Better idea, Ben!
It would work as you might expect it to to produce the same results as
the above:
##first make sure your regressor is a matrix:
pur2 <- matrix(purity2, ncol =1)
## convert the data frame variables into a matrix
dat <- as.matrix(gem751be.rpkm[ , 74:35164])
##then
result <- residuals(lm.fit( x= pur2, y = dat))
Cheers,
Bert
On Fri, Aug 9, 2024 at 6:38 PM Ben Bolker <bbol...@gmail.com> wrote:
>
> You can also fit a linear model with a matrix-valued response
> variable, which should be even faster (not sure off the top of my head
> how to get the residuals and reshape them to the dimensions you want)
>
> On Fri, Aug 9, 2024 at 9:31 PM Bert Gunter <bgunter.4...@gmail.com> wrote:
> >
> > See ?lm.fit.
> > I must be missing something, because:
> >
> > results <- sapply(74:35164, \(i) residuals(lm.fit(purity2,
> > gem751be.rpkm[, i] )))
> >
> > would give you a 751 x 35091 matrix of the residuals from each of the
> > regressions.
> > I assume it will be considerably faster than all the overhead you are
> > carrying in your current code, but of course you'll have to try it and
> > see. ... Assuming that I have interpreted your request correctly.
> > Ignore if not.
> >
> > Cheers,
> > Bert
> >
> > On Fri, Aug 9, 2024 at 4:50 PM Yuan Chun Ding via R-help
> > <r-help@r-project.org> wrote:
> > >
> > > Dear R users,
> > >
> > > I am running the following code below, the gem751be.rpkm is a dataframe
> > > with dim of 751 samples by 35164 variables, 73 phenotypic variables in
> > > the furst to 73rd column and 35091 genomic variables or genes in the 74th
> > > to 35164th columns. What I need to do is to calculate the residuals for
> > > each gene using the simple linear regression model of genelist[i] ~
> > > purity2;
> > >
> > > The following code is running, it takes long time, but I have an
> > > expensive ThinkStation window computer.
> > > Can you provide a fast way to do it?
> > >
> > > Thank you,
> > >
> > > Ding
> > >
> > > ---------------------------------------------------------------------------------
> > >
> > >
> > > gem751be.rpkm <-merge(gem751be10, as.data.frame(t(rna849.fpkm2)),
> > > + by.x="id2",by.y=0)
> > > > row.names(gem751be.rpkm)<-gem751be.rpkm$id3
> > > >
> > > > colnames(gem751be.rpkm)<-gsub(colnames(gem751be.rpkm),pattern="-",replacement="_")
> > > > genelist <- gem751be.rpkm %>% dplyr::select(74:35164)
> > > > residuals <- NULL
> > > > for (i in 1:length(genelist)) {
> > > + #i=1
> > > + formula <- reformulate("purity2", response=names(genelist)[i])
> > > + model <- lm(formula, data = gem751be.rpkm)
> > > + resi <- as.data.frame(residuals(model))
> > > + colnames(resi)[1]<-names(genelist)[i]
> > > + resi <-as.data.frame(t(resi))
> > > + residuals <- rbind(residuals, resi)
> > > + }
> > >
> > >
> > >
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