Displaying 5 results from an estimated 5 matches for "pi_i".
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p_i
2024 Jan 04
1
Obtaining a value of pie in a zero inflated model (fm-zinb2)
Are you referring to the zeroinfl() function in the countreg package? If
so, I think
predict(fm_zinb2, type = "zero", newdata = some.new.data)
will give you pi for each combination of covariate values that you
provide in some.new.data
where pi is the probability to observe a zero from the point mass component.
As to your second question, I'm not sure that's possible, for any
2005 Sep 21
2
Help on optim
Dear R-help,
I am new to optim function and need some help with optimization.
Problem description: I am trying to optimize a weights vector
such that it produce maximum value for a function maxVal. The
optimization is subjected to constraint. The constraints are a) Min
weight should be greater than or equal to Zero. b) Max weight should
be less than or equal to 1 c) Sum of the
2002 Jan 25
0
rpart subsets
...he categories. Here
// we make any zeros a very small number so that the rpart run will
not fail.
for (i=0; i<numclass; i++) {
if (freq[i] == 0) freq[i] = 0.00000000001;
}
#endif
for (i=0; i<numclass; i++) {
prior[i] /= freq[i];
aprior[i] /= (temp * freq[i]); /* pi_i / n_i */
}
}
The calculation of the priors (existing code) above shows where I put the
fix in giniinit (in gini.c). However, at this point the rpart run will
complete but will have many NaN's in the rpart$frame for yval's. This is
because of an oversight in rpart.s when calculating...
2002 May 03
3
Regression models for ordinal responses ??
Hello list,
Is there any mean to fit models for ordinal response other than multinomial
polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)?
I am particularly interested in continuation-ratio model and
adjacent-category logit model. It is for the sake of epidemiology in
wild-living populations!
Many thanks,
Emmanuelle Fromont
2002 Jan 28
0
rpart subset fix
...he categories. Here
// we make any zeros a very small number so that the rpart run will
not fail.
for (i=0; i<numclass; i++) {
if (freq[i] == 0) freq[i] = 0.00000000001;
}
#endif
for (i=0; i<numclass; i++) {
prior[i] /= freq[i];
aprior[i] /= (temp * freq[i]); /* pi_i / n_i */
}
}
The calculation of the priors (existing code) above shows where I put the
fix in giniinit (in gini.c). However, at this point the rpart run will
complete but will have many NaN's in the rpart$frame for yval's. This is
because of an oversight in rpart.s when calculating...