I'm working with a large data set with repeated patients over multiple months with ordered outcomes on a severity scale from 1 to 5. I was able to analyze the first set of patients using the polr function to run a basic ordinal logistic regression model, but now want to analyze association across all the time points using a longitudinal ordinal logistic model. I can't seem to find any clear documentation online or on this site so far explaining which package to use and how to use it. I am also an R novice so any simple explanations would be incredibly useful. Based on some initial searching it seems like the mixor function might be what I need though I am not sure how it works. I found it on this site
https://cran.r-project.org/web/packages/mixor/vignettes/mixor.pdf
Would appreciate a simple explanation of how to use this function if this is the right one, or would happily take any alternate suggestions with an explanation.
Thank you in advance for your help!
Related
I have a dataset containing repeated measures and quite a lot of variables per observation. Therefore, I need to find a way to select explanatory variables in a smart way. Regularized Regression methods sound good to me to address this problem.
Upon looking for a solution, I found out about the glmmLasso package quite recently. However, I have difficulties defining a model. I found a demo file online, but since I'm a beginner with R, I had a hard time understanding it.
(demo: https://rdrr.io/cran/glmmLasso/src/demo/glmmLasso-soccer.r)
Since I cannot share the original data, I would suggest you use the soccer dataset (the same dataset used in glmmLasso demo file). The variable team is repeated in observations and should be taken as a random effect.
# sample data
library(glmmLasso)
data("soccer")
I would appreciate if you can explain the parameters lambda and family, and how to tune them.
I’m currently working with a dataset that has lots zeros in the predictor variables as well as the response variable too. The response variable is continuous and it is very skewed to the right.
I’m trying to apply a discrete-continous model where in the first level i perform a binomial logit model to model the zero o and in the second level i perform a regression model for nonzero observations.
Stata program allows you to do this type of analysis very easily but i am using RStudio and did not find any clear packages that implement such apprach. I’d greatly appreciate it if someone can point me to which package i should be using and showing an example would be greatly appreciated too.
I am new to using R as I usually use Stata. I want to estimate a state space model on some time series data with time varying coefficients. From what I have gathered this is not possible to do in Stata.
I have downloaded the dlm package in R and I am trying to run the dlmModReg command to regress my dependent variable on a single explanatory variable. I would like to allow the intercept and beta coefficient to vary over time.
If anyone could show me an example of the code I want to run I think that would be enough for me to work out how to do this. The examples I have found online are vague or use terminology that I am not familiar with as a new R user. Any help or comments are greatly appreciated.
I have a panel dataset, which is unbalanced. I created a pooled model and now need to predict and input the missing values of the dataset. How can it be done?
Here is a printscreen of my data: https://imagizer.imageshack.us/v2/1366x440q90/661/RAH3uh.jpg
Thank you!
First of all it looks like you have a too broad question in here. If you're really asking about how you should predict values for your spreadsheet (i.e cells: Z6,AA6,...,AM22,...); yes you have a HUGE questions =]. Just a hint, in your following questions, you should be more specific, like: I have THIS data related to Households in Belarus. I've searched about predicting models for that and tried XPTO1 and XPTO2. How can I decide which one is better?
So, what I really mean here is that predicting is not exactly a function like SUM, that you can apply to your data and that's it. Prediction is a whole discipline, with a bunch of methods that should be tested to different cases. For example, to predict the Z6 cell in your data, you should to ask yourself what other data can contribute to infer data missing information? In some cases the simple average value for the past 5 years will be enough, in some other, a lot more should be considered.
I recommend you to first take a look at some basic material that covers simple models, like linear models, play with them, try to understand the accuracy of obtained predictions... That will finally solve your problem, or will at least help you to ask the community more "answerable" questions.
One last tip: there is a new SO's sister Q&A community that may be more appropriate to ask questions about prediction models: https://datascience.stackexchange.com/
Good luck.
pardon the newbie question, as I just started learning R a couple weeks ago (but intend to use it actively from now on). However, I could use some help if you already have a working example.
In order to determine own price elasticity coefficients for our each of our products (~100) in each of our states, I want to be able to write a multiple regression that regresses Units on a variety of independent variables. That's straightforward. However, I would like R to be able to cycle through EACH product within a particular state, THEN move onto the next state in the data file, and start the regression on the first product, repeating the cycle.
I have attached an example of what I'm trying to accomplish. I would also like R at the end to export the regression coefficients (and summaries, p-value, t-stat) into a separate worksheet.
Does anyone have an example similar to this? I'm comfortable enough to read the source code and make modifications to fit my needs, but certainly not yet comfortable at this point to write one from scratch. And, alas, I am tired of copying/pasting into Minitab/Excel (which is what i've been using up to this point) to run regressions 1,000 times.
Appreciate any help you could offer!