Hi, I'm analyzing a large number of simulations using lm(), a sample of the resulting data is pasted below. In some simulations, the response variable doesn't vary, ie:> tmp[[2]]$richness[1] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 When I analyze this using R version 2.8.0 (2008-10-20) on a linux cluster, I get an appropriate result: ## begin R ## summary(lm(richness ~ het, data = tmp[[2]])) Call: lm(formula = richness ~ het, data = tmp[[2]]) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 40 0 Inf <2e-16 *** het 0 0 NA NA --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 0 on 23 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 23 DF, p-value: NA ## end R ## This is good, as when I extract the Adjusted R-squared and slope I get NaN and 0, which are easily identified in my aggregate analysis, so I can deal with them appropriately. However, this isn't always the case: ## begin R ## tmp[[1]]$richness [1] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 [26] 40 40 40 40 40 40 40 40 40 40 40 summary(lm(richness ~ het, data = tmp[[1]])) Call: lm(formula = richness ~ het, data = tmp[[1]]) Residuals: Min 1Q Median 3Q Max -8.265e-14 1.689e-15 2.384e-15 2.946e-15 4.022e-15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.000e+01 8.418e-15 4.752e+15 <2e-16 *** het 1.495e-14 4.723e-14 3.160e-01 0.754 --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 1.44e-14 on 34 degrees of freedom Multiple R-squared: 0.5112, Adjusted R-squared: 0.4968 F-statistic: 35.56 on 1 and 34 DF, p-value: 9.609e-07 ## end R ## This is a problem, as when I plot the adj. R sq as part of an aggregate analysis of a large number of simulations, it appears to be a very strong regression. I wouldn't have caught this except it was exceptionally high for the simulation parameters. It also differs by more than rounding error from the results with R 2.8.1 running on my laptop (Debian GNU/Linux), i.e., adj. R sq 0.5042 vs 0.4968. Furthermore, on my laptop, none of the analyses produce a NaN adj. R sq, even for data that do produce that result on the cluster. Both my laptop and the linux cluster have na.action set to na.omit. Is there something else I can do to ensure that lm() returns slope == 0 and adj.R.sq == NaN when the response variable is fixed? Thanks for any suggestions, Tyler Data follows: `tmp` <- list(structure(list(richness = c(40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40), range = c(0.655084651733024, 0.579667533660137, 0.433092220907644, 0.62937198839679, 0.787891987978164, 0.623511540624239, 0.542744487102066, 0.905937570175433, 0.806802881350753, 0.680413208666325, 0.873426339019084, 0.699982832956593, 0.697716600618959, 0.952729864926405, 0.782938474636578, 1.03899695305995, 0.715075858219333, 0.579749205792549, 1.20648999819246, 0.648677938600964, 0.651883559714785, 0.997318331273967, 0.926368116052012, 0.91001274146868, 1.20737951037620, 1.12006560586723, 1.09806272133903, 0.9750792390176, 0.356496202035743, 0.612018080768747, 0.701905693862144, 0.735857916053381, 0.991787489781244, 1.07247435214078, 0.60061903319766, 0.699733090379818), het = c(0.154538307084452, 0.143186508136608, 0.0690948358402777, 0.132337152911839, 0.169037344105692, 0.117783183361602, 0.117524251767612, 0.221161206774407, 0.204574928003633, 0.170571000779693, 0.204489357007294, 0.131749663515638, 0.154127894997213, 0.232672587431942, 0.198610891796736, 0.260497696582693, 0.129028191256682, 0.128717975847452, 0.254300896783617, 0.113546727236817, 0.142220347446853, 0.24828642688332, 0.194340945175726, 0.190782985783610, 0.214676796387244, 0.252940213066992, 0.22362832797347, 0.182423482989676, 0.0602332226418674, 0.145400861749859, 0.141297315445974, 0.139798699247632, 0.222815139716421, 0.211971297234962, 0.120813579628747, 0.150590744533818), n.rich = c(40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40)), .Names = c("richness", "range", "het", "n.rich")), structure(list(richness = c(40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40), range = c(0.753203162648624, 0.599708526308711, 0.714477274087683, 0.892359682406808, 0.868440625159371, 0.753239521511417, 1.20164969658467, 1.20462111558583, 1.13142122690491, 0.95241921975703, 1.13214481653550, 0.827528954009827, 1.14827745443481, 0.936048043180592, 0.874649332193952, 1.38844778296649, 0.985016220913809, 1.18166853164661, 0.784679773255876, 0.94894149080785, 0.770312904574722, 1.10203660758219, 1.15624067277321, 0.692776967548628, 0.79343712876973), het = c(0.170481207967181, 0.108265674755723, 0.123316519598517, 0.220631611141464, 0.160460967122565, 0.145032358811883, 0.293678286125082, 0.284769842125969, 0.258637372765782, 0.18303781265474, 0.265304220319150, 0.194784967445680, 0.248055723803990, 0.204658616507612, 0.167203828355069, 0.287030735881294, 0.247639113771915, 0.269348295820692, 0.111409735752589, 0.209076579513581, 0.176890183224181, 0.249378876987384, 0.260323833307383, 0.177061093736427, 0.172263958005774 ), n.rich = c(40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40)), .Names = c("richness", "range", "het", "n.rich")))
Rolf Turner
2009-Jan-20 21:01 UTC
[R] inconsistent lm results with fixed response variable
Oh for Pete's sake! Computers use floating point arithmetic. Your residual standard error in case 2 (i.e. 1.44e-14) *is* 0, but floating point arithmetic can't quite see that this is so. Put in a check for the RSE being 0, and ``over- ride'' the adjusted R squared to be NA (or NaN, or whatever floats your boat) in such instances. The all.equal() function might be useful to you: > x <- 1.44e-14 > all.equal(x,0) [1] TRUE (Caution: Trap for Young Players: If x and y are ``really'' different, then all.equal(x,y) doesn't return FALSE as you might expect, but rather a description of the difference between x and y --- which may be complicated if x and y are complicated objects. The function isTRUE() is useful here.) cheers, Rolf Turner On 21/01/2009, at 9:21 AM, tyler wrote:> Hi, > > I'm analyzing a large number of simulations using lm(), a sample of > the > resulting data is pasted below. In some simulations, the response > variable doesn't vary, ie: > >> tmp[[2]]$richness > [1] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 > 40 40 40 40 > > When I analyze this using R version 2.8.0 (2008-10-20) on a linux > cluster, I get an appropriate result: > > > ## begin R ## > > summary(lm(richness ~ het, data = tmp[[2]])) > > Call: > lm(formula = richness ~ het, data = tmp[[2]]) > > Residuals: > Min 1Q Median 3Q Max > 0 0 0 0 0 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 40 0 Inf <2e-16 *** > het 0 0 NA NA > --- > Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > Residual standard error: 0 on 23 degrees of freedom > Multiple R-squared: , Adjusted R-squared: > F-statistic: on 1 and 23 DF, p-value: NA > > ## end R ## > > This is good, as when I extract the Adjusted R-squared and slope I get > NaN and 0, which are easily identified in my aggregate analysis, so I > can deal with them appropriately. > > However, this isn't always the case: > > ## begin R ## > > tmp[[1]]$richness > [1] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 > 40 40 40 40 > [26] 40 40 40 40 40 40 40 40 40 40 40 > > summary(lm(richness ~ het, data = tmp[[1]])) > > Call: > lm(formula = richness ~ het, data = tmp[[1]]) > > Residuals: > Min 1Q Median 3Q Max > -8.265e-14 1.689e-15 2.384e-15 2.946e-15 4.022e-15 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 4.000e+01 8.418e-15 4.752e+15 <2e-16 *** > het 1.495e-14 4.723e-14 3.160e-01 0.754 > --- > Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > Residual standard error: 1.44e-14 on 34 degrees of freedom > Multiple R-squared: 0.5112, Adjusted R-squared: 0.4968 > F-statistic: 35.56 on 1 and 34 DF, p-value: 9.609e-07 > > ## end R ## > > This is a problem, as when I plot the adj. R sq as part of an > aggregate > analysis of a large