代码搜索：multivariate
找到约 564 项符合「multivariate」的源代码
代码结果 564
www.eeworm.com/read/440070/7694865
html contents.html
Nonlinear Time Series Routines
TISEAN 2.1: Table of Contents
Generating time series
A few
www.eeworm.com/read/474986/6798897
m hotellingt2.m
function [HotellingT2] = HotellingT2(X,alpha)
%Hotelling T-Squared testing procedures for multivariate samples.
%
% Syntax: function [HotellingT2] = HotellingT2(X,alpha)
%
% Inputs:
www.eeworm.com/read/137827/13294476
pdf distributed multivariate regression using wavelet-based collective data mining.pdf
www.eeworm.com/read/189641/8464199
m add_control_noise.m
function [V,G]= add_control_noise(V,G,Q, addnoise)
% Add random noise to nominal control values
if addnoise == 1
% V= V + randn(1)*sqrt(Q(1,1)); % if assume Q is diagonal
% G= G + randn(
www.eeworm.com/read/453400/7421741
sas aconova2.sas
options nodate nonumber;
title 'Multivariate Analysis of Covariance';
proc format;
value groupfmt 1='Hydrolysate-I' 2='Hydrolysate-II' 3='Casein';
data ancova2;
do i=1 to 8;
do group=1 to
www.eeworm.com/read/319478/13450967
res emvptmp.res
NSF MATH DATA (V = 1, B = 13 GRADERS [BLOCKS], R = 11 PRE & POST RESPONSES)
EXACT MATCHED-PAIRS MULTIVARIATE PERMUTATION TEST:
DISTANCE EXPONENT: 1.00
WITH 11 RESPONSES AND 13 BLOCK
www.eeworm.com/read/319478/13450968
res rmvptmp.res
NSF MATH DATA (V = 1, B = 13 GRADERS [BLOCKS], R = 11 PRE & POST RESPONSES)
MATCHED-PAIRS MULTIVARIATE PERMUTATION TEST:
DISTANCE EXPONENT: 1.00
WITH 11 RESPONSES AND 13 BLOCKS.
AV
www.eeworm.com/read/319478/13451004
res mvptmp.res
NSF MATH DATA (V = 1, B = 13 GRADERS [BLOCKS], R = 11 PRE & POST RESPONSES)
MATCHED-PAIRS MULTIVARIATE PERMUTATION TEST:
DISTANCE EXPONENT: 1.00
WITH 11 RESPONSES AND 13 BLOCKS.
AV
www.eeworm.com/read/216806/14991718
m add_control_noise.m
function [V,G]= add_control_noise(V,G,Q, addnoise)
% Add random noise to nominal control values
if addnoise == 1
% V= V + randn(1)*sqrt(Q(1,1)); % if assume Q is diagonal
% G= G + randn(
www.eeworm.com/read/393504/8281418
m add_control_noise.m
function [V,G]= add_control_noise(V,G,Q, addnoise)
% Add random noise to nominal control values
if addnoise == 1
% V= V + randn(1)*sqrt(Q(1,1)); % if assume Q is diagonal
% G= G + randn(