Use French cleat to hold things

Use French cleat to hold things

• Wikipedia. What is the French cleat?
• Popular Mechanics. How to Build a French Cleat Shelf to Hold Virtually Anything
• Family Handyman. Custom Garage Storage (Video)

Interview with J.J. Allaire

Interview with J.J. Allaire - the founder of RStudio
by Joseph Rickert
Welcome to “R Views”, the new R Community blog from RStudio. For this first post, I sat down with J.J. Allaire, RStudio’s founder and CEO, to discuss RStudio’s history, its mission and JJ’s vision for its future. In a short time, we touched on a wide range of subjects including RStudio’s business, the growth of the R language, the importance of the R Consortium to the R Community and J.J.’s advice to anyone coming to R for the first time. We hope you enjoy this “snapshot” of RStudio’s place in the R world. full text
You can also read a Chinese version here.

'Big Fat Fix' Film Challenges Mediterranean Diet

'Big Fat Fix' Film Challenges Mediterranean Diet
An Interview With Cardiologist Aseem Malhotra
Editor's Note:  Cardiologist Aseem Malhotra, MBChB, MRCP, talks about his new documentary The Big Fat Fix, which sent him to Pioppi, Italy, the village where Ancel Keys researched diet and cardiovascular health. A regular contributor to the BMJ and major UK newspapers on the topic of dietary health, Dr Malhotra believes that the demonization of fat let sugar off the hook as the real culprit in the diabetes, obesity, and cardiovascular disease epidemic, and that we need to rethink our approach to exercise. ... Full Text.


This article is an another interesting opinion based on facts and viewed from a different angle. This interview reminds me the Michael Pollan's book In Defense of Food published in 2008: Food – Not Nutrients – Is The Fundamental Unit In Nutrition. (PBS Documentary In Defense of Food in Dec. 2015, PBS Newshour and on YouTube).
Food Insight (2015). 4 Food Rules You Won’t Find in Michael Pollan’s ‘In Defense of Food’

Microbiome: Fibre for the future

Microbiome: Fibre for the future
Nautre: Eric Martens
A chronic lack of dietary fibre has been found to reduce the diversity of bacteria in the guts of mice. This effect is not fully reversed when fibre is reintroduced, and increases in severity over multiple generations. ... Full text

Battle of the data science Venn Diagrams

Battle of the Data Science Venn Diagrams
by David Taylor    

Data science is a rather fuzzily defined field; some of the definitions I've heard are:

• "Work that takes more programming skills than most statisticians have, and more statistics skills than a programmer has."
• "Applied statistics, but in San Francisco."
• "The field of people who decide to print 'Data Scientist' on their business cards and get a salary bump."

Personally, I've recently decided to avoid the controversy by calling myself a data spelunker. (Data miners are out of vogue anyway.)
As a field in search of a definition, it's unsurprising that you can find a lot of different attempts to define it.
As a field full of data nerds with a penchant for visualization, it's also unsurprising that a lot of them use Venn diagrams. (Fun fact: John Venn, who invented the eponymous diagrams, and his son filed a patent in 1909 for an lawn bowling machine.)... Full Text

Stata: Get out-of-sample file predictions

Stata: Get out-of-sample file predictions
Example:
     webuse auto,clear

          regress mpg weight foreign

     est store regxb

     

     preserve

       webuse newautos,clear

       est restore regxb

       predict mpg

       list

     restore

Stata: display system date

Stata: display system date

• .di "system date:" c(current_date)
• .di "system date:" "$S_DATE"
• .di %td_CY-N-D  date("`c(current_date)'","DMY") // "` '" are not necessary
• .di %td_CY-N-D  date("$S_DATE","DMY")
• .di "system year: " year(date(c(current_date),"DMY") // w/o `' around c(current_date)
• .di "system month: " month(date(c(current_date),"DMY"))
• .di "system day:" day(date(c(current_date),"DMY"))
• .di "system year: " year(date("$S_DATE","DMY"))
• .di "system month: " month(date("$S_DATE","DMY"))
• .di "system day:" day(date("$S_DATE","DMY"))
•  more examples:
• Works ('local' with '=')
• local dd=day(date(c(current_date),"DMY"))
• local mm=month(date(c(current_date),"DMY"))
• local yy=year(date(c(current_date),"DMY"))
• log using "output_`yy'_`mm'_`dd'.log", replace
• log close
• Doesn't work ('local' without '=')
• local dd day(date(c(current_date),"DMY"))
• local mm=month(date(c(current_date),"DMY"))
• local yy=year(date(c(current_date),"DMY"))
• log using "output_`yy'_`mm'_`dd'.log", replace
• log close // invalid 'DMY' r(198)
• Works ('global' with '=')
• global dd=day(date(c(current_date),"DMY"))
• global mm=month(date(c(current_date),"DMY"))
• global yy=year(date(c(current_date),"DMY"))
• log using "output $yy-$mm-$dd.log", replace
• log close

