lm() with same formula but different column/factor combinations in data frame
Thanks for replying Gabor. I checked the leaps() function and i think it is intended to find the best combination of predictors in the linear model. Does leaps have a way to combine different factor columns in my data frame as follows : I have the regression model fixed. The combination of predictor variables used always remains the same. UncDmd ~ M1 + M2 + M3 + M4 + M5 + M6 + M7 + M8 + M9 + M10 + M11 I want to get the coefficients in this linear model when different combinations of factors (select a combination from first four columns of the data frame) and their levels are taken from a data frame(apply lm model for a each combination of levels within the selected factor columns). Thus corresponding to each combination, the data used to determine the model coefficients will be different. I am attaching the data and R files (long method using loops) that I use to get the result. Currently, I modify keys to get different combinations. Also, note in the script, the data frame is named LRO1. Thanks again, Murtaza On Fri, Dec 26, 2008 at 12:58 PM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
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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