[Genabel-commits] r1226 - branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc

Sun May 19 16:58:16 CEST 2013

Author: lckarssen
Date: 2013-05-19 16:58:15 +0200 (Sun, 19 May 2013)
New Revision: 1226

Modified:
   branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ChangeLog
   branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ProbABEL_manual.tex
Log:
In ProbABEL coxfix 0.3.0 branch: updated doc/ directory to trunk (r1225). 


Modified: branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ChangeLog
===================================================================

--- branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ChangeLog	2013-05-19 14:56:20 UTC (rev 1225)
+++ branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ChangeLog	2013-05-19 14:58:15 UTC (rev 1226)
@@ -1,3 +1,12 @@
+***** v.0.4.0 (2013.)
+* Fixed bug #2575: ProbABEL chooses weird file names if the -o option is
+  not specified.
+* Fixed bug #2598: The prepare_data.R script is mentioned in the manual,
+  but not distributed in .deb or .tar.gz
+* Fixed bug #2772: palogist with mmscore crashes (memory issue)
+* Fixed bug #2529: ProbABEL doesn't warn when a required file is not
+  specified on the command line
+
 ***** v.0.3.0 (2013.01.01)
 * This is a major rewrite of several important parts of the ProbABEL
   code. ProbABEL can now make use of the Eigen matrix library

Modified: branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ProbABEL_manual.tex
===================================================================
--- branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ProbABEL_manual.tex	2013-05-19 14:56:20 UTC (rev 1225)
+++ branches/ProbABEL-pacox/v.0.3.0/ProbABEL/doc/ProbABEL_manual.tex	2013-05-19 14:58:15 UTC (rev 1226)
@@ -13,6 +13,7 @@
 \usepackage{titleref}
 \usepackage{amsmath}
 \usepackage{makeidx}
+\usepackage[dvipsnames]{xcolor}
 \usepackage[pdftex,hyperfootnotes=false,pdfpagelabels]{hyperref}
 \hypersetup{%
   linktocpage=false, % If true the page numbers in the toc are links
@@ -23,7 +24,8 @@
   bookmarksopen=true, bookmarksopenlevel=1, hypertexnames=true, %
   pdfhighlight=/O, %hyperfootnotes=true,%nesting=true,%frenchlinks,%
   pdfauthor={\textcopyright\ Y.~Aulchenko, M.~Struchalin, L.C.~Karssen},
-  pdfsubject={ProbABEL manual}
+  pdfsubject={ProbABEL manual},
+  colorlinks=true, urlcolor=MidnightBlue, linkcolor=blue %
 }
 % get the links to the figures and tables right:
 \usepackage[all]{hypcap} % to be loaded after hyperref package
@@ -137,19 +139,19 @@
 
 \verbatiminput{test.mlinfo}
 
-Note that header line is present in the file. The file describes
+Note that a header line is present in the file. The file describes
 five SNPs.
 
 \subsection{Genomic predictor file}
 \label{ssec:dosein}
 
 Again, in the simplest scenario this is an MLDOSE or MLPROB file
-generated by MaCH and \texttt{minimac}.  Such file starts with two special
-columns plus, for each of the SNPs under consideration, a column
-containing the estimated allele 1 dose (MLDOSE).  In an MLPROB file,
-two columns for each SNP correspond to posterior probability that
-person has two ($P_{A_1A_1}$) or one ($P_{A_1A_2}$) copies of allele
-1.  The first ``special'' column is made of the sequential id,
+generated by MaCH and/or \texttt{minimac}.  Such file starts with two
+special columns plus, for each of the SNPs under consideration, a
+column containing the estimated allele 1 dose (MLDOSE).  In an MLPROB
+file, two columns for each SNP correspond to posterior probability
+that person has two ($P_{A_1A_1}$) or one ($P_{A_1A_2}$) copies of
+allele 1.  The first ``special'' column is made of the sequential id,
 followed by an arrow followed by study ID (the one specified in the
 MaCH input files). The second column contains the method keyword
 (e.g.~``MLDOSE'').
@@ -494,9 +496,10 @@
 
 
 \section{Preparing input files}
-In the \texttt{bin} directory you can find the
-\texttt{prepare\_data.R} file -- an R script that arranges phenotypic
-data in right format. Please read this script for details.
+After installing \PA{} you can find the \texttt{prepare\_data.R} file
+in the \texttt{scripts} directory. It is an R script that arranges
+phenotypic data in the right format. Please read this script for
+details.
 
 \section{Memory use and performance}
 Maximum likelihood regression is implemented in
@@ -528,7 +531,7 @@
 expectation
 \begin{equation}
   E[\mathbf{Y}] = \mathbf{X}\, \boldsymbol{\beta}
-\label{expectation}
+\label{eq:expectation}
 \end{equation}
 and variance-covariance matrix
 $$
@@ -651,15 +654,15 @@
 logarithm of every value contained in the vector $\pi$.
 
 \subsubsection{Robust variance-covariance matrix of parameter estimates}
-For a linear model, these are computed using formula
+For a linear model, these are computed using the equation
 $$
 \var_r = (\mathbf{X}^T\mathbf{X})^{-1} (\mathbf{X}^T\mathbf{R}\mathbf{X})
 (\mathbf{X}^T\mathbf{X})^{-1},
 $$
 where $\mathbf{R}$ is a diagonal matrix containing squares of residuals
 of $\mathbf{Y}$. The
-same formula may be used for ``standard'' analysis, in which case
-the elements of the $\mathbf{R}$ matrix are constant, namely mean
+same equation may be used for ``standard'' analysis, in which case
+the elements of the $\mathbf{R}$ matrix are constant, namely the mean
 residual sum of squares (the estimate of $\sigma^2$).
 
 Similar to that, the robust matrix is computed for logistic regression with
@@ -691,7 +694,7 @@
 E[\mathbf{Y}] = \mathbf{X} \mathbf{\beta},
 $$
 identical to that defined for a linear model
-(cf.~section~\ref{expectation}). To account for correlations between
+(cf.~Eq.~\ref{eq:expectation}). To account for correlations between
 the phenotypes of relatives which may be induced by family relations
 the variance-covariance matrix is defined to be proportional to the
 linear combination of the identity matrix $\mathbf{I}$ and the
@@ -713,17 +716,18 @@
 previously (Aulchenko \emph{et al}., 2007).
 
 \subsubsection{Two-step score test for association}
-A two-step score test approach is therefore used to decrease the computational
-burden. Let us first re-define the expectation of the trait by splitting the
-design matrix in two parts, the ''base'' part $\mathbf{X}_x$, which includes all
-terms not changing across all SNP models fit in GWAS (e.g. effects of sex, age, etc.),
-and the part including SNP information, $\mathbf{X_g}$:
+A two-step score test approach is therefore used to decrease the
+computational burden. Let us first re-define the expectation of the
+trait by splitting the design matrix in two parts, the ``base'' part
+$\mathbf{X}_x$, which includes all terms not changing across all SNP
+models fit in GWAS (e.g.\ effects of sex, age, etc.), and the part
+including SNP information, $\mathbf{X_g}$:
 $$
 E[\mathbf{Y}] = \mathbf{X}_x \mathbf{\beta}_x +
 \mathbf{X}_g \mathbf{\beta}_g.
 $$
-Note that the latter design matrix may include not only the main SNP effect, but
-e.g.\ SNP by environment interaction terms.
+Note that the latter design matrix may include not only the main SNP
+effect, but e.g.\ SNP by environment interaction terms.
 
 In the first step, a linear mixed model not including SNP effects
 $$

