Even though I already have some experience working with R I would still consider myself a beginner. For my current research project, I need to run a zero-inflated negative binomial regression with fixed effects. The mixed_model() function seems to be the only way to do this in R. However, I find the manual to the function very challenging and thus I am looking for some help and explanations here in the community.
I want to run a regression with violent_events as the dependent and project_sum as the dependent variable. Additionally, I want to control for gdp, population_size and education. My units of analysis are different districts, which can be identified via the district_id variable. For each district, I have data for the years 2004 to 2010.
My initial attempt looked like this:
gm1 <- mixed_model(violent_events ~ sproject_sum + gdp + population_size + education,
random = year | district_id, data = DF,
family = zi.negative.binomial(), zi_fixed = ~ district_id)
Of course, the code is not working. I would be grateful for suggestions and particularly explanations that let me understand the *mixed_model()+ function better.
Related
How to run Latent Class Growth Modelling (LCGM) with a multinomial response variable in R (using the flexmix package)?
And how to stratify each class by a binary/categorical dependent variable?
The idea is to let gender shape the growth curve by cluster (cf. Mikolai and Lyons-Amos (2017, p. 194/3) where the stratification is done by education. They used Mplus)
I think I might have come close with the following syntax:
lcgm_formula <- as.formula(rel_stat~age + I(age^2) + gender + gender:age)
lcgm <- flexmix::stepFlexmix(.~ .| id,
data=d,
k=nr_of_classes, # would be 1:12 in real analysis
nrep=1, # would be 50 in real analysis to avoid local maxima
control = list(iter.max = 500, minprior = 0),
model = flexmix::FLXMRmultinom(lcgm_formula,varFix=T,fixed = ~0))
,which is close to what Wardenaar (2020,p. 10) suggests in his methodological paper for a continuous outcome:
stepFlexmix(.~ .|ID, k = 1:4,nrep = 50, model = FLXMRglmfix(y~ time, varFix=TRUE), data = mydata, control = list(iter.max = 500, minprior = 0))
The only difference is that the FLXMRmultinom probably does not support varFix and fixed parameters, altough adding them do produce different results. The binomial equivalent for FLXMRmultinom in flexmix might be FLXMRglm (with family="binomial") as opposed FLXMRglmfix so I suspect that the restrictions of the LCGM (eg. fixed slope & intercept per class) are not specified they way it should.
The results are otherwise sensible, but model fails to put men and women with similar trajectories in the same classes (below are the fitted probabilities for each relationship status in each class by gender):
We should have the following matches by cluster and gender...
1<->1
2<->2
3<->3
...but instead we have
1<->3
2<->1
3<->2
That is, if for example men in class one and women in class three would be forced in the same group, the created group would be more similar than the current first row of the plot grid.
Here is the full MVE to reproduce the code.
Got similar results with another dataset with diffent number of classes and up to 50 iterations/class. Have tried two alternative ways to predict the probabilities, with identical results. I conclude that the problem is most likely in the model specification (stepflexmix(...,model=FLXMRmultinom(...) or this is some sort of label switch issue.
If the model would be specified correctly and the issue is that similar trajectories for men/women end up in different classes, is there a way to fix that? By for example restricting the parameters?
Any assistance will be highly appreciated.
This seems to be a an identifiability issue apparently common in mixture modelling. In other words the labels are switched so that while there might not be a problem with the modelling as such, men and women end up in different groups and that will have to be dealt with one way or another
In the the new linked code, I have swapped the order manually and calculated the predictions with by hand.
Will be happy to hear, should someone has an alternative approach to deal with the label swithcing issue (like restricting parameters or switching labels algorithmically). Also curious if the model could/should be specified in some other way.
A few remarks:
I believe that this is indeed performing a LCGM as we do not specify random effects for the slopes or intercepts. Therefore I assume that intercepts and slopes are fixed within classes for both sexes. That would mean that the model performs LCGM as intended. By the same token, it seems that running GMM with random intercept, slope or both is not possible.
Since we are calculating the predictions by hand, we need to be able to separate parameters between the sexes. Therefore I also added an interaction term gender x age^2. The calculations seems to slow down somewhat, but the estimates are similar to the original. It also makes conceptually sense to include the interaction for age^2 if we have it for age already.
varFix=T,fixed = ~0 seem to be reduntant: specifying them do not change anything. The subsampling procedure (of my real data) was unaffected by the set.seed() command for some reason.
