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.
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I need to call a forecast model from R within Anlylogic, and return the resulting outputs in R. It is a specific timeseries that I have built in R, and just copying the coefficients to Anylogic is not efficient. I have seen a couple of older posts on similar questions, but I am not sure I can follow. Any advice would be very appreciated.
I have a regression forecast model that uses predictors to provide a forecast along with Prediction Intervals. I need these outputs to be updated by the different values of the predictors and then used in Anylogic.
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 used the nowcast function from R package to use dynamic factor model to nowcast GDP using the extracted factors. I Have tried multiple combination of the initial variables and finally obtained this model which all variables in it seems significant and teh ales obtained for my variable of interest is acceptable.
enter image description here
But I can't find any reference about what tests on residuals that I need to do in order to validate this model.
I am really struggling and have been stuck in this for a month, I need to submit my graduation project this weekend and I really need this model to work. so any help will be very much appreciated. Thank you.
Update 1:
This is teh acf plot n residuals suggested by the same package nowcasting, I think my model passes that test and therefore I can use it. right?
enter image description here
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!
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.