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

l1line - linregfs2 - log10 - lpderxest - ludecomp - lvtest
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 |.
leafnum Gives the number of leaves (terminal nodes) in a regression tree.
lgamma lgamma computes logarithm of the gamma function.

lgenci auxiliary quantlet for cointegration
library library loads an xplore library

lik
line Convenient function for plotting results. Similar to plot but uses lines instead.
linreg 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 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<position>, otherwise the name of the object at this position.
lo Calculation of the Lo statistic for long-range dependence. 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. The quantlet returns the estimated statistic with its corresponding order. If the estimated statistic is outside the interval (0.809, 1.862), which is the 95 percent confidence interval for no long-memory, a star symbol * is displayed in the third column. The other critical values are in Lo's paper.

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 function is the series. The second optional argument is the vector of bandwidth, i.e., the parameter specifying the number of harmonic frequencies around zero to be considered. By default, the macro uses the automatic bandwidth given in Lobato and Robinson. If the user provides his own vector of bandwidths, then the function returns the value of the test for each component of the bandwidth vector. If the value of the test is in the lower tail of the standard normal distribution, the null hypothesis of I(0) is rejected against the alternative that the series displays long-memory. If the value of the test is in the upper tail of the standard normal distribution, the null hypothesis I(0) is rejected against the alternative that the series is antipersistent.
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.

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.

logarithmic performs a logarithmic transformation of the selected variables in ISTA. The transformed variables can replace the original ones or can be appended on the end of data.x. The type is set automatically to continuous.
logfile If a command was typed in the command line of the input window and <Enter> was pressed then the command is written to the logfile.
logit performs a logit (log((x/(1-x))) transformation of the selected variables in ISTA. The transformed variables can replace the original ones or can be appended on the end of data.x. The type is set automatically to continuous.
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
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.
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 polynomial kernel regression. The computation uses WARPing.
lregxestp estimates a multivariate regression function using local polynomial kernel regression with Quartic kernel.
lrseev LRS estimator for EV model
lts Computes the least trimmed squares estimate for the coefficients of a linear model.
ludecomp ludecomp computes the lu decomposition of a 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, 21.9.2000