An example:
load(url('BROKEN LINK'))
head(sdat)
library(plm)
fem = plm(y~T+G:t,data=sdat,effect="twoways",model="within",index=c("ID","t"))
summary(fem)
lsdvm = lm(y~ID+T+G:t,data=sdat)
summary(lsdvm)
fem$coef
fem is the fixed-effects model (fit with plm), and lsdv is the equivalent least-squares dummy variable model (fit with lm)
It is clear that plm is estimating the coefficients, and indeed that the coefficients are identical in the two models, as they should be. But when I go to summarize the results, plm is having a hard time, and I'm pretty sure that the reason is the timeXgroup fixed effects, some of which need to be auto-omitted because of the dummy variable trap. (lm, for example, seems to know how to automatically remove variables that are exact linear combinations of each other).
How do I get around this? I'd prefer to stay with plm, as it gives much more parsimonious output than lm with dummy variables for each cross-sectional unit.
Related
I need to perform glm (poisson) estimations with fixed-effects (say merely unit FE) and several regressors (RHS variables). I have an unbalanced panel dataset where most (~90%) observations have missing values (NA) for some but not all regressors.
fixest::feglm() can handle this and returns my fitted model.
However, to do so, it (and fixest::demean too) removes observations that have at least one regressor missing, before constructing the fixed-effect means.
In my case, I am afraid this implies not using a significant share of available information in the data.
Therefore, I would like to demean my variables by hand, to be able to include as much information as possible in each fixed-effect dimension's mean, and then run feglm on the demeaned data. However, this implies getting negative dependent variable values, which is not compatible with Poisson. If I run feglm with "poisson" family and my manually demeaned data, I (coherently) get: "Negative values of the dependent variable are not allowed for the "poisson" family.". The same error is returned with data demeaned with the fixest::demean function.
Question:
How does feglm handle negative values of the demeaned dependent variable? Is there a way (like some data transformation) to reproduce fepois on a fixed-effect in the formula with fepois on demeaned data and a no fixed-effect formula?
To use the example from fixest::demean documentation (with two-way fixed-effects):
data(trade)
base = trade
base$ln_dist = log(base$dist_km)
base$ln_euros = log(base$Euros)
# We center the two variables ln_dist and ln_euros
# on the factors Origin and Destination
X_demean = demean(X = base[, c("ln_dist", "ln_euros")],
fe = base[, c("Origin", "Destination")])
base[, c("ln_dist_dm", "ln_euros_dm")] = X_demean
and I would like to reproduce
est_fe = fepois(ln_euros ~ ln_dist | Origin + Destination, base)
with
est = fepois(ln_euros_dm ~ ln_dist_dm, base)
I think there are two main problems.
Modelling strategy
In general, it is important to be able to formally describe the estimated model.
In this case it wouldn't be possible to write down the model with a single equation, where the fixed-effects are estimated using all the data and other variables only on the non-missing observations. And if the model is not clear, then... maybe it's not a good model.
On the other hand, if your model is well defined, then removing random observations should not change the expectation of the coefficients, only their variance. So again, if your model is well specified, you shouldn't worry too much.
By suggesting that observations with missing values are relevant to estimate the fixed-effects coefficients (or stated differently, that they are used to demean some variables) you are implying that these observations are not randomly distributed. And now you should worry.
Just using these observations to demean the variables wouldn't remove the bias on the estimated coefficients due to the selection to non-missingness. That's a deeper problem that cannot be removed by technical tricks but rather by a profound understanding of the data.
GLM
There is a misunderstanding with GLM. GLM is a super smart trick to estimate maximum likelihood models with OLS (there's a nice description here). It was developed and used at a time when regular optimization techniques were very expensive in terms of computational time, and it was a way to instead employ well developed and fast OLS techniques to perform equivalent estimations.
GLM is an iterative process where typical OLS estimations are performed at each step, the only changes at each iteration concern the weights associated to each observation. Therefore, since it's a regular OLS process, techniques to perform fast OLS estimations with multiple fixed-effects can be leveraged (as is in the fixest package).
So actually, you could do what you want... but only within the OLS step of the GLM algorithm. By no means you should demean the data before running GLM because, well, it makes no sense (the FWL theorem has absolutely no hold here).
I'm currently trying to use a GAM to calculate a rough estimation of expected goals model based purely on the commentary data from ESPN. However, all the data is either a categorical variable or a logical vector, so I'm not sure if there's a way to smooth, or if I should just use the factor names.
Here are my variables:
shot_where (factor): shot location (e.g. right side of the box)
assist_class (factor): type of assist (cross, through ball, pass)
follow_corner (logical): whether the shot follows a corner
shot_with (factor): right foot, left food, header
follow_set_piece (logical): whether the shot follows a set piece
I think I should just use the formula as just the variable names.
model <- bam(is_goal ~ shot_where + assist_class + follow_set_piece + shot_where + follow_corner + shot_where:shot_with, family = "binomial", method = "REML")
The shot_where and shot_with would incorporate any interactions between these two varaibles.
