592 research outputs found
zoo: S3 Infrastructure for Regular and Irregular Time Series
zoo is an R package providing an S3 class with methods for indexed totally
ordered observations, such as discrete irregular time series. Its key design
goals are independence of a particular index/time/date class and consistency
with base R and the "ts" class for regular time series. This paper describes
how these are achieved within zoo and provides several illustrations of the
available methods for "zoo" objects which include plotting, merging and
binding, several mathematical operations, extracting and replacing data and
index, coercion and NA handling. A subclass "zooreg" embeds regular time series
into the "zoo" framework and thus bridges the gap between regular and irregular
time series classes in R.Comment: 24 pages, 5 figure
Structural Change in (Economic) Time Series
Methods for detecting structural changes, or change points, in time series
data are widely used in many fields of science and engineering. This chapter
sketches some basic methods for the analysis of structural changes in time
series data. The exposition is confined to retrospective methods for univariate
time series. Several recent methods for dating structural changes are compared
using a time series of oil prices spanning more than 60 years. The methods
broadly agree for the first part of the series up to the mid-1980s, for which
changes are associated with major historical events, but provide somewhat
different solutions thereafter, reflecting a gradual increase in oil prices
that is not well described by a step function. As a further illustration, 1990s
data on the volatility of the Hang Seng stock market index are reanalyzed.Comment: 12 pages, 6 figure
Implementing a Class of Permutation Tests: The coin Package
The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and flexible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests---such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data---can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality.
Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi
Behavior of QQ-Plots and Genomic Control in Studies of Gene-Environment Interaction
Genome-wide association studies of gene-environment interaction (GxE GWAS) are becoming popular. As with main effects GWAS, quantile-quantile plots (QQ-plots) and Genomic Control are being used to assess and correct for population substructure. However, in GE work these approaches can be seriously misleading, as we illustrate; QQ-plots may give strong indications of substructure when absolutely none is present. Using simulation and theory, we show how and why spurious QQ-plot inflation occurs in GE GWAS, and how this differs from main-effects analyses. We also explain how simple adjustments to standard regression-based methods used in GE GWAS can alleviate this problem
Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Multivariate analysis of psychological dat
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach
Accurate and high-resolution snowfall and fresh snow forecasts are important
for a range of economic sectors as well as for the safety of people and
infrastructure, especially in mountainous regions. In this article a new
hybrid statistical postprocessing method is proposed, which combines
standardized anomaly model output statistics (SAMOS) with ensemble copula
coupling (ECC) and a novel re-weighting scheme to produce spatially and
temporally high-resolution probabilistic snow forecasts. Ensemble forecasts
and hindcasts of the European Centre for Medium-Range Weather Forecasts
(ECMWF) serve as input for the statistical postprocessing method, while
measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the
eastern European Alps. The results demonstrate that the new hybrid method
allows one not only to provide reliable high-resolution forecasts, but also
to combine different data sources with different temporal resolutions to
create hourly probabilistic and physically consistent predictions.</p
NWP-based lightning prediction using flexible count data regression
A method to predict lightning by postprocessing numerical weather prediction
(NWP) output is developed for the region of the European Eastern Alps.
Cloud-to-ground (CG) flashes â detected by the ground-based Austrian
Lightning Detection & Information System (ALDIS) network â are counted on
the 18Ă18 km2 grid of the 51-member NWP ensemble of the European
Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the
target quantity in count data regression models for the occurrence of
lightning events and flash counts of CG. The probability of lightning
occurrence is modelled by a Bernoulli distribution. The flash counts are
modelled with a hurdle approach where the Bernoulli distribution is combined
with a zero-truncated negative binomial. In the statistical models the
parameters of the distributions are described by additive predictors, which
are assembled using potentially nonlinear functions of NWP covariates.
Measures of location and spread of 100 direct and derived NWP covariates
provide a pool of candidates for the nonlinear terms. A combination of
stability selection and gradient boosting identifies the nine (three) most
influential terms for the parameters of the Bernoulli (zero-truncated
negative binomial) distribution, most of which turn out to be associated with
either convective available potential energy (CAPE) or convective
precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final
model to provide credible inference of effects, scores, and
predictions. The selection of terms and MCMC sampling are applied for data of
the year 2016, and out-of-sample performance is evaluated for 2017. The
occurrence model outperforms a reference climatology â based on 7 years of
data â up to a forecast horizon of 5Â days. The flash count model is
calibrated and also outperforms climatology for exceedance probabilities,
quantiles, and full predictive distributions.</p
Future Dominance by Quaking Aspen Expected Following Short-Interval, Compounded Disturbance Interaction
The spatial overlap of multiple ecological disturbances in close succession has the capacity to alter trajectories of ecosystem recovery. Widespread bark beetle outbreaks and wildfire have affected many forests in western North America in the past two decades in areas of important habitat for native ungulates. Bark beetle outbreaks prior to fire may deplete seed supply of the host species, and differences in fireârelated regeneration strategies among species may shift the species composition and structure of the initial forest trajectory. Subsequent browsing of postfire tree regeneration by large ungulates, such as elk (Cervus canadensis), may limit the capacity for regeneration to grow above the browse zone to form the next forest canopy. Five standâreplacing wildfires burned ~60,000 ha of subalpine forest that had previously been affected by severe ( \u3e90% mortality) outbreaks of spruce beetle (SB, Dendroctonus rufipennis) in Engelmann spruce (Picea engelmannii) in 2012â2013 in southwestern Colorado. Here we examine the drivers of variability in abundance of newly established conifer tree seedlings [spruce and subalpine fir (Abies lasiocarpa)] and resprouts of quaking aspen (Populus tremuloides) following the shortâinterval sequence of SB outbreaks and wildfire (2â8 yr between SB outbreak and fire) at sites where we previously reconstructed severities of SB and fire. We then examine the implications of ungulate browsing for forest recovery. We found that abundances of postfire spruce seedling establishment decreased substantially in areas of severe SB outbreak. Prolific aspen resprouting in stands with live aspen prior to fire will favor an initial postfire forest trajectory dominated by aspen. However, preferential browsing of postfire aspen resprouts by ungulates will likely slow the rate of canopy recovery but browsing is unlikely to alter the species composition of the future forest canopy. Collectively, our results show that SB outbreak prior to fire increases the vulnerability of spruceâfir forests to shifts in forest type (conifer to aspen) and physiognomic community type (conifer forest to nonâforest). By identifying where compounded disturbance interactions are likely to limit recovery of forests or tree species, our findings are useful for developing adaptive management strategies in the context of warming climate and shifting disturbance regimes
- âŠ