Mercurial > octave-antonio
changeset 3499:3e3e14ad5149
[project @ 2000-01-31 05:18:07 by jwe]
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--- a/scripts/linear-algebra/commutation_matrix.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/linear-algebra/commutation_matrix.m Mon Jan 31 05:18:13 2000 +0000 @@ -41,7 +41,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{K}(@var{m},@var{n}) * vec (@var{A}) = vec (@var{A}') +## @math{K(m,n) * vec(A) = vec(A')} ## @end ifinfo ## for all ## @iftex @@ -50,7 +50,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{m} by @var{n} +## @math{m} by @math{n} ## @end ifinfo ## matrices ## @iftex @@ -59,7 +59,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{A}. +## @math{A}. ## @end ifinfo ## ## If only one argument @var{m} is given, @@ -69,7 +69,7 @@ ## @end tex ## @end iftex ## @ifinfo -## K(m,m) +## @math{K(m,m)} ## @end ifinfo ## is returned. ##
--- a/scripts/linear-algebra/duplication_matrix.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/linear-algebra/duplication_matrix.m Mon Jan 31 05:18:13 2000 +0000 @@ -23,7 +23,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{D}_@var{n} +## @math{Dn} ## @end ifinfo ## which is the unique ## @iftex @@ -32,7 +32,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{n}^2 by @var{n}*(@var{n}+1)/2 +## @math{n^2} by @math{n*(n+1)/2} ## @end ifinfo ## matrix such that ## @iftex @@ -41,7 +41,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{D}_@var{n} \cdot vech (@var{A}) = vec (@var{A}) +## @math{Dn vech (A) = vec (A)} ## @end ifinfo ## for all symmetric ## @iftex @@ -50,7 +50,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{n} by @var{n} +## @math{n} by @math{n} ## @end ifinfo ## matrices ## @iftex @@ -59,7 +59,7 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{A}. +## @math{A}. ## @end ifinfo ## ## See Magnus and Neudecker (1988), Matrix differential calculus with
--- a/scripts/linear-algebra/krylov.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/linear-algebra/krylov.m Mon Jan 31 05:18:13 2000 +0000 @@ -17,9 +17,9 @@ ## Software Foundation, 59 Temple Place, Suite 330, Boston, MA 02111 USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{U}, @var{H}, @var{nu}] =} krylov (@var{A}, @var{V}, @var{k}, @var{eps1}, @var{pflg}); +## @deftypefn {Function File} {[@var{u}, @var{h}, @var{nu}] =} krylov (@var{a}, @var{v}, @var{k}, @var{eps1}, @var{pflg}); ## construct orthogonal basis U of block Krylov subspace; -## [V AV A^2*V ... A^(k+1)*V]; +## [v a*v a^2*v ... a^(k+1)*v]; ## method used: householder reflections to guard against loss of ## orthogonality ## eps1: threshhold for 0 (default: 1e-12) @@ -29,12 +29,12 @@ ## 1 : pivoting performed ## ## outputs: -## Uret: orthogonal basis of block krylov subspace -## H: Hessenberg matrix; if V is a vector then A U = U H -## otherwise H is meaningless -## nu: dimension of span of krylov subspace (based on eps1) -## if B is a vector and k > m-1, krylov returns H = the Hessenberg -## decompostion of A. +## u: orthogonal basis of block krylov subspace +## h: Hessenberg matrix; if v is a vector then a u = u h +## otherwise h is meaningless +## nu: dimension of span of krylov subspace (based on eps1) +## if b is a vector and k > m-1, krylov returns h = the Hessenberg +## decompostion of a. ## ## Reference: Hodel and Misra, "Partial Pivoting in the Computation of ## Krylov Subspaces", to be submitted to Linear Algebra and its
