| Analysing Ecological Data |
| | Generalised linear modelling |
| | Principal component analysis and redundancy analysis |
| | Common trends and sudden changes |
| | Identifying the seasonal component |
| | Common trends: Dynamic factor analysis |
| | Sudden changes: Chronological clustering |
| | Correspondence analysis and canonical correspondence analysis |
| | Additive and generalised additive modelling |
| | The random intercept and slope model |
| | Model selection and validation |
| | Another mixed modelling example |
| | Methodes and algorithms of ecological analysis |
| | >>Note: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands |
| | Linear regression results |
| | Generalised least squares results |
| | Multivariate analysis results |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Univariate and multivariate analysis applied on a Dutch sandy beach community |
| | Analysing the data using univariate methods |
| | Analysing the data using multivariate methods |
| | Discussion and conclusions |
| | Investigating the effects of rice farming on aquatic birds with mixed modelling |
| | Getting familiar with the data |
| | The optimal model in terms of random components |
| | Validating the optimal linear mixed model |
| | Multivariate analyses of South-American zoobenthic species - spoilt for choice |
| | Introduction and the underlying questions |
| | Study site and sample collection |
| | The transformation plus RDA approach |
| | Principal component analysis applied to harbour porpoise fatty acid data |
| | Principal component analysis |
| | Principal component analysis results |
| | Simpler alternatives to PCA |
| | Univariate methods to analyse abundance of decapod larvae |
| | Linear regression results |
| | Additive modelling results |
| | How many samples to take? |
| | Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico |
| | Canonical correspondence analysis |
| | Estimating common trends in Portuguese fisheries landings |
| | Spatial modelling of forest community features in the Volzhsko-Kamsky reserve |
| | Models of boreality without spatial auto-correlation |
| | Models of boreality with spatial auto-correlation |
| | Sea level change and salt marshes in the Wadden Sea: A time series analysis |
| | Interaction between hydrodynamical and biological factors |
| | Time series analysis of Hawaiian waterbirds |
| | Endangered Hawaiian waterbirds |
| | Three ways to estimate trends |
| | Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal |
| | Generalised additive modeling |
| | Generalised linear modeling |
| | Crop pollination by honeybees in Argentina using additive mixed modelling |
| | Abstracting the information |
| | Common trends in demersal communities on the Newfoundland-Labrador Shelf |
| | Classification trees and radar detection of birds for North Sea wind farms |
| | A tree for birds, clutter and more clutter |
| | Multivariate analyses of morphometric turtle data - size and shape |
| | Classic approaches related to PCA |
| | Applying PCA to the original turtle data |
| | Classic morphometric data analysis |
| | A geometric morphometric approach |
| | Redundancy analysis and additive modelling applied on savanna tree data |
| | Fish stock identification through neural network analysis of parasite fauna |
| | Horse mackerel in the northeast Atlantic |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Principal component analysis and redundancy analysis |
| | The underlying principle of PCA |
| | Two technical explanations |
| | PCA regression to deal with collinearity |
| | Chord and Hellinger transformations |
| | Partial RDA and variance partitioning |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Additive and generalised additive modelling |
| | Estimate the smoother and amount of smoothing |
| | Additive models with multiple explanatory variables |
| | Choosing the amount of smoothing |
| | Model selection and validation |
| | Generalised additive modelling |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Methodes and algorithms of ecological analysis |
| | Data management and software |
| | Bivariate linear regression |
| | Multiple linear regression |
| | Partial linear regression |
| | Applied statistical theory |
| | Association among species: R analysis |
| | Association between sites: Q analysis |
| | Hypothesis testing with measures of association |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Correspondence analysis and canonical correspondence analysis |
| | When to use PCA, CA, RDA or CCA |
| | Three rationales for correspondence analysis |
| | Gaussian regression and extensions |
| | Understanding the CCA triplot |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
| | Analysis and modelling of lattice data |
| | Numerical representation of the lattice structure |
| | The numerical output for the sparrow data |
| | Principal coordinate analysis and non-metric multidimensional scaling |
| | Principal coordinate analysis |
| | Non-metric multidimensional scaling |
| | Using what we have already seen before |
| | Auto-regressive integrated moving average models with exogenous variables |
| | Spatially continuous data analysis and modeling |
| | Spatially continuous data |
| | Geostatistical functions and assumptions |
| | Exploratory variography analysis |
| | Geostatistical modelling: Kriging |
| | A full spatial analysis of the bird radar data |
| | Analysing Ecological Data |
| | Zuur, Alain F., Ieno, Elena N., Smith, Graham M. |
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