Recycle/reuse returned results in Stata

Recycle/reuse returned results in Stata

• UCLA: "How can I access information stored after I run a command in Stata (returned results)?" 
• The Stata Blog: Drukker (2015). Programming an estimation command in Stata: Where to store your stuff
• Stackoverflow (2014).Saving coefficients and standard errors as variables
• Lembcke (2009). Advanced Stata Topics
• SSCC. An Introduction to Mata
• Stata commands are grouped into 4 major categories: r-class, e-class, s-class, and n-class commands. Also a c-class contains the values of system parameters and settings, along with certain constants.
• The commands produce the statistical results are either r-class or e-class. e-class commands produce the estimation results, others are belong to r-class.
• After submitting "contrast", Stata generates a L matrix (r(L)), you can check the contrast coefficients using "matrix list r(L)".
• If don't know what results are outputted, use "return list" or "ereturn list" to find them. The scalar results from a r-class can be used with the "r(...)" and scalar results from e-class command can be used with "e(...)". Here, "..." is the name showed using "return list" or "ereturn list". The use of results in matrix form is a little tricky. "_b[...]" or "_se[...]" have to be used; here, "..." is the variable name of a coefficient in the model. The results for a constant is used as "_b[_cons]" for beta coefficient or "_se[_cons]" for standard error. A matric results can also converted into a matrix: "mat B=e(b)", then "disp B[rowno, colon]".
• To show variance-covariance matrix, use: "estat vce" or just simple "matlist e(V)", and to show correlation, use: "estat vce, correlation".
• You can "estimate store" and "estimate restore" a set of estimates with a name in memory, in such way, the following command will not be erased. If want to save and use it as a permanent file, you can use "estimate save" and "estimate use".
• A single number can been converted into scalar, for example, "scalar xyz=_b[agecat]". However, the scalar has to be used with a pseudofunction scalar(), for example, "display scalar(xyz)" (more info)
• The e(V) and e(b) matrices can be converted into variables of a dataset using "svmat" (convert variables into matrix using "mkmat"), which is similar to "putmat and getmat" of mata (matrix ref.):
• mat D = e(b)', e(b)'
• svmat double D, name(coef)
• mat se1=vecdiag(e(V))
• mat se2=vecdiag(e(V))
• mat SE = se1, se2
• svmat SE, name(se)
• The "ereturn display" can use the e(V) and e(b) matrices to return a r-class matrix "r(table)"
• "margins" also gives e-class results:
• webuse dollhill3,clear
• poisson deaths i.smokes##c.agecat, exposure(pyears)
• est store tempreg
• margins smokes, gen(dhat) predict(ir) // undocumented gen()
• mean dhat1 // for smokes = 0
• scalar dhat1=_b[dhat1] // .00810452
• margins smokes, eydx(agecat) predict(ir) post
• scalar eydxsmokes0=_b[0.smokes] // 1.046826
• est restore tempreg
• margins smokes, dydx(agecat) predict(ir) post
• scalar dydxsmokes0=_b[0.smokes] // .00848402
• disp scalar(dydxsmokes0)/scalar(dhat1) // gives 1.046826
• Gould(2010).Mata Matters; (2011).Mata, the missing manual. Baum(2009).Using Mata to work more effectively in Stata
• putmat and getmat - Put Stata variables into Mata and vice versa
• mata r2=(1\2\3)
• mata b=st_matrix("e(b)")'
• mata se=sqrt(diagonal(st_matrix("e(V)")))
• getmata r2 b se, force
• vwls b r2, sd(se)
• reg b r2
• Rename "rowname" and "colname" of a matrix

     program estmatrename, eclass
       matrix BB = e(b)
       matrix colnames BB = "1.race" "2.race" "3.race"
       ereturn repost b = BB, rename
       matrix VV = e(V)
       matrix colnames VV = "1.race" "2.race" "3.race"
       matrix rownames VV = "1.race" "2.race" "3.race"
       ereturn repost V = VV
     end

• total heartatk [pw=swgt], over(race)
• estmatrename
• lincom (_b[3.race]-_b[1.race])/2
• test _b[1.race]=_b[2.race]
• contrast {race 1 -1 0}
• contrast p(1).race
• Convert ln(RR) into RR and percent change
• webuse dollhill3
• poisson deaths smokes i.agecat,exposure(pyears) irr margins agecat, predict(ir) post
• qui nlcom (lnRR21:ln((_b[2.agecat]/_b[1.agecat])))(lnRR31:ln((_b[3.agecat]/_b[1.agecat]))) (lnRR41:ln((_b[4.agecat]/_b[1.agecat]))), post
• ereturn disp,eform(RR) cformat(%5.2f) pformat(%5.4f)
• mat rtable=r(table)'
• mat RR=rtable[1...,"b"],rtable[1...,"ll".."ul"]
• mata st_matrix("pctable",(st_matrix("RR"):-1):*100)
• mat coln pctable=RR LL UL
• matlist pctable, format(%10.2f)r

