This R tutorial describes how to perform a Principal Component Analysis (PCA) using the built-in R functions prcomp() and princomp(). You will learn how to predict new individuals and variables.
Aug 08, 2011 · ...say you have a multivariate dataset and a two-way factorial design - you do a PERMANOVA and the aov-table (adonis is using ANOVA or "sum"-contrasts) tells you there is an interaction - how to proceed when you want to go deeper into the analysis?
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Pros & cons of mean imputation - Examples in R & SPSS - Alternatives for mean substitution Mean Imputation in R (Example). Before we can start with the example, we need some data with missing...

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  • Details. The purpose of the nullmodel function is to create an object, where all necessary statistics of the input matrix are calculated only once. This information is reused, but not recalculated in each step of the simulation process done by the simulate method.

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Most standard distributions in R have a family of commands - pdf/pmf, cdf/cmf, quantile, random As nico wrote, they're not implemented in R. Assuming we work in 1..k, those functions should look like

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  • This script uses anosim (Analysis of similarities) to analyze if groups vary by timepoint. Output is an R value (from -1 to 1) and a p value. The more a category influences microbiota, the larger the R values will be, and the smaller the p value will be.
  • 「遺伝子発現レベルでの種感受性分布 SSD」 Yan Z., Yang N., Wang X., Wang W., Meng S. and Liu Z., 2012, Preliminary analysis of species sensitivity distribution based on gene expression effect, Sci. China Earth Sci., 55(6), 907-913. Cd, Cu, Znの種の感受性分布(SSD)を遺伝子発現レベルで求めてみたよ、論文。Cdで同様のことをおこなっている ...

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Why do we have to convert the categorical value into factor (in R) or dummy variables before applying machine learning algorithms (especially linear regression)?

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F I G U R E 2 (a) Boxplot of population trophic niche breadth (estimated as distance to centroid, PERMDISP) of P. bifasciatus (Pbi) in streams with co-occurring characid species (S1, S2, and S3) and in allopatry (S4, S5, and S6).

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My question concerns the practical difference between the == and %in% operators in R. I have run into an instance at work where filtering with either operator gives different results (e.g. one results on 800 rows, and the other 1200). I have run into this problem in the past and am able to validate in a way that ensures I get the results I desire.

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This script uses anosim (Analysis of similarities) to analyze if groups vary by timepoint. Output is an R value (from -1 to 1) and a p value. The more a category influences microbiota, the larger the R values will be, and the smaller the p value will be.

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levels of salinity and dissolved oxygen as exhibited by PCA ordination. PERMDISP revealed that Bahia Grande undergoes higher temporal variability than the reference sites, which is considered to be an indication of instability. Bahia Grande appears to be trending similarly in terms of ecosystem metabolism however, the basin is functioning

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