[adegenet-commits] r921 - pkg/R pkg/inst/doc pkg/inst/doc/figs www

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Mon Jun 20 20:36:02 CEST 2011 

Author: jombart
Date: 2011-06-20 20:36:00 +0200 (Mon, 20 Jun 2011)
New Revision: 921

Modified:
   pkg/R/dapc.R
   pkg/R/fstat.R
   pkg/inst/doc/adegenet-basics.Rnw
   pkg/inst/doc/adegenet-basics.tex
   pkg/inst/doc/adegenet-dapc.Rnw
   pkg/inst/doc/adegenet-dapc.pdf
   pkg/inst/doc/adegenet-dapc.tex
   pkg/inst/doc/figs/base-040.pdf
   pkg/inst/doc/figs/base-063.pdf
   pkg/inst/doc/figs/base-065.pdf
   pkg/inst/doc/figs/base-068.pdf
   pkg/inst/doc/figs/base-070.pdf
   pkg/inst/doc/figs/base-071.pdf
   pkg/inst/doc/figs/base-072.pdf
   pkg/inst/doc/figs/base-073.pdf
   pkg/inst/doc/figs/base-074.pdf
   pkg/inst/doc/figs/base-075.pdf
   pkg/inst/doc/figs/base-077.pdf
   pkg/inst/doc/figs/base-078.pdf
   pkg/inst/doc/figs/base-080.pdf
   pkg/inst/doc/figs/base-082.pdf
   pkg/inst/doc/figs/base-083.pdf
   pkg/inst/doc/figs/base-098.pdf
   pkg/inst/doc/figs/base-099.pdf
   pkg/inst/doc/figs/base-caexpl.pdf
   pkg/inst/doc/figs/base-mon1.pdf
   pkg/inst/doc/figs/base-mon4.pdf
   pkg/inst/doc/figs/base-mon6.pdf
   pkg/inst/doc/figs/base-njAA.pdf
   pkg/inst/doc/figs/base-pcaaflp.pdf
   pkg/inst/doc/figs/base-sumry.pdf
   pkg/inst/doc/figs/dapc-006.pdf
   pkg/inst/doc/figs/dapc-010.pdf
   pkg/inst/doc/figs/dapc-011.pdf
   pkg/inst/doc/figs/dapc-012.pdf
   pkg/inst/doc/figs/dapc-013.pdf
   pkg/inst/doc/figs/dapc-014.pdf
   pkg/inst/doc/figs/dapc-015.pdf
   pkg/inst/doc/figs/dapc-017.pdf
   pkg/inst/doc/figs/dapc-018.pdf
   pkg/inst/doc/figs/dapc-019.pdf
   pkg/inst/doc/figs/dapc-022.pdf
   pkg/inst/doc/figs/dapc-023.pdf
   pkg/inst/doc/figs/dapc-024.pdf
   pkg/inst/doc/figs/dapc-025.pdf
   pkg/inst/doc/figs/dapc-027.pdf
   pkg/inst/doc/figs/dapc-029.pdf
   pkg/inst/doc/figs/dapc-031.pdf
   pkg/inst/doc/figs/dapc-036.pdf
   pkg/inst/doc/figs/dapc-037.pdf
   pkg/inst/doc/figs/dapc-040.pdf
   pkg/inst/doc/figs/dapc-042.pdf
   www/documentation.html
   www/literature.html
Log:
All 4 vignettes ready, genomics and basics need proof-reading.
Pkg compiles and passes the check.


Modified: pkg/R/dapc.R
===================================================================
--- pkg/R/dapc.R	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/R/dapc.R	2011-06-20 18:36:00 UTC (rev 921)
@@ -534,7 +534,7 @@
         if(mstree && require(ade4)){
             meanposi <- apply(x$tab,2, tapply, grp, mean)
             D <- dist(meanposi)^2
-            tre <- mstree(D)
+            tre <- ade4::mstree(D)
             x0 <- x$grp.coord[tre[,1], axes[1]]
             y0 <- x$grp.coord[tre[,1], axes[2]]
             x1 <- x$grp.coord[tre[,2], axes[1]]

