Generally, I wouldn't drop the intercept term if there isn't a reason for it (see also these answers. If your problem is just getting the same values for the fixed effects as seen in fm2, simply subtract or add the entry2, entry3, $...$ , entry20 from the intercept value (which in this case is the estimate for entry1), e.g.:

entry2: $50.12550625-0.55280604 = 49.5727$

entry20: $50.12550625+0.13853678 = 50.26404$

In a regression output with categorical predictors, the intercept is the expected mean value of $Y$ when all $X=0$.

But as @Roland pointed out, better use the lsmeans() function in the lsmeans package:

> library(lsmeans)
> lsmeans(fm1,"entry")
 entry   lsmean        SE df lower.CL upper.CL
 1     50.12551 0.6053462  2 47.52091 52.73010
 2     49.57270 0.6053462  2 46.96811 52.17729
 3     49.92951 0.6053462  2 47.32491 52.53410
 4     49.27776 0.6053462  2 46.67316 51.88235
 5     49.58035 0.6053462  2 46.97576 52.18495
 6     49.36311 0.6053462  2 46.75852 51.96771
 7     49.63090 0.6053462  2 47.02630 52.23549
 8     49.05924 0.6053462  2 46.45465 51.66384
 9     49.63219 0.6053462  2 47.02760 52.23679
 10    49.22572 0.6053462  2 46.62113 51.83032
 11    49.60636 0.6053462  2 47.00176 52.21095
 12    49.31465 0.6053462  2 46.71005 51.91924
 13    49.13514 0.6053462  2 46.53054 51.73973
 14    49.51608 0.6053462  2 46.91148 52.12067
 15    49.72270 0.6053462  2 47.11810 52.32729
 16    49.76172 0.6053462  2 47.15712 52.36631
 17    49.65226 0.6053462  2 47.04766 52.25685
 18    48.99149 0.6053462  2 46.38689 51.59608
 19    50.15815 0.6053462  2 47.55355 52.76274
 20    50.26404 0.6053462  2 47.65945 52.86864

Confidence level used: 0.95 

Generally, I wouldn't drop the intercept term if there isn't a reason for it (see also these answers. If your problem is just getting the same values for the fixed effects as seen in fm2, simply subtract or add the entry2, entry3, $...$ , entry20 from the intercept value (which in this case is the estimate for entry1), e.g.:

entry2: $50.12550625-0.55280604 = 49.5727$

entry20: $50.12550625+0.13853678 = 50.26404$

In a regression output with categorical predictors, the intercept is the expected mean value of $Y$ when all $X=0$.

But as @Roland pointed out, better use the lsmeans() function in the lsmeans package:

> library(lsmeans)
> lsmeans(fm1,"entry")
 entry   lsmean        SE df lower.CL upper.CL
 1     50.12551 0.6053462  2 47.52091 52.73010
 2     49.57270 0.6053462  2 46.96811 52.17729
 3     49.92951 0.6053462  2 47.32491 52.53410
 4     49.27776 0.6053462  2 46.67316 51.88235
 5     49.58035 0.6053462  2 46.97576 52.18495
 6     49.36311 0.6053462  2 46.75852 51.96771
 7     49.63090 0.6053462  2 47.02630 52.23549
 8     49.05924 0.6053462  2 46.45465 51.66384
 9     49.63219 0.6053462  2 47.02760 52.23679
 10    49.22572 0.6053462  2 46.62113 51.83032
 11    49.60636 0.6053462  2 47.00176 52.21095
 12    49.31465 0.6053462  2 46.71005 51.91924
 13    49.13514 0.6053462  2 46.53054 51.73973
 14    49.51608 0.6053462  2 46.91148 52.12067
 15    49.72270 0.6053462  2 47.11810 52.32729
 16    49.76172 0.6053462  2 47.15712 52.36631
 17    49.65226 0.6053462  2 47.04766 52.25685
 18    48.99149 0.6053462  2 46.38689 51.59608
 19    50.15815 0.6053462  2 47.55355 52.76274
 20    50.26404 0.6053462  2 47.65945 52.86864

Confidence level used: 0.95 

Generally, I wouldn't drop the intercept term if there isn't a reason for it (see also these answers. If your problem is just getting the same values for the fixed effects as seen in fm2, simply subtract or add the entry2, entry3, $...$ , entry20 from the intercept value (which in this case is the estimate for entry1), e.g.:

entry2: $50.12550625-0.55280604 = 49.5727$

entry20: $50.12550625+0.13853678 = 50.26404$

Generally, I wouldn't drop the intercept term if there isn't a reason for it (see also these answers. If your problem is just getting the same values for the fixed effects as seen in fm2, simply subtract or add the entry2, entry3, $...$ , entry20 from the intercept value (which in this case is the estimate for entry1), e.g.:

entry2: $50.12550625-0.55280604 = 49.5727$

entry20: $50.12550625+0.13853678 = 50.26404$

In a regression output with categorical predictors, the intercept is the expected mean value of $Y$ when all $X=0$.

But as @Roland pointed out, better use the lsmeans() function in the lsmeans package:

> library(lsmeans)
> lsmeans(fm1,"entry")
 entry   lsmean        SE df lower.CL upper.CL
 1     50.12551 0.6053462  2 47.52091 52.73010
 2     49.57270 0.6053462  2 46.96811 52.17729
 3     49.92951 0.6053462  2 47.32491 52.53410
 4     49.27776 0.6053462  2 46.67316 51.88235
 5     49.58035 0.6053462  2 46.97576 52.18495
 6     49.36311 0.6053462  2 46.75852 51.96771
 7     49.63090 0.6053462  2 47.02630 52.23549
 8     49.05924 0.6053462  2 46.45465 51.66384
 9     49.63219 0.6053462  2 47.02760 52.23679
 10    49.22572 0.6053462  2 46.62113 51.83032
 11    49.60636 0.6053462  2 47.00176 52.21095
 12    49.31465 0.6053462  2 46.71005 51.91924
 13    49.13514 0.6053462  2 46.53054 51.73973
 14    49.51608 0.6053462  2 46.91148 52.12067
 15    49.72270 0.6053462  2 47.11810 52.32729
 16    49.76172 0.6053462  2 47.15712 52.36631
 17    49.65226 0.6053462  2 47.04766 52.25685
 18    48.99149 0.6053462  2 46.38689 51.59608
 19    50.15815 0.6053462  2 47.55355 52.76274
 20    50.26404 0.6053462  2 47.65945 52.86864

Confidence level used: 0.95