I am trying to learn R after using Stata and I must say that I love it. But now I am having some trouble. I am about to do some multiple regressions with Panel Data so I am using the plm package.
Now I want to have the same results with plm in R as when I use the lm function and Stata when I perform a heteroscedasticity robust and entity fixed regression.
Let's say that I have a panel dataset with the variables Y, ENTITY, TIME, V1.
I get the same standard errors in R with this code
lm.model<-lm(Y ~ V1 + factor(ENTITY), data=data)
coeftest(lm.model, vcov.=vcovHC(lm.model, type="HC1))
as when I perform this regression in Stata
xi: reg Y V1 i.ENTITY, robust
But when I perform this regression with the plm package I get other standard errors
plm.model<-plm(Y ~ V1 , index=C("ENTITY","YEAR"), model="within", effect="individual", data=data)
coeftest(plm.model, vcov.=vcovHC(plm.model, type="HC1))
Have I missed setting some options?
Does the plm model use some other kind of estimation and if so how?
Can I in some way have the same standard errors with plm as in Stata with , robust
By default the plm package does not use the exact same small-sample correction for panel data as Stata. However in version 1.5 of plm (on CRAN) you have an option that will emulate what Stata is doing.
plm.model<-plm(Y ~ V1 , index=C("ENTITY","YEAR"), model="within",
effect="individual", data=data)
coeftest(plm.model, vcov.=function(x) vcovHC(x, type="sss"))
This should yield the same clustered by group standard-errors as in Stata (but as mentioned in the comments, without a reproducible example and what results you expect it's harder to answer the question).
For more discussion on this and some benchmarks of R and Stata robust SEs see Fama-MacBeth and Cluster-Robust (by Firm and Time) Standard Errors in R.
See also:
Clustered standard errors in R using plm (with fixed effects)
Is it possible that your Stata code is different from what you are doing with plm?
plm's "within" option with "individual" effects means a model of the form:
yit = a + Xit*B + eit + ci
What plm does is to demean the coefficients so that ci drops from the equation.
yit_bar = Xit_bar*B + eit_bar
Such that the "bar" suffix means that each variable had its mean subtracted. The mean is calculated over time and that is why the effect is for the individual. You could also have a fixed time effect that would be common to all individuals in which case the effect would be through time as well (that is irrelevant in this case though).
I am not sure what the "xi" command does in STATA, but i think it expands an interaction right ? Then it seems to me that you are trying to use a dummy variable per ENTITY as was highlighted by #richardh.
For your Stata and plm codes to match you must be using the same model.
You have two options:(1) you xtset your data in stata and use the xtreg option with the fe modifier or (2) you use plm with the pooling option and one dummy per ENTITY.
Matching Stata to R:
xtset entity year
xtreg y v1, fe robust
Matching plm to Stata:
plm(Y ~ V1 + as.factor(ENTITY) , index=C("ENTITY","YEAR"), model="pooling", effect="individual", data=data)
Then use vcovHC with one of the modifiers. Make sure to check this paper that has a nice review of all the mechanics behind the "HC" options and the way they affect the variance covariance matrix.
Hope this helps.
Related
I am adjusting a mixed effects model which, due to the observed heteroscedasticity, it was necessary to include an effect to accommodate it. Therefore, using the lme function of the nlme package, this was easy to be solved, see the code below:
library(nlme)
library(lme4)
Model1 <- lme(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5),
random = ~1|Var6, Data1, method="REML",
weights = varIdent(form=~1|Var7))
#Var6: It is a factor with several levels.
#Var7: It is a Dummy variable.
However, I need to readjust the model described above using the lme4 package, that is, using the lmer function. It is known and many are the materials that inform some limitations existing in the lme4, such as, for example, modeling heteroscedasticity. What motivated me to readjust this model is the fact that I have an interest in using a specific package that in cases of mixed models it only accepts if they are adjusted through the lmer function. How could I resolve this situation? Below is a good part of the model adjusted using the lmer function, however, this model is not considering the effect to model the observed heteroscedasticity.