Modified: pkg/R/fstat.R
===================================================================
--- pkg/R/fstat.R	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/R/fstat.R	2011-06-20 18:36:00 UTC (rev 921)
@@ -71,6 +71,9 @@
     temp <- pop(x)
     levPop <- levels(temp)
     allPairs <- combn(1:length(levPop), 2)
+    if(!is.matrix(allPairs)){
+        allPairs <- matrix(allPairs,nrow=2)
+    }
 
     vecRes <- numeric()
     for(i in 1:ncol(allPairs)){

Modified: pkg/inst/doc/adegenet-basics.Rnw
===================================================================
--- pkg/inst/doc/adegenet-basics.Rnw	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/inst/doc/adegenet-basics.Rnw	2011-06-20 18:36:00 UTC (rev 921)
@@ -519,7 +519,7 @@
 Here is an example using a toy dataset 'AFLP.txt' that can be downloaded
 from the adegenet website, section 'Documentation':
 <<aflpread>>=
-dat <- read.table("http://adegenet.r-forge.r-project.org/files/AFLP.txt",header=TRUE)
+dat <- read.table(system.file("files/AFLP.txt",package="adegenet"), header=TRUE)
 dat
 @
 \noindent The function \texttt{df2genind} is used to obtain a genind object:

Modified: pkg/inst/doc/adegenet-basics.tex
===================================================================
--- pkg/inst/doc/adegenet-basics.tex	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/inst/doc/adegenet-basics.tex	2011-06-20 18:36:00 UTC (rev 921)
@@ -645,8 +645,8 @@
 > pop(obj)
 \end{Sinput}
 \begin{Soutput}
- [1] 3 2 3 4 1 2 2 3 2 4
-Levels: 3 2 4 1
+ [1] 2 2 2 4 2 3 3 3 3 1
+Levels: 2 4 3 1
 \end{Soutput}
 \begin{Sinput}
 > pop(obj) <- rep("newPop", 10)
@@ -754,16 +754,16 @@
 \end{Sinput}
 \begin{Soutput}
        loc 1  loc 2  loc 3 
-ind 1  "6998" "2427" "1766"
-ind 2  "7026" "1444" "1141"
-ind 3  "8956" "6567" "7466"
-ind 4  "3866" "5401" "4620"
-ind 5  "4031" "3217" "9901"
-ind 6  "5834" "9524" "3020"
-ind 7  "1959" "6515" "7494"
-ind 8  "0088" "5036" "1201"
-ind 9  "6723" "0982" "5113"
-ind 10 "1950" "5585" "4833"
+ind 1  "0652" "7624" "1903"
+ind 2  "6442" "4143" "6414"
+ind 3  "9753" "5886" "5226"
+ind 4  "3754" "3336" "6535"
+ind 5  "2245" "5586" "9674"
+ind 6  "7438" "1568" "5184"
+ind 7  "8488" "8386" "5272"
+ind 8  "1293" "8206" "1952"
+ind 9  "9159" "6431" "9701"
+ind 10 "8507" "5481" "0490"
 \end{Soutput}
 \begin{Sinput}
 > obj <- df2genind(temp, ploidy = 4, sep = "")
@@ -778,12 +778,12 @@
 S4 class:  genind
 @call: df2genind(X = temp, sep = "", ploidy = 4)
 
- at tab:  10 x 30 matrix of genotypes
+ at tab:  10 x 29 matrix of genotypes
 
 @ind.names: vector of  10 individual names
 @loc.names: vector of  3 locus names
 @loc.nall: number of alleles per locus
- at loc.fac: locus factor for the  30 columns of @tab
+ at loc.fac: locus factor for the  29 columns of @tab
 @all.names: list of  3 components yielding allele names for each locus
 @ploidy:  4
 @type:  codom
@@ -835,7 +835,7 @@
 from the adegenet website, section 'Documentation':
 \begin{Schunk}
 \begin{Sinput}
-> dat <- read.table("http://adegenet.r-forge.r-project.org/files/AFLP.txt", 
+> dat <- read.table(system.file("files/AFLP.txt", package = "adegenet"), 
 +     header = TRUE)
 > dat
 \end{Sinput}
@@ -1038,19 +1038,19 @@
 \end{Sinput}
 \begin{Soutput}
          1   2   3   4   5  
-genot. 1 "c" "c" "c" "t" "a"
-genot. 2 "a" "a" "a" "c" "c"
-genot. 3 "a" "g" "t" "c" "a"
+genot. 1 "g" "g" "a" "t" "g"
+genot. 2 "g" "c" "g" "g" "c"
+genot. 3 "t" "g" "t" "c" "a"
 \end{Soutput}
 \begin{Sinput}
 > obj <- df2genind(dat, ploidy = 1)
 > truenames(obj)
 \end{Sinput}
 \begin{Soutput}
-         1.a 1.c 2.a 2.c 2.g 3.a 3.c 3.t 4.c 4.t 5.a 5.c
-genot. 1   0   1   0   1   0   0   1   0   0   1   1   0
-genot. 2   1   0   1   0   0   1   0   0   1   0   0   1
-genot. 3   1   0   0   0   1   0   0   1   1   0   1   0
+         1.g 1.t 2.c 2.g 3.a 3.g 3.t 4.c 4.g 4.t 5.a 5.c 5.g
+genot. 1   1   0   0   1   1   0   0   0   0   1   0   0   1
+genot. 2   1   0   1   0   0   1   0   0   1   0   0   1   0
+genot. 3   0   1   0   1   0   0   1   1   0   0   1   0   0
 \end{Soutput}
 \end{Schunk}
 
