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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 ) here's a video that no one who has ever seriously used r would need - but might find useful to send along with syntax that a newbie needs to run for some reason: 041 did someone send you a script file or computer code for the r programming language?

sixty two-minute r twotorials now available

Here's how to run it! Using R for Analyzing Loans, Portfolios and Risk: From Academic Theory to Financial Practice. Dr.

Using R for Analyzing Loans, Portfolios and Risk: From Academic Theory to Financial Practice

Sanjiv Das, Professor of Finance and Chair of the Finance Department at Santa Clara University's Leavey School of Business He will present: An R-based model for optimizing loan modifications on distressed home loans, and the economics of these modifications.A goal-based portfolio optimization model for investors who use derivatives.Using network modeling tools in R to detect systemically risky financial institutions.Using R for web delivery of financial models and random generation of pedagogical problems. 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(). "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="*", + xlab="data points",ylab="log(1/R)") #We merge the correlation spectrum with #the sample spectra treated with MSC. We can se the correlation plot between -1.00 and 1.00. 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") Then, extract just the closing prices from each set:

Using R for Analyzing Loans, Portfolios and Risk: From Academic Th... Optimization and Mathematical Programming. This CRAN task view contains a list of packages which offer facilities for solving optimization problems.

Optimization and Mathematical Programming

Although every regression model in statistics solves an optimization problem they are not part of this view. If you are looking for regression methods, the following views will contain useful starting points: Multivariate, SocialSciences, Robust among others. The focus of this task view is on Optimization Infrastructure Packages , General Purpose Continuous Solvers , Mathematical Programming Solvers and Specific Applications in Optimization . Packages are categorized in these three sections. Many packages provide functionality for more than one of the subjects listed at the end of this task view. If you think that some package is missing from the list, please let me know. Optimization Infrastructure Packages General Purpose Continuous Solvers Package stats offers several general purpose optimization routines. Mathematical Programming Solvers Interfaces to Open Source Optimizers. 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. For example, the function forecast.Arima makes forecasts based on the results produced by arima. The function forecast.ts makes forecasts using ets models (if the data are non-seasonal or the seasonal period is 12 or less) or stlf (if the seasonal period is 13 or more). Usage.