[adegenet-commits] r889 - in pkg/inst/doc: . figs

[noreply at r-forge.r-project.org]

Author: jombart
Date: 2011-05-30 18:09:02 +0200 (Mon, 30 May 2011)
New Revision: 889

Added:
   pkg/inst/doc/adegenet-dapc.tex
Modified:
   pkg/inst/doc/adegenet-dapc.Rnw
   pkg/inst/doc/adegenet-dapc.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
Log:
Vignette DAPC finished.


Modified: pkg/inst/doc/adegenet-dapc.Rnw
===================================================================
--- pkg/inst/doc/adegenet-dapc.Rnw	2011-05-30 15:23:44 UTC (rev 888)
+++ pkg/inst/doc/adegenet-dapc.Rnw	2011-05-30 16:09:02 UTC (rev 889)
@@ -378,7 +378,7 @@
 plot. We also use the same symbol for all individuals, but use bigger dots and transparent colours
 to have a better feel for the density of individuals on the factorial plane.
 <<fig=TRUE>>=
-scatter(dapc1, scree.da=FALSE, bg="white", pch=20,  cell=0, cstar=0, col=transp(myCol),
+scatter(dapc1, scree.da=FALSE, bg="white", pch=20,  cell=0, cstar=0, col=myCol, solid=.4,
         cex=3,clab=0, leg=TRUE, txt.leg=paste("Cluster",1:6))
 @
 
@@ -391,7 +391,7 @@
 PCA eigenvalues retained in dimension-reduction step (retained eigenvalues in black, similar to
 using \texttt{scree.pca=TRUE}).
 <<fig=TRUE>>=
-scatter(dapc1, ratio.pca=0.3, bg="white", pch=20,  cell=0, cstar=0, col=transp(myCol),
+scatter(dapc1, ratio.pca=0.3, bg="white", pch=20,  cell=0, cstar=0, col=myCol, solid=.4,
         cex=3, clab=0, mstree=TRUE, scree.da=FALSE,
         posi.pca="bottomright", leg=TRUE, txt.leg=paste("Cluster",1:6))
 
@@ -426,7 +426,7 @@
 %%%%%%%%%%%%%%%%
 
 In DAPC, the variable actually analyzed are principal components of a PCA.
-Loadings of these variables are generally uninformative, since PCs themself do not all have
+Loadings of these variables are generally uninformative, since PCs themselves do not all have
 straightforward interpretations.
 However, we can also compute contributions of the alleles, which can turn out to be very informative.
 In general, there are many alleles and their contribution is best plotted for a single discriminant
@@ -435,7 +435,7 @@
 Variable contributions are stored in the \texttt{var.contr} slot of a \texttt{dapc} object.
 They can be plotted using \texttt{loadingplot}.
 We illustrate this using the seasonal influenza dataset \texttt{H3N2}, which contains 1903 isolates
-genotyped for 125 SNPs of hemagglutinin segment (see \texttt{?H3N2}):
+genotyped for 125 SNPs located in the hemagglutinin segment (see \texttt{?H3N2}):
 <<>>=
 data(H3N2)
 H3N2
@@ -447,7 +447,7 @@
 second one shows the originality of 2006 strains.
 <<fig=TRUE>>=
 myPal <- colorRampPalette(c("blue","gold","red"))
-scatter(dapc.flu, col=transp(myPal(6)), scree.da=FALSE, cell=0, cex=2, bg="white",cstar=0)
+scatter(dapc.flu, col=transp(myPal(6)), scree.da=FALSE, cell=1.5, cex=2, bg="white",cstar=0)
 @
 
 We can assess which alleles most highlight the originality of 2006 using \texttt{loadingplot}:
@@ -457,9 +457,9 @@
 @
 
 \noindent \texttt{temp} is a list invisibly returned by \texttt{loadingplot} which contains the most
-contributing alleles.
+contributing alleles (i.e., contributions above a given threshold -- argument \texttt{threshold}).
 In this case, SNPs \texttt{906} and \texttt{399} reflect most the temporal evolution of the virus.
-We can look into their frequencies over 2002-2006:
+We can look into their allele frequencies over 2002-2006:
 <<echo=TRUE,fig=TRUE>>=
 temp <- seploc(H3N2)
 snp906 <- truenames(temp[["906"]])$tab
@@ -487,9 +487,9 @@
 \subsection{Interpreting group memberships}
 %%%%%%%%%%%%%%%%
 Besides scatterplots of discriminant functions, group memberships of DAPC can be exploited.
-Note that caution should be taken when interpreting group memberships of a DAPC based on many PCs,
-which can be unstable (see section below).
-Despite possible bias due to overfitting, group memberships can be used as indicators of how
+Note that caution should be taken when interpreting group memberships of a DAPC based on too many
+PCs, as there are risks of overfitting the discriminant functions (see section below).
+But despite this possible bias, group memberships can be used as indicators of how
 clear-cut genetic clusters are.
 Note that this is most useful for groups defined by an external criteria, i.e. defined biologically, as opposed to identified by $k$-means.
 It is less useful for groups identified using \texttt{find.clusters}, since we expect $k$-means to
@@ -497,7 +497,7 @@
 \\
 
