BIOOPS.INFO
BioopsR code demo of Linear discriminant analysis (LDA) Quadratic discriminant analysis (QDA) k-nearest neighbor (KNN) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 …
http://www.bioops.info/
R code demo of Linear discriminant analysis (LDA) Quadratic discriminant analysis (QDA) k-nearest neighbor (KNN) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 …
http://www.bioops.info/
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Bioops | bioops.info Reviews
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R code demo of Linear discriminant analysis (LDA) Quadratic discriminant analysis (QDA) k-nearest neighbor (KNN) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 …
bioops.info
Bioops
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Bin/sh) (P(A B)=P(B A)*P(A)/P(B). Is maintained by the inventor of lasso and provides most important references for lasso. Elements of Statistical Learning. Describes the lasso in detail. Lasso in R: lars. Least Angle Regression, Lasso and Forward Stagewise, and glmnet. Lasso and elastic-net regularized generalized linear models (Note: lars() function from the lars package is probably much slower than glmnet() from glmnet.). The adaptive lasso paper. Adaptive lasso in R. Adaptive.lasso function in lqa.
Online ‘Statistical Learning’ Course Starts Jan 21 2014 - Bioops
http://bioops.info/2014/01/stat-learning-starts
Bin/sh) (P(A B)=P(B A)*P(A)/P(B). Online ‘Statistical Learning’ Course Starts Jan 21 2014. The free online course Statistical Learning. Will start tomorrow. It will be taught by Rob Tibshirani and Trevor Hastie. It is an excellent opportunity to learn directly from the two famous professors in the field and the authors of two great textbooks on statistical learning, An Introduction to Statistical Learning, with Applications in R. And The Elements of Statistical Learning.
The Consistent Estimator of Bernouli Distribution - Bioops
http://bioops.info/2015/01/simulation-bernoulli-consitent-estimator
Bin/sh) (P(A B)=P(B A)*P(A)/P(B). The Consistent Estimator of Bernouli Distribution. This is a simple post showing the basic knowledge of statistics, the consistency. For Bernoulli distribution, $ Y sim B(n,p) $, $ hat{p}=Y/n $ is a consistent estimator of $ p $, because:. For any positive number $ epsilon $. Here is the simulation to show the estimator is consitent. Estimate p on different number of trials. Then, 100 repetitions. Convert n*B observations to a n*B matrix. Obs mat ,. Phat mat ,.
Estimate Gamma Distribution Parmaters Using MME and MLE - Bioops
http://bioops.info/2015/01/gamma-mme-mle
Bin/sh) (P(A B)=P(B A)*P(A)/P(B). Estimate Gamma Distribution Parmaters Using MME and MLE. This post shows how to estimate gamma distribution parameters using (a) moment of estimation (MME) and (b) maximum likelihood estimate (MLE). The probability density function of Gamma distribution is. We can calculate the MLE of $ alpha $ using the Newton-Raphson method. For $ k =1,2, $. Below is the R code. Initiate the convergence and alpha value. Initiate two vectors to store alpha and beta in each step.
Blog Archive - Bioops
http://bioops.info/blog/archives
Bin/sh) (P(A B)=P(B A)*P(A)/P(B). Solving Bridge Regression Using Local Quadratic Approximation (LQA). The Consistent Estimator of Bernouli Distribution. Permutation Test for Principal Component Analysis. Demo of Support Vector Machine. Linear Regression With Cross Validation. Estimate Gamma Distribution Parmaters Using MME and MLE. CFDA Approved BGI’s Next Generation Sequencing Diagnostic Products. Online ‘Statistical Learning’ Course Starts Jan 21 2014. Simulate Multivariate Normal Distribution Using R.
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bioops (Bioops) · GitHub
https://github.com/bioops
Jul 25, 2012. Crowdsourcing nodulation gene network in Lotus japonicus. R Markdown Jekyll htmlwidgets. Conserved and specific legume co-expression networks. 13 contributions in the last year. Summary of pull requests, issues opened, and commits. Learn how we count contributions. Bioops has no activity during this period. You can't perform that action at this time. You signed in with another tab or window. Reload. To refresh your session. You signed out in another tab or window. Reload.
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Bioops
Bin/sh) (P(A B)=P(B A)*P(A)/P(B). R code demo of. Linear discriminant analysis (LDA). Quadratic discriminant analysis (QDA). Https:/ raw.githubusercontent.com/bioops/mis scripts/master/statistics/data/admission.txt. Different symbols represents true classifications. Different colors are predicted classifications. Different symbols represents true classifications. Different colors are predicted classifications. Tune parameter using cross-validation (CV). CV error vs k. Using the optimal parameter. Update ...
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