I have a continuous variable which I divide in 3 class. Now I have run the multinomial logistics model to predict the occurrence of any particular class.
My question is can I convert the predicted probabilities of multinomial logistics model to calculated a continuous variables. If yes, what is the formula of the same.
In the binomial distribution the expected values is calculated as np. Can I use the same logic here too?
For example: I have a score which range from 0-2 in my historical series. I classify it in 3 classess (0-06, 0.6-1 and >1) and run the multinomial logistics model. If I will use the final model to score my testing data. I would get the output as a probability. Is there any way I can convert this probability into a numeric values which can represent my score?
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My outcome variable has a range from 0-8.5 with many zero values. The values contain decimals. The distribution is also right-skewed. I understand models like zero-inflated poisson and zero-inflated negative binomial assume that the outcome is a count ie. values 0,1,2 etc. Is there an alternative GLM that can predict this type of data?
I need to specifically use a GLM in this case.
Is there a simple way to decide for a category based on the predicted probabilities of an ordinal logistic regression?
In the binary case, I have so far set the criterion based on the distribution of the base data set. But this is not possible in the ordinal case.
The output of the zeroinfl regression from pscl provides a list of coefficients under "count model coefficients" as well as a list of coefficients under "zero-inflation model coefficients."
Given the interest is to follow the z inflated model, what is the utility of the count model coefficients? Is it simply provided for reference?
Your zero inflated regression consists of two models. The zero part is usually a binomial part, such as a logit or probit model, and accounts for the probability that Y is not zero. The count part is usually a model for count data (usually integers), such as a poisson or negative binomial model, and only considers those observations that are not zero. When you compare the number of observations of both models, e.g. using summary(fit), you will see the difference. In sum, your zero model calculates the probability that an observations is not zero, the count model fits a model on those observations that are not zero.
This zero inflated regression is similar to a hurdle model. You can read more on this at Cross Validated: What is the difference between zero-inflated and hurdle models?. BTW that platform is actually better suited for this kind of merely statistical questions.
I am trying to perform logistic regression on data that contains a binary outcome. However, I do not have access to the outcome data.
I've calculated probabilities of a "1" outcome for each subject by assigning "risk points" to certain values of each variable and adding them up for each subject, so that the probability of a "1" is (sum of subject's risk points) / (total number of possible risk points). I then took the log of the odds ratio to calculate the logit, so I have a list of logit values between -3 and 2 for each subject.
However, I would like to use logistic regression to evaluate which variables have the greatest effect on the outcome probabilities. Is there a way in R to perform logistic regression using only the predictive variables and logit, without the binary outcome data? I have tried using glm and it does not work, because in order to do logisitic regression you need binary outcome data.
Thank you!
I have conducted a logistic regression on a binary dependent variable and 5 independent variables. The dataframe I drew these variables from is survey data asking whether a person has voted for or against a policy change (binary dependent variable), with the other variables being questions regarding their income, location and other such personal information that could inform whether they would vote for or against the vote.
Having conducted the regression, I'd now like to calculate the predicted probability that each person would have voted yes/no to see how informative those variables are. In total my dataframe has information on 3000 people and I'd like to calculate the predicted probability of voting for/against for every single row/person.
What methods are available for doing so?
Appreciate the help!
You can use the predict function in order to calculate the predicted probabilities.
predict(model, newdata, type="response")
With model our logistic regression (the result of the glm() function), newdata a dataset which contains all the variables defined in our model and for all the individuals for which you want a probability.