Exclude first factor from intercept in R
I'm doing a multi-variable regression in R, where one variable is set as a factor. If I do the regression, the intercept is set to the first factor and all following factors are set relative to that.
So if I do the following regression:
pe1n = lm(return_1_yr_abs~size+fe_year+retained_binary,data=rtn_1_yr)
I get following output:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.516160 15.031941 1.165 0.2457
size -0.001619 0.001214 -1.333 0.1845
fe_year2 -3.532222 16.954761 -0.208 0.8352
fe_year3 6.759767 16.303710 0.415 0.6790
fe_year4 -73.769141 39.464482 -1.869 0.0635 .
fe_year6 -27.113332 24.232382 -1.119 0.2649
fe_year7 -17.705589 26.214533 -0.675 0.5004
fe_year8 -12.726297 28.849918 -0.441 0.6597
fe_year9 1.832507 15.557559 0.118 0.9064
fe_year10 5.282221 12.871480 0.410 0.6821
fe_year11 13.584084 16.376386 0.829 0.4081
retained_binary -6.457557 12.922218 -0.500 0.6180
R takes the first year as intercept and now sets all other variables in relation to it. What I want to do is have the average return across all variables as intercept incl. statistical values. And all other variables should be set in relation to that. I already searched after a few solutions and tried them out, but nothing gives me my desired results:
- change contrasts to sum instead treatment -> last year is missing since contrasts are one less than variables
- offset intercept by the mean and have no intercept -> no statistical output regarding the mean
- set intercept manually to mean -> no statistical output
The final result I'm looking for would look like this:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.70148 X X X
size X X X X
fe_year1 5.81468 X X X
fe_year2 2.27679 X X X
and so on, with every year until 11 included.
I hope the question is clear enough. Thanks!
r regression
asked Nov 13 '18 at 13:50
DenskoDensko
113
add a comment |
I'm doing a multi-variable regression in R, where one variable is set as a factor. If I do the regression, the intercept is set to the first factor and all following factors are set relative to that.
So if I do the following regression:
pe1n = lm(return_1_yr_abs~size+fe_year+retained_binary,data=rtn_1_yr)
I get following output:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.516160 15.031941 1.165 0.2457
size -0.001619 0.001214 -1.333 0.1845
fe_year2 -3.532222 16.954761 -0.208 0.8352
fe_year3 6.759767 16.303710 0.415 0.6790
fe_year4 -73.769141 39.464482 -1.869 0.0635 .
fe_year6 -27.113332 24.232382 -1.119 0.2649
fe_year7 -17.705589 26.214533 -0.675 0.5004
fe_year8 -12.726297 28.849918 -0.441 0.6597
fe_year9 1.832507 15.557559 0.118 0.9064
fe_year10 5.282221 12.871480 0.410 0.6821
fe_year11 13.584084 16.376386 0.829 0.4081
retained_binary -6.457557 12.922218 -0.500 0.6180
R takes the first year as intercept and now sets all other variables in relation to it. What I want to do is have the average return across all variables as intercept incl. statistical values. And all other variables should be set in relation to that. I already searched after a few solutions and tried them out, but nothing gives me my desired results:
- change contrasts to sum instead treatment -> last year is missing since contrasts are one less than variables
- offset intercept by the mean and have no intercept -> no statistical output regarding the mean
- set intercept manually to mean -> no statistical output
The final result I'm looking for would look like this:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.70148 X X X
size X X X X
fe_year1 5.81468 X X X
fe_year2 2.27679 X X X
and so on, with every year until 11 included.
I hope the question is clear enough. Thanks!
r regression
asked Nov 13 '18 at 13:50
DenskoDensko
113
Better formatting ispe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)
– sindri_baldur
Nov 13 '18 at 14:05
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Your desired result may not be possible through anlm()call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.
– Parfait
Nov 13 '18 at 15:20
share you data please
– Mike
Nov 13 '18 at 16:31
add a comment |
I'm doing a multi-variable regression in R, where one variable is set as a factor. If I do the regression, the intercept is set to the first factor and all following factors are set relative to that.
