fupt_chd, chd) ~ sex + age + othercov + snp1 + snp2 )"
+snpd1 <- "prob[, indexHet]"
+snpd2 <- "prob[, indexHom]"
+prob.2df.R <- run.model(model.fn.0, model.fn, snpd1, snpd2)
colnames(prob.2df.R) <- cols2df
rownames(prob.2df.R) <- NULL
stopifnot( all.equal(resPa2df, prob.2df.R, tol=tol) )
Modified: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palinear.R
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palinear.R 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palinear.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -45,30 +45,8 @@
cat("Comparing R output with ProbABEL output\t\t")
-run.model <- function(model0.txt, model.txt, snpdata) {
- resultR <- data.frame()
- for (i in 3:dim(dose)[2]) {
- indexHom <- 3 + ( i - 3 ) * 2
- indexHet <- indexHom + 1
- snp <- eval(parse(text=snpdata))
+source("run_model_linear.R")
- noNA <- which( !is.na(snp) )
- model.0 <- eval(parse(text=model0.txt))
- model <- eval(parse(text=model.txt))
- sm <- summary(model)$coef[4, 1:2]
- lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
-
- rsq <- Rsq[i-2]
- if( rsq < rsq.thresh) {
- row <- c(rsq, NaN, NaN, NaN)
- } else {
- row <- c(rsq, sm[1], sm[2], lrt)
- }
- resultR <- rbind(resultR, row)
- }
- return(resultR)
-}
-
model.fn.0 <- "lm( height[noNA] ~ sex[noNA] + age[noNA] )"
model.fn <- "lm( height ~ sex + age + snp )"
@@ -80,6 +58,7 @@
stopifnot( all.equal(resPaAddDose, dose.add.R, tol=tol) )
cat("additive ")
+
## Additive model, probabilities
snpprob <- "doseFromProb[, i]"
prob.add.R <- run.model(model.fn.0, model.fn, snpprob)
Modified: branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palogist.R
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palogist.R 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/R-tests/run_models_in_R_palogist.R 2013-11-18 23:33:21 UTC (rev 1397)
@@ -45,31 +45,8 @@
cat("Comparing R output with ProbABEL output\t\t")
-run.model <- function(model0.txt, model.txt, snpdata) {
- resultR <- data.frame()
- for (i in 3:dim(dose)[2]) {
- indexHom <- 3 + ( i - 3 ) * 2
- indexHet <- indexHom + 1
- snp <- eval(parse(text=snpdata))
+source("run_model_logist.R")
- noNA <- which( !is.na(snp) )
- model.0 <- eval(parse(text=model0.txt))
- model <- eval(parse(text=model.txt))
- sm <- summary(model)$coef[5, 1:2]
- lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
-
- rsq <- Rsq[i-2]
- if( rsq < rsq.thresh) {
- row <- c(rsq, NaN, NaN, NaN)
- } else {
- row <- c(rsq, sm[1], sm[2], lrt)
- }
- resultR <- rbind(resultR, row)
- }
- return(resultR)
-}
-
-
model.fn.0 <-
"glm( chd[noNA] ~ sex[noNA] + age[noNA] + othercov[noNA], family=binomial)"
model.fn <- "glm( chd ~ sex + age + othercov + snp, family=binomial )"
Modified: branches/ProbABEL-pvals/ProbABEL/checks/check_dose_input.sh
