by-lmby.RdThe data is split into strata according to the levels of the grouping factors and individual lm fits are obtained for each stratum.
lm_by(data, formula, id = NULL, ...)
lmBy(formula, data, id = NULL, ...)A dataframe
A linear model formula object of the form
y ~ x1 + ... + xn | g1 + ... + gm. In the formula object, y represents
the response, x1, ..., xn the covariates, and the grouping
factors specifying the partitioning of the data according to
which different lm fits should be performed.
A formula describing variables from data which are to be available also in the output.
Additional arguments passed on to lm().
A list of lm fits.
bb <- lmBy(1 / uptake ~ log(conc) | Treatment, data=CO2)
coef(bb)
#> (Intercept) log(conc)
#> nonchilled 0.1440697 -0.01825196
#> chilled 0.1669465 -0.01964886
fitted(bb)
#> $nonchilled
#> 1 2 3 4 5 6 7
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#> 8 9 10 11 12 13 14
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#> 15 16 17 18 19 20 21
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#> 43 44 45 46 47 48 49
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#> 50 51 52 53 54 55 56
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#> 57 58 59 60 61 62 63
#> 0.06095250 0.04980222 0.04329220 0.03715092 0.03064091 0.02516341 0.01798962
#>
#> $chilled
#> 22 23 24 25 26 27 28
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#> 29 30 31 32 33 34 35
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#> 36 37 38 39 40 41 42
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#> 64 65 66 67 68 69 70
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#> 71 72 73 74 75 76 77
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#> 78 79 80 81 82 83 84
#> 0.07746803 0.06546436 0.05845611 0.05184481 0.04483656 0.03893984 0.03121701
#>
residuals(bb)
#> $nonchilled
#> 1 2 3 4 5
#> 0.0015474990 -0.0169074783 -0.0145565675 -0.0102692027 -0.0023122957
#> 6 7 8 9 10
#> 0.0003467923 0.0071993013 0.0125769107 -0.0131721785 -0.0163380218
#> 11 12 13 14 15
#> -0.0132274781 -0.0060103658 -0.0010088224 0.0045837478 0.0007758940
#> 16 17 18 19 20
#> -0.0189380176 -0.0184783039 -0.0133979540 -0.0073308843 -0.0023843685
#> 21 43 44 45 46
#> 0.0039884064 0.0333871216 0.0022811182 -0.0051242607 -0.0038175898
#> 47 48 49 50 51
#> 0.0017215519 0.0057007858 0.0101793985 0.0223808323 -0.0043476697
#> 52 53 54 55 56
#> -0.0106124611 -0.0057043822 0.0002232899 0.0069909291 0.0137564161
#> 57 58 59 60 61
#> 0.0275430742 0.0017441766 -0.0045325098 -0.0013086292 0.0044468117
#> 62 63
#> 0.0104237769 0.0179816074
#>
#> $chilled
#> 22 23 24 25 26 27
#> -0.007045494 -0.023970588 -0.025452808 -0.022943079 -0.014067327 -0.010691257
#> 28 29 30 31 32 33
#> -0.005377213 0.030058852 -0.028834327 -0.029884680 -0.026071617 -0.018929822
#> 34 35 36 37 38 39
#> -0.012273178 -0.007632101 -0.011242864 -0.017845316 -0.032209389 -0.022433048
#> 40 41 42 64 65 66
#> -0.019129616 -0.013687319 -0.007062417 0.017770066 0.001649730 -0.003207489
#> 67 68 69 70 71 72
#> 0.001065240 0.006445494 0.006105200 0.014445094 0.052402100 0.022254935
#> 73 74 75 76 77 78
#> 0.022844705 0.025078264 0.035163443 0.034052856 0.038227438 0.016871593
#> 79 80 81 82 83 84
#> -0.009908808 -0.002590186 0.004021109 0.011029364 0.013970208 0.019034250
#>
summary(bb)
#>
#> Call:
#> lm(formula = 1/uptake ~ log(conc), data = wd)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.018938 -0.007001 -0.000393 0.005422 0.033387
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.144070 0.014634 9.845 3.03e-12 ***
#> log(conc) -0.018252 0.002494 -7.318 6.79e-09 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.01213 on 40 degrees of freedom
#> Multiple R-squared: 0.5724, Adjusted R-squared: 0.5618
#> F-statistic: 53.55 on 1 and 40 DF, p-value: 6.79e-09
#>
#>
#> Call:
#> lm(formula = 1/uptake ~ log(conc), data = wd)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.032209 -0.016901 -0.004292 0.016265 0.052402
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.166947 0.025977 6.427 1.19e-07 ***
#> log(conc) -0.019649 0.004427 -4.438 6.95e-05 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.02154 on 40 degrees of freedom
#> Multiple R-squared: 0.33, Adjusted R-squared: 0.3132
#> F-statistic: 19.7 on 1 and 40 DF, p-value: 6.95e-05
#>
coef(summary(bb))
#> $nonchilled
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.14406966 0.014634155 9.844754 3.026663e-12
#> log(conc) -0.01825196 0.002494095 -7.318068 6.789702e-09
#>
#> $chilled
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.16694651 0.02597669 6.426782 1.187822e-07
#> log(conc) -0.01964886 0.00442720 -4.438214 6.950357e-05
#>
coef(summary(bb), simplify=TRUE)
#> stratum parameter Estimate Std. Error t value Pr(>|t|)
#> 1 nonchilled (Intercept) 0.14406966 0.014634155 9.844754 3.026663e-12
#> 2 nonchilled log(conc) -0.01825196 0.002494095 -7.318068 6.789702e-09
#> 3 chilled (Intercept) 0.16694651 0.025976687 6.426782 1.187822e-07
#> 4 chilled log(conc) -0.01964886 0.004427200 -4.438214 6.950357e-05