Exploratory movement analysis and report building with R package stmove

Abstract Background As GPS tags and data loggers have become lighter, cheaper, and longer-lasting, there has been a growing influx of data on animal movement. Simultaneously, methods of analyses and software to apply such methods to movement data have expanded dramatically. Even so, for many interdisciplinary researchers and managers without familiarity with the field of movement ecology and the open-source tools that have been developed, the analysis of movement data has remained an overwhelming challenge. Description Here we present stmove , an R package designed to take individual relocation data and generate a visually rich report containing a set of preliminary results that ecologists and managers can use to guide further exploration of their data. Not only does this package make report building and exploratory data analysis (EDA) simple for users who may not be familiar with the extent of available analytical tools, but it sets forth a framework of best practice analyses, which offers a common starting point for the interpretation of terrestrial movement data. Results Using data from African elephants ( Loxodonta africana ) collected in southern Africa, we demonstrate stmove ’s report building function through the main analyses included: path visualization, primary statistic calculation, summary in space and time, and space-use construction. Conclusions The stmove package provides consistency and increased accessibility to managers and researchers who are interested in movement analysis but who may be unfamiliar with the full scope of movement packages and analytical tools. If widely adopted, the package will promote comparability of results across movement ecology studies

Combining multiple observational data sources to estimate causal effects

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly-used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators

Using principal stratification in analysis of clinical trials

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in supplemental materials

Making Software Cost Data Available for Meta-Analysis

In this paper we consider the increasing need for meta-analysis within empirical software engineering. However, we also note that a necessary precondition to such forms of analysis is to have both the results in an appropriate format and sufficient contextual information to avoid misleading inferences. We consider the implications in the field of software project effort estimation and show that for a sample of 12 seemingly similar published studies, the results are difficult to compare let alone combine. This is due to different reporting conventions. We argue that a protocol is required and make some suggestions as to what it should contain

Regional Income Disparities and Convergence Clubs in Indonesia: New District-Level Evidence 2000-2017

Reducing regional income disparities is a central challenge for promoting sustainable development in Indonesia. In particular, the prospect for these disparities to be reduced in the post-decentralization period has become a major concern for policymakers in Indonesia. Motivated by this background, this paper re-examines the regional convergence hypothesis at the district level in Indonesia over the 2000-2017 period. Using a novel data set, this study investigates the formation of multiple convergence clubs using non-linear dynamic factor model. The results indicate that Indonesian districts form five convergence clubs, implying that the growth of income per capita in 514 districts can be clustered into five common trends. From the lens of spatial distribution, two common occasions can be observed. First, districts belonging to the the same province tend be in the same club and second, the highest club is dominated by districts with specific characteristic (i.e., big cities or natural resources rich regions). From a policy standpoint, the identification of multiple convergence clubs at significantly different levels of income allows regional policy makers to identify districts facing similar challenges

Two-Sample Two-Stage Least Squares (TSTSLS) estimates of earnings mobility: how consistent are they? [WP]

Academics and policymakers have shown great interest in cross-national comparisons of intergenerational earnings mobility. However, producing consistent and comparable estimates of earnings mobility is not a trivial task. In most countries researchers are unable to observe earnings information for two generations. They are thus forced to rely upon imputed data instead. This paper builds upon previous work by considering the consistency of the intergenerational correlation (ρ) as well as the elasticity (β), how this changes when using a range of different instrumental (imputer) variables, and highlighting an important but infrequently discussed measurement issue. Our key finding is that, while TSTSLS estimates of β and ρ are both likely to be inconsistent, the magnitude of this problem is much greater for the former than it is for the latter. We conclude by offering advice on estimating earnings mobility using this methodology

Competition Policy and Productivity Growth: An Empirical Assessment

This paper empirically investigates the effectiveness of competition policy by estimating its impact on Total Factor Productivity (TFP) growth for 22 industries in 12 OECD countries over the period 1995-2005. We ?nd a robust positive and signi?cant effect of competition policy asmeasured by newly created indexes. We provide several arguments and results based on instrumental variables estimators as well as non-linearities to support the claim that the established link can be interpreted in a causal way. At a disaggregated level, the effect on TFP growth is particularly strong for speci?c aspects of competition policy related to its institutional setup and antitrust activities (rather than merger control). The effect is strengthened by good legal systems, suggesting complementarities between competition policy and the ef?ciency of law enforcement institutions