--- a/scripts/polynomial/poly.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/polynomial/poly.m Mon Jan 31 05:18:13 2000 +0000 @@ -19,7 +19,7 @@ ## -*- texinfo -*- ## @deftypefn {Function File} {} poly (@var{a}) -## If @var{a} is a square @var{N}-by-@var{N} matrix, @code{poly (@var{a})} +## If @var{a} is a square @math{N}-by-@math{N} matrix, @code{poly (@var{a})} ## is the row vector of the coefficients of @code{det (z * eye (N) - a)}, ## the characteristic polynomial of @var{a}. If @var{x} is a vector, ## @code{poly (@var{x})} is a vector of coefficients of the polynomial
--- a/scripts/polynomial/residue.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/polynomial/residue.m Mon Jan 31 05:18:13 2000 +0000 @@ -58,9 +58,9 @@ ## @end ifinfo ## ## @noindent -## where @var{M} is the number of poles (the length of the @var{r}, -## @var{p}, and @var{e} vectors) and @var{N} is the length of the @var{k} -## vector. +## where @math{M} is the number of poles (the length of the @var{r}, +## @var{p}, and @var{e} vectors) and @math{N} is the length of the +## @var{k} vector. ## ## The argument @var{tol} is optional, and if not specified, a default ## value of 0.001 is assumed. The tolerance value is used to determine
--- a/scripts/polynomial/roots.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/polynomial/roots.m Mon Jan 31 05:18:13 2000 +0000 @@ -20,7 +20,7 @@ ## -*- texinfo -*- ## @deftypefn {Function File} {} roots (@var{v}) ## -## For a vector @var{v} with @var{N} components, return +## For a vector @var{v} with @math{N} components, return ## the roots of the polynomial ## @iftex ## @tex
--- a/scripts/signal/arch_fit.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/arch_fit.m Mon Jan 31 05:18:13 2000 +0000 @@ -25,22 +25,22 @@ ## @end example ## ## @noindent -## in which @var{e}(@var{t}) is @var{N}(0, @var{h}(@var{t})), given a -## time-series vector @var{y} up to time @var{t}-1 and a matrix of -## (ordinary) regressors @var{x} up to @var{t}. The order of the -## regression of the residual variance is specified by @var{p}. +## in which @math{e(t)} is @math{N(0, h(t))}, given a time-series vector +## @var{y} up to time @math{t-1} and a matrix of (ordinary) regressors +## @var{x} up to @math{t}. The order of the regression of the residual +## variance is specified by @var{p}. ## ## If invoked as @code{arch_fit (@var{y}, @var{k}, @var{p})} with a ## positive integer @var{k}, fit an ARCH(@var{k}, @var{p}) process, -## i.e., do the above with the @var{t}-th row of @var{x} given by +## i.e., do the above with the @math{t}-th row of @var{x} given by ## ## @example ## [1, y(t-1), ..., y(t-k)] ## @end example ## ## Optionally, one can specify the number of iterations @var{iter}, the -## updating factor @var{gamma}, and initial values @var{a0} and @var{b0} -## for the scoring algorithm. +## updating factor @var{gamma}, and initial values @math{a0} and +## @math{b0} for the scoring algorithm. ## @end deftypefn ## Author: KH <Kurt.Hornik@ci.tuwien.ac.at>
--- a/scripts/signal/arch_rnd.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/arch_rnd.m Mon Jan 31 05:18:13 2000 +0000 @@ -21,15 +21,15 @@ ## follows the model ## ## @example -## y(t) = b(1) + b(2) * y(t-1) + ... + b(lb) * y(t-lb+1) + e(t), +## y(t) = b(1) + b(2) * y(t-1) + @dots{} + b(lb) * y(t-lb+1) + e(t), ## @end example ## ## @noindent -## where e(t), given @var{y} up to time @var{t}-1, is @var{N}(0, -## @var{h}(@var{t})), with +## where @math{e(t)}, given @var{y} up to time @math{t-1}, is +## @math{N(0, h(t))}, with ## ## @example -## h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(la) * e(t-la+1)^2 +## h(t) = a(1) + a(2) * e(t-1)^2 + @dots{} + a(la) * e(t-la+1)^2 ## @end example ## @end deftypefn
