Kondofersky, Ivan

English

Open Access LMU

1] <− rep( &quot; numeric &quot;

10: coef() function for class ﬂm

11: summary() function for class ﬂm

12: print() function for class summary.ﬂm

13: print() function for class ﬂm

15: Plotting of bootstrap based conﬁdence intervals plot .

2: predict() function for class emu_fd

4: coef() function for class emu_fd coef .emu_fd

5: summary() function for class emu_fd summary.emu_fd <− function( object ,

7: predict() function for class ﬂm

9: residuals() function for class ﬂm

adding curves and constants together ret

at what points to plot the curves time <− seq(x$reg$yfdPar$fd$basis$rangeval [1]

begin{ l s t l i s t i n g }[ caption = plot () function for class emu\_fd

cat( &quot; for further data analysis .\n\n&quot; ) } A.2. Computation and display of bootstrap based conﬁdence intervals Listing 14: Computation of bootstrap based conﬁdence intervals bootstd <− function( object ,

cat( &quot;\n&quot; ) cat( &quot; Estimated functional coefficients (summarized) :\n&quot;

cat( &quot;Number of observations : &quot; , x$nobs , &quot;\n&quot; ) cat( &quot;Mean R squared : &quot; , x$Rsq , &quot;\n&quot; )

cla [ i ] == &quot; numeric &quot;

Constant Coefficients &quot; , xlab = &quot; &quot; , ylab = &quot; &quot; , xaxt = &quot;n&quot; , yaxt = &quot;n&quot;

defining how many plots to plot and how to arrange them nr_plots

else fds <− c( fds , i )

else if (nr_plots <= 14) op <− par(mfrow = c( ceiling (nr_plots/2) ,2

emu: Interface to the Emu Speech Database System,

end{ l s t l i s i t n g }

Estimated numeric coefficients :\n&quot;

fd (time , x$reg$betaestlist [ [ i ] ] $fd ) , type=&quot; l &quot;

fds <− c( fds , i ) } # defining how many plots to plot and how to arrange them

fitted .emu_fd <− function( object , type ,

ﬁtted() function for class ﬂm

flm <− function( object ,

flm <− function( object , type = &quot; curves &quot;

for ( i in 1: length(

for ( i in 1: length(co)

for ( i in 1: length(x$reg$xfdlist ) ){ iiA R FUNCTIONS A.1 Generic functions for class ﬂm if (nrow(coef(x$reg$betaestlist

for ( i in 1:nrow( ret$coefs

for ( i in fds ){ plot(time , eval . fd (time ,

Functional data analysis.

Functional linear model of phonetic data . Please use generic functions such as\n\n&quot; ) cat( &quot; \t summary(

Functional regression of phonetic data .\n\n&quot;

has a wrong type of class in newdata !

if ( type == &quot; curves &quot; ){ return( object$reg$yhatfdobj$fd )

Listing 3: residuals() function for class emu_fd residuals .emu_fd <− function( object , emuobject ,

marking the constant and functional variables cons <−

nam <− rep(NA, length( tracktimes (emuobject)

ncol = 100 , nrow = 2) for ( j in 1:100) perz

nr_plots <− length( fds ) + as .numeric( ! is .

plot functional data for ( i in fds ){

predict .emu_fd <− function( object ,

print(x$functional_coef) cat( &quot;\n&quot;

R FUNCTIONS A.1 Generic functions for class ﬂm Listing 8: plot() function for class ﬂm

R functions A.1. Generic functions for class ﬂm Listing 1: ﬁtted() function for class emu_fd

Residual sum of squares : &quot; , x$RSS, &quot;\n&quot; ) viiA R FUNCTIONS A.2 Computation and display of bootstrap based conﬁdence intervals cat( &quot;Mean residual : &quot; , x$MR, &quot;\n&quot; )

ret ) <− &quot; flmboot &quot; return( ret )

ret ) <− &quot;summary. flm &quot; return( ret )

return also other types , not just curves return( ret )

return fitted object if newdata = NULL iA R FUNCTIONS A.1 Generic functions for class ﬂm if ( is . null (newdata)

Std . Deviation &quot; ) nobs <−

stop( &quot;New data has not the same length as test data ! &quot; ) for ( i in 1: length( object$reg$xfdlist ) ){

sum( residuals ( object , emuobject = emuobject , type=&quot; fd_ samples &quot;

Time&quot; , ylab = &quot; &quot;

Too many variables . Please plot manually ! &quot; ) on. exit (par(op) ) # at what points to plot the curves

type == &quot; emupoints &quot;

type curves # the fitted curves are returned

type emupoints # for every discrete point recorded a fitted point is being computed

Analysis of Functional Phonetic Data

http://epub.ub.uni-muenchen.de/12149/1/MA_Kondofersky_ohneMnr.pdf

Analysis of Functional Phonetic Data

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