The new model specification becomes:
lcgm_formula <- as.formula(rel_stat~ age + I(age^2) +gender + age:gender + I(age^2):gender)
lcgm <- flexmix::flexmix(.~ .| id,
data=d,
k=nr_of_classes, # would be 1:12 in real analysis
#nrep=1, # would be 50 in real analysis to avoid local maxima (and we would use the stepFlexmix function instead)
control = list(iter.max = 500, minprior = 0),
model = flexmix::FLXMRmultinom(lcgm_formula))
And the plots:
I'm not sure how to make this example reproducible - but I'm having an issue with my fixest implementation of a simple event study plot based on coefficients. Wondering if anyone has suggestions for ways to fix this issue.
I have longitudinal data in which I'm looking into the impact of a policy on mother employment. Since the treatment (a reduction in benefits) is blanketed across all effected obs at once - no need to worry about staggard D-I-D treatment effect.
Treated obs are those that made use of the benefit before it was taken away - control group is everyone else. Where treatment periods are normalized with 0 being the quarter of the event.
I'm using the specification below:
employ_mother <- feols(paid_emp ~ i (time_to_treat, treated_group,ref = -1) +
age_dv + age_dv^2 + nkids_dv + marital_status + regions|quarter ,
data = dta, cluster = dta$pidp)
iplot(ctx_employ_mother,
xlab = 'Time to treatment',
main = 'Mother in Employment')
For which the output graph looks like so:
Mother Employment Graph
I'm trying to understand why all of the pre-treatment coefficients are below 0 and rising. When I try the same specification in STATA - my results look normal- with pre-treatment coefficients hovering around 0 with a positive effect after the treatment period begins.
Would really appreciate any help with this.
Thanks!
Long story short:
I need to run a multinomial logit regression with both individual and time fixed effects in R.
I thought I could use the packages mlogit and survival to this purpose, but I am cannot find a way to include fixed effects.
Now the long story:
I have found many questions on this topic on various stack-related websites, none of them were able to provide an answer. Also, I have noticed a lot of confusion regarding what a multinomial logit regression with fixed effects is (people use different names) and about the R packages implementing this function.
So I think it would be beneficial to provide some background before getting to the point.
Consider the following.
In a multiple choice question, each respondent take one choice.
Respondents are asked the same question every year. There is no apriori on the extent to which choice at time t is affected by the choice at t-1.
Now imagine to have a panel data recording these choices. The data, would look like this:
set.seed(123)
# number of observations
n <- 100
# number of possible choice
possible_choice <- letters[1:4]
# number of years
years <- 3
# individual characteristics
x1 <- runif(n * 3, 5.0, 70.5)
x2 <- sample(1:n^2, n * 3, replace = F)
# actual choice at time 1
actual_choice_year_1 <- possible_choice[sample(1:4, n, replace = T, prob = rep(1/4, 4))]
actual_choice_year_2 <- possible_choice[sample(1:4, n, replace = T, prob = c(0.4, 0.3, 0.2, 0.1))]
actual_choice_year_3 <- possible_choice[sample(1:4, n, replace = T, prob = c(0.2, 0.5, 0.2, 0.1))]
# create long dataset
df <- data.frame(choice = c(actual_choice_year_1, actual_choice_year_2, actual_choice_year_3),
x1 = x1, x2 = x2,
individual_fixed_effect = as.character(rep(1:n, years)),
time_fixed_effect = as.character(rep(1:years, each = n)),
stringsAsFactors = F)
I am new to this kind of analysis. But if I understand correctly, if I want to estimate the effects of respondents' characteristics on their choice, I may use a multinomial logit regression.
In order to take advantage of the longitudinal structure of the data, I want to include in my specification individual and time fixed effects.
To the best of my knowledge, the multinomial logit regression with fixed effects was first proposed by Chamberlain (1980, Review of Economic Studies 47: 225–238). Recently, Stata users have been provided with the routines to implement this model (femlogit).
In the vignette of the femlogit package, the author refers to the R function clogit, in the survival package.
According to the help page, clogit requires data to be rearranged in a different format:
library(mlogit)
# create wide dataset
data_mlogit <- mlogit.data(df, id.var = "individual_fixed_effect",
group.var = "time_fixed_effect",
choice = "choice",
shape = "wide")
Now, if I understand correctly how clogit works, fixed effects can be passed through the function strata (see for additional details this tutorial). However, I am afraid that it is not clear to me how to use this function, as no coefficient values are returned for the individual characteristic variables (i.e. I get only NAs).
library(survival)
fit <- clogit(formula("choice ~ alt + x1 + x2 + strata(individual_fixed_effect, time_fixed_effect)"), as.data.frame(data_mlogit))
summary(fit)
Since I was not able to find a reason for this (there must be something that I am missing on the way these functions are estimated), I have looked for a solution using other packages in R: e.g., glmnet, VGAM, nnet, globaltest, and mlogit.
Only the latter seems to be able to explicitly deal with panel structures using appropriate estimation strategy. For this reason, I have decided to give it a try. However, I was only able to run a multinomial logit regression without fixed effects.
