What is rank regression?
The rank regression is a simple technique which engages replacing the data with their corresponding ranks. Additionally, we simply fit a line through the (rank of the) points and therefore no assumptions are needed to employ this approach.
What is outcome variable in regression?
The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted “Y” and the independent variables are denoted by “X”.
How to use rank order in multiple regression?
All you’d have to do is convert all data into ranks, run it through a regular old multiple regression, then use the L statistic to find your p -values. However, I feel like inference will not be too useful in your case. Not quite sure of the data’s context, but simply using Spearman correlation or scatterplots might be more telling.
Can a regression model be a dependent variable?
I have a bunch of explanatory variables and would like to explain the rank of the firm on the basis of these variables. My inclination is to use a regression model, but am concerned about the fact that the dependent variable is limited, it can only be a positive discrete number.
How many variables can be used in logistic regression?
It has been suggested that the data should contain at least ten events for each variable entered into a logistic regression model. Hence, if we wish to find predictors of mortality using a sample in which there have been sixty deaths, we can study no more than 6 (=60/10) predictor variables.
Which is the best ordinal regression for this problem?
Ordinal regression is ideal for this problem in my opinion. There is no problem other than computational burden caused by having as many unique Y as there are observations. The R rms package’s orm function solves the computational burden problem using a special sparse matrix representation.