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/check_dose_input.sh 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/check_dose_input.sh 2013-11-18 23:33:21 UTC (rev 1397)
@@ -1,4 +1,4 @@
-#!/bin/sh
+#!/bin/bash
#
# This script tests whether dose data without a MaCH/minimac-style
# arrow is read correctly by palinear (and by palogist, since reading
Modified: branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.dose.fvd
===================================================================
(Binary files differ)
Modified: branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.dose.fvi
===================================================================
(Binary files differ)
Modified: branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.map
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.map 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.map 2013-11-18 23:33:21 UTC (rev 1397)
@@ -4,3 +4,4 @@
rs8102615 211970 A T
rs8105536 212033 A G
rs2312724 217034 C T
+rs3174230 7845238 G C
Modified: branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mldose
===================================================================
--- branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mldose 2013-11-18 23:29:25 UTC (rev 1396)
+++ branches/ProbABEL-pvals/ProbABEL/checks/inputfiles/test.mldose 2013-11-18 23:33:21 UTC (rev 1397)
@@ -1,200 +1,200 @@
-1->id636728 MLDOSE 0.678 1.622 1.732 1.585 1.867
-2->id890314 MLDOSE 0.66 1.621 1.737 1.59 1.869
-3->id102874 MLDOSE 0.684 1.623 1.731 1.585 1.866
-4->id200949 MLDOSE 0.667 NaN 1.728 1.576 1.866
-5->id336491 MLDOSE 0.66 1.609 1.737 1.577 1.869
-6->id988766 MLDOSE 0.687 1.622 1.738 1.59 1.868
-7->id21999 MLDOSE 0.67 1.62 1.735 1.588 1.867
-8->id433893 MLDOSE 0.663 1.615 1.734 1.572 1.869
-9->id688932 MLDOSE 0.672 1.619 1.729 1.585 1.862
-10->id394203 MLDOSE 0.666 1.619 1.729 1.58 1.863
-11->id995678 MLDOSE 0.672 1.619 1.733 1.583 1.867
-12->id694339 MLDOSE 0.666 1.615 1.733 1.59 1.869
-13->id256455 MLDOSE 0.673 1.617 1.737 1.584 1.869
-14->id14836 MLDOSE 0.672 1.62 1.737 1.582 1.869
-15->id817128 MLDOSE 0.666 1.609 1.735 1.589 1.868
-16->id803325 MLDOSE 0.678 1.62 1.73 1.591 1.868
-17->id521287 MLDOSE 0.661 1.618 1.736 1.585 1.87
-18->id701472 MLDOSE 0.666 1.621 1.731 1.585 1.867
-19->id850010 MLDOSE 0.669 1.615 1.736 1.581 1.868
-20->id268483 MLDOSE 0.669 1.618 1.732 1.582 1.865
-21->id738781 MLDOSE 0.666 1.611 1.73 1.59 1.862
-22->id28411 MLDOSE 0.66 1.62 1.735 1.57 1.869
-23->id541635 MLDOSE 0.669 1.622 1.725 1.558 1.868
-24->id751101 MLDOSE 0.669 1.624 1.738 1.589 1.869
-25->id826300 MLDOSE 0.666 1.605 1.736 1.586 1.869
-26->id884387 MLDOSE 0.661 1.604 1.734 1.587 1.868
-27->id492414 MLDOSE 0.666 1.619 1.733 1.583 1.866
