Solved – What data cleaning to do for logit regression with only dummies

logitrregression

Does anyone know what exact data cleaning steps one need to undertake in order to clean data for a logit regression (not a logistic regression)?

I have only time variables, meaning year and month, as my independent variables, and I am using R.

A logit regression is simply a normal linear regression where the DV have been transformed with the following formula:

logit(y) = ln(y/(1-y) for

An example:

3 of 12 people gets cured from taking a pill in period 3 ->
ln(0.25/(1-0.25)

5 of 25 people gets cured taking a pill in period 5 ->
ln(0.20/(1-0.20)

One can use the logit transformation if you have ratios and in many papers and books it is closely related to the logistic regression.

Best Answer

Data cleaning is data cleaning. It doesn't matter whether you are doing regression (any type) or something else, bad data will mess things up.

So... Are any of your DV incorrect and or impossible? Are your dates correctly coded?

etc.

But, if you have many time points, shouldn't you be looking at some sort of time-series model? Regression (any type) with time series data is fraught.

Related Solutions

Solved – Are ecologists the only ones who didn’t know that the arcsine is asinine

I teach it to public health students for two reasons:

one of my colleagues teach it (in the introduction course) as magic recipe, I show them the Delta method and how it is derived;
I think the Delta method and variance stabilizing transformations are not asinine and can be useful. The confidence interval computed using arcsin transform with correction of continuity is not perfect but behaves reasonnably well, and for small samples it is much much better¹ than the Wald procedure, which is still widely used.

As John for psychology and neuroscience, I think many people in epidemiology don’t even care, they just use linear models in a push-button way.

¹ Pires, Amado, 2008. Interval estimators for a binomial proportion.

Solved – Simulating Multinomial Logit Data with R

It is really simple to generate multinomial logit regression data. All you need to keep in mind are the normalizing assumptions.

# covariate matrix
mX = matrix(rnorm(1000), 200, 5)

# coefficients for each choice
vCoef1 = rep(0, 5)
vCoef2 = rnorm(5)
vCoef3 = rnorm(5)

# vector of probabilities
vProb = cbind(exp(mX%*%vCoef1), exp(mX%*%vCoef2), exp(mX%*%vCoef3))

# multinomial draws
mChoices = t(apply(vProb, 1, rmultinom, n = 1, size = 1))
dfM = cbind.data.frame(y = apply(mChoices, 1, function(x) which(x==1)), mX)

Here mChoices and dfM$y encode the same information differently.

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Related Solutions

Solved – Are ecologists the only ones who didn’t know that the arcsine is asinine

Solved – Simulating Multinomial Logit Data with R

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