--- a/scripts/signal/arch_test.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/arch_test.m Mon Jan 31 05:18:13 2000 +0000 @@ -29,35 +29,35 @@ ## I.e., the model is ## ## @example -## y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t), +## y(t) = b(1) * x(t,1) + @dots{} + b(k) * x(t,k) + e(t), ## @end example ## ## @noindent -## given @var{y} up to @var{t}-1 and @var{x} up to @var{t}, -## @var{e}(@var{t}) is @var{N}(0, @var{h}(@var{t})) with +## given @var{y} up to @math{t-1} and @var{x} up to @math{t}, +## @math{e}(t) is @math{N(0, h(t))} with ## ## @example -## h(t) = v + a(1) * e(t-1)^2 + ... + a(p) * e(t-p)^2, +## h(t) = v + a(1) * e(t-1)^2 + @dots{} + a(p) * e(t-p)^2, ## @end example ## ## @noindent -## and the null is @var{a}(1) == ... == @var{a}(@var{p}) == 0. +## and the null is @math{a(1)} == @dots{} == @math{a(p)} == 0. ## -## If the second argument is a scalar integer, @var{k}, perform the same -## test in a linear autoregression model of order @var{k}, i.e., with +## If the second argument is a scalar integer, @math{k}, perform the same +## test in a linear autoregression model of order @math{k}, i.e., with ## ## @example -## [1, y(t-1), ..., y(t-@var{k})] +## [1, y(t-1), @dots{}, y(t-@var{k})] ## @end example ## ## @noindent -## as the @var{t}-th row of @var{x}. +## as the @math{t}-th row of @var{x}. ## ## Under the null, LM approximately has a chisquare distribution with -## @var{p} degrees of freedom and @var{pval} is the @var{p}-value (1 +## @var{p} degrees of freedom and @var{pval} is the @math{p}-value (1 ## minus the CDF of this distribution at LM) of the test. ## -## If no output argument is given, the @var{p}-value is displayed. +## If no output argument is given, the @math{p}-value is displayed. ## @end deftypefn ## Author: KH <Kurt.Hornik@ci.tuwien.ac.at>
--- a/scripts/signal/autocor.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/autocor.m Mon Jan 31 05:18:13 2000 +0000 @@ -18,7 +18,7 @@ ## @deftypefn {Function File} {} autocor (@var{x}, @var{h}) ## Return the autocorrelations from lag 0 to @var{h} of vector @var{x}. ## If @var{h} is omitted, all autocorrelations are computed. -## If @var{X} is a matrix, the autocorrelations of each column are +## If @var{x} is a matrix, the autocorrelations of each column are ## computed. ## @end deftypefn
--- a/scripts/signal/diffpara.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/diffpara.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,21 +15,20 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{d}, @var{D}]} = diffpara (@var{x}, @var{a}, @var{b}) +## @deftypefn {Function File} {[@var{d}, @var{dd}]} = diffpara (@var{x}, @var{a}, @var{b}) ## Return the estimator @var{d} for the differencing parameter of an ## integrated time series. ## -## The frequencies from @code{[2*pi*@var{a}/@var{T}, -## 2*pi*@var{b}/@var{T}]} are used for the estimation. If @var{b} is -## omitted, the interval @code{[2*pi/@var{T}, 2*pi*@var{a}/@var{T}]} is -## used. If both @var{b} and @var{a} are omitted then @code{@var{a} = -## 0.5 * sqrt(@var{T})} and @code{@var{b} = 1.5 * sqrt(@var{T})} is -## used, where @var{T} is the sample size. If @var{x} is a matrix, the -## differencing parameter of each column is estimated. +## The frequencies from @math{[2*pi*a/t, 2*pi*b/T]} are used for the +## estimation. If @var{b} is omitted, the interval +## @math{[2*pi/T, 2*pi*a/T]} is used. If both @var{b} and @var{a} are +## omitted then @math{a = 0.5 * sqrt (T)} and @math{b = 1.5 * sqrt (T)} +## is used, where @math{T} is the sample size. If @var{x} is a matrix, +## the differencing parameter of each column is estimated. ## ## The estimators for all frequencies in the intervals -## described above is returned in @var{D}. The value of @var{d} is -## simply the mean of @var{D}. +## described above is returned in @var{dd}. The value of @var{d} is +## simply the mean of @var{dd}. ## ## Reference: Brockwell, Peter J. & Davis, Richard A. Time Series: ## Theory and Methods Springer 1987.
