The modeltime R library offers a wide range of features for model evaluation, selection, and forecasting using the tidymodels ecosystem. Time-series analysis of sea ice in the Arctic polar regions performed using the modeltime library suggests that the Arctic sea will be nearly ice-free in the very near future.
Passive-microwave instrumentation on satellites has allowed for the monitoring of Arctic sea ice coverage since the late 1970s, showing a long-term downward trend due to both natural variability and climate change. The rate of decline in Arctic sea ice has varied over the past 43 years, with some periods showing faster rates of loss than others. This blog post explores the temporal changes in Arctic sea ice extent using the R language for statistical computing and visualization.
The post compares differences between simple random sampling and geographically stratified sampling. Stratified random sampling improves the spatial distribution of sample point locations by stratifying the field geographically and sampling randomly within each stratum. Different sampling patterns are compared using Monte Carlo simulations on a simulated population.
Environmental data are frequently left-censored, indicating that some values are less than the limit of quantification for the analytical methods employed. These data are problematic because censored (non-detect) values are known only to range between zero and the censoring limit. This complicates analysis of the data, including estimating statistical parameters, characterizing data distributions, and conducting inferential statistics. This post demonstrates various procedures and methods that are available in R for analyzing data containing a mixture of detects and non-detects. These methods make few or no assumptions about the data, or substitute arbitrary values (e.g., one-half the detection or reporting limit) for the non-detects.
Combining principal component analysis (PCA) and clustering methods are useful for reducing the dimension of a data set into a few continuous variables containing the most important information in the data. This post illustrates how to combine PCA and clustering methods to identify patterns in a data set using the R language for statistical computing and visualization.
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