-28->id268871 MLDOSE 0.669 1.621 1.731 1.576 1.869
-29->id627354 MLDOSE 0.666 1.615 1.735 1.59 1.869
-30->id503932 MLDOSE 0.66 1.62 1.735 1.586 1.867
-31->id163442 MLDOSE 0.675 1.621 1.737 1.586 1.869
-32->id317797 MLDOSE 0.654 1.625 1.739 1.57 1.87
-33->id687857 MLDOSE 0.667 1.621 1.734 1.586 1.869
-34->id871570 MLDOSE 0.666 1.61 1.731 1.576 1.864
-35->id724067 MLDOSE 0.666 1.619 1.736 1.583 1.868
-36->id874076 MLDOSE 0.676 1.617 1.738 1.585 1.869
-37->id927863 MLDOSE 0.67 1.62 1.734 1.586 1.865
-38->id369805 MLDOSE 0.663 1.616 1.73 1.583 1.867
-39->id668376 MLDOSE 0.675 1.617 1.736 1.582 1.869
-40->id717362 MLDOSE 0.66 1.62 1.736 1.59 1.869
-41->id665504 MLDOSE 0.669 1.618 1.732 1.578 1.867
-42->id336637 MLDOSE 0.661 1.618 1.734 1.576 1.865
-43->id60633 MLDOSE 0.669 1.619 1.731 1.579 1.866
-44->id848600 MLDOSE 0.664 1.615 1.737 1.576 1.869
-45->id169514 MLDOSE 0.664 1.615 1.737 1.583 1.869
-46->id690732 MLDOSE 0.663 1.619 1.737 1.586 1.868
-47->id684760 MLDOSE 0.667 1.62 1.734 1.578 1.866
-48->id553502 MLDOSE 0.672 1.608 1.732 1.59 1.869
-49->id214917 MLDOSE 0.669 1.618 1.727 1.588 1.865
-50->id849169 MLDOSE 0.664 1.624 1.733 1.588 1.867
-51->id941921 MLDOSE 0.667 1.619 1.731 1.588 1.862
-52->id784646 MLDOSE 0.663 1.603 1.739 1.584 1.87
-53->id520954 MLDOSE 0.664 1.619 1.732 1.581 1.864
-54->id996355 MLDOSE 0.66 1.605 1.736 1.586 1.865
-55->id96730 MLDOSE 0.664 1.619 1.736 1.573 1.868
-56->id673442 MLDOSE 0.666 1.609 1.735 1.575 1.867
-57->id68305 MLDOSE 0.672 1.618 1.735 1.585 1.866
-58->id653025 MLDOSE 0.678 1.617 1.738 1.585 1.868
-59->id208543 MLDOSE 0.664 1.618 1.734 1.58 1.868
-60->id335725 MLDOSE 0.666 1.616 1.714 1.593 1.845
-61->id980400 MLDOSE 0.663 1.607 1.733 1.588 1.867
-62->id869939 MLDOSE 0.667 1.619 1.733 1.588 1.865
-63->id297563 MLDOSE 0.673 1.614 1.72 1.585 1.866
-64->id852663 MLDOSE 0.666 1.603 1.729 1.585 1.87
-65->id162070 MLDOSE 0.666 1.619 1.728 1.572 1.868
-66->id272875 MLDOSE 0.66 1.605 1.737 1.584 1.869
-67->id163787 MLDOSE 0.661 1.616 1.723 1.584 1.87
-68->id422204 MLDOSE 0.663 1.615 1.734 1.576 1.867
-69->id120197 MLDOSE 0.66 1.62 1.731 1.588 1.87
-70->id33660 MLDOSE 0.658 1.62 1.736 1.571 1.869
-71->id803855 MLDOSE 0.669 1.616 1.732 1.586 1.865
-72->id255048 MLDOSE 0.675 1.616 1.726 1.582 1.866
-73->id690936 MLDOSE 0.657 1.588 1.738 1.59 1.87
-74->id126807 MLDOSE 0.667 1.612 1.731 1.589 1.864
-75->id99016 MLDOSE 0.667 1.612 1.734 1.59 1.864
-76->id883847 MLDOSE 0.666 1.611 1.736 1.587 1.869
-77->id354523 MLDOSE 0.669 1.62 1.734 1.584 1.868
-78->id737255 MLDOSE 0.666 1.623 1.733 1.569 1.867
-79->id990941 MLDOSE 0.675 1.62 1.734 1.585 1.869
-80->id25464 MLDOSE 0.673 1.62 1.737 1.579 1.868
-81->id918375 MLDOSE 0.672 1.621 1.737 1.58 1.869
-82->id537828 MLDOSE 0.666 1.613 1.729 1.585 1.869