--- a/scripts/signal/fftshift.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/fftshift.m Mon Jan 31 05:18:13 2000 +0000 @@ -18,17 +18,17 @@ ## @deftypefn {Function File} {} fftshift (@var{v}) ## Perform a shift of the vector @var{v}, for use with the @code{fft} ## and @code{ifft} functions, in order the move the frequency 0 to the -## centre of the vector or matrix. +## center of the vector or matrix. ## -## If @var{v} is a vector of @var{E} elements corresponding to @var{E} -## time samples spaced of @var{Dt} each, then @code{fftshift (fft +## If @var{v} is a vector of @math{E} elements corresponding to @math{E} +## time samples spaced of @math{Dt} each, then @code{fftshift (fft ## (@var{v}))} corresponds to frequencies ## ## @example -## f = linspace (-@var{E}/(4*@var{Dt}), (@var{E}/2-1)/(2*@var{Dt}), @var{E}) +## f = linspace (-E/(4*Dt), (E/2-1)/(2*Dt), E) ## @end example ## -## If @var{v} is a matrix, does the same holds for rows and columns. +## If @var{v} is a matrix, the same holds for rows and columns. ## @end deftypefn ## Author: Vincent Cautaerts <vincent@comf5.comm.eng.osaka-u.ac.jp>
--- a/scripts/signal/fractdiff.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/signal/fractdiff.m Mon Jan 31 05:18:13 2000 +0000 @@ -16,8 +16,8 @@ ## -*- texinfo -*- ## @deftypefn {Function File} {} fractdiff (@var{x}, @var{d}) -## Compute the fractional differences @code{(1-@var{L})^@var{d} * @var{x}} -## where @var{L} denotes the lag-operator and @var{d} is greater than -1. +## Compute the fractional differences @math{(1-L)^d x} where @math{L} +## denotes the lag-operator and @math{d} is greater than -1. ## @end deftypefn ## Author: FL <Friedrich.Leisch@ci.tuwien.ac.at>
--- a/scripts/statistics/base/gls.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/base/gls.m Mon Jan 31 05:18:13 2000 +0000 @@ -27,8 +27,8 @@ ## @end tex ## @end iftex ## @ifinfo -## @code{@var{y} = @var{x} * @var{b} + @var{e}} with @code{mean (@var{e}) = -## 0} and @code{cov (vec (@var{e})) = (@var{s}^2)*@var{o}}, +## @math{y = x b + e} with @math{mean (e) = 0} and +## @math{cov (vec (e)) = (s^2) o}, ## @end ifinfo ## where ## @iftex @@ -39,28 +39,26 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{Y} is a @var{T} by @var{p} matrix, @var{X} is a @var{T} by @var{k} -## matrix, @var{B} is a @var{k} by @var{p} matrix, @var{E} is a @var{T} by -## @var{p} matrix, and @var{O} is a @var{T}@var{p} by @var{T}@var{p} -## matrix. +## @math{y} is a @math{t} by @math{p} matrix, @math{x} is a @math{t} by +## @math{k} matrix, @math{b} is a @math{k} by @math{p} matrix, @math{e} +## is a @math{t} by @math{p} matrix, and @math{o} is a @math{t p} by +## @math{t p} matrix. ## @end ifinfo ## ## @noindent -## Each row of Y and X is an observation and each column a variable. -## -## The return values @var{beta}, @var{v}, and @var{r} are defined as -## follows. +## Each row of @var{y} and @var{x} is an observation and each column a +## variable. The return values @var{beta}, @var{v}, and @var{r} are +## defined as follows. ## ## @table @var ## @item beta -## The GLS estimator for @var{b}. +## The GLS estimator for @math{b}. ## ## @item v -## The GLS estimator for @code{@var{s}^2}. +## The GLS estimator for @math{s^2}. ## ## @item r -## The matrix of GLS residuals, @code{@var{r} = @var{y} - @var{x} * -## @var{beta}}. +## The matrix of GLS residuals, @math{r = y - x beta}. ## @end table ## @end deftypefn
--- a/scripts/statistics/base/kurtosis.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/base/kurtosis.m Mon Jan 31 05:18:13 2000 +0000 @@ -19,7 +19,7 @@ ## -*- texinfo -*- ## @deftypefn {Function File} {} kurtosis (@var{x}) -## If @var{x} is a vector of length @var{N}, return the kurtosis +## If @var{x} is a vector of length @math{N}, return the kurtosis ## @iftex ## @tex ## $$
