Displaying 6 results from an estimated 6 matches for "logodds".
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2007 Apr 02
2
Why does lmList() fail when lm() doesn't?
Dear r-helpers,
Can anyone suggest why lm() doesn't complain here:
summary(osss.lm1 <- lm(logOdds ~ c.setSize %in% task, data = osss))
whereas in package:nlme (and in package:lme4)
osss.lmL <- lmList(logOdds ~ c.setSize %in% task | subj, data = osss)
# Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") :
# contrasts can be applied only to factors with 2 or more levels...
2012 Jun 08
1
Testing relationships in logistic regression
...q") - speaks about
deviances and degrees of freedom. I can see how to determine whether each
term is predictive - 1-pchisq(deviance, df) - but how can I tell whether
the relationship between 'z' and 'Y' is the same or different at each level
of 'x'? ie, is the change in logodds of Y for 1z significantly different
from the change in logodds of Y for 0z?
I have had no luck tracking this down using Google. Many thanks to those
who take up my question!
*Peter M Milne* *Dept of Linguistics**, University of Ottawa*
Tel: (613) 562-5800 x1125 | Fax: (613) 562-5141
aix2.uottawa....
2011 Jun 22
2
VGAM constraints-related puzzle
...e linear predictor x. There is a mechanistic reason to believe
this is sensible. So I'd like to fit a model
\eta_j = \beta_{ (j) 0 } + \beta_{ (j) x } f(x)
where both the function f(x) and its scaling coefficients \beta_{ (j)
x } are fit simultaneously. Here \eta_j is the linear predictor, the
logodds of outcome j vs the reference outcome. I cannot see how to fit
exactly this. Instead I seem to be able to do the following:
vgam(formula = y ~ s(x), family = multinomial)
fits the model
\eta_j = \beta_{ (j) 0 } + \beta_{ (j) x } f_j (x)
i.e. a different function f_j (x) is fit for each outcome.
v...
2012 Mar 19
1
glm: getting the confidence interval for an Odds Ratio, when using predict()
Say I fit a logistic model and want to calculate an odds ratio between 2
sets of predictors. It is easy to obtain the difference in the predicted
logodds using the predict() function, and thus get a point-estimate OR. But
I can't see how to obtain the confidence interval for such an OR.
For example:
model <- glm(chd ~age.cat + male + lowed, family=binomial(logit))
pred1 <- predict(model, newdata=data.frame(age.cat=1,male=1,lowed=1))...
2011 Mar 17
1
generalized mixed linear models, glmmPQL and GLMER give very different results that both do not fit the data well...
...ubject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER).
I wanted to fit the following model: for problem i, person j:
logodds ( Y_ij ) = b_0j + b_1j TYPE_ij
with b_0j = b_00 + b_01 POWER_j + u_0j
and b_1j = b_10 + b_11 POWER_j
I think it makes sense, but I'm not sure.
Here are the observed cell means:
conflict noconflict
control 0.6896552 0.9568966
high 0.6935484...
2005 Jun 24
1
comparing strength of association instead of strength of evidence?
Hi,
I asked this question before, which was hidden in a bunch of
questions. I repharse it here and hope I can get some help this time:
I have 2 contingency tables which have the same group variable Y. I
want to compare the strength of association between X1/Y and X2/Y. I
am not sure if comparing p-values IS the way even though the
probability of seeing such "weird" observation under H0