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POR- Polynomial Linear model coefficients not fit predicted values of model
 

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  1. POR- Polynomial Linear model coefficients not fit predicted values of model
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    <p>I am trying to fit some models to some data and the resulting model predicts sensible values and the plots seem correct. But when extracting the coefficients and plotting the functions separately, they make no sense!. I am obviously doing something wrong, so please can someone tell me where the error is? </p> <p>Data:</p> <pre><code>dput(distcur) structure(list(id1 = c(1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6 ), range = c(-39.898125, -21.448125, -11.07, -3.22875, 3.776484375, 12.309609375, 22.399453125, 39.235078125), meanrat = c(20.2496, 17.7504273504274, 12.76875, 2.475, -1.4295652173913, -3.9603305785124, -14.7008547008547, -19.7366666666667)), .Names = c("id1", "range", "meanrat"), row.names = 9:16, class = "data.frame") library(ggplot2) id = 1.6 degree = 3 press_x &lt;- seq(min(distcur$range), max(distcur$range), length = 500) moddist3b &lt;- lm(meanrat ~ poly(range, degree), distcur) valsdist = data.frame(predict(moddist3b, data.frame(range = press_x))) colnames(valsdist) = "pred" valsdist$id1 = id allvals = cbind(valsdist, press_x) summary(moddist3b) #test plot pdf(paste("mod-",measure,id )) TITLE = paste("Distance ID: ", id, "Model = line, Points = exp1") p = ggplot(allvals, aes(x=press_x, y=pred, colour=factor(id1))) + geom_line() + geom_point(data=distcur, aes(shape=factor(id1), x = range, y = meanrat, colour = factor(id1))) + ylim(-100, 100) + labs(title=TITLE) + ylab("Mean Rating (%)") + xlab(measure) print(p) dev.off() </code></pre> <p><img src="https://i.stack.imgur.com/x2MIE.png" alt="Plot of model vs points"></p> <p>I know the image is really bad quality, but it shows that it is correct. However the coefficients obtained from the model used to build the function look nothing like that plot: </p> <pre><code>summary(moddist3b) Call: lm(formula = meanrat ~ poly(range, degree), data = distcur) Residuals: 9 10 11 12 13 14 15 16 -0.20134 0.44939 1.65996 -2.80500 -1.14594 2.98617 -0.92081 -0.02244 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 1.6770 0.8281 2.025 0.1128 poly(range, degree)1 -37.7155 2.3423 -16.102 8.7e-05 *** poly(range, degree)2 -2.9435 2.3423 -1.257 0.2773 poly(range, degree)3 6.4888 2.3423 2.770 0.0503 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.342 on 4 degrees of freedom Multiple R-squared: 0.9853, Adjusted R-squared: 0.9743 F-statistic: 89.51 on 3 and 4 DF, p-value: 0.0004019 </code></pre> <p>Giving function y = 6.49x^3 −2.94x^2 − 37.72x + 1.68</p> <p>Plotting that on google clearly shows that the function is nothing like the plot from R (from the model) </p> <p><a href="https://www.google.com/search?q=6.49x%5E3%20%E2%88%922.94x%5E2%20%E2%88%92%2037.72x%20%2b%201.68&amp;ie=utf-8&amp;oe=utf-8&amp;aq=t&amp;rls=org.mozilla:en-US:unofficial&amp;client=iceweasel-a&amp;channel=fflb#client=iceweasel-a&amp;rls=org.mozilla:en-US:unofficial&amp;channel=fflb&amp;sclient=psy-ab&amp;q=6.49*x%5E3%20-2.94*x%5E2%20-%2037.72*x%20%2b%201.68&amp;oq=6.49*x%5E3%20-2.94*x%5E2%20-%2037.72*x%20%2b%201.68&amp;gs_l=serp.3...3610.3975.1.4155.2.2.0.0.0.0.107.147.1j1.2.0...0.0...1c.1.14.psy-ab.4C6De6gdmtg&amp;pbx=1&amp;bav=on.2,or.r_qf.&amp;bvm=bv.47008514,d.d2k&amp;fp=5e81885614cfda4f&amp;biw=1440&amp;bih=667" rel="nofollow noreferrer">https://www.google.com/search?q=6.49x^3+%E2%88%922.94x^2+%E2%88%92+37.72x+%2B+1.68&amp;ie=utf-8&amp;oe=utf-8&amp;aq=t&amp;rls=org.mozilla:en-US:unofficial&amp;client=iceweasel-a&amp;channel=fflb#client=iceweasel-a&amp;rls=org.mozilla:en-US%3Aunofficial&amp;channel=fflb&amp;sclient=psy-ab&amp;q=6.49*x^3+-2.94*x^2+-+37.72*x+%2B+1.68&amp;oq=6.49*x^3+-2.94*x^2+-+37.72*x+%2B+1.68&amp;gs_l=serp.3...3610.3975.1.4155.2.2.0.0.0.0.107.147.1j1.2.0...0.0...1c.1.14.psy-ab.4C6De6gdmtg&amp;pbx=1&amp;bav=on.2,or.r_qf.&amp;bvm=bv.47008514,d.d2k&amp;fp=5e81885614cfda4f&amp;biw=1440&amp;bih=667</a></p>
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      1. POR- Polynomial Linear model coefficients not fit predicted values of model
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      1. VTUpMod
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      1. VTUpMod
    1. COJust a guess, but you probably want to enclose your independent arguments with `I(poly(range,degree))` so the `formula` is interpreted the way you want it to be. Things like `+` and `*` have a different meaning in R-formulas.
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      1. POR- Polynomial Linear model coefficients not fit predicted values of model
      1. USCarl Witthoft
    2. CO@CarlWitthoft Adding the `I` gave the exact same model, however the values predicted were almost a horizontal line, which is further away from the experiment points. The coefficients are still the same as in my question. No idea why it affected predict, but I still don't have a function for the line that is plotted.
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      1. POR- Polynomial Linear model coefficients not fit predicted values of model
      1. USunixsnob
 

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