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.
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Unfortunately, I had convergence (and singularity) issues when calculating my GLMM analysis models in R. When I tried it in SPSS, I got no such warning message and the results are only slightly different. Does it mean I can interpret the results from SPSS without worries? Or do I have to test for singularity/convergence issues to be sure?
You have two questions. I will answer both.
First Question
Does it mean I can interpret the results from SPSS without worries?
You do not want to do this. The reason being is that mixed models have a very specific parameterization. Here is a screenshot of common lme4 syntax from the original article about lme4 from the author:
With this comes assumptions about what your model is saying. If for example you are running a model with random intercepts only, you are assuming that the slopes do not vary by any measure. If you include correlated random slopes and random intercepts, you are then assuming that there is a relationship between the slopes and intercepts that may either be positive or negative. If you present this data as-is without knowing why it produced this summary, you may fail to explain your data in an accurate way.
The reason as highlighted by one of the comments is that SPSS runs off defaults whereas R requires explicit parameters for the model. I'm not surprised that the model failed to converge in R but not SPSS given that SPSS assumes no correlation between random slopes and intercepts. This kind of model is more likely to converge compared to a correlated model because the constraints that allow data to fit a correlated model make it very difficult to converge. However, without knowing how you modeled your data, it is impossible to actually know what the differences are. Perhaps if you provide an edit to your question that can be answered more directly, but just know that SPSS and R do not calculate these models the same way.
Second Question
Or do I have to test for singularity/convergence issues to be sure?
SPSS and R both have singularity checks as a default (check this page as an example). If your model fails to converge, you should drop it and use an alternative model (usually something that has a simpler random effects structure or improved optimization).
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 basically trying to do what is described in this question R Loop for Variable Names to run linear regression model (i.e. swapping out one independent variable while all others stay constant)
except that I'd like to run a classification model as opposed to a linear regression. Any classification model will do, the simpler the better. Can anyone provide a basic guideline of what this kind of code would look like?
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 huge data which has about 2,000 variables and about 10,000 observations.
Initially, I wanted to run a regression model for each one with 1999 independent variables and then do stepwise model selection.
Therefore, I would have 2,000 models.
However, unfortunately R presented errors because of lack of memory..
So, alternatively, I have tried to remove some independent variables which are low correlation value- maybe lower than .5-
With variables which are highly correlated with each dependent variable, I would like to run regression model..
I tried to do follow codes, even melt function doesn't work because of memory issue.. oh god..
test<-data.frame(X1=rnorm(50,mean=50,sd=10),
X2=rnorm(50,mean=5,sd=1.5),
X3=rnorm(50,mean=200,sd=25))
test$X1[10]<-5
test$X2[10]<-5
test$X3[10]<-530
corr<-cor(test)
diag(corr)<-NA
corr[upper.tri(corr)]<-NA
melt(corr)
#it doesn't work with my own data..because of lack of memory.
Please help me.. and thank you so much in advance..!
In such a situation if might be worth trying sparsity inducing techniques such as the Lasso. Here a sparse subset of variables is selected by constraining the sum of absolute values of the regression coefficients.
This will give you a reduced subset of variables which are the most relevant (and due to the nature of the Lasso algorithm also the most correlated, which was what you were looking for)
In R you can use the LARS package and information about the Lasso can be found here:
http://www-stat.stanford.edu/~tibs/lasso.html
Also a very good resource is: http://www-stat.stanford.edu/~tibs/ElemStatLearn/