The <code>BD</code> data frame has 89 rows and 5 columns. There are 89 runs with 
four buffer components (KCL, MgCl2, KP04, dnTP) systematically varied in 
a space-filliing design. The response is the DNA amplification rate.

datasets

For curve, surface and function fitting with an emphasis
on splines, spatial data, geostatistics, and spatial statistics. The major methods
include cubic, and thin plate splines, Kriging, and compactly supported
covariance functions for large data sets. The splines and Kriging methods are
supported by functions that can determine the smoothing parameter
(nugget and sill variance) and other covariance function parameters by cross
validation and also by restricted maximum likelihood. For Kriging
there is an easy to use function that also estimates the correlation
scale (range parameter). A major feature is that any covariance function
implemented in R and following a simple format can be used for
spatial prediction. There are also many useful functions for plotting
and working with spatial data as images. This package also contains
an implementation of sparse matrix methods for large spatial data
sets and currently requires the sparse matrix (spam) package. Use
help(fields) to get started and for an overview. The fields source
code is deliberately commented and provides useful explanations of
numerical details as a companion to the manual pages. The commented
source code can be viewed by expanding the source code version
and looking in the R subdirectory. The reference for fields can be generated
by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
of this package was supported in part by the National Science Foundation Grant
1417857, the National Center for Atmospheric Research, and Colorado School of Mines.
See the Fields URL
for a vignette on using this package and some background on spatial statistics.

Douglas Nychka

fields

Tools for Spatial Data

Reinhard Furrer

John Paige

Stephan Sain

Florian Gerber

Matthew Iverson

Rider Johnson

BD function

This data frame contains the following columns:
<dl>
<dt>KCl</dt>
<dd>Buffer component.</dd><dt>MgCl2</dt>
<dd>Buffer component.</dd><dt>KPO4</dt>
<dd>Buffer component.</dd><dt>dNTP</dt>
<dd>Buffer component, deoxyribonucleotides.</dd><dt>lnya</dt>
<dd>Exponential amplification rate on a log scale, i.e. the actual amplification 
rate.</dd>
</dl>

Format

Data frame of the effect of buffer compositions on DNA strand displacement 
amplification. A 4-d regression data set with 
with replication. This is a useful test data set for exercising function
fitting methods. — BD

This data frame contains the following columns:
<dl>
<dt>KCl</dt>
<dd>Buffer component.</dd>

<dt>MgCl2</dt>
<dd>Buffer component.</dd>

<dt>KPO4</dt>
<dd>Buffer component.</dd>

<dt>dNTP</dt>
<dd>Buffer component, deoxyribonucleotides.</dd>

<dt>lnya</dt>
<dd>Exponential amplification rate on a log scale, i.e. the actual amplification 
rate.</dd>


</dl>

Data frame of the effect of buffer compositions on DNA strand displacement 
amplification. A 4-d regression data set with 
with replication. This is a useful test data set for exercising function
fitting methods.

BD: Data frame of the effect of buffer compositions on DNA strand displacement amplification. A 4-d regression data set with with replication. This is a useful test data set for exercising function fitting methods.

Description

Arguments

Format

See Also

Examples