Maximum Likelihood Estimation - MLE - in R using the plm package

Lucca coding

I have unbalanced panel data and I want to fit this type of regression:

Pr(y=1|xB) = G(xB+a)

where "y" is a binary variable, "x" is a vector of explanatory variables, and "B" is my coefficient.

I want to implement a random effects model with maximum likelihood estimation, but I don't understand what I need to change in the function of the plm(package plm) CRAN guide (vignette) . As far as I use this code:

library(plm)
p_finale <- plm.data(p_finale, index=c("idnumber","Year"))
attach(p_finale)
y <- (TotalDebt_dummy)
X_tot <- cbind(Size,ln_Age,liquidity,Asset_Tangibility,profitability,growth, sd_cf_risk1, family_dummy,family_manager,
               sd_cf_risk1*family_dummy,
               Ateco_A,Ateco_C,Ateco_D,Ateco_E,Ateco_F,Ateco_G,Ateco_H,Ateco_I,Ateco_J,Ateco_M,Ateco_N,
               Ateco_Q,Ateco_R)

model1 <- plm(y~X_tot+factor(Year),data = p_finale, model="random")

I included the entire code, but I think the only thing that needs to be changed is the plm.

Spiral 123

plmThe functions in the package do not use the maximum likelihood method for model estimation. It uses the GLS approach commonly found in econometrics.plm

See the section on plm with nlme and lme4 in the first vignette of the package ("Panel Data Econometrics Using R: The plm Package" ( https://cran.rstudio.com/web/packages/plm/vignettes /A_plmPackage.html ). This section explains the differences between the methods and provides code examples for both (with references to packages nlmeand lme4maximum likelihood methods).

Related

Maximum Likelihood Estimation of Gamma Density in R

Mercier I just simulated 100 random observations from a gamma density with alpha (shape parameter) = 5 and lambda (rate parameter) = 5: x=rgamma(100,shape=5,rate=5) Now I want to find maximum likelihood estimates of alpha and lambda with a function that will r

Maximum Likelihood Estimation of Gamma Density in R

Mercier I just simulated 100 random observations from a gamma density with alpha (shape parameter) = 5 and lambda (rate parameter) = 5: x=rgamma(100,shape=5,rate=5) Now I want to find maximum likelihood estimates of alpha and lambda with a function that will r

Maximum Likelihood Estimation of Gamma Density in R

Mercier I just simulated 100 random observations from a gamma density with alpha (shape parameter) = 5 and lambda (rate parameter) = 5: x=rgamma(100,shape=5,rate=5) Now I want to find maximum likelihood estimates of alpha and lambda with a function that will r

Maximum Likelihood Estimation Using Step Functions

Simon L I want to fit a step function (two parameters) to some data. The code below doesn't do the job. I wonder round()if there is something wrong with this argument. However, I've also tried dividing the parameters to make small (say 0.001) changes to the pa

Problems implementing MLE estimation in the bbmle package (for R)

will I am trying to validate the MLEs for $\alpha$ , $\beta$ and $\lambda$ obtained for the Logistic-Lomax distribution by Zubair et al. in their paper titled A Study of Logistic-Lomax Distribution when using Dataset 1 . The paper uses the following code to do

Maximum Likelihood Estimation for Binomial Data

Luna I need to find maximum likelihood estimates for binomial data vectors. One like this: binvec <- rbinom(1000, 1, 0.5) I try to create the function first and then use to optimize optim(). llbinom <- function(theta, x) {return(sum(dbinom(x=x,

Maximum Likelihood Estimation for Binomial Data

Luna I need to find maximum likelihood estimates for binomial data vectors. One like this: binvec <- rbinom(1000, 1, 0.5) I try to create the function first and then use to optimize optim(). llbinom <- function(theta, x) {return(sum(dbinom(x=x,

What exactly does maximum likelihood estimation mean?

username When training a model, we usually use MLE to estimate the model. I know this means that the most likely data for this learned model is our training set. But I want to know if its probability exactly matches 1? Bogatron You are almost right. The likeli

What exactly does maximum likelihood estimation mean?

username When training a model, we usually use MLE to estimate the model. I know this means that the most likely data for this learned model is our training set. But I want to know if its probability exactly matches 1? Bogatron You are almost right. The likeli

What exactly does maximum likelihood estimation mean?

username When training a model, we usually use MLE to estimate the model. I know this means that the most likely data for this learned model is our training set. But I want to know if its probability exactly matches 1? Bogatron You are almost right. The likeli