[R] problem with lm, and summary.lm

Gabor Grothendieck ggrothendieck at gmail.com
Sun Nov 16 17:31:02 CET 2008


Try upgrading to R 2.8.0 patched.  This works for me
using R 2.8.0 patched from Nov 10th:

library(zoo)
z <- 1:10
x <- z*z
y <- x*z
lm(z ~ x + y)
summary(lm(z ~ x + y))

> packageDescription("zoo")$Version
[1] "1.5-4"
> R.version.string # Vista
[1] "R version 2.8.0 Patched (2008-11-10 r46884)"


On Sun, Nov 16, 2008 at 7:32 AM, Tolga Uzuner <tolga.uzuner at gmail.com> wrote:
> Dear R Users,
>
> I am having a weird problem. I have three zoo time series, foo, bar and baz.
> I run a simple linear regression with foo as the dependent and bar+baz as
> independents. Even though the regression runs fine, summary seems to
> fail.The code is below. I am happy to send the data along. I am on R 2.8.0
> and Windows XP SP2. Traceback (below, a ton of numbers cut out to make it
> readable but I can provide the data). reveals the problem is in a function
> called gt. sessioninfo is at the bottom.
>
> Any suggestions ? I upgraded to 2.8.0 this morning after replaced 2.7.1 and
> I almost feel the new version is at fault but I could be inferring too
> much...
>
> Thanks in advance,
> Tolga
>
> cooks.distance also reveals the same problem.
>
>> length(foo)
> [1] 258
>> length(foo)
> [1] 258
>> length(bar)
> [1] 258
>> length(baz)
> [1] 258
>> regrlm<-lm(foo~bar+baz)
>> regrlm
>
> Call:
> lm(formula = foo ~ bar + baz)
>
> Coefficients:
> (Intercept)          bar          baz   1082.39        12.72    -20176.67
>> summary(regrlm)
>
> Call:
> lm(formula = foo ~ bar + baz)
>
> Residuals:
> Error in if (xi == xj) 0L else if (xi > xj) 1L else -1L :
>  argument is of length zero
>> traceback()
> 19: .gt(c(145.181456007549, 118.279525850693, 111.250750147955,
> 89.1393551953539,
> MANY MANY NUMBERS
>   -67.9948569260507, -146.080176235300), 250L, 246L)
> 18: switch(ties.method, average = , min = , max = .Internal(rank(x[!nas],
>       ties.method)), first = sort.list(sort.list(x[!nas])), random =
> sort.list(order(x[!nas],
>       stats::runif(sum(!nas)))))
> 17: rank(x, ties.method = "min", na.last = "keep")
> 16: as.vector(rank(x, ties.method = "min", na.last = "keep"))
> 15: xtfrm.default(x)
> 14: xtfrm(x)
> 13: FUN(X[[1L]], ...)
> 12: lapply(z, function(x) if (is.object(x)) xtfrm(x) else x)
> 11: order(x, na.last = na.last, decreasing = decreasing)
> 10: `[.zoo`(x, order(x, na.last = na.last, decreasing = decreasing))
> 9: x[order(x, na.last = na.last, decreasing = decreasing)]
> 8: sort.default(x, partial = unique(c(lo, hi)))
> 7: sort(x, partial = unique(c(lo, hi)))
> 6: quantile.default(resid)
> 5: quantile(resid)
> 4: structure(quantile(resid), names = nam)
> 3: print.summary.lm(list(call = lm(formula = foo ~ bar + baz), terms = foo ~
>      bar + baz, residuals = c(145.181456007549, 118.279525850693,
> MANY MANY NUMBERS   -97.6817272270226, -101.621851940748, -67.9948569260507,
> -146.080176235300
>  ), coefficients = c(1082.39330190496, 12.7191319384837, -20176.6660075191,
>  36.7646530199551, 0.752346859475059, 1097.00127070372, 29.4411401439708,
>  16.9059414262171, -18.3925639343844, 5.30095123419022e-84,
> 1.60626441787295e-43,
>  1.15247513614373e-48), aliased = c(FALSE, FALSE, FALSE), sigma =
> 90.0587318356495,
>      df = c(3L, 255L, 3L), r.squared = 0.767559392535633, adj.r.squared =
> 0.765736328947677,
