Integrating a multiple regression equation (including covariates) into ggplot2 graph - r
I am currently generating simple linear regression plots in ggplot2 with the following code (toy example)
library(ggplot2)
Data<-data.frame(Age=c(40,41,42,43,44,45,46,47,48,49),
Height=c(185,184,183,182,181,186,187,188,189,190),
Sex=c("Male","Male","Male","Male","Male","Female","Female","Female","Female","Female"),
Weight=c(84,83,82,81,80,85,86,87,88,89),
BMI=c(20,21,22,23,24,25,26,27,28,29))
points<-c("#FF9999","turquoise")
lines<-c("dark red","blue")
linetypes<-c(1,1)
x<-ggplot(Data,aes(x=Age,y=Weight))+
geom_point(aes(fill=factor(Sex)),size=4,alpha=1,shape=21,color="transparent")+
geom_smooth(aes(color=Sex,linetype=Sex),method="lm",formula=y~x,lwd=1,se=F)+
scale_fill_manual(values=points,labels=c("Female","Male"))+
scale_colour_manual(values=lines)+
scale_linetype_manual(values=linetypes)+
theme_classic(base_size=15)+
theme(legend.position="top",legend.direction="horizontal")+
labs(fill="Points",colour="Lines",x='Age',y='Weight')+
guides(fill=guide_legend(override.aes=list(shape=c(16,16),alpha=1,size=4,color=c("#FF9999","turquoise"))))+
guides(colour=guide_legend(override.aes=list(linetype=c(1,1),alpha=1,size=1,color=c("dark red","blue"))))+
guides(linetype="none")
x
I am stuck as how to add one-or-more covariates, in order to transform the plots from simple to multiple linear regression, e.g., from
Weight~Age to Weight~Age+Height+BMI
I am sure that it must have something to do with the method="lm" and formula=y~x commands, but I am not clear on exactly what changes need to be made.
Any help would be very much appreciated. Thank you.
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You can also just use the result of your lm like this: model <- lm(log10(brain)~log10(body)) plot(log10(brain)~log10(body)) abline(model,col=2)
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Order lattice panel by regression intercept
Example dataset here
Let us build a simple lattice plot from this data for linear regression, with separate panels for each Subject
xyplot(Measurement~HOL|Subject,groups=Treatment,data=Data,
type=c('p','r'),auto.key=T,aspect="xy")
The issue is, I would like to visually inspect if the slopes-and-intercepts are correlated. Thus, I would like to order the panels by linear-regression intercept as opposed to by Subject (this was done in Douglas Bates' book "lme4: Mixed-effects modeling with R" Figure 3.1, but I cannot find example code). I know I can change the order of panels by hand by adding
index.cond=list(c(1,2,3, etc))
But this is extraordinarily inefficient, especially since I would like to do this for multiple response variables.
Does anyone have an automated way to do this? I am also open to attempting this in ggplot2 if it has any built in functions, but as I understand, there is no way to easily change the aspect to a 45degree such as the
aspect="xy"
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Thank you in advance for any thoughts
If you want to order by regression intercept, it's best to run the regression. For example, with your data we can do
cf<-sapply(Data$Subject, function(x)
coef(lm(Measurement~HOL, data=subset(Data, Subject==x))))
which will give a slope/intercept for each person, we can then create a new factor of Subjects ordered by the intercept with
Sx<-reorder(Data$Subject, cf[1,])
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xyplot(Measurement~HOL|Sx,groups=Treatment,data=Data,
type=c('p','r'),auto.key=T,aspect="xy")
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