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

Library: stats
See also: gls linregfs linregres doglm

Quantlet: linreg
Description: linreg computes the Generalized Least Squares estimate for the coefficients of a linear model.

Usage: {b,bse,bstan,bpval} = linreg (x, y{,opt, om})
Input:
x n x p x d1 x ... x dn array
y n x 1 x d1 x ... x dn array
opt string vector
om n x n x d1 x ... x dn array
Output:
b p x 1 x d1 x ... x dn array
bse p x 1 x d1 x ... x dn array
bstan p x 1 x d1 x ... x dn array
bpval p x 1 x d1 x ... x dn array

Note:

Example:

library("stats")

setenv("outputstringformat", "%s")    

randomize(1964)

n = 500

x = normal(n,3)

beta = #(10, 2, 3)

u = 0.5 * normal(n) 

y = x*beta .+ u

{beta,se,betastan,p} = linreg(x,y)  

Result:

Contents of out

[ 1,] 

[ 2,] A  N  O  V  A                   SS      df     MSS       F-test   P-value

[ 3,] _________________________________________________________________________

[ 4,] Regression                 61494.937     3 20498.312   80164.745   0.0000

[ 5,] Residuals                    126.828   496     0.256

[ 6,] Total Variation            61621.765   499   123.491

[ 7,] 

[ 8,] Multiple R      = 0.99897

[ 9,] R^2             = 0.99794

[10,] Adjusted R^2    = 0.99793

[11,] Standard Error  = 0.50567

[12,] 

[13,] 

[14,] PARAMETERS         Beta         SE         StandB        t-test   P-value

[15,] ________________________________________________________________________

[16,] b[ 0,]=         -0.0058       0.0227       0.0000        -0.254   0.6001

[17,] b[ 1,]=         10.0019       0.0215       0.9501       465.977   0.0000

[18,] b[ 2,]=          1.9906       0.0221       0.1839        90.263   0.0000

[19,] b[ 3,]=          3.0249       0.0231       0.2667       130.817   0.0000

Example:

library("stats")

randomize(1964)

n = 50

x = normal(n,3)

beta = #(10, 2, 3)

u = 0.5 * normal(n) 

y = x*beta .+ u

covar = (0.5.*x[,2] .+ 0.3.*x[,3]+ 0.2*x[,1]).^2

y = x*beta .+ sqrt(covar).*u

{beta,se,betastan,p} = linreg(x, y,"nointercept"|"display"|"omega",diag(covar))   

Result:



You see the display:

A  N  O  V  A                 SS       df     MSS      F-test     P-value

_________________________________________________________________________

Regression                 4782.570     3  1594.190    8526.286   0.0000

Residuals                     8.601    46     0.187

Total Variation            4846.290    49    98.904

Multiple R      = 0.99340

R^2             = 0.98685

Adjusted R^2    = 0.99811

Standard Error  = 0.43240

PARAMETERS         Beta        SE         StandB      t-test      P-value

_________________________________________________________________________

b[ 1,]=         10.0297      0.0643       0.9793      155.865     0.0000

b[ 2,]=          2.0544      0.0518       0.2452       39.661     0.0000

b[ 3,]=          2.9838      0.0706       0.2673       42.291     0.0000  


Library: stats
See also: gls linregfs linregres doglm

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

Author: Susanne Hannappel 961122
(C) MD*TECH Method and Data Technologies, 21.9.2000