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library(fsdaR)
data(poison)
## We do not need these, since the formula interface can be used
y <- poison[, ncol(poison)]
X <- poison[, -ncol(poison)]
## out <- fsrfan(Y~.-1, data=poison) # formula, intercept=FALSE
## out <- fsrfan(X, y, intercept=FALSE)
## out <- fsrfan(X, y, intercept=FALSE, family="YJall", plot=TRUE, ylim=c(-14, 3))
## out <- fsrfan(X, y, intercept=FALSE, plot=TRUE, ylim=c(-14, 3))
## All default parameters, use the native MCR plot
out <- fsrfan(Y~.-1, data=poison, plot=TRUE, ylim=c(-14, 3))
## The R plots
plot(out)
plot(out, conflev=c(0.9, 0.95, 0.99))
##================================================================
## fsrfan() with all default options.
##
## Store values of the score test statistic for the five most common
## values of $\lambda$. Produce also a fan plot and display it on the screen.
## Common part to all examples: load 'wool' data set.
data(wool)
head(wool)
dim(wool)
## Function FSRfan stores the score test statistic.
## In this case we use the five most common values of lambda are considered
out <- fsrfan(cycles~., data=wool)
## fanplot(out)
## The fan plot shows the log transformation is diffused throughout the data
## and does not depend on the presence of particular observations.
##======================
## fsrfan() with optional arguments.
## Produce a personalized fan plot with required font sizes for labels and axes.
## !!! actually these parameters do not work also in MATLAB !!!
## [out]=FSRfan(y,X,'plots',1,'FontSize',16,'SizeAxesNum',16);
data(wool)
out <- fsrfan(cycles~., data=wool, plot=TRUE)
plot(put)
##======================
##
## Example specifying 'lambda'.
## Produce a fan plot for each value of 'lambda' in the vector 'la'.
## Extract in matrix 'Un' the units which entered the search in each step
data(wool)
out <- fsrfan(cycles~., data=wool, la=c(-1, -0.5, 0, 0.5), plot=TRUE)
plot(out)
out$Un[,2,]
##======================
## Example specifying the confidence level and the initial starting point for monitoring.
## Construct the fan plot specifying the confidence level and the initial starting point
## for monitoring.
data(wool)
out <- fsrfan(cycles~., data=wool, init=ncol(wool)+1, nsamp=0, conflev=0.95, plots=TRUE)
plot(out, conflev=0.95)
##=====================
## Example with starting point based on LTS.
## Extract all subsamples, construct a fan plot specifying the confidence level
## and the initial starting point for monitoring based on p+2 observations,
## strong line width for lines associated with the confidence bands.
data(wool)
out <- fsrfan(cycles~., data=wool, init=ncol(wool)+1, nsamp=0, lms=0, lwd.env=3, plot=TRUE)
plot(out, lwd.env=3)
##=====================
## Fan plot using the loyalty cards data.
## In this example, 'la' is the vector contanining the most common values
## of the transformation parameter.
## Store the score test statistics for the specified values of lambda
## and automatically produce the fan plot
data(loyalty)
head(loyalty)
dim(loyalty)
## la is a vector contanining the most common values of the transformation parameter
out <- fsrfan(amount_spent~., data=loyalty, la=c(0, 1/3, 0.4, 0.5), init=ncol(loyalty)+1, plot=TRUE, lwd=3)
plot(out, lwd=3)
## The fan plot shows that even if the third root is the best value of the transformation
## parameter at the end of the search, in earlier steps it lies very close to the upper
## rejection region. The best value of the transformation parameter seems to be the one
## associated with la=0.4, which is always the confidence bands but at the end of search,
## due to the presence of particular observations it goes below the lower rejection line.
##=====================
## Compare BoxCox with Yeo and Johnson transformation.
## Store values of the score test statistic for the five most common
## values of lambda. Produce also a fan plot and display it on the screen.
## Common part to all examples: load wool dataset.
data(wool)
## Store the score test statistic using Box Cox transformation.
outBC <- fsrfan(cycles~., data=wool, nsamp=0)
## Store the score test statistic using Yeo and Johnson transformation.
outYJ <- fsrfan(cycles~., data=wool, family="YJ", nsamp=0)
## Not yet fully implemented
## fanplot(outBC, main="Box Cox")
## fanplot(outYJ,main="Yeo and Johnson")
plot(outBC, main="Box Cox")
plot(outYJ, main="Yeo and Johnson")
cat("\nMaximum difference in absolute value: ",
max(max(abs(outYJ$Score - outBC$Score), na.rm=TRUE)), "\n")
##=====================
## This and the following two examples from the FSDA Help of GSRfan()
## cannot be translated from MATLAB because we do not have yet
## the function normYJ() in fsdaR.
##
## Example of monitoring of score test for positive and negative obseravations.
set.seed(10)
n <- 200
X <- matrix(rnorm(n * 3), nrow=n, ncol=3)
beta <- c(1, 1, 1)
sig <- 0.5
y <- X %*% beta + sig * rnorm(n)
outlmori <- lm(y~X)
cat("Fit in the true scale\nR2 of the model in the true scale:",
summary(outlmori)$r.squared, "\n")
regspmplot(y, X)
##[~,~,BigAx]=yXplot(y,X,'tag','ori');
##title(BigAx,'Data in the original scale')
## Find the data to transform
la <- -0.25
##ytra <- normYJ(y, [], la, 'inverse', true);
##if any(isnan(ytra))
## disp('response with missing values')
##disp('Fit in the transformed scale')
##disp('R2 of the model in the wrong (inverse) scale')
##outlmtra=fitlm(X,ytra);
##disp(outlmtra.Rsquared.Ordinary)
##[~,~,BigAx]=yXplot(ytra,X,'tag','tra','namey','Data to transform (zoom of y [0 500])','ylimy',[0 500]);
##title(BigAx,'Data in the inverse scale')
## out <- fsrfan(ytra~X, la=c(-0.5, -0.25, 0),family="YJpn", plot=TRUE, init=round(n/2))
##title('Extended fan plot')
##=====================
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