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Forecasts and ggplot. The forecast package uses the base R graphics for all plots, but some people may prefer to use the nice graphics available using the ggplot2 package.

Forecasts and ggplot

In the following two posts, Frank Davenport shows how it can be done: To leave a comment for the author, please follow the link and comment on his blog: Research tips » R. offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more... Regression – covariate adjustment. Linear regression is one of the key concepts in statistics [wikipedia1, wikipedia2].

Regression – covariate adjustment

However, people are often confuse the meaning of parameters of linear regression - the intercept tells us the average value of y at x=0, while the slope tells us how much change of y can we expect on average when we change x for one unit - exactly the same as in the linear function, though we use averages here due to noise. Today colleague got confused with the meaning of adjusting covariate (x variable) and the effect of parameter estimates. By shifting the x scale, we also shift the point at which intercept is estimated. I made the following graph to demonstrate this point in the case of nested regression of y on x within a group factor having two levels.

R code to produce this plots is shown on bottom. Sixty two-minute r twotorials now available. Sixty twotorials now posted. two minutes each. new video highlights: <- ifelse( you are not yet fluent in the r language , help you learn more , provide quicklinks to answer colleagues' r questions )

sixty two-minute r twotorials now available

Using R for Analyzing Loans, Portfolios and Risk: From Academic Theory to Financial Practice. User Input in R vs Python. Both R and Python have facilities where the coder can write a script which requests a user to input some information.

User Input in R vs Python

In Python 2.6, the main function for this task is raw_input (in Python 3.0, it’s input()). In R, there are a series of functions that can be used to request an input from the user, including readline(), cat(), and scan(). However, I find the readline() function to be the optimal function for this task. In the following blocks of code, I show how to perform the same task using both R and Python. num = 0 while(num < 3 ){ num = num + 1 name <- readline(“Hey dude, what’s your name = “) if( name == “quit” ) { break } if( name == “Bieber” ) { cat( “Welcome “, name ) break } } while num < 3: num = num + 1 name = raw_input(“Hey dude, what’s your name = “) if name == “Bieber”: print “Welcome “, name break if name == “quit”: break To leave a comment for the author, please follow the link and comment on his blog: Abraham Mathew » R.

"Correlation / Covariance" Spectrum (This time with "R") I treat this matter with other software´s, and of course you can do the same with "R".Once I have the spectra of my samples with a math treatment, I want to draw a correlation spectrum to see which wavelengths have better correlation with the constituent of interest.

"Correlation / Covariance" Spectrum (This time with "R")

In this example I want to see the correlation of the wavelengths treated with MSC (Multiple Scatter Correction) respect to the Moisture value of the Demo file, but only in the NIR range (1100 to 2498 nm = 700 data points). >Xmsc<-demoNIR_msc$NIRmsc>Ymoi<-demoNIR_msc$Moisture>cor_spec<-cor(Ymoi,Xmsc[,1:700])>matplot(wave_nir,t(cor_spec),lty=1,pch="*", R: Functions for extreme value distributions. Finance. By Daniel Hanson, QA Data Scientist, Revolution Analytics Introduction and Data Setup Last time, we included a couple of examples of plotting a single xts time series using the plot(.) function (ie, said function included in the xts package).


Today, we’ll look at some quick and easy methods for plotting overlays of multiple xts time series in a single graph. As this information is not explicitly covered in the examples provided with xts and base R, this discussion may save you a bit of time. To start, let’s look at five sets of cumulative returns for the following ETF’s: SPY SPDR S&P 500 ETF TrustQQQ PowerShares NASDAQ QQQ TrustGDX Market Vectors Gold Miners ETFDBO PowerShares DB Oil Fund (ETF)VWO Vanguard FTSE Emerging Markets ETF We first obtain the data using quantmod, going back to January 2007: library(quantmod)tckrs <- c("SPY", "QQQ", "GDX", "DBO", "VWO")getSymbols(tckrs, from = "2007-01-01")

Using R for Analyzing Loans, Portfolios and Risk: From Academic Th... Optimization and Mathematical Programming. Boxcox {MASS} Box-Cox Transformations for Linear Models Description Computes and optionally plots profile log-likelihoods for the parameter of the Box-Cox power transformation.

boxcox {MASS}

Usage. Sem. OpenMx - Advanced Structural Equation Modeling. Lavaan. Forecast {forecast} Description forecast is a generic function for forecasting from time series or time series models.

forecast {forecast}

The function invokes particular methods which depend on the class of the first argument.