[Genabel-commits] r1382 - in pkg/ProbABEL: checks/R-tests checks/inputfiles checks/verified_results doc src
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Nov 11 23:42:12 CET 2013
Author: lckarssen
Date: 2013-11-11 23:42:12 +0100 (Mon, 11 Nov 2013)
New Revision: 1382
Added:
pkg/ProbABEL/checks/R-tests/run_model_coxph.R
pkg/ProbABEL/checks/R-tests/run_model_linear.R
pkg/ProbABEL/checks/R-tests/run_model_logist.R
Modified:
pkg/ProbABEL/checks/R-tests/run_R_test_pacox.sh
pkg/ProbABEL/checks/R-tests/run_models_in_R_pacox.R
pkg/ProbABEL/checks/R-tests/run_models_in_R_palinear.R
pkg/ProbABEL/checks/R-tests/run_models_in_R_palogist.R
pkg/ProbABEL/checks/inputfiles/test.dose.fvd
pkg/ProbABEL/checks/inputfiles/test.dose.fvi
pkg/ProbABEL/checks/inputfiles/test.map
pkg/ProbABEL/checks/inputfiles/test.mldose
pkg/ProbABEL/checks/inputfiles/test.mlinfo
pkg/ProbABEL/checks/inputfiles/test.mlprob
pkg/ProbABEL/checks/inputfiles/test.prob.fvd
pkg/ProbABEL/checks/inputfiles/test.prob.fvi
pkg/ProbABEL/checks/verified_results/height_base_add.out.txt
pkg/ProbABEL/doc/ChangeLog
pkg/ProbABEL/src/coxph_data.cpp
pkg/ProbABEL/src/coxph_data.h
pkg/ProbABEL/src/main.cpp
Log:
Improving ProbABEL's Cox PH module.
This is a changeset that was started more than a month ago, shortly
after the release of v4.0. It aims to improve the error handling (and
detection) in pacoxph. In v4.1 the 'flag' variable returned by
coxfit2() was checked, leading to several warnings (see the forum at:
http://forum.genabel.org/viewtopic.php?f=10&t=825). With this commit
the checks of the coxfit2() output are more extensive and inline with
what the R-package survival does.
Note that this commit is work in progress. At the moment the R-tests
for palinear and palogist fail.
Also note that one of the checks is only implemented when the Eigen
libraries are used. Maybe implementation without Eigen will come in
the future, but I'm inclined to say that that won't happen.
checks/inputfiles/test.prob.fvd,
checks/inputfiles/test.mldose,
checks/inputfiles/test.mlinfo,
checks/inputfiles/test.prob.fvi,
checks/inputfiles/test.mlprob,
checks/inputfiles/test.dose.fvd,
checks/inputfiles/test.map,
checks/inputfiles/test.dose.fvi,
checks/verified_results/height_base_add.out.txt:
- In order trigger these warnings a SNP was added to the genotype data
in the checks/inputfiles. This SNP has only one person with a
different dosage than the others.
src/main.cpp:
- Raise MAXITER from 10 to 20, with 10 several SNPs didn't converge.
- In order to print the name of the trouble-causing SNP the mlinfo
information must be passed to the coxph_regression object (around
line 450, 740)
src/coxph_data.h:
- Pass the mlinfo to the estimate() function of the coxph_reg object
- Set several of the function parameters of estimate() to const
src/coxph_data.cpp:
- Pass the mlinfo to the estimate() function of the coxph_reg object
- Set several of the function parameters of estimate() to const
- Include the checks of the coxfit2.c output. Based on the checks
done by R's survival package. If a test fails, set all betas and
sebetas to NaN for that SNP. R handles this more subtlely by only
replacing the troublesome betas and sebetas with NaN (in our type
of analysis only the SNP beta, not the betas of the other
covariates).
checks/R-tests/run_model_coxph.R,
checks/R-tests/run_model_logist.R,
checks/R-tests/run_R_test_pacox.sh,
checks/R-tests/run_models_in_R_pacox.R,
checks/R-tests/run_models_in_R_palinear.R,
checks/R-tests/run_model_linear.R,
checks/R-tests/run_models_in_R_palogist.R:
- Major overhaul of the R-tests where the ProbABEL results are
compared to those from R. Needed to accomodate the warnings
generated by the troublesome SNP.
- Split the actual run_model() function for each type of regression
into a separate file for clarity.
- The test for Cox regression now works, the others don't (yet)
This change was partially supported by the Erasmus Medical Centre, Rotterdam.
Modified: pkg/ProbABEL/checks/R-tests/run_R_test_pacox.sh
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_R_test_pacox.sh 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/R-tests/run_R_test_pacox.sh 2013-11-11 22:42:12 UTC (rev 1382)
@@ -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 ${srcdir} ]; then
srcdir="."
