I'm unconfident that whether my understanding on fixed effect and random effect is correct:
Fixed effect= variable that make inferences about the specific levels.
Random effect= variable that make inferences about and generalise to a wider population.
The aim for my model is to suggest the kind of videos to create on youtube so that they become popular and get large number of views.
I have 6 variables in my glm model:
- Channel – YouTube account the video was uploaded from ( all account names, e.g.Netflix, star wars etc)
- Views – Number of times the video was viewed ( that observed over unequal time interval)
- Comments_disabled – Whether the channel disabled other users from commenting on the video (no = comments enabled, yes = comments disabled)
- Theme – Category of the video (e.g. ‘Drama’, ‘Family’ etc)
- Weeks – Number of weeks available on YouTube to date
- Tags – Number of tags, key words assigned to the video that users can search for within YouTube
I defined them as:
Fixed effect: 2, 4, 5
Random effect: 1, 3, 6
I have categorized tags as random effect but I am not very certain about it.
And what is the main difference between a fixed effect model, a random effect model and a mixed model? From my understanding of these three models, fixed effect model = all variables are fixed effects, random effect model = all variables are random effects and mixed model = both fixed effects and random effects variables are in the model ?
Also, is it possible to get a glm model that only includes fixed effects?
I used the code below in glm
glm( views ~ weeks, data = "youtube" , family = "poisson", link = "log")
and keep saying
Error in eval(predvars, data, env) :
invalid 'envir' argument of type 'character'.
I'm not sure where I went wrong here. Any help would be appreciated.
edit: I have figured out my code, it shall be
glm( views ~ weeks, data = "youtube" , family = "poisson" (link = "log"))