I would like to run a fixed effect Poisson model with panel data in R, with a count variable as the outcome, and the log of the population as an offset variable (i.e. modeling a rate). However, using the example dataset below, I get the same results when I run the two models m1 and m2. I'd be grateful if anyone could point out what I'm doing wrong in terms of specifying m1, or offer a solution using a different package? Many thanks
library(AER)
data(Fatalities)
library(pglm)
m1 <- pglm(fatal ~ beertax + as.factor(year) + offset(log(pop)), index = c("state"), model = "within", effect="individual", data = Fatalities, family = poisson)
summary(m1)
m2 <- pglm(fatal ~ beertax + as.factor(year), index = c("state"), model = "within", effect="individual", data = Fatalities, family = poisson)
summary(m2)
One direct solution is by using glm instead, with dummy variables for year and state:
fit_model <- glm(fatal ~ beertax + as.factor(year) + as.factor(state) + offset(log(pop)) , data = Fatalities, family = poisson)
which gives the same result in STATA (at least using this command: xtpoisson fatal beertax year1-year7, fe offset(log_pop)).
This approach is not feasible when the number of states is reasonably large. In CRAN, there is the novel fixest package (https://cran.r-project.org/web/packages/fixest/index.html) that provides a fast solution with robust standard errors.
Related
I am running a linear mixed effects models using the "nlme" package looking at stress and lifestyle as predictors of change in cognition over 4 years in a longitudinal dataset. All variables in the model are continuous variables.
I am able to create the model and get the summary statistics using this code:
mod1 <- lme(MS ~ age + sex + edu + GDST1*Time + HLI*Time + GDST1*HLI*Time, random= ~ 1|ID, data=NuAge_long, na.action=na.omit)
summary(mod1)
I am trying to use the "interactions" package to probe the 3-way interaction:
sim_slopes(model = mod1, pred = Time, modx = GDST1, mod2 = HLI, data = NuAge_long)
but am receiving this error:
Error in if (tcol == "df") tcol <- "t val." : argument is of length zero
I am also trying to plot the interaction using the same "interactions" package:
interact_plot(model = mod1, pred = Time, modx = GDST1, mod2 = HLI, data = NuAge_long)
and am receiving this error:
Error in UseMethod("family") : no applicable method for 'family' applied to an object of class "lme"
I can't seem to find what these errors mean and why I'm getting them. Any help would be appreciated!
From ?interactions::sim_slopes:
The function is tested with ‘lm’, ‘glm’,
‘svyglm’, ‘merMod’, ‘rq’, ‘brmsfit’, ‘stanreg’ models. Models
from other classes may work as well but are not officially
supported. The model should include the interaction of
interest.
Note this does not include lme models. On the other hand, merMod models are those generated by lme4::[g]lmer(), and as far as I can tell you should be able to fit this model equally well with lmer():
library(lme4)
mod1 <- lmer(MS ~ age + sex + edu + GDST1*Time + HLI*Time + GDST1*HLI*Time
+ (1|ID), data=NuAge_long)
(things will get harder if you want to specify correlation structures, e.g. correlation = corAR1(), which works for lme() but not lmer() ...)
I have estimated a Tobit model using the censReg package, along with the censReg function. Alternatively, the same Tobit model is estimated using the tobit function in the AER package.
Now, I really like to have some goodness of fit statistic, such as the Pseudo-R2. However, whenever I try to estimate this, the output returns as NA. For example:
Tobit <- censReg(Listing$occupancy_rate ~ ., left = -Inf, right = 1, data = Listing)
PseudoR2(Tobit, which = "McFadden")
[1] NA
So far, I have only seen reported Pseudo-R2's when people use Stata. Does anyone know how to estimate it in R?
Alternatively, Tobit estimates the (log)Sigma, which is basically the standard deviation of the residuals. Could I use this to calculate the R2?
All help is really appreciated.
