Dear R-Users,

 

How can I put a pre-defined regression model into to an object of class lm
in order to use the predict.lm function.

 

A simplified example:

 

I would normally run a regression analysis on a dataset, 

 

> germany<-lm(RENT~AGE1, in.mi01)

> summary(germany)

 

Call:

lm(formula = RENT ~ AGE1, data = in.mi01)

 

Residuals:

    Min      1Q  Median      3Q     Max 

-12.193  -3.646  -1.009   2.101  49.025 

 

Coefficients:

             Estimate Std. Error t value Pr(>|t|)    

(Intercept) 13.283639   0.063020  210.78   <2e-16 ***

AGE1        -0.091030   0.003231  -28.17   <2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

Residual standard error: 6.044 on 19802 degrees of freedom

Multiple R-Squared: 0.03854,    Adjusted R-squared: 0.03849 

F-statistic: 793.7 on 1 and 19802 DF,  p-value: < 2.2e-16

 

and then use the predict.lm function which uses the above specified
regression equation: RENT=13.283639-0.091030*AGE1 

 

But I want to skip the lm function and specify my own regression equation
RENT= 15 -0.15*AGE1 and then use the predict.lm function. However, in order
to use the predict.lm function I need an object of class lm. Is there any
way to do so? Or maybe somebody has another solution? 

 

Thanks in advance,

 

Simon

 

 


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