--- a/scripts/statistics/base/ols.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/base/ols.m Mon Jan 31 05:18:13 2000 +0000 @@ -28,9 +28,8 @@ ## @end tex ## @end iftex ## @ifinfo -## @code{@var{y} = @var{x}*@var{b} + @var{e}} with -## @code{mean (@var{e}) = 0} and @code{cov (vec (@var{e})) = kron (@var{s}, -## @var{I})}. +## @math{y = x b + e} with +## @math{mean (e) = 0} and @math{cov (vec (e)) = kron (s, I)}. ## @end ifinfo ## where ## @iftex @@ -40,9 +39,9 @@ ## @end tex ## @end iftex ## @ifinfo -## @var{y} is a @var{t} by @var{p} matrix, @var{X} is a @var{t} by @var{k} -## matrix, @var{B} is a @var{k} by @var{p} matrix, and @var{e} is a @var{t} -## by @var{p} matrix. +## @math{y} is a @math{t} by @math{p} matrix, @math{x} is a @math{t} by +## @math{k} matrix, @math{b} is a @math{k} by @math{p} matrix, and +## @math{e} is a @math{t} by @math{p} matrix. ## @end ifinfo ## ## Each row of @var{y} and @var{x} is an observation and each column a
--- a/scripts/statistics/base/skewness.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/base/skewness.m Mon Jan 31 05:18:13 2000 +0000 @@ -19,7 +19,7 @@ ## -*- texinfo -*- ## @deftypefn {Function File} {} skewness (@var{x}) -## If @var{x} is a vector of length @var{N}, return the skewness +## If @var{x} is a vector of length @math{n}, return the skewness ## @iftex ## @tex ## $$
--- a/scripts/statistics/distributions/hypergeometric_rnd.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/distributions/hypergeometric_rnd.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,8 +15,8 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {} hypergeometric_rnd (@var{N}, @var{m}, @var{t}, @var{n}) -## Generate a row vector containing a random sample of size @var{N} from +## @deftypefn {Function File} {} hypergeometric_rnd (@var{n_size}, @var{m}, @var{t}, @var{n}) +## Generate a row vector containing a random sample of size @var{n_size} from ## the hypergeometric distribution with parameters @var{m}, @var{t}, and ## @var{n}. ##
--- a/scripts/statistics/tests/f_test_regression.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/tests/f_test_regression.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,8 +15,8 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{pval}, @var{f}, @var{df_num}, @var{df_den}] =} f_test_regression (@var{y}, @var{X}, @var{R}, @var{r}) -## Perform an F test for the null hypothesis R * b = r in a classical +## @deftypefn {Function File} {[@var{pval}, @var{f}, @var{df_num}, @var{df_den}] =} f_test_regression (@var{y}, @var{x}, @var{rr}, @var{r}) +## Perform an F test for the null hypothesis rr * b = r in a classical ## normal regression model y = X * b + e. ## ## Under the null, the test statistic @var{f} follows an F distribution
--- a/scripts/statistics/tests/hotelling_test.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/tests/hotelling_test.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,12 +15,12 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{pval}, @var{Tsq}] =} hotelling_test (@var{x}, @var{m}) +## @deftypefn {Function File} {[@var{pval}, @var{tsq}] =} hotelling_test (@var{x}, @var{m}) ## For a sample @var{x} from a multivariate normal distribution with unknown ## mean and covariance matrix, test the null hypothesis that @code{mean ## (@var{x}) == @var{m}}. ## -## Hotelling's T^2 is returned in @var{Tsq}. Under the null, +## Hotelling's T^2 is returned in @var{tsq}. Under the null, ## @math{(n-p) T^2 / (p(n-1))} has an F distribution with @math{p} and ## @math{n-p} degrees of freedom, where @math{n} and @math{p} are the ## numbers of samples and variables, respectively.
