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

L

LA LB LC LD LE LF LG LH LI LJ LK LL LM LN LO LP LQ LR LS LT LU LV LW LX LY LZ
l1fit
Estimate L1 regression (least absolute deviation regression) of y on x.
l1line
l1line computes the least absolute deviation line from scatterplot data. It gives the estimate b0 and b1 that minimizes sum_i=1,n |y_i - b0 - b1 x_i |.
lab2rgb
converts an L*a*b* colour vector into an RGB vector.
levene
levene runs Levene-test
levf
returns the limited expected value.
lgamma
lgamma computes logarithm of the gamma function.
lgenci
auxiliary quantlet for cointegration
library
library loads an xplore library
linapprox
interpolates linearly from given discretized one-dimensional functions on the same grid.
line
convenient function for plotting results. Similar to plot but draws lines connecting the points.
linreg
Computes the Generalized Least Squares estimate for the coefficients of a linear model.
linregbs
linregbs computes a backward elimination of a multiple linear regression model.
linregfs
linregfs computes a simple forward selection for a multiple linear regression model.
linregfs2
computes a forward selection for a multiple linear regression model.
linregopt
sets optional parameters for linregbs, linregfs2 and linregstep.
linregres
linregres computes some residual analysis for a linear regression.
linregstep
linregstep computes a stepwise regression for a multiple linear regression model.
list
list generates lists from given objects. If an object is temporary the name of the component is el, otherwise the name of the object at this position.
lms
Computes the least median of squares estimate for the coefficients of a linear model.
lo
Calculation of the Lo statistic for long-range dependence.
lobrob
Semiparametric test for I(0) of a time series against fractional alternatives, i.e., long-memory and antipersistence. The test is semiparametric in the sense that it does not depend on a specific parametric form of the spectrum in the neighborhood of the zero frequency. The first argument of the fu
lochomest
calls the quantlet "lochomtest" and performs the estimation of the volatility for the whole time series at regular grid points.
lochomtest
computes the most recent interval of homogeneity of the volatilities of financial time series data and the local mean relative to this interval.
locpol
locpol computes the local polynomial estimator. It is using the quartic kernel.
locpoldis
locpoldis computes the local polynomial estimator without mixed terms but allows for including a linear part in the regression model. It is using the quartic kernel.
locpolex
computes the local polynomial estimator with an arbitrary kernel.
log
log returns the natural logarithm of the elements of an array.
log10
log10 returns the logarithm base 10 of the elements of an array.
log1p
log1p computes the natural logarithm of (1+x) accurately even for tiny x.
logfile
If a command was typed in the command line of the input window and was pressed then the command is written to the logfile.
loglikBurr
auxiliary function for estBurr.
loglikgamma
auxiliary quantlet for estgamma.
loglikmixexp
auxiliary quantlet for estmixexp.
loglikPareto
auxiliary quantlet for estPareto.
loglikWeibull
auxiliary quantlet for estWeibull
logsplinefit
estimates density function using splines
logsplinesummary
summarizes the results of estimation of quantlet logsplinefit
looreg
computes the Nadaraya-Watson leave-one-out estimator without binning using the quartic kernel. Prior to estimation, looreg sorts the data. The sorted data, along with the sorted leave-one-out regression estimates, are returned as an output.
lorenz
calculates the measures of concentration of Gini and Herfindahl
lowdiscrepancy
computes the first n d-dimensional sequence elements of the low-discrepancy generator seqnum.
lowess
lowess computes the robust locally weighted regression. Fitted values are computed at each of the given values x.
lpderest
estimates the q-th derivative of a regression function using local polynomial kernel regression. The computation uses WARPing.
lpderrot
determines a rule-of-thumb bandwidth for univariate local polynomial derivatives estimation using the Quartic kernel.
lpderxest
estimates the q-th derivative of a regression function using local polynomial kernel regression with Quartic kernel.
lpdist
computes the so-called Lp-distances between the rows of a data matrix. In the case p=1 (absolute metric) or p=2 (Euclidean metric) one should favour the function DISTANCE.
lplocband
Estimates the derivative of a regression function (including the 0th derivative) by local polynomial fits on a grid. This quantlet can be used for univariate or multivariate regression estimation.
lpregest
estimates a regression function using local polynomial kernel regression. The computation uses WARPing.
lpregrot
determines a rule-of-thumb bandwidth for univariate local polynomial kernel regression using the Quartic kernel.
lpregxest
estimates a univariate regression function using local polynomial kernel regression with Quartic kernel.
lprotint
lprotint computes the integral of the (p+1)st derivative of a polynomial of order (p+3), this function is used to find rule-of-thumb bandwidth for local polynomial regression and derivative estimation
lregestp
estimates a multivariate regression function using local linear kernel regression. The computation uses WARPing.
lregxestp
estimates a multivariate regression function using local linear kernel regression with Quartic kernel.
lrseev
LRS estimator for an Extreme Value model
lsdcheckfit
auxiliary quantlet for logspline density estimation - checks if list "fit" is compatible with output of logsplinefit quantlet
lts
Computes the least trimmed squares estimate for the coefficients of a linear model.
ludecomp
computes the LU decomposition of a square matrix, ; where L represents the lower ; and U the upper triangular matrix.
lvtest
This quantlet tests for significance of a subset or of the whole set of continuous regresssors in a nonparametric regression.

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, 05.02.2006 Impressum