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sciruby.com | ||
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www.rdatagen.net
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| | | | | Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e.one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. My goal here is to show that at least writing the code need not be overwhelming. | |
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matbesancon.xyz
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| | | | | Learning by doing: predicting the outcome. | |
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aosmith.rbind.io
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| | | | | I walk through an example of simulating data from a binomial generalized linear mixed model with a logit link and then exploring estimates of over/underdispersion. | |
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nhigham.com
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| | | A norm on $latex \mathbb{C}^{m \times n}$ is unitarily invariant if $LATEX \|UAV\| = \|A\|$ for all unitary $latex U\in\mathbb{C}^{m \times m}$ and $latex V\in\mathbb{C}^{n\times n}$ and for all $latex A\in\mathbb{C}^{m \times n}$. One can restrict the definition to real matrices, though the term unitarily invariant is still typically used. Two widely used matrix norms... | ||