Model2 <- lmer(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5)+(1|Var6),
Data1, REML=T)
Regarding the choice of the random effect (Var6) and the inclusion of the effect to consider the heterogeneity by levels of the variable (Var7), these were carefully analyzed, however, I will not put here the whole procedure so as not to be an extensive post and to be more objective .
This is hackable. You need to add an observation-level random effect that is only applied to the group with the larger residual variance (you need to know this in advance!), via (0+dummy(Var7,"1")|obs); this has the effect of multiplying each observation-level random effect value by 1 if the observation is in group "1" of Var7, 0 otherwise. You also need to use lmerControl() to override a few checks that lmer does to try to make sure you are not adding redundant random effects.
Data1$obs <- factor(seq(nrow(Data1)))
Model2 <- lmer(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5) + (1|Var6) +
(0+dummy(Var7,"1")|obs),
Data1, REML=TRUE,
control=lmerControl(check.nobs.vs.nlev="ignore",
check.nobs.vs.nRE="ignore"))
all.equal(REMLcrit(Model2), c(-2*logLik(Model1))) ## TRUE
all.equal(fixef(Model1), fixef(Model2), tolerance=1e-7)
If you want to use this model with hnp you need to work around the fact that hnp doesn't pass the lmerControl option properly.
library(hnp)
d <- function(obj) resid(obj, type="pearson")
s <- function(n, obj) simulate(obj)[[1]]
f <- function(y.) refit(Model2, y.)
hnp(Model2, newclass=TRUE, diagfun=d, simfun=s, fitfun=f)
You might also be interested in the DHARMa package, which does similar simulation-based diagnostics.
I have been doing variable selection for a modeling problem.
I have used trial and error for the selection (adding / removing a variable) with a decrease in error. However, I have the challenge as the number of variables grows into the hundreds that manual variable selection can not be performed as the model takes 1/2 hour to compute, rendering the task impossible.
Would you happen to know of any other packages than the regsubsets from the leaps package (which when tested with the same trial and error variables produced a higher error, it did not include some variables which were lineraly dependant - excluding some valuable variables).
You need a better (i.e. not flawed) approach to model selection. There are plenty of options, but one that should be easy to adapt to your situation would be using some form of regularization, such as the Lasso or the elastic net. These apply shrinkage to the sizes of the coefficients; if a coefficient is shrunk from its least squares solution to zero, that variable is removed from the model. The resulting model coefficients are slightly biased but they have lower variance than the selected OLS terms.
Take a look at the lars, glmnet, and penalized packages
Try using the stepAIC function of the MASS package.
Here is a really minimal example:
library(MASS)
data(swiss)
str(swiss)
lm <- lm(Fertility ~ ., data = swiss)
lm$coefficients
## (Intercept) Agriculture Examination Education Catholic
## 66.9151817 -0.1721140 -0.2580082 -0.8709401 0.1041153
## Infant.Mortality
## 1.0770481
st1 <- stepAIC(lm, direction = "both")
st2 <- stepAIC(lm, direction = "forward")
st3 <- stepAIC(lm, direction = "backward")
summary(st1)
summary(st2)
summary(st3)
You should try the 3 directions and ckeck which model works better with your test data.
Read ?stepAIC and take a look at the examples.
EDIT
It's true stepwise regression isn't the greatest method. As it's mentioned in GavinSimpson answer, lasso regression is a better/much more efficient method. It's much faster than stepwise regression and will work with large datasets.
Check out the glmnet package vignette:
http://www.stanford.edu/~hastie/glmnet/glmnet_alpha.html
A newbie question: does anyone know how to run a logistic regression with clustered standard errors in R? In Stata it's just logit Y X1 X2 X3, vce(cluster Z), but unfortunately I haven't figured out how to do the same analysis in R. Thanks in advance!
You might want to look at the rms (regression modelling strategies) package. So, lrm is logistic regression model, and if fit is the name of your output, you'd have something like this:
fit=lrm(disease ~ age + study + rcs(bmi,3), x=T, y=T, data=dataf)
fit
robcov(fit, cluster=dataf$id)
bootcov(fit,cluster=dataf$id)
You have to specify x=T, y=T in the model statement. rcs indicates restricted cubic splines with 3 knots.