-83->id682778 MLDOSE 0.658 1.621 1.737 1.573 1.869
-84->id587547 MLDOSE 0.669 1.62 1.738 1.585 1.868
-85->id670874 MLDOSE 0.666 1.618 1.734 1.58 1.867
-86->id444459 MLDOSE 0.66 1.617 1.734 1.577 1.87
-87->id777456 MLDOSE 0.663 1.615 1.732 1.581 1.865
-88->id452384 MLDOSE 0.666 1.625 1.738 1.588 1.869
-89->id826975 MLDOSE 0.669 1.619 1.731 1.582 1.865
-90->id519567 MLDOSE 0.66 1.621 1.732 1.586 1.869
-91->id84292 MLDOSE 0.663 1.618 1.737 1.582 1.868
-92->id124432 MLDOSE 0.684 1.621 1.736 1.591 1.867
-93->id800145 MLDOSE 0.681 1.621 1.737 1.587 1.869
-94->id153857 MLDOSE 0.661 1.617 1.739 1.585 1.87
-95->id587157 MLDOSE 0.663 1.619 1.736 1.584 1.866
-96->id506262 MLDOSE 0.664 1.613 1.719 1.592 1.855
-97->id634462 MLDOSE 0.673 1.623 1.737 1.585 1.869
-98->id687592 MLDOSE 0.664 1.615 1.726 1.584 1.869
-99->id955526 MLDOSE 0.663 1.621 1.737 1.566 1.869
-100->id181850 MLDOSE 0.66 1.603 1.736 1.586 1.869
-101->id159506 MLDOSE 0.667 1.621 1.735 1.585 1.868
-102->id609051 MLDOSE 0.675 1.618 1.73 1.59 1.86
-103->id963886 MLDOSE 0.663 1.612 1.739 1.581 1.87
-104->id405792 MLDOSE 0.682 1.621 1.737 1.589 1.869
-105->id494172 MLDOSE 0.66 1.607 1.736 1.58 1.868
-106->id964637 MLDOSE 0.663 1.616 1.73 1.59 1.861
-107->id799355 MLDOSE 0.669 1.62 1.735 1.585 1.868
-108->id157111 MLDOSE 0.658 1.601 1.731 1.588 1.87
-109->id114524 MLDOSE 0.666 1.603 1.729 1.585 1.87
-110->id954931 MLDOSE 0.676 1.62 1.733 1.585 1.868
-111->id827034 MLDOSE 0.664 1.613 1.737 1.579 1.869
-112->id689645 MLDOSE 0.657 1.611 1.736 1.59 1.868
-113->id281585 MLDOSE 0.673 1.621 1.736 1.584 1.869
-114->id885624 MLDOSE 0.672 1.619 1.734 1.585 1.869
-115->id577871 MLDOSE 0.67 1.616 1.731 1.585 1.864
-116->id238796 MLDOSE 0.667 1.62 1.734 1.585 1.867
-117->id481035 MLDOSE 0.664 1.62 1.737 1.588 1.867
-118->id972713 MLDOSE 0.666 1.611 1.73 1.589 1.869
-119->id905484 MLDOSE 0.66 1.62 1.728 1.572 1.869
-120->id713511 MLDOSE 0.664 1.605 1.733 1.585 1.866
-121->id512328 MLDOSE 0.67 1.616 1.737 1.584 1.869
-122->id703534 MLDOSE 0.67 1.615 1.731 1.59 1.862
-123->id409904 MLDOSE 0.678 1.622 1.734 1.585 1.868
-124->id577169 MLDOSE 0.669 1.616 1.734 1.582 1.867
-125->id813971 MLDOSE 0.657 1.606 1.72 1.591 1.85
-126->id558483 MLDOSE 0.669 1.62 1.737 1.59 1.868
-127->id892784 MLDOSE 0.669 1.621 1.733 1.586 1.866
-128->id611178 MLDOSE 0.666 1.611 1.735 1.582 1.866
-129->id192732 MLDOSE 0.666 1.618 1.735 1.59 1.868
-130->id917280 MLDOSE 0.66 1.602 1.736 1.567 1.869
-131->id435876 MLDOSE 0.678 1.619 1.739 1.586 1.87
-132->id980722 MLDOSE 0.663 1.62 1.735 1.572 1.869
-133->id308273 MLDOSE 0.661 1.617 1.737 1.582 1.868
-134->id476685 MLDOSE 0.675 1.618 1.733 1.584 1.867
-135->id315883 MLDOSE 0.669 1.612 1.736 1.573 1.869
-136->id935945 MLDOSE 0.663 1.614 1.733 1.58 1.865