Added: pkg/ProbABEL/checks/R-tests/run_model_coxph.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_model_coxph.R (rev 0)
+++ pkg/ProbABEL/checks/R-tests/run_model_coxph.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -0,0 +1,98 @@
+##' This function runs the actual models for Cox regression. It is
+##' called by run_models_in_R_pacox.R
+##'
+##'
+##' @title run.model
+##' @param model0.txt String containing the null model (without SNP term)
+##' @param model.txt String containing the alternative model (with SNP
+##' term)
+##' @param snpcomponent1 String telling how the SNP term is defined
+##' @param snpcomponent2 String telling how the second SNP term is
+##' defined (only used in the 2 df model). By default this term is
+##' constant ("1")
+##' @return A data frame containing the coefficients from the
+##' regression analysis and some other variables, such that this
+##' output can be compared to the ProbABEL output.
+##' @author L.C. Larsen
+run.model <- function(model0.txt, model.txt,
+ snpcomponent1, snpcomponent2="1") {
+
+ if (snpcomponent2 != "1") {
+ ## SNP component 2 is not constant: assume we run the 2df
+ ## model.
+ twoDF = TRUE
+ } else {
+ twoDF = FALSE
+ }
+
+ resultR <- data.frame()
+
+ for (i in 3:dim(dose)[2]) {
+ indexHom <- 3 + ( i - 3 ) * 2
+ indexHet <- indexHom + 1
+ snp1 <- eval(parse(text=snpcomponent1))
+ snp2 <- eval(parse(text=snpcomponent2))
+ snp <- snp1 + snp2
+
+ noNA <- which( !is.na(snp) )
+ model.0 <- eval(parse(text=model0.txt))
+
+ ## Evaluate the model. The whole tryCatch is needed to catch
+ ## problems with non-converging regression.
+ model = tryCatch({
+ list(
+ eval(parse(text=model.txt)),
+ list(message="no warnings")
+ )
+ }, warning = function(war) {
+ return(list(
+ eval(parse(text=model.txt)),
+ war)
+ )
+ })
+
+ if ( grepl("infinite", model[[2]]$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)
+}
Added: pkg/ProbABEL/checks/R-tests/run_model_linear.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_model_linear.R (rev 0)
+++ pkg/ProbABEL/checks/R-tests/run_model_linear.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -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)$coefficients
+ if ( dim(coeff)[1] != 4 ) {
+ sm <- c(NaN, NaN)
+ } else {
+ sm <- coeff[4, c("Estimate", "Std. Error")]
+ }
+
+ lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
+ rsq <- Rsq[i-2]
+ if( rsq < rsq.thresh) {
+ row <- c(rsq, NaN, NaN, NaN)
+ } else {
+ row <- c(rsq, sm[1], sm[2], lrt)
+ }
+ resultR <- rbind(resultR, row)
+ }
+ return(resultR)
+}
Added: pkg/ProbABEL/checks/R-tests/run_model_logist.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_model_logist.R (rev 0)
+++ pkg/ProbABEL/checks/R-tests/run_model_logist.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -0,0 +1,23 @@
+run.model <- function(model0.txt, model.txt, snpdata) {
+ resultR <- data.frame()
+ for (i in 3:dim(dose)[2]) {
+ indexHom <- 3 + ( i - 3 ) * 2
+ indexHet <- indexHom + 1
+ snp <- eval(parse(text=snpdata))
+
+ noNA <- which( !is.na(snp) )
+ model.0 <- eval(parse(text=model0.txt))
+ model <- eval(parse(text=model.txt))
+ sm <- summary(model)$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: pkg/ProbABEL/checks/R-tests/run_models_in_R_pacox.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_models_in_R_pacox.R 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/R-tests/run_models_in_R_pacox.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -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: pkg/ProbABEL/checks/R-tests/run_models_in_R_palinear.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_models_in_R_palinear.R 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/R-tests/run_models_in_R_palinear.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -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)$coef[4, 1:2]
- lrt <- 2 * ( logLik( model ) - logLik( model.0 ) )
-
- rsq <- Rsq[i-2]
- if( rsq < rsq.thresh) {
- row <- c(rsq, NaN, NaN, NaN)
- } else {
- row <- c(rsq, sm[1], sm[2], lrt)
- }
- resultR <- rbind(resultR, row)
- }
- return(resultR)
-}
-
model.fn.0 <- "lm( height[noNA] ~ sex[noNA] + age[noNA] )"
model.fn <- "lm( height ~ sex + age + snp )"
@@ -80,6 +58,7 @@
stopifnot( all.equal(resPaAddDose, dose.add.R, tol=tol) )
