My question has to do with using the RSGHB package for predicting choice probabilities per alternative by applying mixed logit models (variation across respondents) with correlated coefficients.
I understand that the choice probabilities are simulated on an individual level and in order to get preference share an average of the individual shares would do. All the sources I have found treat each prediction as a separate simulation which makes the whole process cumbersome if many predictions are needed.
Since one can save the respondent specific coefficient draws wouldn't it be faster to simply apply the logit transform to each each (vector of) coefficient draw? Once this is done new or existing alternatives could be calculated faster than rerunning a whole simulation process for each required alternative. For the time being using a fitted() approach will not help me understand how prediction actually works.
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Maybe anyone can help me with this question. I conducted a follow-up study and obviously now have to face missing data. Now I am considering how to impute the missing data at best using MLM in R (f.e. participants concluded the follow up 2 survey, but not the follow up 1 survey, therefore I am missing L1 predictors for my longitudinal analysis).
I read about Multiple Imputation of multilevel data using the pan package (Schafer & Yucel, 2002) and came across the following code:
imp <- panImpute(data, formula = fml, n.burn = 1000, n.iter = 100, m = 5)
Yet, I have troubles understanding it completely. Is there maybe another way to impute missing data in R? Or maybe somebody could illustrate the process of the imputation method a bit more detailed, that would be so great! Do I have to conduct the imputation for every model I built in my MLM? (f.e. when I compared, whether a random intercept versus a random intercept and random slope model fits better for my data, do I have to use the imputation code for every model, or do I use it at the beginning of all my calculations?)
Thank you in advance
Is there maybe another way to impute missing data in R?
There are other packages. mice is the one that I normally use, and it does support multilevel data.
Do I have to conduct the imputation for every model I built in my MLM? (f.e. when I compared, whether a random intercept versus a random intercept and random slope model fits better for my data, do I have to use the imputation code for every model, or do I use it at the beginning of all my calculations?)
You have to specify the imputation model. Basically that means you have to tell the software which variables are predicted by which other variables. Since you are comparing models with the same fixed effect, and only changing the random effects (in particular comparing models with and without random slopes), the imputation model should be the same in both cases. So the workflow is:
perform the imputations;
run the model on all the imputed datasets,
pool the results (typically using Rubin's rules)
So you will need to do this twice, to end up with 2 sets of pooled results - one for each model. The software should provide functionality for doing all of this.
Having said all of that, I would advise against choosing your model based on fit statistics and instead use expert knowledge. If you have strong theoretical reasons for expecting slopes to vary by group, then include random slopes. If not, then don't include them.
Working with a dataset of ~200 observations and a number of variables. Unfortunately, none of the variables are distributed normally. If it possible to extract a data subset where at least one desired variable will be distributed normally? Want to do some statistics after (at least logistic regression).
Any help will be much appreciated,
Phil
If there are just a few observations that skew the distribution of individual variables, and no other reasons speaking against using a particular method (such as logistic regression) on your data, you might want to study the nature of "weird" observations before deciding on which analysis method to use eventually.
I would:
carry out the desired regression analysis (e.g. logistic regression), and as it's always required, carry out residual analysis (Q-Q Normal plot, Tukey-Anscombe plot, Leverage plot, also see here) to check the model assumptions. See whether the residuals are normally distributed (the normal distribution of model residuals is the actual assumption in linear regression, not that each variable is normally distributed, of course you might have e.g. bimodally distributed data if there are differences between groups), see if there are observations which could be regarded as outliers, study them (see e.g. here), and if possible remove them from the final dataset before re-fitting the linear model without outliers.
However, you always have to state which observations were removed, and on what grounds. Maybe the outliers can be explained as errors in data collection?
The issue of whether it's a good idea to remove outliers, or a better idea to use robust methods was discussed here.
as suggested by GuedesBF, you may want to find a test or model method which has no assumption of normality.
Before modelling anything or removing any data, I would always plot the data by treatment / outcome groups, and inspect the presence of missing values. After quickly looking at your dataset, it seems that quite some variables have high levels of missingness, and your variable 15 has a lot of zeros. This can be quite problematic for e.g. linear regression.
Understanding and describing your data in a model-free way (with clever plots, e.g. using ggplot2 and multiple aesthetics) is much better than fitting a model and interpreting p-values when violating model assumptions.
A good start to get an overview of all data, their distribution and pairwise correlation (and if you don't have more than around 20 variables) is to use the psych library and pairs.panels.
dat <- read.delim("~/Downloads/dput.txt", header = F)
library(psych)
psych::pairs.panels(dat[,1:12])
psych::pairs.panels(dat[,13:23])
You can then quickly see the distribution of each variable, and the presence of correlations among each pair of variables. You can tune arguments of that function to use different correlation methods, and different displays. Happy exploratory data analysis :)
I want to run a linear regression model with a large number of variables and I want an R function to iterate on good combinations of these variables and give me the best combination.
The glmulti package will do this fairly effectively:
Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.
Unsolicited advice follows:
HOWEVER. Please be aware that while this approach can find the model that minimizes within-sample error (the goodness of fit on your actual data), it has two major problems that should make you think twice about using it.
this type of data-driven model selection will almost always destroy your ability to make reliable inferences (compute p-values, confidence intervals, etc.). See this CrossValidated question.
it may overfit your data (although using the information criteria listed in the package description will help with this)
There are a number of different ways to characterize a "best" model, but AIC is a common one, and base R offers step(), and package MASS offers stepAIC().
summary(lm1 <- lm(Fertility ~ ., data = swiss))
slm1 <- step(lm1)
summary(slm1)
slm1$anova
I am trying to run a latent class analysis with covariates using polca package. However, every time I run the model, the multinomial logit coefficients result different. I have considered the changes in the order of the classes and I set up a very high number of replications (nrep=1500). However, rerunning the model I obtain different results. For example, I have 3 classes (high, low, medium). No matter the order in which the classes are considered in the estimation, the multinomial model will give me different coefficient for the same combinations after different estimations (such as low vs high and medium vs high). Should I increase further the number of repetitions in order to have stable results? Any idea of why is this happening? I know with the function set.seed() I can replicate the results but I would like to obtain stable estimates to be able to claim the validity of the results. Thank you very much!
From the manual (?poLCA):
As long as probs.start=NULL, each function call will use different
(random) initial starting parameters
you need to use set.seed() or set probs.start in order to get consistent results across function calls.
Actually, if with different starting points you are not converging, you have a data problem.
LCA uses a kind of maximum likelihood estimation. If there is no convergence, you have an under-identification problem: you have too little information to estimate the number of classes that you have. Lower class numbers might run, or you will have to make some a-priori restrictions.
You might wish to read Latent Class and Latent Transition Analysis by Collins. It was a great help for me.
I am trying to analyze a panel data which includes observations for each US state collected across 45 years.
I have two predictor variables that vary across time (A,B) and one that does not vary (C). I am especially interested in knowing the effect of C on the dependent variable Y, while controlling for A and B, and for the differences across states and time.
This is the model that I have, using plm package in R.
random <- plm(Y~log1p(A)+B+C, index=c("state","year"),model="random",data=data)
My reasoning is that with a time invariant variable I should be using random rather than fixed effect model.
My question is: Is my model and thinking correct?
Thank you for your help in advance.
You base your answer about the decision between fixed and random effect soley on computational grounds. Please see the specific assumptions associated with the different models. The Hausman test is often used to discriminate between the fixed and the random effects model, but should not be taken as the definite answer (any good textbook will have further details).
Also pooled OLS could yield a good model, if it applies. Computationally, pooled OLS will also give you estimates for time-invariant variables.