# state formula
formula_mlogit <- formula("choice ~ 1| x1 + x2")
# run multinomial regression
fit <- mlogit(formula_mlogit, data_mlogit)
summary(fit)
If I understand correctly how mlogit works, here's what I have done.
By using the function mlogit.data, I have created a dataset compatible with the function mlogit. Here, I have also specified the id of each individual (id.var = individual_fixed_effect) and the group to which individuals belongs to (group.var = "time_fixed_effect"). In my case, the group represents the observations registered in the same year.
My formula specifies that there are no variables correlated with a specific choice, and which are randomly distributed among individuals (i.e., the variables before the |). By contrast, choices are only motivated by individual characteristics (i.e., x1 and x2).
In the help of the function mlogit, it is specified that one can use the argument panel to use panel techniques. To set panel = TRUE is what I am after here.
The problem is that panel can be set to TRUE only if another argument of mlogit, i.e. rpar, is not NULL.
The argument rpar is used to specify the distribution of the random variables: i.e. the variables before the |.
The problem is that, since these variables does not exist in my case, I can't use the argument rpar and then set panel = TRUE.
An interesting question related to this is here. A few suggestions were given, and one seems to go in my direction. Unfortunately, no examples that I can replicate are provided, and I do not understand how to follow this strategy to solve my problem.
Moreover, I am not particularly interested in using mlogit, any efficient way to perform this task would be fine for me (e.g., I am ok with survival or other packages).
Do you know any solution to this problem?
Two caveats for those interested in answering:
I am interested in fixed effects, not in random effects. However, if you believe there is no other way to take advantage of the longitudinal structure of my data in R (there is indeed in Stata but I don't want to use it), please feel free to share your code.
I am not interested in going Bayesian. So if possible, please do not suggest this approach.
I had previously been using simple stats in Statistica, but required R for my masters research. I am trying to run the following code to test for any significant interactions, and it is just running forever. If I simplify the model by taking month out, then it runs, but biologically it makes sense that month is significant so I would really like this to run including month as a factor. Once I run the model, the stop sign in R studio just stays present for hours, what could be the reason for this? Like I said I'm very new and it has been really difficult to learn this on my own. I am working with presence/absence data (as %) which I do cbind as my dependent variable. SO far this is what my coad looks like:
library(car)
library(languageR)
library(AICcmodavg)
library(lme4)
Scat <- read.csv("Scat2.csv", header=T)
attach(Scat)
names(Scat)
y <- cbind(Present,Absent)
ScatData <- glmer(y ~ Estate * Species * Month * Content * (1|Site) + Min + Max,family=binomial)
summary(ScatData)
Once I get to running the actual model, I don't even get to do the summary because R is not done computing the results of the actual model. I ran the model for approximately 4 hours, and when I clicked on the stop sign, I received this message:
Warning message:
In (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
failure to converge in 10000 evaluations
I would really appreciate some input on this matter.
You have a few problems with your model specification. Your model
y ~ Estate * Species * Month * Content * (1|Site) + Min + Max
is asking for all the main effects and interactions of estate, species, month, content, and site, which is incredibly complex.
Also, you have specified site as a random effect and asked for its interaction with fixed effects. I'm not sure whether that's possible, but it certainly seems wrong. You should decide whether you want site to be a fixed effect or a random effect.
If you post a minimal replicable example, I can give more specific advice.
Let's say I have the following data and I'm interested in examining some counterfactuals. In particular, I want to examine whether there would be changes in predicted income given a change in income. The best way I can think to do this is to write a loop that runs this regression 1:n. However, how do I also make adjustments to the data frame while running through the loop. I'm really hoping that there is a base R function or something in a package that someone can point me to.
df = data.frame(year=c(2000,2001,2002,2003,2004,2005,2006,2007,2009,2010),
income=c(100,50,70,80,50,40,60,100,90,80),
age=c(26,30,35,30,28,29,31,34,20,35),
gpa=c(2.8,3.5,3.9,4.0,2.1,2.65,2.9,3.2,3.3,3.1))
df
mod = lm(income ~ age + gpa, data=df)
summary(mod)
Here are some counter factuals that may be worth considering when looking at the relationship between age, gpa, and income.
# What is everyone in the class had a lower/higher gpa?
df$gpa2 = df$gpa + 0.55
# what if one person had a lower/higher gpa?
df$gpa2[3] = 1.6
# what if the most recent employee/person had a lower/higher gpa?
df[10,4] = 4.0
With or without looping, what would be the best way to "simulate" a large (1000+) number of regression models in order examine various counter factuals, and then save those results in some data structure? Is there a "counter factual" analysis package which could save me a bit of work?