The 21 greatest graduation speeches of the last 60 years

Vox: The 21 greatest graduation speeches of the last 60 years
by German Lopez on May 11, 2016
"Graduation speeches are the last opportunity for a high school or college to educate its students. It's unsurprising, then, that these institutions often pull in some of the world's most powerful people to leave an equally powerful impression on their students. Here are the best of those speeches and some of the sections that resonate the most..." (May 11, 2016)
To read and watch the full article on the Vox website here.

R! Books

R! Books

• Oscar: Big Book of R collection
• RStudio: Cheatsheets
• W.N. Venables. An Introduction to R
• Hadley Wickham & Garrett Grolemund (2017). R for Data Science
• Hadley Wickham. Advanced R
• Winston Chang: R Graphics Cookbook, 2nd
• Christoph Hank: Introduction to econometrics with R
• Neale Batra: R for applied epidemiology and public health
• David Dalpiaz: Applied Statistics with R (HTML version) GitHub
• Colin Gillespie. Efficient R programming
• Hadley Wickham (2015). R Packages
• Yihui Xie. bookdown: Authoring Books and Technical Documents with R Markdown
• Yihui Xie. R Markdown: The Definitive Guide
• Julia Silge: Text Mining with R
• Patrick Burns (2011). The R Inferno
• Daniel Navarro. Learning Statistics with R
• Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten: An Introduction to Statistical Learning, with Applications in R 2nd. (pdf) with the excellent self-paced video training course. (here is the 15-hours of video training video abstracted by the Data School (YouTube))
• Trevor Hastie (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• Norman Matloff. The Art of R Programming (part)
• Michael Crawley (2007). The R Book
• Bolker (2007).Ecological models and data in R (2007 draft). Appendix (w/ delta method)
• Winston Chang. Cookbook for R
• Verzani. simpleR - Using R for Introductory Statistics 
• Kerns. Introduction to Probability and Statistics Using R
• Peng. R Programming for Data Science, The Art of Data Science, Exploratory Data Analysis with R
• Yakir. Introduction to Statistical Thinking (With R, Without Calculus) 
• Aragón. Population Health Data Science with R
• 赵鹏, 谢益辉, 黄湘云 现代统计图形 (Modern Statistical Graphics)

how to configure R! environment

Where/how to configure R start-up environment
There are several approaches can be used to customize the R working environment such as options and library directory etc. at R start-up:

• Modify the R original profile file directly. The "Rprofile.site" is under the directory ".\R directory name\etc\". At both startup and end, the R will use the "Rprofile.site" file, then looks for the user-defined ".Rprofile" file in the current working directory (run "getwd()" to find the current location of working directory) or in the user's R home directory (run "R.home()" or "Sys.getenv("R_HOME")"to find where it is). You can edit the "Rprofile.site" file or create a ".Rprofile" file to customize the startup. For more information see Initialization at start of an R session and Customizing startup. I am using R-Portable and prefer to create a ".Rprofile" in the same directory of "R-Portable.exe" file. In such way, I don't need to dig deep and edit the R original setting.
• to lists all the options can be set, run "names(options())"
• to show the value of an item, run "options("option name")", for example:
•  "options("digits")" shows "$digits, [1] 7", which means the number will be shown 7 digits.
• "options("defaultPackages")" shows the packages attached by default when R starts up
• to modify the values of an option item, run "options(xxx=yyy), for example:
• "options(digits=15)" changes the digit number into 15. Notes: this is for setting full length of number but not number of decimal places. To set the number of decimal, try such as "round(4/3, digits=2)" with 2 decimal places but not in "options()" unfortunately.
• to set the directory of personal R library, create a ".Rprofile" file in the working directory and include ".libPaths(c(.libPaths(),"c:/myRlib directory name")", save it.
• or, edit "Rprofile.site", add line: Add line: ".libPaths(c(.libPaths(),"c:/myRlib directory name")"
• When use RStudio as the IDE, modify the options file ("Options.R") under the ".\Rstudio directory name\R\". The option setting overwrites the option setting in R profiles both "Rprofile.site" and ".Rprofile".
• to set the directory of personal R library, edit file "Options.R", add line: ".libPaths(c(.libPaths(),"c:/myRlib directory name")",  then save the "Options.R".
• or, to use ".Rprofile", this file needs be in the working directory when not in a project (to set this master working directory using RStudio GUI: tools -> Global options... -> change the "Default working directory(when not in a project):"). Also you can change R.home() under the "R version:".
• By the way, the options and the directory of package library can also be changed after the start-up of R.
• de Vries (2015).Best practices for handling packages in R projects
• Gillespie. R startup

Doing Basic Calculus Using R!

