search for: reparameterisation

I have a question about the basis functions of cubic regression spline in mgcv. Are there some ways I can get the exact forms of the basis functions and the penalty matrix that are used in mgcv? Thanks in advance! Yan [[alternative HTML version deleted]]

ggplot2 ------------------------------------------------------------ ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and avoid bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce

ggplot2 ------------------------------------------------------------ ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and avoid bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce

...sp. drop or add only terms such that marginality is preserved. Finally, about your singular matrix t(mm)%*%mm. This is in fact Example 2.1 in Case 2 discussed above. As discussed there, in Stata and in R the drop of the continuous variable has no effect on the degrees of freedom here: it is just a reparameterisation of the full model, protecting you against losing marginality... Hence the model.matrix 'mm' is still square and nonsingular after the drop of X1, unless of course when a row is removed from the matrix 'design' when before creating 'mm'. Arie On Sun, Oct 15, 2017 at 7:0...

...gt;> preserved. >> >> Finally, about your singular matrix t(mm)%*%mm. This is in fact >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata >> and in R the drop of the continuous variable has no effect on the >> degrees of freedom here: it is just a reparameterisation of the full >> model, protecting you against losing marginality... Hence the >> model.matrix 'mm' is still square and nonsingular after the drop of >> X1, unless of course when a row is removed from the matrix 'design' >> when before creating 'mm'. &g...

...>> Finally, about your singular matrix t(mm)%*%mm. This is in fact >> >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata >> >> and in R the drop of the continuous variable has no effect on the >> >> degrees of freedom here: it is just a reparameterisation of the full >> >> model, protecting you against losing marginality... Hence the >> >> model.matrix 'mm' is still square and nonsingular after the drop of >> >> X1, unless of course when a row is removed from the matrix 'design' >> >> w...

...ch that marginality is > preserved. > > Finally, about your singular matrix t(mm)%*%mm. This is in fact > Example 2.1 in Case 2 discussed above. As discussed there, in Stata > and in R the drop of the continuous variable has no effect on the > degrees of freedom here: it is just a reparameterisation of the full > model, protecting you against losing marginality... Hence the > model.matrix 'mm' is still square and nonsingular after the drop of > X1, unless of course when a row is removed from the matrix 'design' > when before creating 'mm'. > > Arie...

...ur singular matrix t(mm)%*%mm. This is in fact >> >> >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata >> >> >> and in R the drop of the continuous variable has no effect on the >> >> >> degrees of freedom here: it is just a reparameterisation of the full >> >> >> model, protecting you against losing marginality... Hence the >> >> >> model.matrix 'mm' is still square and nonsingular after the drop of >> >> >> X1, unless of course when a row is removed from the matrix 'desi...

...gt;> > >> Finally, about your singular matrix t(mm)%*%mm. This is in fact > >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata > >> and in R the drop of the continuous variable has no effect on the > >> degrees of freedom here: it is just a reparameterisation of the full > >> model, protecting you against losing marginality... Hence the > >> model.matrix 'mm' is still square and nonsingular after the drop of > >> X1, unless of course when a row is removed from the matrix 'design' > >> when before creat...

...ly, about your singular matrix t(mm)%*%mm. This is in fact > >> >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata > >> >> and in R the drop of the continuous variable has no effect on the > >> >> degrees of freedom here: it is just a reparameterisation of the full > >> >> model, protecting you against losing marginality... Hence the > >> >> model.matrix 'mm' is still square and nonsingular after the drop of > >> >> X1, unless of course when a row is removed from the matrix 'design' >...

...(mm)%*%mm. This is in fact > >> >> >> Example 2.1 in Case 2 discussed above. As discussed there, in > Stata > >> >> >> and in R the drop of the continuous variable has no effect on the > >> >> >> degrees of freedom here: it is just a reparameterisation of the > full > >> >> >> model, protecting you against losing marginality... Hence the > >> >> >> model.matrix 'mm' is still square and nonsingular after the drop > of > >> >> >> X1, unless of course when a row is removed...