search for: myfit2

Hi, I have problem getting the standard deviation from the manova output. I have used the manova function: myfit <- manova(cbind(y1, y2) ~ x1 + x2 + x3, data=mydata) . I tried to get the predicted values and their standard deviation by using: predict(myfit, type="response", se.fit=TRUE) But the problem is that I don't get the standard deviation values, I only

..., objdta[ , c( "a", "b" ) ] , FUN = function( x ) if ( all( is.na( x ) ) ) NA else sum( x, na.rm=TRUE ) ) objdtass2min <- objdtass2[ which.min( objdtass2$x ), ] myfit2 <- nlsLM( y ~ myfun2( a, log1ab, r = 2, t = x ) , data = mydata , start = list( a = 2000 , log1ab = 4.60517 ) , lower = c( 1000, 0 ) , upper = c( 3000, 8.0063 )...

..."b" ) ] > , FUN = function( x ) > if ( all( is.na( x ) ) ) NA > else sum( x, na.rm=TRUE ) > ) > objdtass2min <- objdtass2[ which.min( objdtass2$x ), ] > > myfit2 <- nlsLM( y ~ myfun2( a, log1ab, r = 2, t = x ) > , data = mydata > , start = list( a = 2000 > , log1ab = 4.60517 > ) > , lower = c( 1000, 0 ) > , upper =...

I ran the following script. I satisfied the constraint by making a*b a single parameter, which isn't always possible. I also ran nlxb() from nlsr package, and this gives singular values of the Jacobian. In the unconstrained case, the svs are pretty awful, and I wouldn't trust the results as a model, though the minimum is probably OK. The constrained result has a much larger sum of squares.

https://cran.r-project.org/web/views/Optimization.html (Cran's optimization task view -- as always, you should search before posting) In general, nonlinear optimization with nonlinear constraints is hard, and the strategy used here (multiplying by a*b < 1000) may not work -- it introduces a discontinuity into the objective function, so gradient based methods may in particular be