number of simulations, it appears to be a very > strong regression. I wouldn't have caught this except it was > exceptionally high for the simulation parameters. It also differs by > more than rounding error from the results with R 2.8.1 running on my > laptop (Debian GNU/Linux), i.e., adj. R sq 0.5042 vs 0.4968. > Furthermore, on my laptop, none of the analyses produce a NaN adj. > R sq, > even for data that do produce that result on the cluster. > > Both my laptop and the linux cluster have na.action set to na.omit. Is > there something else I can do to ensure that lm() returns slope == 0 > and adj.R.sq == NaN when the response variable is fixed? > > Thanks for any suggestions, > > Tyler > > Data follows: > > `tmp` <- > list(structure(list(richness = c(40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40, 40), range = c(0.655084651733024, 0.579667533660137, > 0.433092220907644, > 0.62937198839679, 0.787891987978164, 0.623511540624239, > 0.542744487102066, > 0.905937570175433, 0.806802881350753, 0.680413208666325, > 0.873426339019084, > 0.699982832956593, 0.697716600618959, 0.952729864926405, > 0.782938474636578, > 1.03899695305995, 0.715075858219333, 0.579749205792549, > 1.20648999819246, > 0.648677938600964, 0.651883559714785, 0.997318331273967, > 0.926368116052012, > 0.91001274146868, 1.20737951037620, 1.12006560586723, > 1.09806272133903, > 0.9750792390176, 0.356496202035743, 0.612018080768747, > 0.701905693862144, > 0.735857916053381, 0.991787489781244, 1.07247435214078, > 0.60061903319766, > 0.699733090379818), het = c(0.154538307084452, 0.143186508136608, > 0.0690948358402777, 0.132337152911839, 0.169037344105692, > 0.117783183361602, > 0.117524251767612, 0.221161206774407, 0.204574928003633, > 0.170571000779693, > 0.204489357007294, 0.131749663515638, 0.154127894997213, > 0.232672587431942, > 0.198610891796736, 0.260497696582693, 0.129028191256682, > 0.128717975847452, > 0.254300896783617, 0.113546727236817, 0.142220347446853, > 0.24828642688332, > 0.194340945175726, 0.190782985783610, 0.214676796387244, > 0.252940213066992, > 0.22362832797347, 0.182423482989676, 0.0602332226418674, > 0.145400861749859, > 0.141297315445974, 0.139798699247632, 0.222815139716421, > 0.211971297234962, > 0.120813579628747, 0.150590744533818), n.rich = c(40, 40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40)), .Names = c("richness", "range", "het", "n.rich")), > structure(list(richness = c(40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40, 40, 40, 40, 40, 40, 40), range = c(0.753203162648624, > 0.599708526308711, > 0.714477274087683, 0.892359682406808, 0.868440625159371, > 0.753239521511417, > 1.20164969658467, 1.20462111558583, 1.13142122690491, > 0.95241921975703, > 1.13214481653550, 0.827528954009827, 1.14827745443481, > 0.936048043180592, > 0.874649332193952, 1.38844778296649, 0.985016220913809, > 1.18166853164661, > 0.784679773255876, 0.94894149080785, 0.770312904574722, > 1.10203660758219, > 1.15624067277321, 0.692776967548628, 0.79343712876973), > het = c(0.170481207967181, > 0.108265674755723, 0.123316519598517, 0.220631611141464, > 0.160460967122565, > 0.145032358811883, 0.293678286125082, 0.284769842125969, > 0.258637372765782, > 0.18303781265474, 0.265304220319150, 0.194784967445680, > 0.248055723803990, > 0.204658616507612, 0.167203828355069, 0.287030735881294, > 0.247639113771915, > 0.269348295820692, 0.111409735752589, 0.209076579513581, > 0.176890183224181, > 0.249378876987384, 0.260323833307383, 0.177061093736427, > 0.172263958005774 > ), n.rich = c(40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, > 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40)), .Names = c > ("richness", > "range", "het", "n.rich"))) > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.###################################################################### Attention: This e-mail message is privileged and confidential. 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