I was using the glmer code for a logistic regression model with 2.5 million observations. However, after I added the multi-level component (a few hundred thousand groups), the data was too large to run in a timely manner on my computer. I want to try a general additive model instead, but I am confused about how to write the code.
The glmer code is as follows:
mylogit.m1a <- glmer(outcome ~
exposure*risk+ tenure.yr + CurrentAge + percap.inc.k + employment + rentership + pop.change + pop.den.k +
(1 | geo_id / house_id),
data = temp, family = "binomial", control = glmerControl(optimizer="bobyqa", calc.derivs=FALSE))
print(Sys.time()-start)
The example I found writes the gam like this:
ga_model = gam(
Reaction ~ Days + s(Subject, bs = 're') + s(Days, Subject, bs = 're'),
data = sleepstudy,
method = 'REML'
)
But I am confused about why there are two bits in parenthesis / what I should put in parenthesis to specify the model correctly.
The details are given in ?smooth.construct.re.smooth.spec:
Exactly how the random effects are implemented is best seen by
example. Consider the model term ‘s(x,z,bs="re")’. This will
result in the model matrix component corresponding to ‘~x:z-1’
being added to the model matrix for the whole model.
So s(Days, Subject, bs = "re") is equivalent to the (0 + Days|Subject) term in the lmer model: both of them encode "random variation in slope with respect to day across subjects"
So your (1 | geo_id / house_id) would be translated to mgcv syntax as
s(geo_id, bs = "re") + s(geo_id, house_id, bs = "re")
(the nesting syntax a/b expands in general to a + a:b).
A couple of other comments:
you should probably use bam() as a drop-in replacement for gam() (much faster)
you may very well run into problems with memory usage: mgcv doesn't use sparse matrices for the random effects terms, so they can get big
I built a fixed effects model using the lm function. I had previously created price clusters of independent variables (cluster number * price) with an end goal of estimating demand elasticities. Am I correct that the factor function ( + factor(Tulsa$Cluster)-1) at the end provides different intercepts (b0's) for each of the pricing cluster independent variables?
model.fixed = lm(Tulsa$ln_volume ~ Tulsa$Price_Cluster_1
+ Tulsa$Price_Cluster_2
+ Tulsa$Price_Cluster_3
+ Tulsa$Price_Cluster_4
+ Tulsa$Price_Cluster_5
+ Tulsa$PC1
+ Tulsa$PC2
+ Tulsa$PC3
+ Tulsa$PC4
+ factor(Tulsa$Cluster)-1,data=Tulsa)
The coefficients for the price clusters are the right direction and make intuitive sense. Thanks in advance.
is there a way to compare (standardized) beta coefficients of one sample and regression without generating two models and conducting an anova? Is there a simpler method with e.g. one function?
For example, if I have this model and would want to compare beta coefficients of SE_gesamt and CE_gesamt (only two variables):
library(lm.beta)
fit1 <- lm(Umint_gesamt ~ Alter + Geschlecht_Dummy + SE_gesamt + CE_gesamt + EmoP_gesamt + Emp_gesamt + IN_gesamt + DN_gesamt + SozID_gesamt, data=dataset)
summary(fit1)
lm.beta(fit1)
All the best,
Karen
I'm trying to include time fixed effects (dummies for years generated with model.matrix) into a PPML regression in R.
Without time fixed effect the regression is:
require(gravity)
my_model <- PPML(y="v", dist="dist",
x=c("land","contig","comlang_ethno",
"smctry","tech","exrate"),
vce_robust=T, data=database)
I've tried to add command fe=c("year") within the PPML function but it doesn't work.
I'd appreciate any help on this.
I would comment on the previous answer but don't have enough reputation. The gravity model in your PPML command specifies v = dist × exp(land + contig + comlang_ethno + smctry + tech + exrate + TimeFE) = exp(log(dist) + land + contig + comlang_ethno + smctry + tech + exrate + TimeFE).
The formula inside of glm should have as its RHS the variables inside the exponential, because it represents the linear predictor produced by the link function (the Poisson default for which is natural log). So in sum, your command should be
glm(v ~ log(dist) + land + contig + comlang_ethno + smctry + tech + exrate + factor(year),
family='quasipoisson')
and in particular, you need to have distance in logs on the RHS (unlike the previous answer).
