學長我想問一下
就是之前學長在介紹盧sir的最後
有告訴我們可以讓R跑很快的指令
哈哈 我突然忘記了 不過我想把他學起來
學長在教導一下^^ 哈哈
※ 引述《seablack (Water)》之銘言：
: Add some useful packages (functions) in R to all.
: Name Illustrations
: 1. boost : Boosting Methods for Real and Simulated Data
: 2. classifly : Explore classification models in high dimensions
: 3. concord : Concordance and reliability
: 4. dyn : Time Series Regression
: 5. EMV : Estimation of Missing Values for a Data Matrix
: 6. FactoMineR: Factor Analysis and Data Mining with R
: 7. fda : Functional Data Analysis
: 8. gclus : Clustering Graphics
: 9. ump : Uniformly Most Powerful Tests
: 10. TwoWaySurvival : Additiv Two-Way Hazards Modelling of
: Right Censored Survival Data
: Reference from http://cran.csie.ntu.edu.tw/src/contrib/PACKAGES.html
: : In R :
: : 1. Function for reading binary data:
: : e.g.,
: : zz = file("D:/Test01.raw", "rb") # Readin binary data
: : N = 2064384/2; # Numbers of data
: : nb = 128; # image size = nb x nb
: : k = 40; # 40th
: : Img = readBin(zz, "integer", n=N,size=2) # using 2 bytes integer read data
: : ZZ = matrix(Img[(1+(k-1)*nb*nb):((k)*nb*nb)],nb,nb); # assign 40th image to ZZ
: : Co = rgb(0:255, 0:255, 0:255, max = 255); #Create gray map
: : image(1:nb, 1:nb,ZZ,col = Co) # display 40th image
: : 2. Function for data mining package:
: : RWeka : Collected many mining tools like those in http://www.cs.waikato.ac.nz/ml/weka/.
: : http://cran.cs.pu.edu.tw/src/contrib/Descriptions/RWeka.html
: : 3. Function for Parallel Virtual Machine (PVM):
: : rpvm : http://cran.cs.pu.edu.tw/src/contrib/Descriptions/rpvm.html
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