[Dplr-commits] r834 - pkg/dplR/vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Apr 23 15:32:39 CEST 2014
Author: mvkorpel
Date: 2014-04-23 15:32:39 +0200 (Wed, 23 Apr 2014)
New Revision: 834
Modified:
pkg/dplR/vignettes/intro-dplR.Rnw
pkg/dplR/vignettes/timeseries-dplR.Rnw
pkg/dplR/vignettes/xdate-dplR.Rnw
Log:
Fixed some typos and addressed a few style issues. The latter point
was mostly about removing the contractions. This is largely a matter
of taste: How informal are the vignettes meant to be? Are the
documents too stiff if there are no contractions? Andy: I will not
object if you overrule some of these changes.
I used TextLint to point out the issues but ignored most of the
complaints. https://github.com/DamienCassou/textlint
Modified: pkg/dplR/vignettes/intro-dplR.Rnw
===================================================================
--- pkg/dplR/vignettes/intro-dplR.Rnw 2014-04-23 09:34:08 UTC (rev 833)
+++ pkg/dplR/vignettes/intro-dplR.Rnw 2014-04-23 13:32:39 UTC (rev 834)
@@ -33,7 +33,7 @@
that an analyst might follow when working with a new tree-ring data set. The
vignette starts with reading in ring widths and plotting them. We describe
a few of the available methods for detrending and then show how to extract
-basic descriptive statistics. We show how to build a and plot simple
+basic descriptive statistics. We show how to build and plot a simple
mean-value chronology. We also show how to build a chronology
using the expressed population signal from the detrended ring widths as an
example of how more complicated analysis can be done using dplR.
@@ -43,7 +43,7 @@
\newpage
\section{Introduction}
-\subsection{What's Covered}
+\subsection{What Is Covered}
The Dendrochronology Program Library in R (dplR) is a package for
dendrochronologists to handle data processing and analysis. This
document gives just a brief introduction of some of the most commonly
@@ -76,7 +76,7 @@
\href{http://www.ncdc.noaa.gov/paleo/treeinfo.html#formats}{Tucson/decadal}
format file of ring widths to the more complex (but richer)
\href{http://www.tridas.org/}{TRiDaS format}. We generally refer to these as
-\code{rwl} objects for ``ring width length'' but there is no reason these can't be
+\code{rwl} objects for ``ring width length'' but there is no reason these cannot be
other types of tree-ring data (e.g., density).
The workhorse function for getting tree-ring data into R is dplR's
@@ -131,7 +131,7 @@
\textbf{By the way, if this is all new to you -- you should stop reading this
vignette and proceed immediately to a good primer on dendrochronology like
\cite{Fritts2001}. This vignette is not intended to teach you about how to do
-tree-ring analysis. It's intended to teach you how to use the package.}
+tree-ring analysis. It is intended to teach you how to use the package.}
A rwi object has the same basic properties as the \code{rwl} object from which it is
made. I.e., it has the same number of rows and columns, the same names, and so
@@ -183,13 +183,13 @@
When \code{detrend} is run on an \code{rwl} object the function loops through
each series. It does this by calling a different function
(\code{detrend.series}) for each column in the \code{rwl} object.
-But, a user can also call \code{detrend.series} and it's useful to do so here
+But, a user can also call \code{detrend.series} and it is useful to do so here
for educational purposes.
-Let's detrend a single series and apply more than one detrending method when we
-call it. We'll call \code{detrend.series} using the verbose mode so that
+Let us detrend a single series and apply more than one detrending method when we
+call it. We will call \code{detrend.series} using the verbose mode so that
we can see the parameters applied for each method. The \code{detrend.series}
-function, produces a plot by default (Figure~\ref{fig:detrend.series}).
+function produces a plot by default (Figure~\ref{fig:detrend.series}).
<<b, fig=TRUE>>=
series <- ca533[, "CAM011"] # extract the series
@@ -218,7 +218,7 @@
information.
