Hi Sergii, >> I have trouble obtaining the same results for nested Anova with two fixed >> factors when using >> lm and aov functions.
There is no difference between the two if you treat them equally, i.e. if you summarize them in the same way. ## Try: anova(e2) summary(e1) ## Or: summary.lm(e1) summary(e2) Your nested model is also unusual: customary/correct is: e2 <- lm( z/x - 1) The nesting introduces an intercept for each level of the nesting factor. Also, x presently is a factor (with 48 levels). Is that what you really want ? Regards, Mark. Sergii Ivakhno wrote: > > Dear R users, > > I have trouble obtaining the same results for nested Anova with two fixed > factors when using lm and aov functions. > > The formulas are: > >> e1=aov(y~x/z) > >> e2=lm(y~x/z) > > > > summary(e1) > > Df Sum Sq Mean Sq F value Pr(>F) > > x 47 260.0 5.5 18.0088 < 2.2e-16 *** > > x:z 195 169.6 0.9 2.8318 < 2.2e-16 *** > > Residuals 14425 4430.3 0.3 > > --- > > Signif. codes: 0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1 > > 2 observations deleted due to missingness > > > > For e2 > > Residual standard error: 0.5542 on 14425 degrees of freedom > > (2 observations deleted due to missingness) > > Multiple R-squared: 0.08839, Adjusted R-squared: 0.07309 > > F-statistic: 5.779 on 242 and 14425 DF, p-value: < 2.2e-16 > > > > > > > > I prefer to use lm, as in my case I want to know the difference between > the first control group and all the other factors though regression > coefficients. The same is true for levels of the nested factor within each > level of the main factor. > > > > Since I am fairly novice to running linear models in R, I am not sure what > can cause this problem; it also seems that lm does not provide the > decomposition of MS into MS(x) and MS(z) and corresponding F-test > statistics. (Is this possible to estimate them from lm output?) > > > > Finally, few words about the dataset: main factor x has 48 levels, > repeated from 60 to 540 times and represents different patients. The > nested factor z has 9 levels, but not all of them occur within levels of > factor x. Although the nested factor levels are independent between each > of the main factor (i.e. they samples taken from different tissues of each > patients), considering the large size of the dataset I was advised on this > forum to use the same encoding of levels of nested factor z at each level > of factor x. I am not sure if this influences QR decomposition and leads > to differences that I observe. > > I would most appreciate your help as after reading help pages I still can > not understand the cause for lm vs aov discrepancy. > > The dataset with three factors can be downloaded from > > http://www.compbio.group.cam.ac.uk/Resources/Sergii_temp/example.RData > > > > Thank you, > > Sergii > > > > > ---------------------------------------------- > Sergii Ivakhno > > PhD student > > Computational Biology Group > Cancer Research UK Cambridge Research Institute > Li Ka Shing Centre > Robinson Way > Cambridge CB2 0RE > England > > +44 (0)1223 404293 (O) > +44 (0)1223 404128 (F) > > http://www.compbio.group.cam.ac.uk <http://www.compbio.group.cam.ac.uk/> / > > > This communication is from Cancer Research UK. Our website is at > www.cancerresearchuk.org. We are a charity registered under number 1089464 > and a company limited by guarantee registered in England & Wales under > number 4325234. Our registered address is 61 Lincoln's Inn Fields, London > WC2A 3PX. Our central telephone number is 020 7242 0200. > > This communication and any attachments contain information which is > confidential and may also be privileged. It is for the exclusive use of > the intended recipient(s). If you are not the intended recipient(s) > please note that any form of disclosure, distribution, copying or use of > this communication or the information in it or in any attachments is > strictly prohibited and may be unlawful. If you have received this > communication in error, please notify the sender and delete the email and > destroy any copies of it. > > E-mail communications cannot be guaranteed to be secure or error free, as > information could be intercepted, corrupted, amended, lost, destroyed, > arrive late or incomplete, or contain viruses. We do not accept liability > for any such matters or their consequences. 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