[Uwgarp-commits] r23 - in pkg/GARPFRM: sandbox vignettes

[noreply at r-forge.r-project.org]

Author: rossbennett34
Date: 2013-11-27 21:21:53 +0100 (Wed, 27 Nov 2013)
New Revision: 23

Modified:
   pkg/GARPFRM/sandbox/regression_example.R
   pkg/GARPFRM/vignettes/RB.Rnw
   pkg/GARPFRM/vignettes/RB.pdf
Log:
Revisions to vignette

Modified: pkg/GARPFRM/sandbox/regression_example.R
===================================================================
--- pkg/GARPFRM/sandbox/regression_example.R	2013-11-27 17:53:06 UTC (rev 22)
+++ pkg/GARPFRM/sandbox/regression_example.R	2013-11-27 20:21:53 UTC (rev 23)
@@ -59,6 +59,10 @@
 # Sample quantiles of SPY returns
 quantile(SPY.ret, probs=c(0, 0.25, 0.5, 0.75, 1))
 
+# Scatter plot of each pair of assets in the returns dataset
+pairs(coredata(returns), pch=18, col=rgb(0,0,100,50,maxColorValue=255))
+lattice::splom(coredata(returns), pch=18, col=rgb(0,0,100,50,maxColorValue=255))
+
 # Sample correlation of returns
 cor(returns)
 

Modified: pkg/GARPFRM/vignettes/RB.Rnw
===================================================================
--- pkg/GARPFRM/vignettes/RB.Rnw	2013-11-27 17:53:06 UTC (rev 22)
+++ pkg/GARPFRM/vignettes/RB.Rnw	2013-11-27 20:21:53 UTC (rev 23)
@@ -35,7 +35,7 @@
 
 \begin{document}
 
-\title{Exploratory Data Analysis, basic probability and statistics}
+\title{Quantitative Analysis}
 \author{Ross Bennett}
 
 \maketitle
@@ -50,7 +50,7 @@
 
 Load the GARPFRM package and the \verb"returns" dataset. The \verb"returns" dataset includes weekly returns for SPY, AAPL, XOM, GOOG, MSFT, and GE from 2005-01-14 to 2013-11-22.
 <<>>=
-library(GARPFRM)
+suppressMessages(library(GARPFRM))
 data(returns)
 @
 
@@ -119,6 +119,17 @@
 # Sample quantiles of SPY returns
 quantile(SPY.ret, probs=c(0, 0.25, 0.5, 0.75, 1))
 
+@
+
+Scatter plot of each pair of assets in the returns dataset.
+<<>>=
+pairs(coredata(returns), pch=18, 
+      col=rgb(0,0,100,50,maxColorValue=255))
+
+@
+
+Correlation and covariance matrices of assets in the returns dataset.
+<<>>=
 # Sample correlation of returns
 cor(returns)
 
@@ -126,6 +137,7 @@
 cov(returns)
 @
 
+
 \subsection{Distributions}
 R has functions to compute the density, distribution function, quantile, and random number generation for several distributions. The continuous distributions covered in chapter 1 are listed here.
 \begin{itemize}
@@ -195,17 +207,20 @@
 <<>>=
 AAPL.ret <- returns[, "AAPL"]
 SPY.ret <- returns[, "SPY"]
+
+# Fitting linear models works with xts objects, but works better with data.frame objects. This is especially true with the predict method for linear models.
+ret.data <- as.data.frame(cbind(AAPL.ret, SPY.ret))
 @
 
 Scatterplot of AAPL and SPY returns.
 <<>>=
-plot(x=coredata(SPY.ret), y=coredata(AAPL.ret), 
+plot(x=ret.data[, "SPY"], y=ret.data[, "AAPL"], 
      xlab="SPY returns", ylab="AAPL returns")
 @
 
 Fit the linear regression model. \verb"AAPL.ret" is the response variable and \verb"SPY.ret" is the explanatory variable.
 <<>>=
-model.fit <- lm(AAPL.ret ~ SPY.ret)
+model.fit <- lm(AAPL ~ SPY, data=ret.data)
 @
 
 The \verb"print" and \verb"summary" methods for \verb"lm" objects are very useful and provide several of the statistics covered in the book.
@@ -244,8 +259,9 @@
 
 Use the \verb"predict" method to calculate the confidence and prediction intervals of the fitted model.
 <<>>=
-model.ci <- predict(object=model.fit, interval="confidence")
-model.pi <- predict(object=model.fit, interval="prediction")
+new <- data.frame(SPY=seq(from=-0.2, to=0.2, length.out=nrow(ret.data)))
+model.ci <- predict(object=model.fit, newdata=new, interval="confidence")
+model.pi <- predict(object=model.fit, newdata=new, interval="prediction")
 @
 
 Plot the residuals of the model.
@@ -256,12 +272,16 @@
 Plot the fitted model with the confidence and prediction intervals.
 <<tidy=FALSE>>=
 plot(x=coredata(SPY.ret), y=coredata(AAPL.ret), 
-     xlab="SPY returns", ylab="AAPL returns")
-abline(model.fit, col="red")
-lines(x=coredata(SPY.ret), y=model.ci[, "upr"], col="blue", lty=1)
-lines(x=coredata(SPY.ret), y=model.ci[, "lwr"], col="blue", lty=1)
-lines(x=coredata(SPY.ret), y=model.pi[, "upr"], col="red", lty=2)
-lines(x=coredata(SPY.ret), y=model.pi[, "lwr"], col="red", lty=2)
+     xlab="SPY returns", ylab="AAPL returns", xlim=c(-0.2, 0.2))
+abline(model.fit, col="black")
+lines(x=model.ci[, "fit"], y=model.ci[, "upr"], col="blue", lty=1)
+lines(x=model.ci[, "fit"], y=model.ci[, "lwr"], col="blue", lty=1)
+lines(x=model.pi[, "fit"], y=model.pi[, "upr"], col="red", lty=2)
+lines(x=model.pi[, "fit"], y=model.pi[, "lwr"], col="red", lty=2)
+legend("topleft", legend=c("Fitted Values",
+                           "95% Confidence Interval", 
+                           "95% Prediction Interval"), 
+       col=c("black", "blue", "red"), lty=c(1, 1, 2), cex=0.8, bty="n")
 @
 
 \subsection{Regression with multiple regressors}
@@ -327,10 +347,11 @@
 names(rsq) <- colnames(ret.e)
 
 par(mfrow=c(2,2))
-barplot(beta1, main="Beta for Market-RF", col=c(2:6))
-barplot(beta2, main="Beta for SMB", col=c(2:6))
-barplot(beta3, main="Beta for HML", col=c(2:6))
-barplot(rsq, main="R Squared Values", col=c(2:6))
+barplot(beta1, main="Beta for Market-RF", col=c(2:6), cex.names=0.8)
+barplot(beta2, main="Beta for SMB", col=c(2:6), cex.names=0.8)
+barplot(beta3, main="Beta for HML", col=c(2:6), cex.names=0.8)
+barplot(rsq, main="R Squared Values", col=c(2:6), cex.names=0.8)
+par(mfrow=c(1,1))
 @
 
 

Modified: pkg/GARPFRM/vignettes/RB.pdf
===================================================================
(Binary files differ)