I'm new to R, and I'm trying to fit a curve onto a dataset, which (for example) might look like this:
(x- value) (y-value)
105 423
115 471
125 567
135 808
145 921.5
155 1040
The x value represents the number of stimuli, and the y values represent motor responses (in uV). These are the average values for 10 subjects, where the x values are the same for each object.
I was told that this dataset usually follows a sigmoidal fit. I tried installing it with the following:
fit <- lm( y ~ poly(x, 3) )
But I'm not sure if this is a suitable way to do this :(
My code looks like this:
p <- ggplot (data, aes(x, y)) +
geom_point(shape= 21, fill= "blue", colour= "black", size=2) +
xlab("X value") + ylab("Y value") +
geom_smooth(method= "lm", se= FALSE, colour= "red", formula=y ~ poly(x, 3, raw=TRUE)) +
geom_errorbar(aes(ymin=y-SE, ymax=y+SE), width=.9)+
ggtitle ("Title")
p
Optional: as soon as I fit the curve, I would also like to get a slope (calculated as the value of the tangent at the steepest point of the curve)
Thanks in advance, any help would be greatly appreciated!