[R] lm() with same formula but different column/factor combinations in data frame
Gabor Grothendieck
ggrothendieck at gmail.com
Fri Dec 26 18:58:38 CET 2008
See the leaps package.
On Fri, Dec 26, 2008 at 12:37 PM, Murtaza Das <murtazadas at gmail.com> wrote:
> Hi,
>
> I am trying to find an efficient way of applying a linear regression
> model to different factor combinations in a data frame.
> I want to obtain the output with minimal or no use of loops if
> possible. Please let me know if this query is unclear.
>
> Thanks,
> Murtaza
>
> ***********************************************************************************************************************************************************
>
> The data frame TEST1 has four factor columns followed by thirteen
> numeric columns defined as :
> 1) Community, levels: "20232"
> 2) WT, levels: "B", "E", "M"
> 3) LTC, levels: "L", "M", "S", "1"
> 4) UC, levels: "1X1", "2X2"
> 5) UncDmd: Response variable in the linear model
> 6-16) M1...M11: Explanatory variables in the linear model
>
> A few sample rows in the data frame are as follows:
>> TEST1[1:15,]
> Community WT LTC UC UncDmd M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
> 1 20232 E L 1X1 1.000000 0 0 0 0 0 0 0 0 0 0 1
> 2 20232 E L 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1
> 3 20232 E M 1X1 1.000000 0 0 0 0 0 0 0 0 0 0 1
> 4 20232 E M 2X2 1.000000 0 0 0 0 0 0 0 0 0 0 1
> 5 20232 E S 1X1 0.000000 0 0 0 0 0 0 0 0 0 0 1
> 6 20232 E S 2X2 0.000000 0 0 0 0 1 0 0 0 0 0 0
> 7 20232 B 1 1X1 0.209117 0 0 0 0 0 0 0 0 0 0 1
> 8 20232 B 1 2X2 0.190605 0 0 0 0 0 0 0 0 0 0 1
> 9 20232 B L 1X1 0.000000 0 0 0 0 1 0 0 0 0 0 0
> 10 20232 B L 2X2 1.000000 0 0 0 0 0 0 0 0 0 0 1
> 11 20232 B M 1X1 4.000000 0 0 0 0 0 0 0 0 0 0 1
> 12 20232 B M 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1
> 13 20232 B S 1X1 0.000000 1 0 0 0 0 0 0 0 0 0 0
> 14 20232 B S 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1
> 15 20232 M 1 1X1 0.618689 0 0 0 0 0 0 0 0 0 1 0
>
> *********************************************************************************************************************************************************
> I need to store the coefficients using lm() for different combinations
> of the 4 factors, or different combinations of 3 factors or different
> combinations of 2 factors or
> differennt combinations of 1 factor.
> The formula remains fixed as:
>> Formula
> UncDmd ~ M1 + M2 + M3 + M4 + M5 + M6 + M7 + M8 + M9 + M10 + M11
>
> So, different models I want to solve in R are :
> 1) Community : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") ) , ])
> 2) WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="B") ) , ])
> 3) WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="E") ) , ])
> 4) WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="M") ) , ])
> 5) LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[3]]=="L") ) , ])
> 6) LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[3]]=="M") ) , ])
> 7) LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[3]]=="S") ) , ])
> 8) LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[3]]=="1L") ) , ])
> 9) UC : lm(Formula,TEST1[ as.logical(
> (TEST1[[4]]=="1X1") ) , ])
> 10) UC : lm(Formula,TEST1[ as.logical(
> (TEST1[[4]]=="2X2") ) , ])
> 11) Community, WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[2]]=="B") ) , ])
> 12) Community, WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[2]]=="E") ) , ])
> 13) Community, WT : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[2]]=="M") ) , ])
> 14) Community, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[3]]=="L") ) , ])
> 15) Community, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[3]]=="M") ) , ])
> 16) Community, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[3]]=="S") ) , ])
> 17) Community, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[3]]=="1") ) , ])
> 18) Community, UC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[4]]=="1X1") ) , ])
> 19) Community, UC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[4]]=="2X2") ) , ])
> 20) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="B") * (TEST1[[3]]=="L") ) , ])
> 21) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="B") * (TEST1[[3]]=="M") ) , ])
> 22) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="B") * (TEST1[[3]]=="S") ) , ])
> 23) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="B") * (TEST1[[3]]=="1") ) , ])
> 24) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="E") * (TEST1[[3]]=="L") ) , ])
> 25) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="E") * (TEST1[[3]]=="M") ) , ])
> 26) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="E") * (TEST1[[3]]=="S") ) , ])
> 27) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="E") * (TEST1[[3]]=="1") ) , ])
> 28) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="M") * (TEST1[[3]]=="L") ) , ])
> 29) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="M") * (TEST1[[3]]=="M") ) , ])
> 30) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="M") * (TEST1[[3]]=="S") ) , ])
> 31) WT, LTC : lm(Formula,TEST1[ as.logical(
> (TEST1[[2]]=="M") * (TEST1[[3]]=="1") ) , ])
> 32) WT, UC :
> ...
> ...
> xx) LTC, UC :
> ...
> xxx) Community, WT, LTC :
> ...
> ...
> and so on upto:
> xxxx) Community, WT, LTC, UC : lm(Formula,TEST1[ as.logical(
> (TEST1[[1]]=="20232") * (TEST1[[2]]=="M") * (TEST1[[3]]=="1") ) *
> (TEST1[[4]]=="2X2"), ])
> ***********************************************************************************************************************************************************
> Desired Output format (or something simlar):
> Factor1 Factor2 Factor3 Factor4 Intercept M1 M2 M3 M4 M5 M6
> M7 M8 M9 M10 M11
> 1) 20232 x x x
> x x x x x x x x x
> 2) B x x x
> x x x x x x x x x
> 3) E x x x
> x x x x x x x x x
> 4) M x x x
> x x x x x x x x x
> 5) L x x x
> x x x x x x x x x
> 6) M x x x
> x x x x x x x x x
> 7) S x x x
> x x x x x x x x x
> 8) 1 x x x
> x x x x x x x x x
> 9) 1X1 x x x
> x x x x x x x x x
> 10) 2X2 x x x
> x x x x x x x x x
> 11) 20232 B x x x x
> x x x x x x x x
> ..
> ..
> and so on..
>
>
> x is the respective coefficient obtained from the linear fit.
>
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> and provide commented, minimal, self-contained, reproducible code.
>
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