Hello I am using `lm(y~poly(x,2))`

to fit a second order polynomial to my data. But I just couldn't find a way to give a known intercept value. How can I fit a polynomial model with a known intercept value (say 'k') using `lm`

? Thanks

Answer:

```
lm(y~-1+x+I(x^2)+offset(k))
```

should do it.

`-1`

suppresses the otherwise automatically added intercept term`x`

adds a linear term`I(x^2)`

adds a quadratic term; the`I()`

is required so that R interprets`^2`

as squaring, rather than taking an interaction between`x`

and itself (which by formula rules would be equivalent to`x`

alone)`offset(k)`

adds the known constant intercept

I don't know whether `poly(x,2)-1`

would work to eliminate the intercept; you can try it and see. Subtracting the offset from your data should work fine, but `offset(k)`

might be slightly more explicit. You might have to make `k`

a vector (i.e. replicate it to the length of the data set, or better include it as a column in the data set and pass the data with `data=...`

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