--- a/scripts/statistics/tests/hotelling_test_2.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/tests/hotelling_test_2.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,13 +15,13 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{pval}, @var{Tsq}] =} hotelling_test_2 (@var{x}, @var{y}) +## @deftypefn {Function File} {[@var{pval}, @var{tsq}] =} hotelling_test_2 (@var{x}, @var{y}) ## For two samples @var{x} from multivariate normal distributions with ## the same number of variables (columns), unknown means and unknown ## equal covariance matrices, test the null hypothesis @code{mean ## (@var{x}) == mean (@var{y})}. ## -## Hotelling's two-sample T^2 is returned in @var{Tsq}. Under the null, +## Hotelling's two-sample T^2 is returned in @var{tsq}. Under the null, ## ## @example ## (n_x+n_y-p-1) T^2 / (p(n_x+n_y-2))
--- a/scripts/statistics/tests/t_test_regression.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/statistics/tests/t_test_regression.m Mon Jan 31 05:18:13 2000 +0000 @@ -15,10 +15,10 @@ ## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {[@var{pval}, @var{t}, @var{df}] =} t_test_regression (@var{y}, @var{x}, @var{R}, @var{r}, @var{alt}) -## Perform an t test for the null hypothesis @code{@var{R} * @var{b} = +## @deftypefn {Function File} {[@var{pval}, @var{t}, @var{df}] =} t_test_regression (@var{y}, @var{x}, @var{rr}, @var{r}, @var{alt}) +## Perform an t test for the null hypothesis @code{@var{rr} * @var{b} = ## @var{r}} in a classical normal regression model @code{@var{y} = -## @var{X} * @var{b} + @var{e}}. Under the null, the test statistic @var{t} +## @var{x} * @var{b} + @var{e}}. Under the null, the test statistic @var{t} ## follows a @var{t} distribution with @var{df} degrees of freedom. ## ## If @var{r} is omitted, a value of 0 is assumed. @@ -26,9 +26,9 @@ ## With the optional argument string @var{alt}, the alternative of ## interest can be selected. If @var{alt} is @code{"!="} or ## @code{"<>"}, the null is tested against the two-sided alternative -## @code{@var{R} * @var{b} != @var{r}}. If @var{alt} is @code{">"}, the -## one-sided alternative @code{@var{R} * @var{b} > @var{r}} is used. -## Similarly for @var{"<"}, the one-sided alternative @code{@var{R} * +## @code{@var{rr} * @var{b} != @var{r}}. If @var{alt} is @code{">"}, the +## one-sided alternative @code{@var{rr} * @var{b} > @var{r}} is used. +## Similarly for @var{"<"}, the one-sided alternative @code{@var{rr} * ## @var{b} < @var{r}} is used. The default is the two-sided case. ## ## The p-value of the test is returned in @var{pval}.
