Comments in line
On 12/01/2015 13:13, Vito M. R. Muggeo wrote:
dear Stanislav,
Your data show two slopes with a kink at around 0. Thus, yet another
approach would be to use segmented regression to fit a piecewise linear
relationship with unknown breakpoint (being estimated as part of model
fitting). While the resulting fitting is likely to be (slightly) worse
than the one coming from splines, the advantage is that you get
interpretable parameter estimates, left and right slopes and breakpoint.
Dear Stanislav
You might also want to search for 'broken stick', another name for this
sort of model. I suppose it is also a linear spline. If you know on
scientific grounds where the breakpoint is you can force its position.
I am not sure how relevant this is but in your original example the
slopes would have been constrained to be equal although I wonder whether
that was really what you intended.
Michael
Relevant syntax is
library(segmented)
o<-glm(DV~IV, data= YourDataFrame, family=binomial)
os<-segmented(o, ~IV, psi=0)
vito
Il 12/01/2015 13.45, Stanislav Aggerwal ha scritto:
Thanks very much Marc and Ben for the helpful suggestions
Stan
On Sun, Jan 11, 2015 at 10:28 PM, Ben Bolker <bbol...@gmail.com> wrote:
If you're going to use splines, another possibility is mgcv::gam (also
part of standard R installation)
require(mgcv)
gam(DV ~ s(IV), data= YourDataFrame, family=binomial)
this has the advantage that the complexity of the spline is
automatically adjusted/selected by the fitting algorithm (although
occasionally you need to use s(IV,k=something_bigger) to adjust the
default *maximum* complexity chosen by the code)
On Sun, Jan 11, 2015 at 5:23 PM, Marc Schwartz <marc_schwa...@me.com>
wrote:
On Jan 11, 2015, at 4:00 PM, Ben Bolker <bbol...@gmail.com> wrote:
Stanislav Aggerwal <stan.aggerwal <at> gmail.com> writes:
I have the following problem.
DV is binomial p
IV is quantitative variable that goes from negative to positive
values.
The data look like this (need nonproportional font to view):
[snip to make gmane happy]
If these data were symmetrical about zero,
I could use abs(IV) and do glm(p
~ absIV).
I suppose I could fit two glms, one to positive and one to
negative IV
values. Seems a rather ugly approach.
[snip]
What's wrong with a GLM with quadratic terms in the predictor
variable?
This is perfectly respectable, well-defined, and easy to implement:
glm(y~poly(x,2),family=binomial,data=...)
or y~x+I(x^2) or y~poly(x,2,raw=TRUE)
(To complicate things further, this is within-subjects design)
glmer, glmmPQL, glmmML, etc. should all support this just fine.
As an alternative to Ben's recommendation, consider using a piecewise
cubic spline on the IV. This can be done using glm():
# splines is part of the Base R distribution
# I am using 'df = 5' below, but this can be adjusted up or down as
may be apropos
require(splines)
glm(DV ~ ns(IV, df = 5), family = binomial, data = YourDataFrame)
and as Ben's notes, is more generally supported in mixed models.
If this was not mixed model, another logistic regression implementation
is in Frank's rms package on CRAN, using his lrm() instead of glm() and
rcs() instead of ns():
# after installing rms from CRAN
require(rms)
lrm(DV ~ rcs(IV, 5), data = YourDataFrame)
Regards,
Marc Schwartz
[[alternative HTML version deleted]]
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