A real data-driven simulation strategy to select an imputation method for mixed-type trait data

May, Jacqueline A.; Feng, Zeny; Adamowicz, Sarah J.

doi:10.1371/journal.pcbi.1010154

Cited by 3 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, while our results agree with others that random forest models (as implemented by the missForest R function) are an accurate imputation method for trait data (Johnson et al, 2021), care should be taken to ensure use of imputation is appropriate. Our findings regarding the utility of imputation are only applicable to continuous trait imputation, as the efficacy of categorical traits imputation was not explored (although see May et al, 2023), and to large trait data sets on the scale of hundreds or thousands of species rather than tens. The utility of imputation in tackling missing and biased data has been shown to depend on the correlation between traits, and extent of phylogenetic autocorrelation (Clavel et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…We tested two ways of dealing with the generated incomplete data sets: (1) removal of species with missing data (complete case analysis) and (2) filling data gaps through imputation. We used missForest imputation, implemented through the missForest (Stekhoven & Bühlmann, 2012), due to its demonstrated accuracy (Hong & Lynn, 2020;May et al, 2023;Penone et al, 2014), and fast computation times.…”

Section: Imputationmentioning

confidence: 99%

“…Accounting for phylogenetic relatedness between species can improve imputation accuracy (May et al, 2023;Penone et al, 2014).…”

Section: Imputationmentioning

confidence: 99%

See 2 more Smart Citations

Functional diversity metrics can perform well with highly incomplete data sets

Stewart,

Carmona,

Clements

et al. 2023

Methods Ecol Evol

View full text Add to dashboard Cite

Characterising changes in functional diversity at large spatial scales provides insight into the impact of human activity on ecosystem structure and function. However, the approach is often based on trait data sets that are incomplete and unrepresentative, with uncertain impacts on functional diversity estimates. To address this knowledge gap, we simulated random and biased removal of data from three empirical trait data sets: an avian data set (9579 species), a plant data set (2185 species) and a crocodilian data set (25 species). For these data sets, we assessed whether functional diversity metrics were robust to data incompleteness with and without using imputation to fill data gaps. We compared two metrics each calculated with two methods: functional richness (calculated with convex hulls and trait probabilities densities) and functional divergence (calculated with distance‐based Rao and trait probability densities). Without imputation, estimates of functional diversity (richness and divergence) for birds and plants were robust when 20%–70% of species had missing data for four out of 11 and two out of six continuous traits, respectively, depending on the severity of bias and method used. However, when missing traits were imputed, functional diversity metrics consistently remained representative of the true value when 70% of bird species were missing data for four out of 11 traits and when 50% of plant species were missing data for two out of six traits. Trait probability densities and distance‐based Rao were particularly robust to missingness and bias when combined with imputation. Convex hull‐based estimations of functional richness were less reliable. When applied to a smaller data set (crocodilians, 25 species), all functional diversity metrics were much more sensitive to missing data. Expanding global morphometric data sets to represent more taxa and traits, and to quantify intraspecific variation, remains a priority. In the meantime, our results show that widely used methods can successfully quantify large‐scale functional diversity even when data are missing for half of species, provided that missing traits are estimated using imputation. We recommend the use of trait probability densities or distance‐based Rao when working with large incomplete data sets and filling data gaps with imputation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Imputationmentioning

confidence: 99%

See 1 more Smart Citation

Functional diversity metrics can perform well with highly incomplete data sets

Stewart,

Carmona,

Clements

et al. 2023

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

Benchmarking imputation methods for categorical biological data

Gendre,

Hauffe,

Pimiento

et al. 2024

Methods Ecol Evol

View full text Add to dashboard Cite

Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, quantify species extinction risks, or evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance, due to incomplete sampling, limited data and resource availability. Several imputation methods exist to predict missing values and recent studies have explored their performance for continuous traits in biological datasets. However, less is known about the accuracy of these methods for categorical traits. Here we explore the performance of different imputation methods on categorical biological traits combining phylogenetic comparative methods, machine learning and deep learning models. To this end, we develop an open‐source R package, to impute trait data while integrating a simulation framework to evaluate their performance on synthetic datasets. We run a range of simulations under different missing rates, mechanisms, biases and evolutionary models. We propose an integration between phylogenetic comparative methods and machine learning imputation, and an ensemble approach, in which selected imputation methods are combined. Our simulations show that this approach provides the most robust and accurate predictions. We applied our imputation pipeline to an incomplete trait dataset of 1015 elasmobranch species (i.e. sharks, rays and skates) and found a high imputation accuracy of the predictions based on an expert‐based assessment of the missing traits. Overall, our R package facilitates the comparison of multiple imputation methods and allows robust predictions of missing trait values. Our study highlights the benefits of coupling phylogenetic evolutionary models with machine learning inference to augment incomplete biological datasets.

show abstract

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

2023

View full text Add to dashboard Cite

Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly complete information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.

show abstract

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

Cited by 3 publications

References 76 publications

Functional diversity metrics can perform well with highly incomplete data sets

Functional diversity metrics can perform well with highly incomplete data sets

Benchmarking imputation methods for categorical biological data

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

Contact Info

Product

Resources

About