-137->id991781 MLDOSE 0.664 1.614 1.731 1.586 1.864
-138->id65199 MLDOSE 0.661 1.619 1.731 1.575 1.869
-139->id226233 MLDOSE 0.661 1.618 1.724 1.59 1.854
-140->id860183 MLDOSE 0.66 1.609 1.734 1.577 1.866
-141->id295209 MLDOSE 0.669 1.614 1.735 1.585 1.867
-142->id544964 MLDOSE 0.669 1.619 1.732 1.582 1.868
-143->id648663 MLDOSE 0.666 1.617 1.734 1.585 1.868
-144->id710165 MLDOSE 0.666 1.615 1.728 1.585 1.869
-145->id392593 MLDOSE 0.666 1.621 1.738 1.569 1.868
-146->id129945 MLDOSE 0.66 1.62 1.733 1.587 1.869
-147->id382621 MLDOSE 0.684 1.621 1.736 1.591 1.867
-148->id901440 MLDOSE 0.661 1.615 1.718 1.59 1.852
-149->id39847 MLDOSE 0.669 1.619 1.729 1.58 1.863
-150->id526460 MLDOSE 0.664 1.614 1.731 1.586 1.863
-151->id477473 MLDOSE 0.672 1.619 1.73 1.589 1.862
-152->id448194 MLDOSE 0.669 1.614 1.737 1.586 1.868
-153->id904184 MLDOSE 0.67 1.618 1.73 1.577 1.868
-154->id747852 MLDOSE 0.664 1.615 1.73 1.578 1.869
-155->id711012 MLDOSE 0.672 1.605 1.736 1.575 1.868
-156->id683879 MLDOSE 0.672 1.619 1.734 1.588 1.867
-157->id789575 MLDOSE 0.658 1.618 1.733 1.591 1.863
-158->id650729 MLDOSE 0.676 1.615 1.733 1.589 1.867
-159->id934302 MLDOSE 0.66 1.62 1.733 1.581 1.866
-160->id555013 MLDOSE 0.667 1.622 1.731 1.574 1.865
-161->id82779 MLDOSE 0.675 1.618 1.73 1.585 1.867
-162->id771444 MLDOSE 0.669 1.616 1.718 1.592 1.856
-163->id821562 MLDOSE 0.675 1.625 1.739 1.58 1.87
-164->id292809 MLDOSE 0.66 1.614 1.731 1.58 1.869
-165->id645690 MLDOSE 0.684 1.624 1.733 1.586 1.867
-166->id223901 MLDOSE 0.666 1.615 1.733 1.576 1.868
-167->id41320 MLDOSE 0.661 1.617 1.732 1.576 1.869
-168->id96181 MLDOSE 0.684 1.621 1.716 1.593 1.867
-169->id147900 MLDOSE 0.658 1.603 1.738 1.577 1.868
-170->id702917 MLDOSE 0.669 1.614 1.731 1.59 1.864
-171->id150640 MLDOSE 0.669 1.621 1.734 1.583 1.868
-172->id518391 MLDOSE 0.66 1.62 1.736 1.589 1.869
-173->id879076 MLDOSE 0.661 1.613 1.738 1.573 1.868
-174->id952031 MLDOSE 0.66 1.621 1.734 1.58 1.866
-175->id10055 MLDOSE 0.67 1.611 1.732 1.589 1.866
-176->id727213 MLDOSE 0.664 1.622 1.733 1.583 1.869
-177->id41961 MLDOSE 0.66 1.62 1.737 1.572 1.867
-178->id257209 MLDOSE 0.673 1.616 1.736 1.588 1.869
-179->id995361 MLDOSE 0.661 1.62 1.723 1.571 1.868
-180->id957918 MLDOSE 0.663 1.618 1.726 1.581 1.869
-181->id975370 MLDOSE 0.67 1.614 1.738 1.583 1.87
-182->id889896 MLDOSE 0.679 1.618 1.733 1.589 1.869
-183->id978164 MLDOSE 0.669 1.62 1.735 1.586 1.868
-184->id90359 MLDOSE 0.675 1.621 1.737 1.58 1.869
-185->id307158 MLDOSE 0.675 1.615 1.71 1.591 1.86
-186->id755940 MLDOSE 0.669 1.618 1.734 1.577 1.869
-187->id995582 MLDOSE 0.666 1.615 1.724 1.591 1.853
-188->id363965 MLDOSE 0.663 1.619 1.736 1.574 1.868
-189->id729124 MLDOSE 0.666 1.611 1.73 1.587 1.86
-190->id871963 MLDOSE 0.67 1.62 1.737 1.585 1.868
