- ggcoxdiagnostics: Diagnostic
**Plots**for Cox Proportional Hazards**Model**with**ggplot2**Examples library ( survival )**coxph**.fit2 <-**coxph**( Surv ( futime , fustat ) ~ age + ecog.ps , data = ovarian ) ggcoxdiagnostics (**coxph**.fit2 , type = "deviance" ) - We see that the intercept is 98.0054 and the slope is 0.9528. By the way – lm stands for “
**linear****model**”. Finally, we can add a best fit line (regression line) to our**plot**by adding the following text at the command line: abline(98.0054, 0.9528) Another line of syntax that will**plot**the regression line is: abline(lm(height ~ bodymass)) - Oh dear, of course, that hasn't worked. Well, the animation part has worked exactly as we wanted, but the trendlines are wrong. Due to the way we built the
**model**, we have have created a parallel slopes type of**linear**regression. In doing that, we've lost the key finding of the data: that the number of fundraising staff is rising faster than the acquisition of new funds. - Search: Ggarrange Examples; Standalone text annotations can be added to figures using fig I show four approaches to make such a
**plot**: using facets and with packages cowplot, egg and patchwork ), so keeping min and max the same across the ... - Therefore, the expression in group 1 (when x = 0) is equal to Beta0; and the expression in group 2 (when x = 1) is equal to Beta0 + Beta1. If this is modelled with: mod1 <- lm (expression ~ group, data = gexp) mod1. The above code outputs an intercept of 2.75 and a slope of 1.58. It is the visualisation of the
**linear model**that I don't understand.