A statnet Tutorial

The statnet suite of R packages contains a wide range of functionality for the statistical analysis of social networks, including the implementation of exponential-family random graph (ERG) models. In this paper we illustrate some of the functionality of statnet through a tutorial analysis of a friendship network of 1,461 adolescents.

ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks

We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and inter-related, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set.

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

Tuning Curves, Neuronal Variability, and Sensory Coding

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron's role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron's most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Mechanisms underlying pituitary hypoplasia and failed cell specification in Lhx3-deficient mice

AbstractThe LIM homeodomain transcription factor, LHX3, is essential for pituitary development in mouse and man. Lhx3 engineered null mice have profound pituitary hypoplasia that we find is attributable to an increase in cell death early in pituitary development. Dying cells are localized to regions of TPIT expression indicating that cell death may contribute to the severe reduction in differentiated corticotrope cells and lower expression of the corticotrope transcription factors, TPIT and NEUROD1. Lhx3 deficiency also results in dorsal ectopic expression of transcription factors characteristic of gonadotropes, SF1 and ISL1, but no gonadotropin expression. This apparent disturbance of cell differentiation may be due, in part, to loss of NOTCH2. NOTCH2 is normally expressed in the pituitary at the boundary between dorsal, proliferating cells and ventral, differentiating cells and is important for maintaining dorsal–ventral patterning in other organs. Thus, Lhx3 contributes significantly to pituitary development by maintaining normal dorsal–ventral patterning, cell survival, and normal expression of corticotrope-specific transcription factors, which are necessary for repressing ectopic gonadotrope differentiation

Illinois Power Company - Baldwin Power Plant: ash-pond effluent boron mixing with the Kaskaskia River

Includes bibliographical references (p.31)."Prepared for Illinois Power Company, Decatur, Illinois"--Cover."October 1995"--Cover

Considerations in water use planning for the Fox River

"September 1995."Cover title."SWS Contract Report 586.""Prepared for the Illinois Dept. of Transportation, Division of Water Resources."Includes bibliographical references

Rock Response in a 12-M Tunnel through a Zone of Low Strength

At the Rocky Mountain Pumped Storage Project a 12 meter diameter power tunnel was excavated through sedimentary rock for 760 meters. Approximately 10% of this tunnel was through Pennington shale that is described as a dark gray massive organic shale. This paper will describe the methods of testing .and rock characterization, the results of instrumentation and monitoring, and the post-construction testing program for the excavation, and conclude with a discussion of the observed rock response in relation to the measured strength and deformation properties. This particular zone of the tunnel required the addition of longer rockbolts, and a discussion of that supplemental rock reinforcement will be included

Analysis of the impact of climate change on groundwater related hydrological fluxes: a multi-model approach including different downscaling methods

Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources. This study presents a modelling approach exploiting the advantages of integrated hydrological modelling and a broad climate model basis. We applied the integrated MIKE SHE model on a perialpine, small catchment in northern Switzerland near Zurich. To examine the impact of climate change we forced the hydrological model with data from eight GCM-RCM combinations showing systematic biases which are corrected by three different statistical downscaling methods, not only for precipitation but also for the variables that govern potential evapotranspiration. The downscaling methods are evaluated in a split sample test and the sensitivity of the downscaling procedure on the hydrological fluxes is analyzed. The RCMs resulted in very different projections of potential evapotranspiration and, especially, precipitation. All three downscaling methods reduced the differences between the predictions of the RCMs and all corrected predictions showed no future groundwater stress which can be related to an expected increase in precipitation during winter. It turned out that especially the timing of the precipitation and thus recharge is very important for the future development of the groundwater levels. However, the simulation experiments revealed the weaknesses of the downscaling methods which directly influence the predicted hydrological fluxes, and thus also the predicted groundwater levels. The downscaling process is identified as an important source of uncertainty in hydrological impact studies, which has to be accounted for. Therefore it is strongly recommended to test different downscaling methods by using verification data before applying them to climate model data