However, I was told I could smooth factor variables as well using the below structure.
model <- bam(is_goal ~ s(shot_where, bs = 'fs') + s(assist_class, bs = 'fs') + as.logical(follow_set_piece) +
as.logical(follow_corner) + s(shot_with, bs = 'fs'), data = model_data, family = "binomial", method = "REML")
This worked for creating a model, but I want to make sure this is a correct method of building the model. I've yet to see any information on using only factor/logical variables in a GAM model, so I thought it was worth asking.
If you only have categorical covariates then you aren't fitting a GAM, whether you fit the model with gam(), bam(), or something else.
What you are doing when you pass factor variables to s() using the fs basis like this
s(f, bs = 'fs')`
is creating a random intercept for each level of the factor f.
There's no smoothing going on here at all; the model is simply exploiting the equivalence of the Bayesian view of smoothing with random effects.
Given that none of your covariates could reasonably be considered random in the sense of a mixed effects model then the only justification for doing what you're doing might be as a computational trick.
Your first model is just a simple GLM (note the typo in the formula as shot_where is repeated twice in the formula.)
It's not clear to me why you are using bam() to fit this model; you're loosing computational efficiency that bam() provides by using method = 'REML'; it should be 'fREML' for bam() models. But as there is no smoothness selection going on in the first model you'd likely be better off using glm() to fit that model. If the issue is large sample sizes, there are several packages that can fit GLMs to large data, for example biglm and it's bigglm() function.
In the second model there is no smoothing going on but there is penalisation which is shrinking the estimates for the random intercepts toward zero. You're likely to get better performance on big data using the lme4 package or TMB and the glmmTMB package to fit what is a GLMM.
This is more of a theoretical question than about R, but let me provide a brief answer. Essentially, the most flexible model you could estimate would be one where you used the variables as factors. It also produces a model that is reasonably easily interpreted - where each coefficient gives you the expected difference in y between the reference level and the level represented by the dummy regressor.
Smoothing splines try to strike the appropriate bias-variance tradeoff. If you've got lots of data and relatively few categories in the categorical variables, there will be no real loss in efficiency for including all of the dummy regressors representing the categories and the bias will also be as small as possible. To the extent that the smoothing spline model is different from the one treating everything as factors, it is likely inducing bias without a corresponding increase in efficiency. If it were me, I would stick with a model that treats all of the categorical variables as factors.
I'm looking for a function for interaction effects visualization which has a correspondence with ivreg or plm. My model is 2sls with fixed effects but it seems there are no packages available for calculating interaction effects in R.
I'd be pleased if someone could solve my concern.
You might want to have a look at interplot(). You can use this function to visualize e.g. the estimated coefficient of regressor X on outcome Y conditional on values of instrument Z by simply plugging in the fitted values from ivreg(). (The confidence intervals are trickier, but you are probably less interested in those in the first instance.)
https://cran.r-project.org/web/packages/interplot/vignettes/interplot-vignette.html
I am using auto.arima from forecast package to create an ARIMAX model.
The dependent variable and the regressors are non-stationary. However, auto.arima() returns a model ARIMA(0,0,0).
Should I worry about this? Should I force auto.arima() to difference my time series, specifying d=1 ?
If I don't put any regressors in my model, it does detect non-stationarity, ending up with ARIMA(0,1,1).
I know the problem is similar to this topic, but my dataset is bigger (about 90 observations), thus the answer given is not satisfying.
auto.arima did nothing wrong. Note you have an additive model:
response = regression + time_series
When you include regressors / covariates, non-stationarity is captured by regressors / covariates, so time series component is simple. For your data, you end up with ARIMA(0,0,0), which is white noise.
When you don't have regressors / covariates, non-stationarity has to be modelled by time series, thus differencing is needed. For your data, you end up with ARIMA(0,1,1).
Of course, those two models are not the same, or even equivalent. If you really want some model selection, use the AIC values by both models. But remember, all models are wrong; some are useful. As long as a model can not be rejected at certain statistical significance, it is useful for prediction purpose.
I have fit my discrete count data using a variety of functions for comparison. I fit a GEE model using geepack, a linear mixed effect model on the log(count) using lme (nlme), a GLMM using glmer (lme4), and a GAMM using gamm4 (gamm4) in R.
I am interested in comparing these models and would like to plot the expected (predicted) values for a new set of data (predictor variables). My goal is to compare the predicted effects for each model under particular conditions (x variables). Of particular interest is the comparison between marginal (GEE) and conditional estimates.
I think my main problem might be getting the new data in the correct form with the correct labels and attributes and such. I am still very much an R novice and struggle with this stuff (no course on this at my university unfortunately).
I currently have fitted models
gee1 lme1 lmer1 gamm1
and can extract their fixed effect coefficients and standard errors without a problem. I also don't have a problem converting them from the log scale or estimating confidence intervals accounting for the random effects.
I also have my new dataframe newdat which has 365 observations of 23 variables (average environmental data for each day of the year).
I am stuck on how to predict new count estimates from this. I played around with the model.matrix function but couldn't get it to work. For example, I tried:
mm = model.matrix(terms(glmm1), newdat) # Error in model.frame.default(object,
# data, xlev = xlev) : object is not a matrix
newdat$pcount = mm %*% fixef(glmm1)
Any suggestions or good references would be greatly appreciated. Can anyone help with the error above?
Getting predictions for lme() and lmer() is documented on http://glmm.wikidot.com/faq