Refits a model with a specified set of coefficients.
Arguments
- model
An R model, e.g., produced by
lmorglm- beta
A numeric vector with
length(coef)model coefficients. If the vector is not named, the coefficients should be given in the same order as incoef(model). If the vector is named, the procedure attempts to match on names.
Details
The function calculates the linear predictor using the new coefficients,
and reformulates the model using the offset
argument. The linear predictor is called
offset, and its coefficient will be 1 by definition.
The new model only fits the intercept, which should be 0
if we set beta = coef(model).
Examples
model0 <- lm(Volume ~ Girth + Height, data = trees)
formula(model0)
#> Volume ~ Girth + Height
#> <environment: 0x55589a85ede0>
coef(model0)
#> (Intercept) Girth Height
#> -57.9876589 4.7081605 0.3392512
deviance(model0)
#> [1] 421.9214
# refit same model
model1 <- fix.coef(model0)
formula(model1)
#> Volume ~ 1
#> <environment: 0x55589a85ede0>
coef(model1)
#> (Intercept)
#> 1.125519e-14
deviance(model1)
#> [1] 421.9214
# change the beta's
model2 <- fix.coef(model0, beta = c(-50, 5, 1))
coef(model2)
#> (Intercept)
#> -62.07097
deviance(model2)
#> [1] 1098.984
# compare predictions
plot(predict(model0), predict(model1))
abline(0, 1)
plot(predict(model0), predict(model2))
abline(0, 1)
# compare proportion explained variance
cor(predict(model0), predict(model0) + residuals(model0))^2
#> [1] 0.94795
cor(predict(model1), predict(model1) + residuals(model1))^2
#> [1] 0.94795
cor(predict(model2), predict(model2) + residuals(model2))^2
#> [1] 0.9228528
# extract offset from constrained model
summary(model2$offset)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 57.00 82.00 87.00 92.24 102.25 140.00
# it also works with factors and missing data
model0 <- lm(bmi ~ age + hyp + chl, data = nhanes2)
model1 <- fix.coef(model0)
model2 <- fix.coef(model0, beta = c(15, -8, -8, 2, 0.2))