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I need to create a probit model without the intercept. So, how can I remove the intercept from a probit model in R?
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So, how can I remove the intercept from a probit model in R? Just add a -1 in your formula as in: glm(y ~ x1 + x2 - 1, family = binomial(link = "probit"), data = yourdata) this will estimate a probit model without intercept.
The intercept (often labeled as constant) is the point where the function crosses the y-axis. In some analysis, the regression model only becomes significant when we remove the intercept, and the regression line reduces to Y = bX + error.
Most multiple regression models include a constant term (i.e., the intercept), since this ensures that the model will be unbiased--i.e., the mean of the residuals will be exactly zero. (The coefficients in a regression model are estimated by least squares--i.e., minimizing the mean squared error.
You don't say how you are intending to fit the probit model, but if it uses R's formula notation to describe the model then you can supply either + 0 or - 1 as part of the formula to suppress the intercept:
mod <- foo(y ~ 0 + x1 + x2, data = bar) or
mod <- foo(y ~ x1 + x2 - 1, data = bar) (both using pseudo R code of course - substitute your modelling function and data/variables.)
If this is a model fitting by glm() then something like:
mod <- glm(y ~ x1 + x2 - 1, data = bar, family = binomial(link = "probit")) should do it (again substituting in your data and variable names as appropriate.)
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