Computation in proximal soil sensing with ‘R’ – introduction and applications Dr. Michael Schirrmann
About the seminar ‘R’ a is free statistical programming environment. Due to its support by a large community including the finest statisticians it has become a sophisticated software package. In the seminar you will learn how to use ‘R’ for analysis of proximal soil sensing data. The focus is on giving you an overview of the ‘R’ environment on beginner level. The seminar covers practical issues in statistical computing which includes data handling in ‘R’, reading data into ‘R’, creating data graphics, writing ‘R’ functions as well as specific tasks related to proximal soil sensing like modelling of spatial data and multivariate calibration of spectrometry data. For practical training with ‘R’ we will use real world examples.
Seminar content • • • • • •
Overview of ‘R’ and the IDE ‘RStudio’ Data handling in ‘R’: Importing and exporting data, data manipulation Descriptive statistics and graphical representation of data Introduction to spatial analysis in ‘R’ Introduction to multivariate data analysis in ‘R’ Specific working examples for geostatistical analysis of spatial data with the ‘R’ package ‘gstat’ and multivariate calibration using the package ‘plsr’
Time and Venue 30 May 2013, 9:00 to 16:00 h Leibniz-Institute for Agricultural Engineering, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Registration Information The maximum number of participants is limited to 16 persons. The seminar is free of charge for attendees of the Global Workshop of Proximal Soil Sensing 2013.
Requirements The seminar is dedicated to beginners and no pre-knowledge of R is expected. Basic programming skills and statistical experience are advantageous. In order to follow the
examples, participants should bring their own laptop computer with the software ‘R’ and ‘RStudio’ pre-installed.
Suggested Readings There is no specific textbook required for this seminar. However, there is a large community and lots of helpful books related to R available. Suggested readings for this seminar might include: •
• • •
An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics by W. N. Venables, D. M. Smith and the R Core Team (accessible via http://cran.r-project.org/doc/manuals/R-intro.pdf) Applied Spatial Data Analysis with R by Roger S. Bivand, Edzer Pebesma and Virgilio Gomez-Rubio (Springer) Multivariate Statistical Analysis in Chemometrics by Kurt Varmusa and Peter Filzmoser (CRC Press) Software for Data Analysis: Programming with R (Statistics and Computing) by John M. Chambers (Springer)
and Graphics by W. N. Venables, D. M. Smith and the R Core Team (accessible via http://cran.r-project.org/doc/manuals/R-intro.pdf). ⢠Applied Spatial Data ...
resistivity - radar and gamma radiometrics - multi-sensor platforms - high resolution digital soil mapping - applications Raphael A. Viscarra Rossel is a scientist at ...
Terms of appointment are for one (1) year, renewable for up to three years, subject to approval. Search will remain open for at least 30 days after the ad appears ...
Candidates must have a Ph.D. in hydrology, atmospheric sciences or in a related field and an interest in working with observational data. Prior experience with ...
ditional barcode technology, it also provides additional ... retail automation, the technology can help bridge the .... readers will have access to wireless net-.
Radio frequency identification has attracted considerable press attention in recent years, and for good reasons: RFID not only replaces tra- ditional barcode ...
Index TermsâChaos, compressive sensing, logistic map. I. INTRODUCTION ... attributes of a signal using very few measurements: for any. -dimensional signal ...
resources than standard space-parallel methods with se- quential time stepping. ...... Friedhoff, S., MacLachlan, S.: A generalized predictive analysis tool for ...
Feb 27, 2018 - Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econo- metrica, 55(5):999â1033, 1987. J. Rust. Structural estimation of markov decision processes. In R. F. Engle and D. L. McFadden, editors, Handbook of
ggplot2: Elegant Graphics for Data Analysis (Use R!) Deep Learning (Adaptive Computation and Machine Learning Series) · Pattern Recognition and Machine Learning (Information Science and Statistics) · Hands-On Machine Learning with Scikit-Learn and Te
approaches, tree-based methods, support vector machines, and clustering. ... Pattern Recognition and Machine Learning (Information Science and Statistics).
Key words. multigrid methods; space-time discretizations; parallel-in-time ... computing leading towards computers with more, but not faster, processors induce ...... inverse bandwidth cost, i.e., the cost per amount of data sent, and γ is the flop
ber has to be finite, we derive high probability bounds on the iteration ... proposed in-network cooperative spectrum sensing at a given iteration. 1. ... To guarantee high spectrum uti- ..... little impact (logarithmic) on the convergence speed.
the soil water flow is already ceased, in the conditions of preventing the evaporation [1, 7, and 9]. Therefore, to determine the soil moisture distribution over time, a plot of 8m x 6m or 48 m2 with no plants was set. The tensiometers and the electr
determine the soil moisture distribution over time, a plot of 8m x 6m or 48 m2 with no plants was set. The ... water distribution on a macro scale, in situ, as it is.
aggregate statistics (aggregates) amid a group of sensor nodes[18,. 29, 14]. .... and broadcasts a group call message, GCM â¡ (groupid = i), to all its neighbors. 1.3 The ...... [15] J. M. Hellerstein, P. J. Haas, and H. J. Wang, âOnline Aggregati
