Remove coefficients from lm output in R -

i remove of as.factor elements output of ordinary least squares lm() model in r. last line doesn't work, example:

frame <- data.frame(y = rnorm(100), x= rnorm(100), block = sample(c("a", "b", "c", "d"), 100, replace = true)) mod <- lm(y ~ x + as.factor(block), data = frame) summary(mod)  summary(mod)$coefficients[3:5,] <- null 

is there way remove of these elements saved `lm' object no longer has them? thanks.

one option use felm function in lfe package.

as stated in package:

the package intended linear models multiple group fixed effects, i.e. 2 or more factors large number of levels. performs similar functions lm, uses special method projecting out multiple group fixed effects normal equations, hence faster.

set.seed(123) frame <- data.frame(y = rnorm(100), x= rnorm(100), block = sample(c("a", "b", "c", "d"), 100, replace = true)) id<-as.factor(frame$block) mod <- lm(y ~ x + id, data = frame) #lm summary(mod) call: lm(formula = y ~ x + id, data = frame)  residuals:      min       1q   median       3q      max  -2.53394 -0.68372  0.04072  0.67805  2.00777   coefficients:             estimate std. error t value pr(>|t|)   (intercept)  0.18115    0.17201   1.053   0.2950   x           -0.08310    0.09604  -0.865   0.3891   idb          0.04834    0.24645   0.196   0.8449   idc         -0.51265    0.25052  -2.046   0.0435 * idd          0.04905    0.26073   0.188   0.8512   --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  residual standard error: 0.9002 on 95 degrees of freedom multiple r-squared:  0.06677,   adjusted r-squared:  0.02747  f-statistic: 1.699 on 4 , 95 df,  p-value: 0.1566   library(lfe) est <- felm(y ~ x| id) summary(est)  call:    felm(formula = y ~ x | id, data = frame)   residuals:      min       1q   median       3q      max  -2.53394 -0.68372  0.04072  0.67805  2.00777   coefficients:   estimate std. error t value pr(>|t|) x -0.08310    0.09604  -0.865    0.389  residual standard error: 0.9002 on 95 degrees of freedom multiple r-squared(full model): 0.06677   adjusted r-squared: 0.02747  multiple r-squared(proj model): 0.00782   adjusted r-squared: -0.03396  f-statistic(full model):1.699 on 4 , 95 df, p-value: 0.1566  f-statistic(proj model): 0.7487 on 1 , 95 df, p-value: 0.3891  

p.s. similar program stata reghdfe.