 Membership probabilities are based on the retained discriminant functions.
-They are stored in \texttt{dapc} objects as the slot \texttt{posterior}:
+They are stored in \texttt{dapc} objects in the slot \texttt{posterior}:
 <<>>=
 class(dapc1$posterior)
 dim(dapc1$posterior)
@@ -509,23 +509,23 @@
 summary(dapc1)
 @
 The slot \texttt{assign.per.pop} indicates the proportions of successful reassignment (based on
-discriminant functions) of individuals to their original clusters. Large values indicate clear-cut
+the discriminant functions) of individuals to their original clusters. Large values indicate clear-cut
 clusters, while low values suggest admixed groups.
 \\
 
 This information can also be visualized using \texttt{assignplot} (see \texttt{?assignplot} for display
-options); here, we chose to represent only the first 50 individuals to make the figure readable:
+options); here, we choose to represent only the first 50 individuals to make the figure readable:
 <<fig=TRUE>>=
 assignplot(dapc1, subset=1:50)
 @
 
 \noindent
-This figure is the simple graphical translation of the table above. Heat colors represent probabilities
+This figure is the simple graphical translation of the \texttt{posterior} table above. Heat colors
+represent membership probabilities
 (red=1, white=0); blue crosses represent the prior cluster provided to DAPC.
-Here, we observe for most individuals that DAPC classification is consistent with the original
-clusters (blue crosses are on red rectangles), except one discrepancie for individual 21, classified
+Here in most individuals, DAPC classification is consistent with the original
+clusters (blue crosses are on red rectangles), except for one discrepancy in individual 21, classified
 in group 1 while DAPC would assign it to group 3.
-
 Such figure is particularly useful when prior biological groups are used, as one may infer admixed
 or misclassified individuals.
 \\
@@ -534,21 +534,21 @@
 (see \code{?compoplot} for customizing the plot).
 We can plot information of all individuals to have a global picture of the clusters composition.
 <<fig=TRUE>>=
-compoplot(dapc1, posi="bottomright", leg.txt=paste("Cluster", 1:6), lab="", ncol=1, xlab="individuals")
+compoplot(dapc1, posi="bottomright", txt.leg=paste("Cluster", 1:6), lab="", ncol=1, xlab="individuals")
 @
 
-But we can have a closer look to a subset of individuals as easily; for instance, for the first 50 individuals:
+\noindent We can also have a closer look at a subset of individuals; for instance, for the first 50 individuals:
 <<fig=TRUE>>=
-compoplot(dapc1, subset=1:50, posi="bottomright", leg.txt=paste("Cluster", 1:6), lab="", ncol=2, xlab="individuals")
+compoplot(dapc1, subset=1:50, posi="bottomright", txt.leg=paste("Cluster", 1:6), lab="", ncol=2, xlab="individuals")
 @
 
 Obviously, we can use the power of R to lead our investigation further. For instance, which are the
 most 'admixed' individuals?
-Say admixed individuals are those having no more than 90\% of probability of membership in a single cluster:
+Let us consider as admixed individuals having no more than 90\% of probability of membership in a single cluster:
 <<fig=TRUE>>=
 temp <- which(apply(dapc1$posterior,1, function(e) all(e<0.9)))
 temp
-compoplot(dapc1, subset=temp, posi="bottomright", leg.txt=paste("Cluster", 1:6), lab="", ncol=2)
+compoplot(dapc1, subset=temp, posi="bottomright", txt.leg=paste("Cluster", 1:6),  ncol=2)
 @
 
 
@@ -572,7 +572,8 @@
 order to assess how clear-cut or admixed the clusters are.
 Unfortunately, retaining too many PCs with respect to the number of individuals can lead to over-fitting the discriminant functions.
 In such case, discriminant function become so "flexible" that they could discriminate almost perfectly any cluster.
-While the main scatterplots are usually unaltered by this process, membership probabilities can become drastically inflated.
+While the main scatterplots are usually unaltered by this process, membership probabilities can
+become drastically inflated for the best-fitting cluster, resulting in apparent perfect discrimination.
 \\
 
