| Library: | metrics |
| See also: | dpls makedesign |
| Quantlet: | redun | |
| Description: | calculates a single redundance and a redundance vector for dpls quantlet as a measure of goodness. |
| Usage: | {red,redm}=redun(b,sk,lk,skl,y) | |
| Input: | ||
| b | a matrix with loadings | |
| sk | a matrix with path coefficients | |
| lk | a matrix with latent variables | |
| skl | a matrix with lagged path coefficients | |
| y | a matrix with manifest variables (indicators) | |
| Output: | ||
| red | a scalar with single redundance value | |
| redm | a vector with redundace values | |
randomize(13409)
library("metrics")
b1=0.3
c1=0.6
s=500
n1=normal(s+1)
n1lag=n1[1:s,]
n1=n1[2:rows(n1),] ;innermodel
n2=b1*n1+c1*n1lag+normal(rows(n1))/5
n=n1~n2
nn=n./sqrt(var(n)) ;loadingsmatrix
p=(1|2|3|4|0|0|0)~(0|0|0|0|5|6|7)
y=nn*p'+normal(rows(n),rows(p))/8
d=(0|1)~(0|0)
dl=(0|1)~(0|0)
w=(1|1|1|1|0|0|0)~(0|0|0|0|1|1|1)
myfit=dpls(w,d,w,dl,y,1,3)
{red,redm}=redun(myfit.b,myfit.sk,myfit.lk,myfit.skl,y)
red
redm
Contents of red [1,] 0.90498 Contents of redm [1,] 0 [2,] 0 [3,] 0 [4,] 0 [5,] 0.90491 [6,] 0.90489 [7,] 0.90515