@@ -2295,7 +2295,7 @@
     do.call("mapply", c(FUN = FUN, args[dovec], MoreArgs = list(args[!dovec]), 
         SIMPLIFY = SIMPLIFY, USE.NAMES = USE.NAMES))
 }
-<environment: 0x525f550>
+<environment: 0x6e2d148>
 \end{Soutput}
 \end{Schunk}
 The output object \texttt{F} can seem a bit cryptic: it is an function embedded within a hidden environment.
@@ -2733,11 +2733,11 @@
 Observation: 0.00492068 
 
 Based on 999 replicates
-Simulated p-value: 0.458 
+Simulated p-value: 0.46 
 Alternative hypothesis: greater 
 
      Std.Obs  Expectation     Variance 
- 0.093756493 -0.004680006  0.010485806 
+ 0.060196288 -0.001260674  0.010544545 
 \end{Soutput}
 \end{Schunk}
 \begin{Schunk}
@@ -2775,8 +2775,8 @@
 Simulated p-value: 0.001 
 Alternative hypothesis: greater 
 
-    Std.Obs Expectation    Variance 
-3.361033347 0.001240891 0.001394103 
+      Std.Obs   Expectation      Variance 
+ 3.4917048386 -0.0009677967  0.0013375798 
 \end{Soutput}
 \end{Schunk}
 \begin{Schunk}
@@ -2981,8 +2981,8 @@
 > replicate(10, pairwise.fst(sim2pop, pop = sample(pop(sim2pop))))
 \end{Sinput}
 \begin{Soutput}
- [1] 0.004182984 0.003325440 0.003499744 0.004075277 0.004365510 0.003540032
- [7] 0.003754623 0.003234999 0.003385460 0.003815465
+ [1] 0.003484945 0.003764490 0.003314752 0.003239110 0.003821221 0.003562231
+ [7] 0.004425530 0.005708968 0.003082894 0.003398615
 \end{Soutput}
 \end{Schunk}
 $F_{ST}$ values in absence of population structure would be one order of magnitude lower (more

Modified: pkg/inst/doc/adegenet-dapc.Rnw
===================================================================
--- pkg/inst/doc/adegenet-dapc.Rnw	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/inst/doc/adegenet-dapc.Rnw	2011-06-20 18:36:00 UTC (rev 921)
@@ -163,7 +163,7 @@
 (and accessible using \texttt{pop(x)}).
 We specify that we want to evaluate up to $k=40$ groups (\texttt{max.n.clust=40}):
 <<eval=TRUE,echo=FALSE>>=
-load("Robjects/grp.RData")
+grp <- find.clusters(x, n.pca=200, n.clust=6)
 @
 <<eval=FALSE>>=
 grp <- find.clusters(x, max.n.clust=40)
@@ -288,7 +288,7 @@
 We run the analysis on the previous toy dataset, using the inferred groups stored in \texttt{grp\$grp}:
 
 <<echo=FALSE>>=
-load("Robjects/dapc1.RData")
+dapc1 <- dapc(x, grp$grp, n.pca=40, n.da=100)
 @
 <<eval=FALSE>>=
 dapc1 <- dapc(x, grp$grp)