-191->id475172 MLDOSE 0.663 1.621 1.737 1.579 1.869
-192->id804699 MLDOSE 0.663 1.619 1.731 1.581 1.868
-193->id625843 MLDOSE 0.667 1.622 1.738 1.583 1.87
-194->id595713 MLDOSE 0.657 1.599 1.736 1.588 1.869
-195->id462604 MLDOSE 0.663 1.616 1.731 1.584 1.867
-196->id106141 MLDOSE 0.672 1.612 1.732 1.589 1.864
-197->id689349 MLDOSE 0.666 1.609 1.738 1.583 1.869
-198->id639003 MLDOSE 0.672 1.618 1.728 1.58 1.863
-199->id393896 MLDOSE 0.663 1.617 1.73 1.579 1.868
-200->id450307 MLDOSE 0.663 1.622 1.739 1.588 1.87
+1->id636728 MLDOSE 0.678 1.622 1.732 1.585 1.867 1.000
+2->id890314 MLDOSE 0.66 1.621 1.737 1.59 1.869 2.000
+3->id102874 MLDOSE 0.684 1.623 1.731 1.585 1.866 2.000
+4->id200949 MLDOSE 0.667 NaN 1.728 1.576 1.866 2.000
+5->id336491 MLDOSE 0.66 1.609 1.737 1.577 1.869 2.000
+6->id988766 MLDOSE 0.687 1.622 1.738 1.59 1.868 2.000
+7->id21999 MLDOSE 0.67 1.62 1.735 1.588 1.867 2.000
+8->id433893 MLDOSE 0.663 1.615 1.734 1.572 1.869 2.000
+9->id688932 MLDOSE 0.672 1.619 1.729 1.585 1.862 2.000
+10->id394203 MLDOSE 0.666 1.619 1.729 1.58 1.863 2.000
+11->id995678 MLDOSE 0.672 1.619 1.733 1.583 1.867 2.000
+12->id694339 MLDOSE 0.666 1.615 1.733 1.59 1.869 2.000
+13->id256455 MLDOSE 0.673 1.617 1.737 1.584 1.869 2.000
+14->id14836 MLDOSE 0.672 1.62 1.737 1.582 1.869 2.000
+15->id817128 MLDOSE 0.666 1.609 1.735 1.589 1.868 2.000
+16->id803325 MLDOSE 0.678 1.62 1.73 1.591 1.868 2.000
+17->id521287 MLDOSE 0.661 1.618 1.736 1.585 1.87 2.000
+18->id701472 MLDOSE 0.666 1.621 1.731 1.585 1.867 2.000
+19->id850010 MLDOSE 0.669 1.615 1.736 1.581 1.868 2.000
+20->id268483 MLDOSE 0.669 1.618 1.732 1.582 1.865 2.000
+21->id738781 MLDOSE 0.666 1.611 1.73 1.59 1.862 2.000
+22->id28411 MLDOSE 0.66 1.62 1.735 1.57 1.869 2.000
+23->id541635 MLDOSE 0.669 1.622 1.725 1.558 1.868 2.000
+24->id751101 MLDOSE 0.669 1.624 1.738 1.589 1.869 2.000
+25->id826300 MLDOSE 0.666 1.605 1.736 1.586 1.869 2.000
+26->id884387 MLDOSE 0.661 1.604 1.734 1.587 1.868 2.000
+27->id492414 MLDOSE 0.666 1.619 1.733 1.583 1.866 2.000
+28->id268871 MLDOSE 0.669 1.621 1.731 1.576 1.869 2.000
+29->id627354 MLDOSE 0.666 1.615 1.735 1.59 1.869 2.000
+30->id503932 MLDOSE 0.66 1.62 1.735 1.586 1.867 2.000
+31->id163442 MLDOSE 0.675 1.621 1.737 1.586 1.869 2.000
+32->id317797 MLDOSE 0.654 1.625 1.739 1.57 1.87 2.000
+33->id687857 MLDOSE 0.667 1.621 1.734 1.586 1.869 2.000
+34->id871570 MLDOSE 0.666 1.61 1.731 1.576 1.864 2.000
+35->id724067 MLDOSE 0.666 1.619 1.736 1.583 1.868 2.000
+36->id874076 MLDOSE 0.676 1.617 1.738 1.585 1.869 2.000
+37->id927863 MLDOSE 0.67 1.62 1.734 1.586 1.865 2.000
+38->id369805 MLDOSE 0.663 1.616 1.73 1.583 1.867 2.000
+39->id668376 MLDOSE 0.675 1.617 1.736 1.582 1.869 2.000
+40->id717362 MLDOSE 0.66 1.62 1.736 1.59 1.869 2.000
