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

names - ngau - nnmain - nnrtest - nyear
names gives the names of all components of a list object.

ndayofmonth returns the day of the month as a number (1-31)
ndayofweek returns the day of the week as a number (1-7, sunday-saturday)
ndw ndw is an auxiliary macro of adedis. It defines the nadaraya watson estimate of the link as a function of the (estimated) index of continuous explanatory variables. In adedis the simpsonint routine is used to integrate over this function.
nelmin nelmin searchs for a minimum of a function. In each iteration step the function is evaluated at a simplex consisting of p+1 points. The simplex contracts until the variance of the evaluated function values is less than eps (or the maximal number of iterations is reached).

neuronal This macro computes different networks of the form single layer feedforward perceptron. The macro can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. It is possible to split the data in a training and a test set. The weight for the cases for the training of the net can be chosen, the numbers of hidden units with ``from, stepwidth, to'' and additional information concerning the weights of the units. Different optional parameters can be chosen to build the architektur of the network. The choice holds for every single net. The default values are chosen in order to solve a linear regression problem. The optional parameters constits of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections (direkt links). The fifth values is the maximum value for the starting weights, the sixth is the weight decay, the seventh the maximum number of iterations and the the last value generates the output concerning the architekur of the net if it is equal to one. The output consits of the Error and MSE of the different nets (MSE for test and trainings data separately if chosen) and the R^2.
new1
newadeslp slope estimation of average derivatives

newest
neweywest Calculation of the Newey and West Heteroskedastic and Autocorrelation Consistent estimator of the variance. The first argument of the quantlet is the series, the second optional argument is the vector of truncation lags of the autocorrelation consistent variance estimator. If the second optional argument is missing, the vector of truncation lags is set to m = 5, 10, 25, 50.

ngau computes the rescaled Gaussian kernel ngau(u) = 5.*gau(5.*u), multivariate.
nhour returns the hour as a number (0-23)
nminute returns the minute as a number (0-59)
nmomnorm Auxiliary routine for ricfil: calculates the n-th moment of a standard normal variate truncated at t, i.e. E [X^n (X<t)] for X~N(0,1)
nmonth returns the month as a number
nn2visu
nnfunc nnfunc computes for a given feed forward network the result for a datavector x.
nninfo shows some information about the actual network
nninit nninit checks if a given network is feedforward network and suggest reorderings.
nnlayer builds a feedforward network
nnmain loads the necessary libraries
nnrdovm nnrdovm optimizes a network for a given dataset.
nnrinfo shows information about the net in the output window.
nnrload loads a network from different files
nnrmatrix
nnrnet trains a one hidden layer feed forward network. The optional parameter param consists of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections. The fifth value is the maximum value for the starting weights, the sixth the weight decay, the seventh the number of maximal iterations and the last value generates some output if equal to one.
nnrpredict estimates for a given net and dataset the response.
nnrsave saves a network in different names
nnrsetnet nnrsetnet sets the internal variables to construct a specific network.
nnrsettrain nnrsettrain sets the internal variables to fill a specific network with data and weights.
nnrtest nnrtest computes for a given network and dataset the y-values.
nntest
nnvisu nnvisu computes the visualization for a given feed forward network by non-metric multidimensional scaling.
normal normal generates arrays up to eight dimensions of pseudo random variables with a standard normal distribution. the algorithm by box-muller is used.
normalt multivariate normality tests
nsecond returns the second as a number (0-59)
numcomp numcomp.xpl tests some mathematical functions whether they give the right result for the matrix x=#(-Inf, 0, 1, Inf, -NaN, NaN)
numint2 Auxiliary routine for rICfil: calculates for dimension p=2 diag(E[ YY' u min(b/|aIhY|,u) ]) and diag(E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_2-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/2} Lambda /u

the integrals are evaluated stepwise, first conditioning on Y and calculated "analytically" using Ewinn, Ew2inn and then the outer integration is done by a Romberg-Procedure along the directions Y, parametrized by a sin-cos-representation.

numint2m Auxiliary routine for rICfil: calculates for dimension p=2 (E[ YY' u min(b/|aIhY|,u) ]) and (E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_2-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/2} Lambda /u

the integrals are evaluated stepwise, first conditioning on Y and calculated "analytically" using Ewinn, Ew2inn and then the outer integration is done by a Romberg-Procedure along the directions Y, parametrized by a sin-cos-representation.

nyear returns the year as a four digit number (YYYY)

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, 21.9.2000