You can use DescTools package to calculate PseudoR2. You have not provided any sample data. So, it is hard for me to run your model. I am using a default dataset like
library(DescTools)
r.glm <- glm(Survived ~ ., data=Untable(Titanic), family=binomial)
PseudoR2(r.glm, c("McFadden"))
For your model, you can use something like
library(AER)
data("Affairs", package = "AER")
fm.tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
data = Affairs)
#Create a function for pseudoR2 calculation
pseudoR2 <- function(obj) 1 - as.vector(logLik(obj)/logLik(update(obj, . ~ 1)))
pseudoR2(fm.tobit)
#>[1] 0.05258401
Or using censReg as you have used
library(censReg)
data("Affairs", package = "AER")
estResult <- censReg(affairs ~ age + yearsmarried + religiousness +
occupation + rating, data = Affairs)
summary(estResult)
pseudoR2(estResult)
#>[1] 0.05258401
You can find the details about pseudoR2 in the following link
R squared in logistic regression
I am using Afrobarometer survey data using 2 rounds of data for 10 countries. My DV is a binary 0-1 variable. I need to use logistic regression, fixed-effects, clustered standard errors (at country), and weighted survey data. A variable for the weights already exists in the dataframe.
I've been looking at help files for the following packages: clogit, glm, pglm, glm2, zelig, bife , etc. Typical errors include: can't add weights, can't do fixed effects, cant do either or etc.
#Glm
t3c1.fixed <- glm(formula = ethnic ~ elec_prox +
elec_comp + round + country, data=afb,
weights = afb$survey_weight,
index c("country", "round"),
family=binomial(link='logit'))
#clogit
t3c1.fixed2 <- clogit(formula = ethnic ~ elec_prox +
elec_comp + round + country, data=afb,
weights = afb$survey_weight,
method=c("within"))
#bife attempt
library(bife)
t3c1.fixed3 <- bife(ethnic ~ elec_prox + elec_comp + round +
country, model = logit,data=afb,
weights = afb$survey_weight,
bias_corr = "ana")
I either get error messages or the code doesn't include one of the conditions I need to include, so I can't use them. In Stata it appears this process is very simple, but in R it seems rather tedious. Any help would be appreciated!
I would check out the survey package which provides everything for which you are asking. The first step is to create the survey object, specify the survey weights and then you are off to the races.
library(survey)
my_survey <- svydesign(ids= ~1, strata = ~country, wts = ~wts, data = your_data)
# Then you can use the survey glm to do what you want via
svy_fit <- svy_glm(ethnic ~ elec_prox +
elec_comp + round + country, data = my_survey, family = binomial())
Or at least I would go down this path given you are using survey data.
I have a problem with the estimation of a panel data model in R.
I want to estimate the effect of a change in the Real GDP and the relative price level in the respective country on the contribution of the tourism sector.
If I use the command
Y <- cbind(ln_Differences_Contribution)
X <- cbind(ln_price_differences, Differences_ln_gdp)
and then
fixed <- plm(Y~X, data=pdata, model = "within")
I do not have an effect for the different years.
Is there anyway I can add a time variable?
If you want to control for the effect of the time period ("years"), you can use the effect argument to additionally specify time effects in a two-way within model:
plm(y ~ x1 + x2, data = pdata, model = "within", effect = "twoways")
Another way would be to explicitly include the time variable in the formula as a factor and specifying a one-way individual model as in your question, assuming your time variable is called year:
plm(y ~ x1 + x2 + factor(year), data = pdata, model = "within")
The output would be a bit "polluted" by the explicit estimates for the years (usually, one is not interested in those).
I would like to plot the effects of variables in interaction terms, using panel data and a FE model.
I have various interaction effects in my equation, for example this one here:
FIXED1 <- plm(GDPPCgrowth ~ FDI * PRIVCR, data = dfp)
I can only find solutions for lm, but not for plm.
So on the x-axis there should be PRIVCR and on the y-axis the effect of FDI on growth.
Thank you for your help!
Lisa
I am not aware of a package that supports plm objects directly. As you are asking for FE models, you can just take an LSDV approach for FE and do the estimation by lm to get an lm object which works with the effects package. Here is an example for the Grunfeld data:
library(plm)
library(effects)
data("Grunfeld", package = "plm")
mod_fe <- plm(inv ~ value + capital + value:capital, data = Grunfeld, model = "within")
Grunfeld[ , "firm"] <- factor(Grunfeld[ , "firm"]) # needs to be factor in the data NOT in the formula [required by package effects]
mod_lsdv <- lm(inv ~ value + capital + value:capital + firm, data = Grunfeld)
coefficients(mod_fe) # estimates are the same
coefficients(mod_lsdv) # estimates are the same
eff_obj <- effects::Effect(c("value", "capital"), mod_lsdv)
plot(eff_obj)