+41->id665504 MLDOSE 0.669 1.618 1.732 1.578 1.867 2.000
+42->id336637 MLDOSE 0.661 1.618 1.734 1.576 1.865 2.000
+43->id60633 MLDOSE 0.669 1.619 1.731 1.579 1.866 2.000
+44->id848600 MLDOSE 0.664 1.615 1.737 1.576 1.869 2.000
+45->id169514 MLDOSE 0.664 1.615 1.737 1.583 1.869 2.000
+46->id690732 MLDOSE 0.663 1.619 1.737 1.586 1.868 2.000
+47->id684760 MLDOSE 0.667 1.62 1.734 1.578 1.866 2.000
+48->id553502 MLDOSE 0.672 1.608 1.732 1.59 1.869 2.000
+49->id214917 MLDOSE 0.669 1.618 1.727 1.588 1.865 2.000
+50->id849169 MLDOSE 0.664 1.624 1.733 1.588 1.867 2.000
+51->id941921 MLDOSE 0.667 1.619 1.731 1.588 1.862 2.000
+52->id784646 MLDOSE 0.663 1.603 1.739 1.584 1.87 2.000
+53->id520954 MLDOSE 0.664 1.619 1.732 1.581 1.864 2.000
+54->id996355 MLDOSE 0.66 1.605 1.736 1.586 1.865 2.000
+55->id96730 MLDOSE 0.664 1.619 1.736 1.573 1.868 2.000
+56->id673442 MLDOSE 0.666 1.609 1.735 1.575 1.867 2.000
+57->id68305 MLDOSE 0.672 1.618 1.735 1.585 1.866 2.000
+58->id653025 MLDOSE 0.678 1.617 1.738 1.585 1.868 2.000
+59->id208543 MLDOSE 0.664 1.618 1.734 1.58 1.868 2.000
+60->id335725 MLDOSE 0.666 1.616 1.714 1.593 1.845 2.000
+61->id980400 MLDOSE 0.663 1.607 1.733 1.588 1.867 2.000
+62->id869939 MLDOSE 0.667 1.619 1.733 1.588 1.865 2.000
+63->id297563 MLDOSE 0.673 1.614 1.72 1.585 1.866 2.000
+64->id852663 MLDOSE 0.666 1.603 1.729 1.585 1.87 2.000
+65->id162070 MLDOSE 0.666 1.619 1.728 1.572 1.868 2.000
+66->id272875 MLDOSE 0.66 1.605 1.737 1.584 1.869 2.000
+67->id163787 MLDOSE 0.661 1.616 1.723 1.584 1.87 2.000
+68->id422204 MLDOSE 0.663 1.615 1.734 1.576 1.867 2.000
+69->id120197 MLDOSE 0.66 1.62 1.731 1.588 1.87 2.000
+70->id33660 MLDOSE 0.658 1.62 1.736 1.571 1.869 2.000
+71->id803855 MLDOSE 0.669 1.616 1.732 1.586 1.865 2.000
+72->id255048 MLDOSE 0.675 1.616 1.726 1.582 1.866 2.000
+73->id690936 MLDOSE 0.657 1.588 1.738 1.59 1.87 2.000
+74->id126807 MLDOSE 0.667 1.612 1.731 1.589 1.864 2.000
+75->id99016 MLDOSE 0.667 1.612 1.734 1.59 1.864 2.000
+76->id883847 MLDOSE 0.666 1.611 1.736 1.587 1.869 2.000
+77->id354523 MLDOSE 0.669 1.62 1.734 1.584 1.868 2.000
+78->id737255 MLDOSE 0.666 1.623 1.733 1.569 1.867 2.000
+79->id990941 MLDOSE 0.675 1.62 1.734 1.585 1.869 2.000
+80->id25464 MLDOSE 0.673 1.62 1.737 1.579 1.868 2.000
+81->id918375 MLDOSE 0.672 1.621 1.737 1.58 1.869 2.000
+82->id537828 MLDOSE 0.666 1.613 1.729 1.585 1.869 2.000
+83->id682778 MLDOSE 0.658 1.621 1.737 1.573 1.869 2.000
+84->id587547 MLDOSE 0.669 1.62 1.738 1.585 1.868 2.000
+85->id670874 MLDOSE 0.666 1.618 1.734 1.58 1.867 2.000
+86->id444459 MLDOSE 0.66 1.617 1.734 1.577 1.87 2.000
+87->id777456 MLDOSE 0.663 1.615 1.732 1.581 1.865 2.000
+88->id452384 MLDOSE 0.666 1.625 1.738 1.588 1.869 2.000
+89->id826975 MLDOSE 0.669 1.619 1.731 1.582 1.865 2.000