 
@@ -629,10 +630,10 @@
 %%%%%%%%%%%%%%%%
 \subsection{Using the $a$-score}
 %%%%%%%%%%%%%%%%
-The trade-off between power of discrimination and over-fitting can be measured by the $a$-score, which is simply the difference between the \% of
+The trade-off between power of discrimination and over-fitting can be measured by the $a$-score, which is simply the difference between the proportion of
 successful reassignment of the analysis (observed discrimination) and values obtained using random
 groups (random discrimination).
-It can be seen as the \% of successful reassignment corrected for the number of retained PCs.
+It can be seen as the proportion of successful reassignment corrected for the number of retained PCs.
 It is implemented by \texttt{a.score}, which relies on repeating the DAPC analysis using randomized
 groups, and computing $a$-scores for each group, and well as the average $a$-score:
 <<>>=
@@ -656,7 +657,7 @@
 \end{center}
 
 
-\noindent Since evaluating solutions for 1, 2, ... 100 retained PCs, as a first approximation the
+\noindent Since evaluating solutions for 1, 2, ... 100 retained PCs is unusefully computer-intensive, as a first approximation the
 method evaluates a few numbers of retained PCs in this range, and uses spline interpolation to
 approximate the optimal number of PCs to retain. Then, one can evaluate all solutions within a
 restrained range using the argument \texttt{n.pca}.
@@ -686,7 +687,7 @@
 
 \noindent Admixture appears to be the strongest between a few breeds (Blonde d'Aquitaine, Bretonne Pie-Noire,
 Limousine and Gascone). Some features are fairly surprising; for instance, the last individual is
-fairly distant from its cluster, but almost 50\% chances to be assigned to two other breeds.
+fairly distant from its cluster, but has almost 50\% chances of being assigned to two other breeds.
 
 
 
@@ -703,16 +704,16 @@
 \subsection{Rationale}
 %%%%%%%%%%%%%%%%
 
-Statistically speaking, supplementary individuals are observations which did not participate to the
-analysis, but which we would like to predict using our model of the data.
+Statistically speaking, supplementary individuals are observations which do not participate to
+constructing a model, but which we would like to predict using a model obtaining on other ("training") data.
 In the context of DAPC, we may know groups for most individuals, but some individuals could be of
 unknown or uncertain group. In this case, we need to exclude individuals from the analysis, and then
 project them as supplementary individuals on the discriminant functions.
-The only requirement for this operation is that supplementary individuals have been typed as the
+The only requirement for this operation is that supplementary individuals have been typed for the
 same loci as the rest of the dataset.
 
-Technically, using supplementary individuals consist in transforming the new data using the centring
-and scaling of the contributing individuals, and then using the same discriminant coefficients as
+Technically, using supplementary individuals consists in transforming the new data using the centring
+and scaling of the "training data", and then using the same discriminant coefficients as
 for the contributing individuals to predict the position of the new individuals onto the
 discriminant functions.
 
@@ -726,7 +727,7 @@
 <<>>=
 data(microbov)
 set.seed(2)
-kept.id <- unlist(tapply(1:nInd(microbov), pop(microbov), function(e) sample(e, 30,replace=FALSE)))
+kept.id <- unlist(tapply(1:nInd(microbov), pop(microbov), function(e) sample(e, 20,replace=FALSE)))
 x <- microbov[kept.id]
 x.sup <- microbov[-kept.id]
 nInd(x)
@@ -756,12 +757,12 @@
 <<fig=TRUE>>=
 col <- rainbow(length(levels(pop(x))))
 col.points <- transp(col[as.integer(pop(x))],.2)
-scatter(dapc4, col=col, bg="white", scree.da=0, grid=FALSE, pch="", cstar=0, clab=0, xlim=c(-10,10), legend=TRUE)
+scatter(dapc4, col=col, bg="white", scree.da=0, pch="", cstar=0, clab=0, xlim=c(-10,10), legend=TRUE)
 par(xpd=TRUE)
 points(dapc4$ind.coord[,1], dapc4$ind.coord[,2], pch=20, col=col.points, cex=5)
 col.sup <- col[as.integer(pop(x.sup))]
 points(pred.sup$ind.scores[,1], pred.sup$ind.scores[,2], pch=15, col=transp(col.sup,.7), cex=2)
-add.scatter.eig(dapc4$eig,15,1,2, posi="topright", inset=.02)
+add.scatter.eig(dapc4$eig,15,1,2, posi="bottomright", inset=.02)
 @
 