Another alternative would be to use the sandwich and lmtest package as follows. Suppose that z is a column with the cluster indicators in your dataset dat. Then
# load libraries
library("sandwich")
library("lmtest")
# fit the logistic regression
fit = glm(y ~ x, data = dat, family = binomial)
# get results with clustered standard errors (of type HC0)
coeftest(fit, vcov. = vcovCL(fit, cluster = dat$z, type = "HC0"))
will do the job.
I have been banging my head against this problem for the past two days; I magically found what appears to be a new package which seems destined for great things--for example, I am also running in my analysis some cluster-robust Tobit models, and this package has that functionality built in as well. Not to mention the syntax is much cleaner than in all the other solutions I've seen (we're talking near-Stata levels of clean).
So for your toy example, I'd run:
library(Zelig)
logit<-zelig(Y~X1+X2+X3,data=data,model="logit",robust=T,cluster="Z")
Et voilà!
There is a command glm.cluster in the R package miceadds which seems to give the same results for logistic regression as Stata does with the option vce(cluster). See the documentation here.
In one of the examples on this page, the commands
mod2 <- miceadds::glm.cluster(data=dat, formula=highmath ~ hisei + female,
cluster="idschool", family="binomial")
summary(mod2)
give the same robust standard errors as the Stata command
logit highmath hisei female, vce(cluster idschool)
e.g. a standard error of 0.004038 for the variable hisei.
In lm and glm models, I use functions coef and confint to achieve the goal:
m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous
coef(m)
confint(m)
Now I added random effect to the model - used mixed effects models using lmer function from lme4 package. But then, functions coef and confint do not work any more for me!
> mix1 = lmer(resp ~ 0 + var1 + var1:var2 + (1|var3))
# var1, var3 categorical, var2 continuous
> coef(mix1)
Error in coef(mix1) : unable to align random and fixed effects
> confint(mix1)
Error: $ operator not defined for this S4 class
I tried to google and use docs but with no result. Please point me in the right direction.
EDIT: I was also thinking whether this question fits more to https://stats.stackexchange.com/ but I consider it more technical than statistical, so I concluded it fits best here (SO)... what do you think?
Not sure when it was added, but now confint() is implemented in lme4.
For example the following example works:
library(lme4)
m = lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
confint(m)
There are two new packages, lmerTest and lsmeans, that can calculate 95% confidence limits for lmer and glmer output. Maybe you can look into those? And coefplot2, I think can do it too (though as Ben points out below, in a not so sophisticated way, from the standard errors on the Wald statistics, as opposed to Kenward-Roger and/or Satterthwaite df approximations used in lmerTest and lsmeans)... Just a shame that there are still no inbuilt plotting facilities in package lsmeans (as there are in package effects(), which btw also returns 95% confidence limits on lmer and glmer objects but does so by refitting a model without any of the random factors, which is evidently not correct).
I suggest that you use good old lme (in package nlme). It has confint, and if you need confint of contrasts, there is a series of choices (estimable in gmodels, contrast in contrasts, glht in multcomp).
Why p-values and confint are absent in lmer: see http://finzi.psych.upenn.edu/R/Rhelp02a/archive/76742.html .
Assuming a normal approximation for the fixed effects (which confint would also have done), we can obtain 95% confidence intervals by
estimate + 1.96*standard error.
The following does not apply to the variance components/random effects.
library("lme4")
mylm <- lmer(Reaction ~ Days + (Days|Subject), data =sleepstudy)
# standard error of coefficient
days_se <- sqrt(diag(vcov(mylm)))[2]
# estimated coefficient
days_coef <- fixef(mylm)[2]
upperCI <- days_coef + 1.96*days_se
lowerCI <- days_coef - 1.96*days_se
I'm going to add a bit here. If m is a fitted (g)lmer model (most of these work for lme too):
fixef(m) is the canonical way to extract coefficients from mixed models (this convention began with nlme and has carried over to lme4)
you can get the full coefficient table with coef(summary(m)); if you have loaded lmerTest before fitting the model, or convert the model after fitting (and then loading lmerTest) via coef(summary(as(m,"merModLmerTest"))), then the coefficient table will include p-values. (The coefficient table is a matrix; you can extract the columns via e.g. ctab[,"Estimate"], ctab[,"Pr(>|t|)"], or convert the matrix to a data frame and use $-indexing.)
