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Apr 19 • 10 tweets • 6 min read Twitter logo

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1/10: 🧵 Welcome to this thread on #regression modeling strategies in #R! We'll discuss key techniques and packages to help you build effective models. Ready to dive in? Let's go! 🚀 #RStats #DataScience #Statistics

2/10: 🌐 Linear Regression: Start with simple & multiple linear regression using 'lm()' function. Check out the 'broom' package for easy-to-use regression output! #RStats cran.r-project.org/web/packages/b…

3/10:🏞️ Polynomial Regression: When data is nonlinear, try polynomial regression! Use 'poly()' to create higher-order terms. Beware of overfitting! #RStats

4/10: 🚆 Ridge, Lasso, & Elastic Net: Use these when you have many correlated predictors. Check out the 'glmnet' package to implement these methods! #RStats cran.r-project.org/web/packages/g…

5/10:🌲 Decision Trees & Random Forests: For non-parametric models, consider these options. Use 'rpart' or 'tree' for decision trees & 'randomForest' for random forests. #RStats cran.r-project.org/web/packages/r…

6/10: 📊 Generalized Additive Models (GAMs): Flexible, non-linear models. Use the 'mgcv' package to implement GAMs. #RStats cran.r-project.org/web/packages/m…

7/10: 🔧 Model Selection: Use techniques like cross-validation & AIC to pick the best model. The 'caret' package is excellent for model selection! #Rstats cran.r-project.org/web/packages/c…

8/10: 🔍 Diagnostics & Validation: Always check assumptions & validate your models! Look into packages like 'car', 'lmtest', 'DHARMa', & 'performance' for diagnostics. #RStats

🎓 Learning Resources: Boost your regression modeling skills with these resources:
1An Introduction to Statistical Learning (James et al.)
2Applied Predictive Modeling (Kuhn and Johnson)
3R for Data Science (Wickham and Grolemund)
#RStats

10/10: 🎉 That's a wrap! We hope you found this thread helpful. Keep exploring, and happy modeling! 📈 #Rstats #DataScience #Statistics

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Apr 18

1/ Bioinformatics is an essential part of modern biology, and R is a powerful programming language that has become the standard tool for bioinformatics analysis. #rstats #bioinformatics #datascience

2/ R provides an extensive collection of packages for bioinformatics analysis, including tools for gene expression analysis, sequencing data analysis, and network analysis. #rstats #bioinformatics

3/ Bioconductor is an open-source software project that provides tools for the analysis and comprehension of genomic data. It contains more than 1,800 packages for bioinformatics analysis. #rstats #bioinformatics

Read 7 tweets

Twitter author

Apr 18

1/ Regularization methods are a crucial part of machine learning models that help to prevent overfitting. In R, there are several popular regularization methods available, including Lasso, Ridge, and Elastic Net. #rstats #datascience #MachineLearning

2/ Lasso (Least Absolute Shrinkage and Selection Operator) is a method that uses L1 regularization to shrink the coefficients of less important features to zero, resulting in a sparse model. It is useful when there are many features with only a few of them being relevant. #rstats

3/ Ridge regression, on the other hand, uses L2 regularization to add a penalty term to the loss function that shrinks the coefficients of less important features towards zero without setting them to zero. It is useful when all features are potentially relevant. #rstats

Read 9 tweets

Twitter author

Apr 18

1/6: Venn diagrams are commonly used in bioinformatics to visualize the overlap of different sets of genes or proteins. There are several R packages available for creating these diagrams, including VennDiagram, ggvenn, and ggVennDiagram. #rstats #datascience #bioinformatics

2/6: VennDiagram is a widely used package for creating classic Venn diagrams with up to six sets. It offers a range of options for customizing the appearance of the diagram, including font size, color, and label placement. #rstats #bioinformatics

3/6: One of the advantages of VennDiagram is the ability to easily incorporate statistical analyses. For example, you can calculate the significance of the overlap between different sets of genes or proteins and display this information on the diagram. #rstats #bioinformatics

Read 6 tweets

Twitter author

Apr 18

1/ Mixed models are a powerful statistical tool for analyzing complex data with both fixed and random effects. R has several great packages for fitting mixed models. #rstats #datascience

2/ One of the most popular packages for mixed models in R is "lme4". This package provides functions for fitting linear and generalized linear mixed models, including models with crossed and nested random effects. #rstats #lme4 cran.r-project.org/web/packages/l…

3/ Another popular mixed model package in R is "nlme". It has similar functionality to "lme4" but is designed to handle longitudinal or repeated-measures data. #rstats #nlme cran.r-project.org/web/packages/n…

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Twitter author

Apr 17

1/ If you're designing experiments, check out the "randomizeR" package in R! It helps you create randomized experimental designs, which can be crucial for avoiding bias and ensuring your results are statistically sound. #rstats #datascience cran.r-project.org/web/packages/r…

2/ Another helpful package is "DoE.base", which offers a wide range of tools for design and analysis of experiments. You can use it to create custom designs, analyze data, and more. #rstats #datascience cran.r-project.org/web/packages/D…

3/ "FrF2" is another package you'll want to consider for experiment design. It helps you create fractional factorial designs, which can save time and resources while still giving you the information you need. #rstats #datascience cran.r-project.org/web/packages/F…

Read 5 tweets

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Apr 17

1/ In 2021, DeepMind made headlines when it announced that it had developed an algorithm called AlphaFold that could predict the 3D structure of proteins with remarkable accuracy. Here's what you need to know about this groundbreaking technology. #bioinformatics #AlphaFold #AI

2/ Proteins are essential building blocks of life, and their structure is critical to understanding how they function. Determining the structure of a protein can be a long and complex process, but AlphaFold is changing that.

3/ AlphaFold uses deep neural networks to predict the 3D structure of a protein based on its amino acid sequence. By training on a vast database of known protein structures, AlphaFold can accurately predict the structure of a protein in a matter of days, rather than years.

Read 7 tweets

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