 \noindent Light dots and ellipses correspond to the original analysis, while more solid squares indicate
@@ -770,10 +771,10 @@
 <<>>=
 mean(as.character(pred.sup$assign)==as.character(pop(x.sup)))
 @
-Around \Sexpr{round(100*mean(as.character(pred.sup$assign)==as.character(pop(x.sup))))} of
+Around \Sexpr{round(100*mean(as.character(pred.sup$assign)==as.character(pop(x.sup))))}\% of
 individuals have been assigned to their actual cluster.
 For more details about which breed was assigned to which cluster, we can display the contingency
-table of the actual cluster vs the inferred one:
+table of the actual cluster \textit{vs} the inferred one:
 <<fig=TRUE>>=
 table.value(table(pred.sup$assign, pop(x.sup)), col.lab=levels(pop(x.sup)))
 @

Modified: pkg/inst/doc/adegenet-dapc.pdf
===================================================================
--- pkg/inst/doc/adegenet-dapc.pdf	2011-05-30 15:23:44 UTC (rev 888)
+++ pkg/inst/doc/adegenet-dapc.pdf	2011-05-30 16:09:02 UTC (rev 889)
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+  153.78 337.37 152.97 338.18 152.97 339.17 c
+B
+  147.13 269.72 m
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+  149.92 271.52 150.73 270.71 150.73 269.72 c
+  150.73 268.73 149.92 267.92 148.93 267.92 c
+  147.94 267.92 147.13 268.73 147.13 269.72 c
+B
+  140.05 245.30 m
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+  142.84 247.10 143.65 246.29 143.65 245.30 c
+  143.65 244.31 142.84 243.50 141.85 243.50 c
+  140.86 243.50 140.05 244.31 140.05 245.30 c
+B
+  99.87 267.01 m
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+  103.47 266.02 102.66 265.21 101.67 265.21 c
+  100.68 265.21 99.87 266.02 99.87 267.01 c
+B
+  159.42 282.11 m
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+  163.02 281.12 162.21 280.31 161.22 280.31 c
+  160.23 280.31 159.42 281.12 159.42 282.11 c
+B
+  153.10 320.82 m
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+  156.70 319.83 155.89 319.02 154.90 319.02 c
+  153.91 319.02 153.10 319.83 153.10 320.82 c
+B
+  136.32 280.63 m
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+  139.92 279.64 139.11 278.83 138.12 278.83 c
+  137.13 278.83 136.32 279.64 136.32 280.63 c
+B
+  165.88 234.06 m
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+  169.48 233.07 168.67 232.26 167.68 232.26 c
+  166.69 232.26 165.88 233.07 165.88 234.06 c
+B
+  121.84 281.08 m
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+  125.44 280.09 124.63 279.28 123.64 279.28 c
+  122.65 279.28 121.84 280.09 121.84 281.08 c
+B
+  163.12 268.27 m
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+  165.91 270.07 166.72 269.26 166.72 268.27 c
+  166.72 267.28 165.91 266.47 164.92 266.47 c
+  163.93 266.47 163.12 267.28 163.12 268.27 c
+B
+  143.06 287.44 m
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+  146.66 286.45 145.85 285.64 144.86 285.64 c
+  143.87 285.64 143.06 286.45 143.06 287.44 c
+B
+  138.85 279.35 m
+  138.85 280.34 139.66 281.15 140.65 281.15 c
+  141.64 281.15 142.45 280.34 142.45 279.35 c
+  142.45 278.36 141.64 277.55 140.65 277.55 c
+  139.66 277.55 138.85 278.36 138.85 279.35 c
+B
+/GS258 gs
 /sRGB cs 1.000 0.000 0.000 scn
+/GS1 gs
 /sRGB CS 1.000 0.000 0.000 SCN
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 147.73 272.66 Tm (l) Tj 0 Tr
-ET
+  147.91 274.39 m
+  147.91 275.38 148.72 276.19 149.71 276.19 c
+  150.70 276.19 151.51 275.38 151.51 274.39 c
+  151.51 273.40 150.70 272.59 149.71 272.59 c
+  148.72 272.59 147.91 273.40 147.91 274.39 c
+B
+/GS258 gs
 /sRGB cs 0.000 1.000 0.000 scn
+/GS1 gs
 /sRGB CS 0.000 1.000 0.000 SCN
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 123.42 241.06 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 119.72 273.87 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 124.09 260.48 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 146.24 295.23 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 121.54 258.61 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 118.18 256.79 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 152.25 287.47 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 101.23 296.88 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 151.50 257.48 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 109.11 282.13 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 130.15 291.26 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 150.44 289.45 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 155.51 309.51 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 131.49 249.88 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 93.65 306.17 Tm (l) Tj 0 Tr
-ET
-BT
-/F1 1 Tf 2 Tr 4.99 0 0 4.99 127.89 255.48 Tm (l) Tj 0 Tr
-ET
-BT
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/adegenet -r 889