As stated above you can get likelihood profile confidence intervals via confint(m); these may be computationally intensive. If you use confint(m, method="Wald") you'll get the standard +/- 1.96SE confidence intervals. (lme uses intervals(m) instead of confint().)
If you prefer to use broom.mixed:
tidy(m,effects="fixed") gives you a table with estimates, standard errors, etc.
tidy(as(m,"merModLmerTest"), effects="fixed") (or fitting with lmerTest in the first place) includes p-values
adding conf.int=TRUE gives (Wald) CIs
adding conf.method="profile" (along with conf.int=TRUE) gives likelihood profile CIs
You can also get confidence intervals by parametric bootstrap (method="boot"), which is considerably slower but more accurate in some circumstances.
To find the coefficient, you can simply use the summary function of lme4
m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous
m_summary <- summary(m)
to have all coefficients :
m_summary$coefficient
If you want the confidence interval, multiply the standart error by 1.96:
CI <- m_summary$coefficient[,"Std. Error"]*1.96
print(CI)
I'd suggest tab_model() function from sjPlot package as alternative. Clean and readable output ready for markdown. Reference here and examples here.
For those more visually inclined plot_model() from the same package might come handy too.
Alternative solution is via parameters package using model_parameters() function.
I am running logistic regressions using R right now, but I cannot seem to get many useful model fit statistics. I am looking for metrics similar to SAS:
http://www.ats.ucla.edu/stat/sas/output/sas_logit_output.htm
Does anyone know how (or what packages) I can use to extract these stats?
Thanks
Here's a Poisson regression example:
## from ?glm:
d.AD <- data.frame(counts=c(18,17,15,20,10,20,25,13,12),
outcome=gl(3,1,9),
treatment=gl(3,3))
glm.D93 <- glm(counts ~ outcome + treatment,data = d.AD, family=poisson())
Now define a function to fit an intercept-only model with the same response, family, etc., compute summary statistics, and combine them into a table (matrix). The formula .~1 in the update command below means "refit the model with the same response variable [denoted by the dot on the LHS of the tilde] but with only an intercept term [denoted by the 1 on the RHS of the tilde]"
glmsumfun <- function(model) {
glm0 <- update(model,.~1) ## refit with intercept only
## apply built-in logLik (log-likelihood), AIC,
## BIC (Bayesian/Schwarz Information Criterion) functions
## to models with and without intercept ('model' and 'glm0');
## combine the results in a two-column matrix with appropriate
## row and column names
matrix(c(logLik(glm.D93),BIC(glm.D93),AIC(glm.D93),
logLik(glm0),BIC(glm0),AIC(glm0)),ncol=2,
dimnames=list(c("logLik","SC","AIC"),c("full","intercept_only")))
}
Now apply the function:
glmsumfun(glm.D93)
The results:
full intercept_only
logLik -23.38066 -26.10681
SC 57.74744 54.41085
AIC 56.76132 54.21362
EDIT:
anova(glm.D93,test="Chisq") gives a sequential analysis of deviance table containing df, deviance (=-2 log likelihood), residual df, residual deviance, and the likelihood ratio test (chi-squared test) p-value.
drop1(glm.D93) gives a table with the AIC values (df, deviances, etc.) for each single-term deletion; drop1(glm.D93,test="Chisq") additionally gives the LRT test p value.
Certainly glm with a family="binomial" argument is the function most commonly used for logistic regression. The default handling of contrasts of factors is different. R uses treatment contrasts and SAS (I think) uses sum contrasts. You can look these technical issues up on R-help. They have been discussed many, many times over the last ten+ years.
I see Greg Snow mentioned lrm in 'rms'. It has the advantage of being supported by several other functions in the 'rms' suite of methods. I would use it , too, but learning the rms package may take some additional time. I didn't see an option that would create SAS-like output.
If you want to compare the packages on similar problems that UCLA StatComputing pages have another resource: http://www.ats.ucla.edu/stat/r/dae/default.htm , where a large number of methods are exemplified in SPSS, SAS, Stata and R.
Using the lrm function in the rms package may give you the output that you are looking for.