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mlm
2012 Mar 16
1
multivariate regression and lm()
...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
### separate linear regressions on y1 and y2 ###
################################################
> f.mlm1 = lm(y1 ~ z1, data=ex7.8); summary(f.mlm1)
Call:
lm(formula = y1 ~ z1, data = ex7.8)
Residuals:
1 2 3 4 5
0 1 -2 1 0
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 *
---...
2005 May 04
3
Multivariate multiple regression
I'd like to model the relationship between m responses Y1, ..., Ym and a
single set of predictor variables X1, ..., Xr. Each response is assumed
to follow its own regression model, and the error terms in each model
can be correlated. My understanding is that although lm() handles
vector Y's on the left-hand side of the model formula, it really just
fits m separate lm models. What should