+90->id519567 MLDOSE 0.66 1.621 1.732 1.586 1.869 2.000
+91->id84292 MLDOSE 0.663 1.618 1.737 1.582 1.868 2.000
+92->id124432 MLDOSE 0.684 1.621 1.736 1.591 1.867 2.000
+93->id800145 MLDOSE 0.681 1.621 1.737 1.587 1.869 2.000
+94->id153857 MLDOSE 0.661 1.617 1.739 1.585 1.87 2.000
+95->id587157 MLDOSE 0.663 1.619 1.736 1.584 1.866 2.000
+96->id506262 MLDOSE 0.664 1.613 1.719 1.592 1.855 2.000
+97->id634462 MLDOSE 0.673 1.623 1.737 1.585 1.869 2.000
+98->id687592 MLDOSE 0.664 1.615 1.726 1.584 1.869 2.000
+99->id955526 MLDOSE 0.663 1.621 1.737 1.566 1.869 2.000
+100->id181850 MLDOSE 0.66 1.603 1.736 1.586 1.869 2.000
+101->id159506 MLDOSE 0.667 1.621 1.735 1.585 1.868 2.000
+102->id609051 MLDOSE 0.675 1.618 1.73 1.59 1.86 2.000
+103->id963886 MLDOSE 0.663 1.612 1.739 1.581 1.87 2.000
+104->id405792 MLDOSE 0.682 1.621 1.737 1.589 1.869 2.000
+105->id494172 MLDOSE 0.66 1.607 1.736 1.58 1.868 2.000
+106->id964637 MLDOSE 0.663 1.616 1.73 1.59 1.861 2.000
+107->id799355 MLDOSE 0.669 1.62 1.735 1.585 1.868 2.000
+108->id157111 MLDOSE 0.658 1.601 1.731 1.588 1.87 2.000
+109->id114524 MLDOSE 0.666 1.603 1.729 1.585 1.87 2.000
+110->id954931 MLDOSE 0.676 1.62 1.733 1.585 1.868 2.000
+111->id827034 MLDOSE 0.664 1.613 1.737 1.579 1.869 2.000
+112->id689645 MLDOSE 0.657 1.611 1.736 1.59 1.868 2.000
+113->id281585 MLDOSE 0.673 1.621 1.736 1.584 1.869 2.000
+114->id885624 MLDOSE 0.672 1.619 1.734 1.585 1.869 2.000
+115->id577871 MLDOSE 0.67 1.616 1.731 1.585 1.864 2.000
+116->id238796 MLDOSE 0.667 1.62 1.734 1.585 1.867 2.000
+117->id481035 MLDOSE 0.664 1.62 1.737 1.588 1.867 2.000
+118->id972713 MLDOSE 0.666 1.611 1.73 1.589 1.869 2.000
+119->id905484 MLDOSE 0.66 1.62 1.728 1.572 1.869 2.000
+120->id713511 MLDOSE 0.664 1.605 1.733 1.585 1.866 2.000
+121->id512328 MLDOSE 0.67 1.616 1.737 1.584 1.869 2.000
+122->id703534 MLDOSE 0.67 1.615 1.731 1.59 1.862 2.000
+123->id409904 MLDOSE 0.678 1.622 1.734 1.585 1.868 2.000
+124->id577169 MLDOSE 0.669 1.616 1.734 1.582 1.867 2.000
+125->id813971 MLDOSE 0.657 1.606 1.72 1.591 1.85 2.000
+126->id558483 MLDOSE 0.669 1.62 1.737 1.59 1.868 2.000
+127->id892784 MLDOSE 0.669 1.621 1.733 1.586 1.866 2.000
+128->id611178 MLDOSE 0.666 1.611 1.735 1.582 1.866 2.000
+129->id192732 MLDOSE 0.666 1.618 1.735 1.59 1.868 2.000
+130->id917280 MLDOSE 0.66 1.602 1.736 1.567 1.869 2.000
+131->id435876 MLDOSE 0.678 1.619 1.739 1.586 1.87 2.000
+132->id980722 MLDOSE 0.663 1.62 1.735 1.572 1.869 2.000
+133->id308273 MLDOSE 0.661 1.617 1.737 1.582 1.868 2.000
+134->id476685 MLDOSE 0.675 1.618 1.733 1.584 1.867 2.000
+135->id315883 MLDOSE 0.669 1.612 1.736 1.573 1.869 2.000
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/genabel -r 1397
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