[R] 95% confidence intercal with glm
Hi I had to use a glm instead of my basic lm on some data due to unconstant variance. now, when I plot the model over the data, how can I easily get the 95% confidence interval that sormally coming from: yv - predict(modelVar,list(aveLength=xv),int=c) matlines(xv,yv,lty=c(1,2,2)) There is no interval argument to pass to the predict function when using a glm, so I was wondering if I had to use an other function thanks -- View this message in context: http://r.789695.n4.nabble.com/95-confidence-intercal-with-glm-tp2716906p2716906.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org 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.
Re: [R] help interpreting a model summary
David Winsemius wrote: On Sep 19, 2010, at 5:59 PM, zozio32 wrote: Thanks for you're long answer. I have to say, I am not fully sure of what you're meaning everywhere. As I said, I am merely following a recipe book, and when things depart from it I am a bit lost. I'll try to answer to each of your paragraphs: 3: I was not wanting to include 3-way interactions, but that's the only way I found to include a 2 way interaction in my piecewise linear model. Yes. That makes sense to me. I could obviously include only angleNoise*reflection, but I thought that was not very consistent with the fact that reflection variable was split in 2. I could may be define the point of separation, and then create 2 separate models in the form lm(weightedDiff ~ angleNoise*reflection). Yes, I see that you did, and that may be helpful in understanding the estimates. The question I would ask you is why you are putting a breakpoint in a physical model. Unless there is a phase change or some discontinuity in effects at that point, I think the breakpoint looks artificial. Is refelction somehow physically connected with the breakpoint? Some sort of acoustic timing phenomenom? Or reflected wave effect in a hydraulic model? I am generating waves in a wave tank and I have some of it bouncing back from the tank wall to mess up my measures, and i want to quantify this effect. weightedDiff is a measure of the difference between the target spectra and what I am measuring. For that I have generated virtual wave elevation with different level of reflection and I am analysing the results of my waves measurement method. Basically, I can observe a strong curvature in my data weightedDiff as a function of reflection. Now, I can try to model this curvature by a square term, a linear piece wise linear model or may be a log model in the form y = a*log(x)+b.I don't like the square model as it will not go to +inf with reflection - +inf. So I've tried the second option with what I thought was not too bad results ... I think I'll investigate the log option now. i have to say that speaking to someone definitely clears up my mind on this. And R is not the cup of tea of people in my department. I am not really thinking that there is a breaking point, but that the influence of the angleNoise perturbations (level of uncertainty in the direction of propagation of my waves) is negligible when the reflection get too high. If I could identify this point, or a king of limit between 2 zones ( one with reflection low enough that angleNoise as to be taken into account, one with reflection so high that angleNoise do not matter any more) that will be helpful. I merely thought that my formulation was a way to combine them together. Basically, i am expecting both parameters to degrade my signal, but I'll not be surprised if passed a certain level of reflection, having noise or not in my angles is not really relevant, hence the interaction parameter. The piecewise linear model is a way to take into account the curvature in the data that I can observe on a straight scatter plot. 1: Thanks for the first part, i think I can make sens of it. ;) I guess I can ignore this parameter in that case. By the way, which type of Anova you refering to: creating a factor with high and low level of interation, and fitting the interation between angleNoise and this new factor? If you took a model with all of the data and fit first a model without the break point and one with the breakpoint and then looked at the output of anova(model1) and annova(model2) the difference in deviance across the two models is distributed (asymptotically anyway) as a chi- square statistic with the difference in number of degrees of freedom. That's a much better basis for deciding whether the addition of the term is statistically significant. yeah, I did that and there is definitely no match for the linear model without the break or square term, or anything. I just need to model this curvature somehow to get a decent model here. As I am using virtual wave elevation, I have over 600 observations point, so the anova test results are not ambiguous at all. 2: first, i was mislead by the meaning of this factor. i only encounter the version were it's TRUE, not FALSE which is the difference. I think I also use important in a wrong way. I should have used significant instead. You had specified a model that that terms with both (reflection = Break[xMin]) and (reflection Break[xMin]) and the lm program threw away all the levels with reflection = Break[xMin]. If you had only specified the the model with only reflection = Break[xMin] you would have gotten an identical model as far as predictions were concerned, but the signs would have been reversed for any of the levels with the inequality term in them. After, i have to admit that I am lost when
[R] help interpreting a model summary