So if I do the following regression:
pe1n = lm(return_1_yr_abs~size+fe_year+retained_binary,data=rtn_1_yr)
I get following output:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.516160 15.031941 1.165 0.2457
size -0.001619 0.001214 -1.333 0.1845
fe_year2 -3.532222 16.954761 -0.208 0.8352
fe_year3 6.759767 16.303710 0.415 0.6790
fe_year4 -73.769141 39.464482 -1.869 0.0635 .
fe_year6 -27.113332 24.232382 -1.119 0.2649
fe_year7 -17.705589 26.214533 -0.675 0.5004
fe_year8 -12.726297 28.849918 -0.441 0.6597
fe_year9 1.832507 15.557559 0.118 0.9064
fe_year10 5.282221 12.871480 0.410 0.6821
fe_year11 13.584084 16.376386 0.829 0.4081
retained_binary -6.457557 12.922218 -0.500 0.6180
R takes the first year as intercept and now sets all other variables in relation to it. What I want to do is have the average return across all variables as intercept incl. statistical values. And all other variables should be set in relation to that. I already searched after a few solutions and tried them out, but nothing gives me my desired results:
- change contrasts to sum instead treatment -> last year is missing since contrasts are one less than variables
- offset intercept by the mean and have no intercept -> no statistical output regarding the mean
- set intercept manually to mean -> no statistical output
The final result I'm looking for would look like this:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.70148 X X X
size X X X X
fe_year1 5.81468 X X X
fe_year2 2.27679 X X X
and so on, with every year until 11 included.
I hope the question is clear enough. Thanks!
r regression
asked Nov 13 '18 at 13:50
DenskoDensko
113
I'm doing a multi-variable regression in R, where one variable is set as a factor. If I do the regression, the intercept is set to the first factor and all following factors are set relative to that.
So if I do the following regression:
pe1n = lm(return_1_yr_abs~size+fe_year+retained_binary,data=rtn_1_yr)
I get following output:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.516160 15.031941 1.165 0.2457
size -0.001619 0.001214 -1.333 0.1845
fe_year2 -3.532222 16.954761 -0.208 0.8352
fe_year3 6.759767 16.303710 0.415 0.6790
fe_year4 -73.769141 39.464482 -1.869 0.0635 .
fe_year6 -27.113332 24.232382 -1.119 0.2649
fe_year7 -17.705589 26.214533 -0.675 0.5004
fe_year8 -12.726297 28.849918 -0.441 0.6597
fe_year9 1.832507 15.557559 0.118 0.9064
fe_year10 5.282221 12.871480 0.410 0.6821
fe_year11 13.584084 16.376386 0.829 0.4081
retained_binary -6.457557 12.922218 -0.500 0.6180
R takes the first year as intercept and now sets all other variables in relation to it. What I want to do is have the average return across all variables as intercept incl. statistical values. And all other variables should be set in relation to that. I already searched after a few solutions and tried them out, but nothing gives me my desired results:
- change contrasts to sum instead treatment -> last year is missing since contrasts are one less than variables
- offset intercept by the mean and have no intercept -> no statistical output regarding the mean
- set intercept manually to mean -> no statistical output
The final result I'm looking for would look like this:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.70148 X X X
size X X X X
fe_year1 5.81468 X X X
fe_year2 2.27679 X X X
and so on, with every year until 11 included.
I hope the question is clear enough. Thanks!
r regression
r regression
asked Nov 13 '18 at 13:50
DenskoDensko
113
asked Nov 13 '18 at 13:50
DenskoDensko
113
edited Nov 13 '18 at 14:07
Densko
asked Nov 13 '18 at 13:50
DenskoDensko
113
asked Nov 13 '18 at 13:50
DenskoDensko
113
asked Nov 13 '18 at 13:50
DenskoDensko
113
113
Better formatting ispe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)
– sindri_baldur
Nov 13 '18 at 14:05
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Your desired result may not be possible through anlm()call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.
– Parfait
Nov 13 '18 at 15:20
share you data please
– Mike
Nov 13 '18 at 16:31
add a comment |
Better formatting ispe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)
– sindri_baldur
Nov 13 '18 at 14:05
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Your desired result may not be possible through anlm()call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.
– Parfait
Nov 13 '18 at 15:20
share you data please
– Mike
Nov 13 '18 at 16:31
Better formatting is
pe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)– sindri_baldur
Nov 13 '18 at 14:05
Better formatting is
pe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)– sindri_baldur
Nov 13 '18 at 14:05
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Your desired result may not be possible through an
lm() call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.– Parfait
Nov 13 '18 at 15:20
Your desired result may not be possible through an
lm() call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.– Parfait
Nov 13 '18 at 15:20
share you data please
– Mike
Nov 13 '18 at 16:31
share you data please
– Mike
Nov 13 '18 at 16:31
add a comment |
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Better formatting is
pe1n = lm(return_1_yr_abs ~ size + fe_year + retained_binary, data=rtn_1_yr)– sindri_baldur
Nov 13 '18 at 14:05
Edited it, thanks!
– Densko
Nov 13 '18 at 14:08
Your desired result may not be possible through an
lm()call as at least one group in a categorical variable must be dropped (n-1) else you face perfect collinearity. You can possibly create a fe_year1 variable calculated during post-estimation.– Parfait
Nov 13 '18 at 15:20
share you data please
– Mike
Nov 13 '18 at 16:31