Dear Listserv,
Here is my latest in a series of simple-seeming questions that dog me. Consider the following data: x <- read.table(textConnection("temperature probability 0.11 9.4 0 2.3 0.38 8.7 0.43 9.2 0.6 15.6 0.47 8.7 0.09 12.8 0.11 9.4 0.01 7.7 0.83 8 0.65 9.3 0.05 7.4 0.34 10.1 0.02 4.8 0.07 9.1 0.6 15.6 0.01 8.4 0.9 9.6 0.83 8 0.12 8.4 0.01 8 0 5 0.11 9.7 0.41 7.4 0.05 9.4 0.09 8.3 0 6.1 0.12 8.4 0.73 7.8 0 4.2"), header = TRUE, as.is = TRUE) closeAllConnections() I modeled the relationship: Probability = f(Temperature), i.e., probability as a function of temperature. I found that there is a significant quadratic term in the model: summary(lm(x[,2] ~ x[,1] + I(x[,1]^2))) Now the question is: how do I plot it? I can do this: plot(x[,2] ~ x[,1]) ...but I would also like to add a line corresponding to the quadratic function. In other words, I want to visually show the relationship among the variables that is being modeled. How do I do it? I think the curve() command will be used, but I don't know how to employ it. Thanks very much in advance. Sincerely, ----------------------------------- Josh Banta, Ph.D Center for Genomics and Systems Biology New York University 100 Washington Square East New York, NY 10003 Tel: (212) 998-8465 http://plantevolutionaryecology.org [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
Hi Josh,
This is by no means the fanciest solution ever, but as there are predict methods for many types of models in R, I thought I would show it this way. ## fit the model model <- lm(probability ~ poly(temperature, 2), data = x) ## create line values dat <- data.frame(temperature = seq(min(x$temperature, na.rm = TRUE), max(x$temperature, na.rm = TRUE), by = .01)) ## add predicted y values dat$yhat <- predict(model, dat) ## plot data plot(probability ~ temperature, data = x) ## add predicted line lines(x = dat$temperature, y = dat$yhat, type = "l") Hope this helps, Josh On Mon, Apr 11, 2011 at 12:29 PM, Josh B <[hidden email]> wrote: > Dear Listserv, > > Here is my latest in a series of simple-seeming questions that dog me. > > Consider the following data: > > x <- read.table(textConnection("temperature probability > 0.11 9.4 > 0 2.3 > 0.38 8.7 > 0.43 9.2 > 0.6 15.6 > 0.47 8.7 > 0.09 12.8 > 0.11 9.4 > 0.01 7.7 > 0.83 8 > 0.65 9.3 > 0.05 7.4 > 0.34 10.1 > 0.02 4.8 > 0.07 9.1 > 0.6 15.6 > 0.01 8.4 > 0.9 9.6 > 0.83 8 > 0.12 8.4 > 0.01 8 > 0 5 > 0.11 9.7 > 0.41 7.4 > 0.05 9.4 > 0.09 8.3 > 0 6.1 > 0.12 8.4 > 0.73 7.8 > 0 4.2"), header = TRUE, as.is = TRUE) > closeAllConnections() > > I modeled the relationship: Probability = f(Temperature), i.e., probability as a > function of temperature. > > I found that there is a significant quadratic term in the model: > > summary(lm(x[,2] ~ x[,1] + I(x[,1]^2))) > > Now the question is: how do I plot it? > > I can do this: > plot(x[,2] ~ x[,1]) > > ...but I would also like to add a line corresponding to the quadratic function. > In other words, I want to visually show the relationship among the variables > that is being modeled. How do I do it? I think the curve() command will be used, > but I don't know how to employ it. > > Thanks very much in advance. > > Sincerely, > ----------------------------------- > Josh Banta, Ph.D > Center for Genomics and Systems Biology > New York University > 100 Washington Square East > New York, NY 10003 > Tel: (212) 998-8465 > http://plantevolutionaryecology.org > [[alternative HTML version deleted]] > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Joshua Wiley Ph.D. Student, Health Psychology University of California, Los Angeles http://www.joshuawiley.com/ ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
In reply to this post by Josh B-3
Hi,
Is it just me or it appears the "temperature" and "probability" should be reversed? -------- Anyhow it should help you to assign your model to a variable (as Joshua did with his own suggestion) yourmodel <-lm(x[,2] ~ x[,1] + I(x[,1]^2)) # again, taking literally the way you formulated it... And you could then access the coefficients individually: c <- coef(yourmodel)[1] # for the intercept b <- coef(yourmodel)[2] a <- coef(yourmodel)[3] To build yourself a vector of predicted y-values based on a vector of x-values ("dat" as per Joshua's post) and pass them to the "lines" function to be plotted over your existing plot. This is less convenient than using the "predict" but sometimes it helps to do the arithmetic at a lower level. HTH |
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