Hello, I am all new here. Thanks for the job done, R really helped me in my thesis lately. However, I am kind of new in statistics, coming from mecanical engineering, and I mostly teached myself with The R Book, so I may do silly things some time. PLease tell me if you think so. Anyway, I've just build up a piecewise linear model to fit some data, including some interaction and i am not sure of how to interpret the summary:. here it is: Call: lm(formula = weightedDiff ~ angleNoise + (reflection Break[xMin]) * reflection + (reflection = Break[xMin]) * reflection + angleNoise:(reflection Break[xMin]) * reflection + angleNoise:(reflection = Break[xMin]) * reflection) Residuals: Min 1Q Median 3QMax -1.073e-03 -1.749e-04 -5.913e-06 1.650e-04 1.311e-03 Coefficients: (4 not defined because of singularities) Estimate Std. Error (Intercept) 0.0134798 0.0001086 angleNoise 0.0004658 0.0002245 reflection Break[xMin]TRUE-0.0028766 0.0001236 reflection 0.0316122 0.0014741 reflection = Break[xMin]TRUE NA NA reflection Break[xMin]TRUE:reflection 0.0683631 0.0027668 reflection:reflection = Break[xMin]TRUENA NA angleNoise:reflection Break[xMin]TRUE 0.0011158 0.0002548 angleNoise:reflection = Break[xMin]TRUENA NA angleNoise:reflection Break[xMin]FALSE:reflection -0.0055751 0.0030620 angleNoise:reflection Break[xMin]TRUE:reflection -0.0343745 0.0049164 angleNoise:reflection:reflection = Break[xMin]TRUE NA NA t value Pr(|t|) (Intercept) 124.079 2e-16 *** angleNoise 2.075 0.0384* reflection Break[xMin]TRUE -23.2652e-16 *** reflection 21.445 2e-16 *** reflection = Break[xMin]TRUE NA NA reflection Break[xMin]TRUE:reflection 24.7082e-16*** reflection:reflection = Break[xMin]TRUE NA NA angleNoise:reflection Break[xMin]TRUE 4.379 1.41e-05 *** angleNoise:reflection = Break[xMin]TRUE NA NA angleNoise:reflection Break[xMin]FALSE:reflection -1.821 0.0692. angleNoise:reflection Break[xMin]TRUE:reflection-6.992 7.35e-12 *** angleNoise:reflection:reflection = Break[xMin]TRUE NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0002885 on 592 degrees of freedom Multiple R-squared: 0.9666, Adjusted R-squared: 0.9662 F-statistic: 2450 on 7 and 592 DF, p-value: 2.2e-16 Basically, I am really not sure of the meaning of this parameter: angleNoise:reflection Break[xMin]FALSE:reflection Overall, my interpretation is that reflection is important , angle Noise also but specially when reflection is below the breaking point. Is that correct? well, sorry for the first long post thanks in advance -- View this message in context: http://r.789695.n4.nabble.com/help-interpreting-a-model-summary-tp2546161p2546161.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org 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.
Re: [R] help interpreting a model summary
Thanks for you're long answer. I have to say, I am not fully sure of what you're meaning everywhere. As I said, I am merely following a recipe book, and when things depart from it I am a bit lost. I'll try to answer to each of your paragraphs: 3: I was not wanting to include 3-way interactions, but that's the only way I found to include a 2 way interaction in my piecewise linear model. I could obviously include only angleNoise*reflection, but I thought that was not very consistent with the fact that reflection variable was split in 2. I could may be define the point of separation, and then create 2 separate models in the form lm(weightedDiff ~ angleNoise*reflection). I merely thought that my formulation was a way to combine them together. Basically, i am expecting both parameters to degrade my signal, but I'll not be surprised if passed a certain level of reflection, having noise or not in my angles is not really relevant, hence the interaction parameter. The piecewise linear model is a way to take into account the curvature in the data that I can observe on a straight scatter plot. 1: Thanks for the first part, i think I can make sens of it. ;) I guess I can ignore this parameter in that case. By the way, which type of Anova you refering to: creating a factor with high and low level of interation, and fitting the interation between angleNoise and this new factor? 2: first, i was mislead by the meaning of this factor. i only encounter the version were it's TRUE, not FALSE which is the difference. I think I also use important in a wrong way. I should have used significant instead. After, i have to admit that I am lost when you're talking about models with reversed inequality... 4. Not much to say here, i knew they were pointless but the results from my formulation of the model. I don't thnik there is a need to removed them no? -- View this message in context: http://r.789695.n4.nabble.com/help-interpreting-a-model-summary-tp2546161p2546292.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org 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.
Re: [R] help interpreting a model summary
Actually, rereading trough my post, I think I understood a bit better now. I have now fit a much simpler to each part of the data, and things looks a bit easier to understand. for the part were reflection break[xmin], i now have: Call: lm(formula = weightedDiff ~ angleNoise * reflection, data = data1) Residuals: Min 1Q Median 3QMax -1.081e-03 -2.296e-04 1.335e-05 2.010e-04 1.287e-03 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept)1.057e-02 7.536e-05 140.273 2e-16 *** angleNoise 1.648e-03 1.542e-04 10.684 2e-16 *** reflection 1.021e-01 3.155e-03 32.361 2e-16 *** angleNoise:reflection -3.877e-02 6.679e-03 -5.804 2.1e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0003556 on 233 degrees of freedom Multiple R-squared: 0.8971, Adjusted R-squared: 0.8957 F-statistic: 676.9 on 3 and 233 DF, p-value: 2.2e-16 and for the other I have: Call: lm(formula = weightedDiff ~ angleNoise * reflection, data = data2) Residuals: Min 1Q Median 3QMax -6.252e-04 -1.391e-04 -1.086e-05 1.365e-04 7.281e-04 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept)1.342e-02 8.197e-05 163.715 2e-16 *** angleNoise 5.577e-04 1.696e-04 3.289 0.00110 ** reflection 3.236e-02 1.127e-03 28.722 2e-16 *** angleNoise:reflection -6.719e-03 2.340e-03 -2.871 0.00433 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0002341 on 359 degrees of freedom Multiple R-squared: 0.8269, Adjusted R-squared: 0.8254 F-statistic: 571.5 on 3 and 359 DF, p-value: 2.2e-16 i hope i can now safely conclude that both parameters are significant, and that their respective slope decreases when reflection is Break[xMin] I also assume that the negative sign on both interaction term can be interpret as angleNoise influence decreaes as reflection increases. Is that a more sensible interpretation of my data? -- View this message in context: http://r.789695.n4.nabble.com/help-interpreting-a-model-summary-tp2546161p2546303.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org 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.
