I have a dataset that has weather=0 if temp is <65 degrees Fahrenheit, weather = 1 if temp is =65 degrees Fahrenheit, and weather = 2 if temp is >68 degrees Fahrenheit. I need to estimate a probability that the temp is between 65 <= weather < 68 degrees Fahrenheit, given the days = 20. Here is the formula and output
multinom(formula = weather ~ days, data = USWeather13)
Which gives the coefficient table:
Coefficients:
(Intercept) days
1 5.142 -.252
2 25.120 .343
Std. Errors:
(Intercept) days
1 1.742 .007
2 1.819 .004
Does anyone know how I can interpret this or figure out this problem?
In your example, weather=0
is the reference level, and you have the coefficients as the log odds ratio of weather=1
or weather=2
for every unit of your predictor Days
.
It's an example without the complete information, but reading your coefficients, it means for every unit increase in days, you reduce the log-odd probability of 1 vs 0 by -.252 and log-odd probability of 2 vs 0 by.343.
If you need to figure the respective probabilities at days=20, you do:
fit = multinom(formula = weather ~ days, data = USWeather13)
predict(fit,newdata=data.frame(days=20),type="prob")
Think this website might provide a good guide on how to interpret the coefficients.
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