\section{Descriptive Statistics}
-Either before or after standardization, it would be natural to want to look at
+Either before or after standardization, it is natural to want to look at
some common (and not-so common) descriptive statistics of a \code{rwl} object. The
\code{rwl.stats} function is typically used on raw ring widths
(the \code{rwl} object) and produces summary statistics. Here are summary
@@ -230,11 +230,11 @@
These are common summary statistics like mean, median, etc. but also statistics
that are more specific to dendrochronology like the first-order autocorrelation
(\code{ar1}) and mean sensitivity (\code{sens1} and \code{sens2}).
-We'd be remiss if we didn't here
+We would be remiss if we did not here
mention that mean sensitivity is actually a terrible statistic that should
rarely, if ever, be used \citep{Bunn2013}.
-It's also easy in dplR to compute commonly used descriptive statistics that
+It is also easy in dplR to compute commonly used descriptive statistics that
describe the correlation between series (both within and between tree
correlations) as well as the expressed population signal and signal-to-noise
ratio for a data set. These are done in dplR using the \code{rwi.stats}
@@ -251,7 +251,7 @@
There is (at least) one other way of looking at the average interseries
correlation of a data set. The \code{interseries.cor} function in dplR
-gives a measure of average interseries correlation that is different than
+gives a measure of average interseries correlation that is different from
the rbar measurements from \code{rwi.stats}. In this function,
correlations are calculated serially between each tree-ring series and a
master chronology built from all the other series in the \code{rwl} object
@@ -289,7 +289,7 @@
An object produced by \code{chron} has a generic S3 moethod for plotting
which calls the \code{crn.plot} function (which has many arguments for
-customization). Here we'll just make a simple plot of the chronology with
+customization). Here we will just make a simple plot of the chronology with
a smoothing spline added. See Figure~\ref{fig:crn.plot.spline}.
<<c, fig=TRUE>>=
plot(ca533.crn, add.spline=TRUE, nyrs=20)
Modified: pkg/dplR/vignettes/timeseries-dplR.Rnw
===================================================================
--- pkg/dplR/vignettes/timeseries-dplR.Rnw 2014-04-23 09:34:08 UTC (rev 833)
+++ pkg/dplR/vignettes/timeseries-dplR.Rnw 2014-04-23 13:32:39 UTC (rev 834)
@@ -39,14 +39,14 @@
\newpage
\section{Introduction}
-\subsection{What's Covered}
+\subsection{What Is Covered}
The Dendrochronology Program Library in R (dplR) is a package for
dendrochronologists to handle data processing and analysis. This
document gives an introduction of some of the functions dealing with
time series in dplR. This vignette does not purport to be any sort of
authority on time series analysis at all! There are many wonderful
R-centric books on time series analysis that can tell you about the theory
-and practice of working with temporal data. For heaven's sake, don't rely on
+and practice of working with temporal data. For heaven's sake, do not rely on
this document!
\subsection{Citing dplR and R}
@@ -95,9 +95,9 @@
\textbf{By the way, if this is all new to you -- you should
proceed immediately to a good primer on dendrochronology like
\cite{Fritts2001}. This vignette is not intended to teach you about how to do
-tree-ring analysis. It's intended to teach you how to use the package.}
+tree-ring analysis. It is intended to teach you how to use the package.}
-Let's make a mean-value chronology of the \code{co021} data after detrending
+Let us make a mean-value chronology of the \code{co021} data after detrending
each series with a frequency response of 50\% at a wavelength of 2/3 of
each series's length. The chronology is plotted in Figure~\ref{fig:crn.plot}.
<<b, fig=TRUE>>=
@@ -113,7 +113,7 @@
\end{figure}
The \code{co021.crn} object has two columns, the first giving the chronology
-and the second the sample depth during that year. We'll start our analysis
+and the second the sample depth during that year. We will start our analysis
on the chronology by looking at its autocorrelation structure using R's
\code{acf} and \code{pacf} functions.
<<c, fig=TRUE>>=
@@ -176,7 +176,7 @@
ARMA(1,1) model -- or an ARIMA(1,0,1). The parsimony principle certainly likes
a nice simple ARMA(1,1) model! Note that we could look at the residuals
(just the first few), model coefficients, etc. quite easily. And indeed the
-residuals are quite clean as we'd expect (Figure~\ref{fig:acf.resid}).
