summary(object, test=c("Roy", "Wilks", "Pillai", ....)

[Ray Haraf <rayharaf@rogers.com>]

Dear All,

 

I am running multivariate multiple regression using lm() and would be very appreciative of your kind and prompt help (as usual) with how to obtain the usual statistics of multivariate analysis, i.e., Wilks' lambda, Pillai's trace, Hotelling-Lawley trace, and Roy's greatest root.

 

I unsuccessfully tried this

> ex7.8 <- data.frame(z1 = c(0, 1, 2, 3, 4),
+  y1 = c(1, 4, 3, 8, 9),
+  y2 = c(-1, -1, 2, 3, 2))
> summary(lm(cbind(y1, y2) ~ z1, data = "">

 

Also, any suggestion on how obtain simultaneous prediction intervals and prediction ellipsoid would be very valued. Thanks for your kind care

 

Here is the result of the above summary() function

Response y1 :

Call:
lm(formula = y1 ~ z1, data = "">

Residuals:
         1          2          3          4          5
 6.880e-17  1.000e+00 -2.000e+00  1.000e+00 -1.326e-16

Coefficients:
            Estimate Std. Error t value Pr(>|t|) 
(Intercept)   1.0000     1.0954   0.913   0.4286 
z1            2.0000     0.4472   4.472   0.0208 *
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Residual standard error: 1.414 on 3 degrees of freedom
Multiple R-squared: 0.8696,     Adjusted R-squared: 0.8261
F-statistic:    20 on 1 and 3 DF,  p-value: 0.02084

Response y2 :

Call:
lm(formula = y2 ~ z1, data = "">

Residuals:
         1          2          3          4          5
 9.931e-17 -1.000e+00  1.000e+00  1.000e+00 -1.000e+00

Coefficients:
            Estimate Std. Error t value Pr(>|t|) 
(Intercept)  -1.0000     0.8944  -1.118   0.3450 
z1            1.0000     0.3651   2.739   0.0714 .
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Residual standard error: 1.155 on 3 degrees of freedom
Multiple R-squared: 0.7143,     Adjusted R-squared: 0.619
F-statistic:   7.5 on 1 and 3 DF,  p-value: 0.07142

>
Kind regards,

Ray