--- a/scripts/strings/lower.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/strings/lower.m Mon Jan 31 05:18:13 2000 +0000 @@ -18,7 +18,8 @@ ## 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {@var{s} =} lower(@var{S}) +## @deftypefn {Function File} {} lower (@var{s}) +## Transform all letters in the string @var{s} to lower case. ## @end deftypefn ## @seealso{tolower}
--- a/scripts/strings/upper.m Mon Jan 31 03:17:16 2000 +0000 +++ b/scripts/strings/upper.m Mon Jan 31 05:18:13 2000 +0000 @@ -18,8 +18,8 @@ ## 02111-1307, USA. ## -*- texinfo -*- -## @deftypefn {Function File} {@var{S} =} upper(@var{s}) -## Transform all letters in a string to upper case. +## @deftypefn {Function File} {} upper (@var{s}) +## Transform all letters in the string @var{s} to upper case. ## @end deftypefn ## @seealso{toupper}
--- a/src/DLD-FUNCTIONS/besselj.cc Mon Jan 31 03:17:16 2000 +0000 +++ b/src/DLD-FUNCTIONS/besselj.cc Mon Jan 31 05:18:13 2000 +0000 @@ -259,7 +259,7 @@ \n\ If thet argumemt @var{opt} is supplied, the result is scaled by the\n\ @code{exp (-I*@var{x})} for @var{k} = 1 or @code{exp (I*@var{x})} for\n\ - @var{K} = 2.\n\ + @var{k} = 2.\n\ \n\ If @var{alpha} is a scalar, the result is the same size as @var{x}.\n\ If @var{x} is a scalar, the result is the same size as @var{alpha}.\n\
--- a/src/DLD-FUNCTIONS/colloc.cc Mon Jan 31 03:17:16 2000 +0000 +++ b/src/DLD-FUNCTIONS/colloc.cc Mon Jan 31 05:18:13 2000 +0000 @@ -36,7 +36,7 @@ DEFUN_DLD (colloc, args, , "-*- texinfo -*-\n\ -@deftypefn {Loadable Function} {[@var{r}, @var{A}, @var{B}, @var{q}] =} colloc (@var{n}, \"left\", \"right\")\n\ +@deftypefn {Loadable Function} {[@var{r}, @var{amat}, @var{bmat}, @var{q}] =} colloc (@var{n}, \"left\", \"right\")\n\ Compute derivative and integral weight matrices for orthogonal\n\ collocation using the subroutines given in J. Villadsen and\n\ M. L. Michelsen, @cite{Solution of Differential Equation Models by\n\
--- a/src/DLD-FUNCTIONS/givens.cc Mon Jan 31 03:17:16 2000 +0000 +++ b/src/DLD-FUNCTIONS/givens.cc Mon Jan 31 05:18:13 2000 +0000 @@ -32,7 +32,7 @@ DEFUN_DLD (givens, args, nargout, "-*- texinfo -*- -@deftypefn {Loadable Function} {@var{G} =} givens (@var{x}, @var{y})\n\ +@deftypefn {Loadable Function} {@var{g} =} givens (@var{x}, @var{y})\n\ @deftypefnx {Loadable Function} {[@var{c}, @var{s}] =} givens (@var{x}, @var{y})\n\ @iftex\n\ @tex\n\ @@ -49,8 +49,8 @@ @end iftex\n\ @ifinfo\n\ Return a 2 by 2 orthogonal matrix\n\ -@code{@var{G} = [@var{c} @var{s}; -@var{s}' @var{c}]} such that\n\ -@code{@var{G} [@var{x}; @var{y}] = [*; 0]} with @var{x} and @var{y} scalars.\n\ +@code{@var{g} = [@var{c} @var{s}; -@var{s}' @var{c}]} such that\n\ +@code{@var{g} [@var{x}; @var{y}] = [*; 0]} with @var{x} and @var{y} scalars.\n\ @end ifinfo\n\ \n\ For example,\n\
--- a/src/oct-hist.cc Mon Jan 31 03:17:16 2000 +0000 +++ b/src/oct-hist.cc Mon Jan 31 05:18:13 2000 +0000 @@ -599,8 +599,8 @@ contents. If the name is omitted, use the default history file\n\ (normally @file{~/.octave_hist}).\n\ \n\ -@item @var{N}\n\ -Only display the most recent @var{N} lines of history.\n\ +@item @var{n}\n\ +Only display the most recent @var{n} lines of history.\n\ \n\ @item -q\n\ Don't number the displayed lines of history. This is useful for cutting\n\