Just make sure that year is a factor, than you can just use the plain-and-simple glm-function as
glm(y ~ dist + year, family = "quasipoisson")
which gives you the results with year as dummies/fixed effects. The robust SE are then calculated with
lmtest::coeftest(EstimationResults.PPML, vcov=sandwich::vcovHC(model.PPML, "HC1"))
The PPML function does nothing more, it just isn't very flexible.
Alternatively to PPML and glm, you can also solve your problem using the function femlm (from package FENmlm) which deals with fixed-effect estimation for maximum likelihood models.
The two main advantages of function femlm are:
you can add as many fixed-effects as you want, and they are dealt with separately leading to computing times without comparison to glm (especially when fixed-effects contain many categories)
standard-errors can be clustered with intuitive commands
Here's an example regarding your problem (with just two variables and the year fixed-effects):
library(FENmlm)
# (default family is Poisson, 'pipe' separates variables from fixed-effects)
res = femlm(v ~ log(dist) + land | year, base)
summary(res, se = "cluster")
This code estimates the coefficients of variables log(dist) and land with year fixed-effects; then it displays the coefficients table with clustered standard-errors (w.r.t. year) for the two variables.
Going beyond your initial question, now assume you have a more complex case with three fixed-effects: country_i, country_j and year. You'd write:
res = femlm(v ~ log(dist) + land | country_i + country_j + year, base)
You can then easily play around with clustered standard-errors:
# Cluster w.r.t. country_i (default is first cluster encountered):
summary(res, se = "cluster")
summary(res, se = "cluster", cluster = "year") # cluster w.r.t. year cluster
# Two-way clustering:
summary(res, se = "twoway") # two-way clustering w.r.t. country_i & country_j
# two way clustering w.r.t. country_i & year:
summary(res, se = "twoway", cluster = c("country_i", "year"))
For more information on the package, the vignette can be found at https://cran.r-project.org/web/packages/FENmlm/vignettes/FENmlm.html.
First of all, I am relatively new in using R and haven't used lavaan (or growth models) before so please excuse my ignorance.
I am doing my thesis and analyzing the U.S. financial industry during the financial crisis of 2007. I therefore have individual banks and several variables for each bank across time (from 2007-2013), some are time-variant (such as ROA or capital adequacy) and some are time-invariant (such as size or age). Some variables are also time-variant but not multi-level since they apply to all firms (such as the average ROA of the U.S. financial industry).
Fist of all, can I use lavaan's growth curve model ("growth") in this instance? The example given on the tutorial is for either time-varying variables (c) that influence the outcome (DV) or time-invariant variables (x1 & x2) which influence the slope (s) and intercept (i). What about time varying variables that influence the slope and intercept? I couldn't find an example for this syntax.
Also, how do I specify the "groups" (i.e. different banks) in my analysis? It is actually possible to do a multi-level growth curve model in lavaan (or R for that matter)?
Last but not least, I could find how to import a multilevel dataset in R. My dataset is basically a 3-dimensional matrix (different variables for different firms across time) so how do I input that via SPSS (or notepad?)?
Any help is much appreciated, I am basically lost on how to implement this model and sincerely need some assistance...
Thank you all in advance for your time!
Harry
edit: Here is the sytanx that I have come with so far. DO you think it makes sense?
ETHthesismodel <- '
# intercept and slope with fixed coefficients
i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4
#regressions (independent variables that influence the slope & intercept)
i ~ high_constr_2007 + high_constr_2008 + ... + low_constr_2007 + low_constr_2008 + ... + ... diff_2013
s ~ high_constr_2007 + high_constr_2008 + ... + low_constr_2007 + low_constr_2008 + ... + ... diff_2013
# time-varying covariates (control variables)
t1 ~ size_2007 + cap_adeq_2007 + brand_2007 +... + acquisitions_2007
t2 ~ size_2008 + cap_adeq_2008 + brand_2008 + ... + acquisitions_2008
...
t7 ~ size_2013 + cap_adeq_2013 + brand_2013 + ... + acquisitions_2013
'
fit <- growth(ETHthesismodel, data = inputdata,
group = "bank")
summary(fit)