Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

T


TA TB TC TD TE TF TG TH TI TJ TK TL TM TN TO TP TQ TR TS TT TU TV TW TX TY TZ
table2
computes a two way table from two-dimensional data.
tableN
tableN returns a N way table for N-dimensional data.
TailCoeffCopula
calculates the lower and upper tail-dependence coefficients for various copulae.
TailCoeffEstimation
estimates the upper or lower tail-dependence coefficient via a bivariate empirical copula.
TailCoeffEstimElliptical
estimates the upper (=lower) tail-dependence coefficient for an elliptically contoured distribution.
TailCoeffLookup
auxiliary table-search quantlet for TailCoeff* quantlets.
taills
Estimates the tail index of fat-tailed distributions
tan
Returns the tangent in radian of the elements of an array.
tanh
Returns the hyperbolic tangent of the elements of an array.
tautstring
auxiliary quantlet for denreg and pmreg, core of taut string method
tdiff
calculates the d'th difference of the time series x. Furthermore, it allows to calculate the s'th seasonal difference. In terms of the backshift (or lag) operator B, the series y_t = (1-B^s)^d x_t is generated. The default values are s=1 and d=1, which generate the first differences of the series x
tgarsim
plots the difference between call option prices calculated by the Black & Scholes model and between risk neutral GARCH or Treshold GARCH models.
timeplot
Generates a display that shows the time series x in multiple windows with user-specified maximum length per window. It is possible to label the abscissa in a yearly format. However, you can not specify the periodicity of the labels (in that case, use timeplotlabel).
timeplotlabel
Generates a display that shows the time series x. The abscissa is labelled automatically, when the labels have the format year:1. One may specify the periodicity of the labels.
timesmain
loads the libraries needed for the quantlets in times
timestest
executes some tests for the quantlets defined in times library
TimeVarAddModel2
estimates a dynamic factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized under the empi
tobit
2-step estimation of a Tobit model
tourasimov
Computes a rotation matrix based on the paper by Asimov (1985).
tourlittle
Computes a little tour rotation matrix.
tourrandom
Computes a random rotation matrix.
trans
trans transposes matrices. This function is equal to the operator '
transform
Transforms the given dataset.
tree
generates from a binary tree an output for plotting.
tri
tri computes the triweight kernel, multivariate
trian
trian computes the triangular kernel, multivariate
trimper
trims a given percentage of a (binned) data matrix
ttest
runs a t-test
tw1d
The teachware quantlet tw1d shows a histogram of the user-defined data and offers an interactive visual analysis of this data by means of box plots (for mean and median) and QQ-plots. Transformations may be applied to the data in order to study the change in distribution and box plots.
twaremain
loads necessary quantlets in order to execute the teachware tware.lib.
twaretest
Executes some tests for the quantlets defined in the teachware tware.lib.
twavemain
Starts the twave lesson when library("twave") is called and generates the global constant twavec which allows to jump immediately to a single task.
twboxcox
allows to find interactively the best parameter for your data for a Box-Cox transformation.
twboxcoxintroduction
generates the introductory text for twboxcox
twboxcoxloop
main loop for twboxcox
twclt
teachware quantlet twclt shows a discrete four point distribution and simulates repeated sampling from this apparently non normal distribution. The variation of the observed mean values around the true mean value (standardized by scale) is shown in a plot. The user may interactively change the numb
twles1
Shows the functions approximation by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
twles2
Compares the data compression of wavelets with fourier basis. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
twles3
Compares the approximation of sines with different frequencies by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles4
Shows the approximation of a sine function which changes its frequency. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles5
Shows how a hard threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, hard threshold by hand and automatically.
twles6
Shows how a soft threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, soft threshold by hand and automatically.
twles7
Shows how a hard threshold behaves on an image and an image plus noise. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles8
Shows the father and mother wavelet for a given basis. You can choose between different wavelet base.
twles9
Shows in the left window the true function plus noise and in the right a translation invariant estimator with k=4*log_2(n) shifts.
twlesson
Starts the twave lessons either interactively or a specific lesson.
twlinreg
teachware quantlet twlinreg gives visual insight into how least squares simple linear regression works, and the relationship between the regression of Y on X, X on Y, and total regression.
twnormalize
teachware quantlet twnormalize shows the distribution of binomials B(n1, p), B(n2, p) and B(n3, p) with increasing n1, n2, n3. One may shift the distribution by the mean value and divide by the standard deviation in order to study the normalizing effect. In addition a normal density may be graphica
twpearson
the teachware quantlet twpearson gives a visual demonstration of the form of the Pearson correlation coefficient. In particular, it shows why the product moment gives a measure of "dependence", and why it is essential to "normalize", i.e. to subtract means, and divide by standard deviations, to pre
twpvalue
teachware quantlet twpvalue computes the p-value of a B(n, p) distribution
twrandomsample
the teachware quantlet twrandomsample asks for a distribution of the numbers {1, 2, 3, 4}, displays a bar chart of the entered values and calculates a test for H0: p{2,3} = 0.5, the hypothesis of uniform distribution.
twskew
teachware quantlet shows effects on skewness and kurtosis by contamination of a normal distribution
twtest
teachware quantlet shows error type I and II in testing simple hypotheses

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 05.02.2006Impressum