cat("additive ")
+
## Additive model, probabilities
snpprob <- "doseFromProb[, i]"
prob.add.R <- run.model(model.fn.0, model.fn, snpprob)
Modified: pkg/ProbABEL/checks/R-tests/run_models_in_R_palogist.R
===================================================================
--- pkg/ProbABEL/checks/R-tests/run_models_in_R_palogist.R 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/R-tests/run_models_in_R_palogist.R 2013-11-11 22:42:12 UTC (rev 1382)
@@ -45,31 +45,8 @@
cat("Comparing R output with ProbABEL output\t\t")
-run.model <- function(model0.txt, model.txt, snpdata) {
- resultR <- data.frame()
- for (i in 3:dim(dose)[2]) {
- indexHom <- 3 + ( i - 3 ) * 2
- indexHet <- indexHom + 1
- snp <- eval(parse(text=snpdata))
+source("run_model_logist.R")
- noNA <- which( !is.na(snp) )
- model.0 <- eval(parse(text=model0.txt))
- model <- eval(parse(text=model.txt))
- sm <- summary(model)$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: pkg/ProbABEL/checks/inputfiles/test.dose.fvd
===================================================================
(Binary files differ)
Modified: pkg/ProbABEL/checks/inputfiles/test.dose.fvi
===================================================================
(Binary files differ)
Modified: pkg/ProbABEL/checks/inputfiles/test.map
===================================================================
--- pkg/ProbABEL/checks/inputfiles/test.map 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/inputfiles/test.map 2013-11-11 22:42:12 UTC (rev 1382)
@@ -4,3 +4,4 @@
rs8102615 211970 A T
rs8105536 212033 A G
rs2312724 217034 C T
+rs3174230 7845238 G C
Modified: pkg/ProbABEL/checks/inputfiles/test.mldose
===================================================================
--- pkg/ProbABEL/checks/inputfiles/test.mldose 2013-11-11 22:03:49 UTC (rev 1381)
+++ pkg/ProbABEL/checks/inputfiles/test.mldose 2013-11-11 22:42:12 UTC (rev 1382)
@@ -1,200 +1,200 @@
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-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
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+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
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+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
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+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
+136->id935945 MLDOSE 0.663 1.614 1.733 1.58 1.865 2.000
+137->id991781 MLDOSE 0.664 1.614 1.731 1.586 1.864 2.000
+138->id65199 MLDOSE 0.661 1.619 1.731 1.575 1.869 2.000
+139->id226233 MLDOSE 0.661 1.618 1.724 1.59 1.854 2.000
+140->id860183 MLDOSE 0.66 1.609 1.734 1.577 1.866 2.000
+141->id295209 MLDOSE 0.669 1.614 1.735 1.585 1.867 2.000
+142->id544964 MLDOSE 0.669 1.619 1.732 1.582 1.868 2.000
+143->id648663 MLDOSE 0.666 1.617 1.734 1.585 1.868 2.000
+144->id710165 MLDOSE 0.666 1.615 1.728 1.585 1.869 2.000
+145->id392593 MLDOSE 0.666 1.621 1.738 1.569 1.868 2.000
+146->id129945 MLDOSE 0.66 1.62 1.733 1.587 1.869 2.000
+147->id382621 MLDOSE 0.684 1.621 1.736 1.591 1.867 2.000
+148->id901440 MLDOSE 0.661 1.615 1.718 1.59 1.852 2.000
+149->id39847 MLDOSE 0.669 1.619 1.729 1.58 1.863 2.000
+150->id526460 MLDOSE 0.664 1.614 1.731 1.586 1.863 2.000
+151->id477473 MLDOSE 0.672 1.619 1.73 1.589 1.862 2.000
+152->id448194 MLDOSE 0.669 1.614 1.737 1.586 1.868 2.000
+153->id904184 MLDOSE 0.67 1.618 1.73 1.577 1.868 2.000
+154->id747852 MLDOSE 0.664 1.615 1.73 1.578 1.869 2.000
+155->id711012 MLDOSE 0.672 1.605 1.736 1.575 1.868 2.000
+156->id683879 MLDOSE 0.672 1.619 1.734 1.588 1.867 2.000
+157->id789575 MLDOSE 0.658 1.618 1.733 1.591 1.863 2.000
+158->id650729 MLDOSE 0.676 1.615 1.733 1.589 1.867 2.000
+159->id934302 MLDOSE 0.66 1.62 1.733 1.581 1.866 2.000
+160->id555013 MLDOSE 0.667 1.622 1.731 1.574 1.865 2.000
+161->id82779 MLDOSE 0.675 1.618 1.73 1.585 1.867 2.000
+162->id771444 MLDOSE 0.669 1.616 1.718 1.592 1.856 2.000
+163->id821562 MLDOSE 0.675 1.625 1.739 1.58 1.87 2.000
+164->id292809 MLDOSE 0.66 1.614 1.731 1.58 1.869 2.000
+165->id645690 MLDOSE 0.684 1.624 1.733 1.586 1.867 2.000
+166->id223901 MLDOSE 0.666 1.615 1.733 1.576 1.868 2.000
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/genabel -r 1382
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