Modified: pkg/inst/doc/adegenet-dapc.pdf
===================================================================
--- pkg/inst/doc/adegenet-dapc.pdf	2011-06-20 17:51:51 UTC (rev 920)
+++ pkg/inst/doc/adegenet-dapc.pdf	2011-06-20 18:36:00 UTC (rev 921)
@@ -106,21 +106,20 @@
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   157.84 89.85 159.46 88.23 159.46 86.25 c
@@ -43358,9 +43357,9 @@
   160.10 109.52 158.48 111.14 158.48 113.12 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   160.55 55.67 m
   160.55 57.65 162.17 59.27 164.15 59.27 c
   166.13 59.27 167.75 57.65 167.75 55.67 c
@@ -43390,9 +43389,9 @@
   163.87 107.04 162.25 108.66 162.25 110.64 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   261.23 58.86 m
   261.23 60.84 262.85 62.46 264.83 62.46 c
   266.81 62.46 268.43 60.84 268.43 58.86 c
@@ -43410,9 +43409,9 @@
   243.09 67.57 241.47 69.19 241.47 71.17 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   244.63 55.32 m
   244.63 57.30 246.25 58.92 248.23 58.92 c
   250.21 58.92 251.83 57.30 251.83 55.32 c
@@ -43436,9 +43435,9 @@
   163.87 107.04 162.25 108.66 162.25 110.64 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -43510,9 +43509,9 @@
   145.90 79.91 144.28 81.53 144.28 83.51 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.04 54.49 m
   241.04 56.47 242.66 58.09 244.64 58.09 c
   246.62 58.09 248.24 56.47 248.24 54.49 c
@@ -43572,9 +43571,9 @@
   243.09 67.57 241.47 69.19 241.47 71.17 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -43604,9 +43603,9 @@
   243.09 67.57 241.47 69.19 241.47 71.17 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -43654,9 +43653,9 @@
   179.83 78.98 178.21 80.60 178.21 82.58 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   257.50 135.49 m
   257.50 137.47 259.12 139.09 261.10 139.09 c
   263.08 139.09 264.70 137.47 264.70 135.49 c
@@ -43710,9 +43709,9 @@
   163.87 107.04 162.25 108.66 162.25 110.64 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   197.67 82.46 m
   197.67 84.44 199.29 86.06 201.27 86.06 c
   203.25 86.06 204.87 84.44 204.87 82.46 c
@@ -43766,9 +43765,9 @@
   163.87 107.04 162.25 108.66 162.25 110.64 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   285.98 57.15 m
   285.98 59.13 287.60 60.75 289.58 60.75 c
   291.56 60.75 293.18 59.13 293.18 57.15 c
@@ -43804,9 +43803,9 @@
   160.10 109.52 158.48 111.14 158.48 113.12 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -43872,9 +43871,9 @@
   137.97 95.02 136.35 96.64 136.35 98.62 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   259.85 57.89 m
   259.85 59.87 261.47 61.49 263.45 61.49 c
   265.43 61.49 267.05 59.87 267.05 57.89 c
@@ -43898,9 +43897,9 @@
   259.33 54.12 257.71 55.74 257.71 57.72 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   252.78 52.78 m
   252.78 54.76 254.40 56.38 256.38 56.38 c
   258.36 56.38 259.98 54.76 259.98 52.78 c
@@ -43936,9 +43935,9 @@
   154.93 118.64 153.31 120.26 153.31 122.24 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -44022,9 +44021,9 @@
   183.93 76.41 182.31 78.03 182.31 80.01 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   284.56 66.68 m
   284.56 68.66 286.18 70.28 288.16 70.28 c
   290.14 70.28 291.76 68.66 291.76 66.68 c
@@ -44054,9 +44053,9 @@
   144.51 85.78 142.89 87.40 142.89 89.38 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.47 71.17 m
   241.47 73.15 243.09 74.77 245.07 74.77 c
   247.05 74.77 248.67 73.15 248.67 71.17 c
@@ -44086,9 +44085,9 @@
   183.93 76.41 182.31 78.03 182.31 80.01 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   241.04 54.49 m
   241.04 56.47 242.66 58.09 244.64 58.09 c
   246.62 58.09 248.24 56.47 248.24 54.49 c
@@ -44106,9 +44105,9 @@
   152.88 75.63 151.26 77.25 151.26 79.23 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   224.84 79.71 m
   224.84 81.69 226.46 83.31 228.44 83.31 c
   230.42 83.31 232.04 81.69 232.04 79.71 c
@@ -44156,9 +44155,9 @@
   145.90 79.91 144.28 81.53 144.28 83.51 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   243.27 60.51 m
   243.27 62.49 244.89 64.11 246.87 64.11 c
   248.85 64.11 250.47 62.49 250.47 60.51 c
@@ -44194,9 +44193,9 @@
   147.97 130.44 146.35 132.06 146.35 134.04 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   239.07 61.83 m
   239.07 63.81 240.69 65.43 242.67 65.43 c
   244.65 65.43 246.27 63.81 246.27 61.83 c
@@ -44220,9 +44219,9 @@
   148.65 106.48 147.03 108.10 147.03 110.08 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
   244.63 55.32 m
   244.63 57.30 246.25 58.92 248.23 58.92 c
   250.21 58.92 251.83 57.30 251.83 55.32 c
@@ -44246,9 +44245,9 @@
   149.37 86.88 147.75 88.50 147.75 90.48 c
 B
 /GS258 gs
-/sRGB cs 1.000 0.675 0.000 scn
+/sRGB cs 1.000 0.671 0.000 scn
 /GS1 gs
-/sRGB CS 1.000 0.675 0.000 SCN
+/sRGB CS 1.000 0.671 0.000 SCN
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/adegenet -r 921

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