[R] for loop using index values
Hi all I have point data along a transect and I want to divide the transect into small blocks of 10m length each. I have named the blocks as a list i.e subset[[i]]. Now the issue is I want to process only those blocks that have at least 100 data points and keep the original index values of those subsets. How do I set the for loop. I have tried the following but it is still processing everything select-which(nrow(subset[[i]])=100 for (i in c(select)){ .. } Thank you in advance, David Gwenzi Graduate Degree Program in Ecology Natural Resources Ecology Lab Colorado State University [[alternative HTML version deleted]] __ 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.
[R] Fwd: Intepreting lm() results with factor
Dear all I have observations done in 4 different classes and the between classes *variance* is too high that I decided to run a model without pooling the *variance*. I used the following code first : model-lm(y~x+factor(class)) and got the following output: Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) 52.41405 17.38161 3.015 0.00658 ** x0.276790.07387 3.747 0.00119 ** factor(class)2 92.68083 32.26645 2.872 0.00912 ** factor(class)3 197.82029 33.24916 5.950 6.63e-06 *** factor(class)4 105.61266 55.18373 1.914 0.06937 . --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 43.07 on 21 degrees of freedom Multiple R-squared: 0.9206,Adjusted R-squared: 0.9055 F-statistic: 60.91 on 4 and 21 DF, p-value: 2.976e-11 My understanding of this output is that class 1 is used as a baseline (constant) and each other class's p values means for example the dependent value in class 2 is significantly different from that of class 1. Now I ran the model again, but without using a constant i.e model-lm(y~x+factor(class)-1) and got the following output: Coefficients: Estimate Std. Error t value Pr(|t|) x0.276790.07387 3.747 0.00119 ** factor(class)1 52.41405 17.38161 3.015 0.00658 ** factor(class)2 145.09488 39.42651 3.680 0.00139 ** factor(class)3 250.23434 40.61189 6.162 4.11e-06 *** factor(class)4 158.02672 64.09549 2.465 0.02238 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 43.07 on 21 degrees of freedom Multiple R-squared: 0.9801,Adjusted R-squared: 0.9754 F-statistic: 207.1 on 5 and 21 DF, p-value: 2.2e-16 Can somebody please tell me how to interpret this one now? what do the classes' P values mean ? Do they merely show if they significantly contribute to the model or whether they are significantly different from the overall mean or not? Does it mean if one class had a p value 0.05 it would mean the observations from that class are not significantly contributing to the model? Thanks in advance David [[alternative HTML version deleted]] __ 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.
[R] R nls results different from those of Excel ??
Hi all I have a set of data whose scatter plot shows a very nice power relationship. My problem is when I fit a Power Trend Line in an Excel spreadsheet, I get the model y= 44.23x^2.06 with an R square value of 0.72. Now, if I input the same data into R and use model -nls(y~ a*x^b , trace=TRUE, data= my_data, start = c(a=40, b=2)) I get a solution with a = 246.29 and b = 1.51. I have tried several starting values and this what I always get. I was expecting to get a value of a close to 44 and that of b close to 2. Why are these values of a and b so different from those Excel gave me. Also the R square value for the nls model is as low as 0.41. What have I done wrong here? Please help. Thanks in advance David [[alternative HTML version deleted]] __ 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.