Open source R for applying machine learning to RPAS remote sensing images

The increase in the number of remote sensing platforms, ranging from satellites to close-range Remotely Piloted Aircraft System (RPAS), is leading to a growing demand for new image processing and classification tools. This article presents a comparison of the Random Forest (RF) and Support Vector Machine (SVM) machine-learning algorithms for extracting land-use classes in RPAS-derived orthomosaic using open source R packages. The camera used in this work captures the reflectance of the Red, Blue, Green and Near Infrared channels of a target. The full dataset is therefore a 4-channel raster image. The classification performance of the two methods is tested at varying sizes of training sets. The SVM and RF are evaluated using Kappa index, classification accuracy and classification error as accuracy metrics. The training sets are randomly obtained as subset of 2 to 20% of the total number of raster cells, with stratified sampling according to the land-use classes. Ten runs are done for each training set to calculate the variance in results. The control dataset consists of an independent classification obtained by photointerpretation. The validation is carried out(i) using the K-Fold cross validation, (ii) using the pixels from the validation test set, and (iii) using the pixels from the full test set. Validation with K-fold and with the validation dataset show SVM give better results, but RF prove to be more performing when training size is larger. Classification error and classification accuracy follow the trend of Kappa index

Pemanfaatan Software Open Source R dalam pemodelan ARIMA

R (R Development Core Team, 2009) merupakan salah satu software open source yang terpopuler dan telah menjadi “lingua franca” atau “bahasa standar” untuk keperluan komputasi statistika saat ini. Dalam tulisan ini, akan dikenalkan dan dibahas penggunaan R untuk komputasi model ARIMA, yang merupakan salah satu model standar yang dikenalkan dalam kuliah analisa runtun waktu. Pengenalan dilakukan dengan menggunakan data empiris dimana komputasi model ARIMA dilakukan dengan menggunakan R versi CLI (command line interface) dan versi GUI (Graphical User Interface) yang merupakan hasil pengembangan terbaru dalam Rosadi, Marhadi dan Rahmatullah (2009). Dalam metodologinya, dikenalkan teknik pemodelan standar dengan menggunakan metode Box-Jenkins, maupun teknik pemilihan model automatik menggunakan ukuran kriteria informasi, seperti yang dibahas di Hyndman dan Khandakar (2008). Kata-kata kunci: R Commander Plug-in, Open Source, automatic ARIM

Analyzing Remote Sensing Data in R: The landsat Package

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.

PEMANFAATAN SOFTWARE OPEN SOURCE “R” UNTUK PENELITIAN AGROKLIMAT

Analysis in agro-climate research often uses long and varied time series data and even involves complex simulation models. Software is required to produce information quickly, precisely, and accurately. Agro-climate research is sometime constrained by the availability proprietary software since cost of proprietary/ licensed software is relatively high. Open source software (OSS) is one solution to overcome this constrain whereas OSS can be used freely. This paper discusses the utilization  of  "R"  for  agro-climatic research that comprise of  available  “R” packages for agro-climate research,  several studies  have applied  “ R”  and  advantage of  “R” over other statistics software. Nowadays, there are many agroclimate researches and studies have utilized R both for spatial and tabular analysis. R can be used for simple statistical analysis such as variance analysis for experimental research and even for complex climate model. Many “R” packages for agro-climate research have been developed. The “R” capabilities on data management, model simulation, modelling and machine learning are “R” advantages that very useful for current agro-climate research.  By using "R" researchers  have greater opportunity to explore the historical agro-climate data. "R" should be developed in agro-climate research with existing packages. Researchers can develop new packages from existing packages to solve agro-climate problems and agricultural issues in general

Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application

Illustration of use of R accrual package. (DOCX 917 kb

Efficient Forward Simulation of Fisher-Wright Populations with Stochastic Population Size and Neutral Single Step Mutations in Haplotypes

In both population genetics and forensic genetics it is important to know how haplotypes are distributed in a population. Simulation of population dynamics helps facilitating research on the distribution of haplotypes. In forensic genetics, the haplotypes can for example consist of lineage markers such as short tandem repeat loci on the Y chromosome (Y-STR). A dominating model for describing population dynamics is the simple, yet powerful, Fisher-Wright model. We describe an efficient algorithm for exact forward simulation of exact Fisher-Wright populations (and not approximative such as the coalescent model). The efficiency comes from convenient data structures by changing the traditional view from individuals to haplotypes. The algorithm is implemented in the open-source R package 'fwsim' and is able to simulate very large populations. We focus on a haploid model and assume stochastic population size with flexible growth specification, no selection, a neutral single step mutation process, and self-reproducing individuals. These assumptions make the algorithm ideal for studying lineage markers such as Y-STR.Comment: 17 pages, 6 figure