Doing Basic Calculus Using R!
Differentiation Rules/Rules for Calculating Derivatives

• Constant: f'(c) = 0, here c as a constant
• Scalar Multiple: f'[cf(x)] = cf'(x)
• Sum and Difference: [f(x) ± g(x)]' = f' (x) ± g' (x)
• Product: [f(x) * g(x)]' = f'(x) * g(x) + f(x) * g'(x)
• Quotient: [f(x) / g(x)]' = [g(x) * f'(x) - f(x) * g'(x)] / g(x)²
• Power: f'(xⁿ) = n * x^(n-1)
• Chain Rule: [f(g(x)]' = f'(g(x)) * g'(x)
• Exponential: f'(e^x) = e^x                  Arbitrary base: f'(b^x) = b^x * lnb
• Logarithmic: f'(ln|x|) = 1/x                  Arbitrary base: f'_b(logx) = 1/(x lnb)

Calculating Derivative and Integration Using R!

• R can symbolically find the derivative of any function by using the function D() with function expression(). R knows how to use the chain rule as well.
• First derivative:     D(expression(x^2), "x") ===> 2 * x
• Higher derivative: D(D(expression(x^2),"x"), "x") ===> 2
• Partial derivative: D(expression((y-x)/y),"x") ===> -(1/y) and D(expression((y-x)/y),"y") ===> 1/y - (y - x)/y^2, which is equal to x/y^2
• with the eval() function, you can get the value using particular values of its parameters: x =10; eval(D(expression(x^2), "x"))  ===> 20
• D(expression(pnorm(x)),"x") ===> dnorm(x)
• D(expression(dnorm(x)),"x") ===> -(x * dnorm(x))
• R  can numerically perform one dimentsional integration using function integrate()
• integrate(dnorm,-Inf,Inf) ===> 1 with absolute error < 9.4e-05
• integrate(dnorm,-2.58,2.58) ===> 0.99012 with absolute error < 1.9e-08
• integrate(function(x) {x^3 + x}, 0, 1) ===> 0.75 with absolute error < 8.3e-15
• Other differentiation related R packages
• Deriv is for symbolic differentiation.
• Ryacas allows R users to access the yacas computer algebra system that does an excellent job of differentiation.
• Use R to Compute Numerical Integrals

Online Calculators: 

• Derivative Calculator, Integral Calculator
• Symbolab: Partial Derivative Calculator
• WolframAlpha: Derivative Calculator
  

Taylor's Series is a series expansion of a function near a point. A real function f(x) which is close to a point a can be estimated as:

• f(x) =∑(f⁽ⁿ⁾(a)/n! * (x - a)ⁿ
• If a = 0, the expansion is known as a Maclaurin series.
• Mathematical Annotation to write math symbols and expressions in R graphics (cheat sheet). 

黄湘云 (2016).R语言做符号计算
R! How can I include Greek letters in my plot labels?

accept-reject algorithm

Accept-reject algorithm
Accept-reject algorithm (acceptance-rejection method) or reject sampling is a simple and general simulation method to decide observations with or without a trait from the probability of a distribution. In this way, we can convert a probability into a dichotomous condition (i.e. yes or no). Basically, there are three steps:

• Step 1. Generate Y from density g [Y = f(x), the pdf of f(x) is the target distribution]
• Sample a point (an x-position) from the proposal density distribution (g) and draw a vertical line at this point, get the density (an y-position) [X ~ g(x)]. The density function of Y has a upper, a constant c, and c is >=1.
• Step 2. Generate U from the uniform distribution on the interval (0, cg(x)) [U = cg(x), the pdf of cg(x) is the proposal distribution]
• Sample uniformly along in the range of x-position (i.e. uniformly from 0 to the maximum of the probability density function) [U ~ runif(0, 1)]
• Step 3. If U <= Y, then set Y = X ("accept"), else repeat Steps 1 and 2

Pr(X|accept) = Pr(accept|X) x Pr(X)/Pr(accept), using Bayes' theorem
Pr(accept|X) = f(x)/cg(x)
Pr(X) = g(x)
Pr(accept) = 1/c
therefore, Pr(X|accept) = f(x)


Example: Stata simulation and define the event

clear
set seed 770488
set obs 1000

gen x = runiform() - .5
gen z = runiform() - .5
gen xb = x + 8*z
 gen y = 1 / (1 + exp(xb)) < runiform() // y defined as 0 or 1
logistic y x z