[R] how to plot a logarithmic regression line
arun
smartpink111 at yahoo.com
Sat Feb 22 22:06:52 CET 2014
HI,
Try ?curve
fit <- lm(Mean_Percent_of_Range~log(No.ofPoints))
coef(fit)
# (Intercept) log(No.ofPoints)
# -74.52645 46.14392
plot(Mean_Percent_of_Range ~ No.ofPoints)
curve(coef(fit)[[1]]+coef(fit)[[2]]*log(x),add=TRUE,col=2)
A.K.
I realize this is a stupid question, and I have honestly tried to find
the answer online, but nothing I have tried has worked. I have two
vectors of data:
"Mean_percent_of_range"
10.90000 17.50000 21.86667 25.00000 25.40000 26.76667 29.53333
32.36667 43.13333 41.80000 50.56667 49.26667 50.36667 51.93333
59.70000 63.96667 62.53333 60.80000 64.23333 66.00000 74.03333
70.40000 77.06667 76.46667 78.13333 89.46667 88.90000 90.03333
91.60000 94.30000 95.50000 96.20000 96.50000 91.40000 98.20000
96.60000 97.40000 99.00000 100.00000
and
"No.ofPoints"
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
39 40 41 42 43
When I plot these, I get a logarithmic curve (as I should for this type of data)
> plot(Mean_Percent_of_Range ~ No.ofPoints)
All that I want to do is plot best fit regression line for that
curve. From what I have read online, it seems like the code to do that
should be
> abline(lm(log(Mean_Percent_of_Range) ~ log(No.ofPoints)))
but that gives me a straight line that isn't even close to fitting the data
How do I plot the line and get the equation of that line and a correlation coefficient?
Thanks
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