>      fstatistic = c(421.027219021081, 2, 255), cov.unscaled =
> c(0.166651523684348,
>      -0.00308410770161002, -3.08083131687658, -0.00308410770161002,
>      6.9788613558326e-05, 0.0263943284503598, -3.08083131687658,
>      0.0263943284503598, 148.375640597725)))
> 2: print(list(call = lm(formula = foo ~ bar + baz), terms = foo ~
>      bar + baz, residuals = c(145.181456007549, 118.279525850693,
> MANY MANY NUMBERS
>  -97.6817272270226, -101.621851940748, -67.9948569260507, -146.080176235300
>  ), coefficients = c(1082.39330190496, 12.7191319384837, -20176.6660075191,
>  36.7646530199551, 0.752346859475059, 1097.00127070372, 29.4411401439708,
>  16.9059414262171, -18.3925639343844, 5.30095123419022e-84,
> 1.60626441787295e-43,
>  1.15247513614373e-48), aliased = c(FALSE, FALSE, FALSE), sigma =
> 90.0587318356495,
>      df = c(3L, 255L, 3L), r.squared = 0.767559392535633, adj.r.squared =
> 0.765736328947677,
>      fstatistic = c(421.027219021081, 2, 255), cov.unscaled =
> c(0.166651523684348,
>      -0.00308410770161002, -3.08083131687658, -0.00308410770161002,
>      6.9788613558326e-05, 0.0263943284503598, -3.08083131687658,
>      0.0263943284503598, 148.375640597725)))
> 1: print(list(call = lm(formula = foo ~ bar + baz), terms = foo ~
>      bar + baz, residuals = c(145.181456007549, 118.279525850693,
> MANY MANY NUMBERS   -97.6817272270226, -101.621851940748, -67.9948569260507,
> -146.080176235300
>  ), coefficients = c(1082.39330190496, 12.7191319384837, -20176.6660075191,
>  36.7646530199551, 0.752346859475059, 1097.00127070372, 29.4411401439708,
>  16.9059414262171, -18.3925639343844, 5.30095123419022e-84,
> 1.60626441787295e-43,
>  1.15247513614373e-48), aliased = c(FALSE, FALSE, FALSE), sigma =
> 90.0587318356495,
>      df = c(3L, 255L, 3L), r.squared = 0.767559392535633, adj.r.squared =
> 0.765736328947677,
>      fstatistic = c(421.027219021081, 2, 255), cov.unscaled =
> c(0.166651523684348,
>      -0.00308410770161002, -3.08083131687658, -0.00308410770161002,
>      6.9788613558326e-05, 0.0263943284503598, -3.08083131687658,
>      0.0263943284503598, 148.375640597725)))
>> sessionInfo()
> R version 2.8.0 (2008-10-20)
> i386-pc-mingw32
>
> locale:
> LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United
> Kingdom.1252;LC_MONETARY=English_United
> Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
> other attached packages:
> [1] lpSolve_5.6.4                leaps_2.7                  [3] nortest_1.0
>                  numDeriv_2006.4-1          [5] bcp_2.1
>  snow_0.3-3                 [7] fArma_270.74                 fBasics_280.74
>             [9] timeSeries_280.78            timeDate_280.80            [11]
> PerformanceAnalytics_0.9.7.1 tseries_0.10-16            [13] quadprog_1.4-11
>              vars_1.4-0                 [15] urca_1.1-7
> MASS_7.2-44                [17] MSBVAR_0.3.2                 coda_0.13-3
>            [19] lattice_0.17-15              xtable_1.5-4               [21]
> KernSmooth_2.22-22           RODBC_1.2-3                [23] corrgram_0.1
>               nlme_3.1-89                [25] lmtest_0.9-21
>  car_1.2-9                  [27] strucchange_1.3-4            sandwich_2.1-0
>             [29] zoo_1.5-4
> loaded via a namespace (and not attached):
> [1] grid_2.8.0  tools_2.8.0
>>
>
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