+residuals are quite clean as we would expect (Figure~\ref{fig:acf.resid}).
\else% If "forecast" is not available
An example was dropped because \code{"forecast"} is not available.
\fi% End of conditional
Modified: pkg/dplR/vignettes/xdate-dplR.Rnw
===================================================================
--- pkg/dplR/vignettes/xdate-dplR.Rnw 2014-04-23 09:34:08 UTC (rev 833)
+++ pkg/dplR/vignettes/xdate-dplR.Rnw 2014-04-23 13:32:39 UTC (rev 834)
@@ -40,7 +40,7 @@
\newpage
\section{Introduction}
-\subsection{What's Covered}
+\subsection{What Is Covered}
The Dendrochronology Program Library in R (dplR) is a package for
dendrochronologists to handle data processing and analysis. This
document gives an introduction of some of the crossdating functions in
@@ -69,7 +69,7 @@
which gives the raw ring widths for Douglas fir \emph{Pseudotsuga menziesii}
at Mesa Verde in Colorado, USA. There are 35 series spanning 788 years.
-We'll rename the \code{co021} object to \code{dat} because we are going to
+We will rename the \code{co021} object to \code{dat} because we are going to
mess around with it and it seems like good practice to rename it. It is a
beautifully sensitive series with long segment lengths, high standard
deviation (relative to ring widths), large first-order autocorrelation,
@@ -97,13 +97,13 @@
\textbf{By the way, if this is all new to you -- you should
proceed immediately to a good primer on dendrochronology like
\cite{Fritts2001}. This vignette is not intended to teach you about how to do
-tree-ring analysis. It's intended to teach you how to use the package.}
+tree-ring analysis. It is intended to teach you how to use the package.}
To demonstrate how crossdating works in dplR, we will take this perfectly
lovely data set and corrupt the dating of one of the series. By doing so we
will be able to reenact one of the most common tasks of the dendrochronologist:
tracking down a misdated core. Here we will take a random series and remove
-one of the years of growth. This simulates a missing ring in the series. We'll
+one of the years of growth. This simulates a missing ring in the series. We will
pick a random year in the core to give us a bit of a challenge in finding it.
<<>>=
@@ -117,7 +117,7 @@
tmp <- c(NA, tmp[-i])
dat[, j] <- tmp
@
-We've now deleted the $i^{th}$ observation from the $j^{th}$ core while making sure that
+We have now deleted the $i^{th}$ observation from the $j^{th}$ core while making sure that
\code{dat} still has the appropriate numbers of rows. By sticking the NA at the
start of the series it is as if we missed a ring while measuring.
@@ -156,7 +156,7 @@
The low correlation between series \code{643114} and the master indicates a dating
problem (Figure~\ref{fig:corr.rwl.plot}). Now that we suspect a dating problem,
-let's take closer look at this problem child. Figure~\ref{fig:corr.seg.plot}
+let us take a closer look at this problem child. Figure~\ref{fig:corr.seg.plot}
shows that series \code{643114} begins to lose correlation with the master at the
end of the 19th century.
@@ -214,7 +214,7 @@
Using a smaller time window and shorter correlation segments we
can try to further isolate the switch from correlation at lag 0 to lag +1.
-We'll, of course, have to be very careful about using such short segments
+We will, of course, have to be very careful about using such short segments
for correlation and be ready to adjust our expectations accordingly.
Fortunately, in this case the trees are so exquisitely sensitive that we can
look at 20-year segments with some confidence as in Figure~\ref{fig:ccf.plot2}.
@@ -275,7 +275,7 @@
We have strong inference now that series \code{643114} is misdated somewhere in a ten
year period around 1885. We have still not revealed whether this is correct
-or not. Let's look at the values for \code{i} and \code{j} and see how we did:
+or not. Let us look at the values for \code{i} and \code{j} and see how we did:
<<>>=
j
colnames(co021)[j]
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