similar to: Query on R-Climdex

Dear All, I have been using RClimdex for indices calculation. After completion of indices calculation, I get few statistical information on each output. Such as R2=3.4 p-value=0.333 Slope estimate= -7.774 and Slope error= 7.891 Now I would like to know, what do they mean and how shall I interpret them. Thank you. Regards. Binaya Pasakhala [[alternative HTML version deleted]]

Dear Sir, I am using RClimDex. I get following error. Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : scan() expected 'a real', got 'T' I have done copy paste of climate data from excel file to notepad and tried to upload. I do not have any knowledge about programming languages. Please help me. Regards, Binaya Pasakhala -- View this

I've recently come across the following results reported from the lm() function when applied to a particular type of admittedly difficult data. When working with small data sets (for instance 3 points) with the same response for different predicting variable, the resulting slope estimate is a reasonable approximation of the expected 0.0, but the p-value of that slope estimate is a surprising

Tim, I think what you're seeing is https://en.wikipedia.org/wiki/Loss_of_significance. Cheers, Mark From: "Glover, Tim" <Tim.Glover at amecfw.com> To: "r-help at r-project.org" <r-help at r-project.org> Date: 09/05/2017 11:37 AM Subject: [R] Interesting behavior of lm() with small, problematic data sets Sent by: "R-help"

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and

Dear List: Below is the validation output of a fitted ordinal logistic model using the bootstrap in the rms package. My interpretation is that most of the corrected indices indicate little overfitting, however the slope seems to indicate that the model is too optimistic. Given that most of the corrected indices seem reasonable, would it be appropriate to use this model on future data if the

Greetings, I would like to use the "boot" function to generate a bootstrap confidence interval for the slope in a SLR that has a zero intercept. My attempt to do this is shown below. Is this the correct implementation of the boot function to solve this problem? In particular, should I be doing anything with the residuals in the "bs" function (e.g., using weighted residuals)?

Hello everybody, I have a question. I would like to get a correlation between constitutive and induced plant defence which I messured on 30 plant species. So I have table with Species, Induced defence (ID), and constitutive defence (CD). Since Induced and constitutive defence are not independant (so called spurious correlation) I should do a randomisation test. I have a syntax of my

Hello, I'm running into a very strange problem: > xrange <- c(-2.5,2.5) > xdim <- 100 > mobility <- 0.1 > slope <- 1.16 > urange <- slope*xrange > udim <- max(slope*xdim,5) > du <- (urange[2]-urange[1])/udim > uvec <- urange[1]+(1:udim-0.5)*du > # type dependent weight function > ckern <-

I am learning ggplot2, and need your help. When I try > p <- ggplot(mtcars, aes(x = wt, y=mpg)) + geom_point() > p + geom_abline(slope=5) (from http://had.co.nz/ggplot2/geom_abline.html) the slope of the abline does not change, but this works: > p + geom_abline(intercept=20) In order to have slope work, I have to use > p + geom_abline(aes(slope=5)) Is it a bug, or is there

Hello there, I am using nlme() to fit a logistic mixed effect model on our data. The outcome variable is binary. I got the error when I wanted to add a group factor to my model. My initial model is as below: model.a <- nlme(response~ 1/(1 + exp( -intercept- u0 - slope*TIME - u1*TIME)), + fixed=intercept+slope~1, random= u0+u1~1 |studentID,

I created a small example to show something that I do a lot of. "scale" data by month and return a data.frame with the output. "id" represents repeated observations over "time" and I want to scale the "slope" variable. The "out" variable shows the output I want. My for..loop does the job but is probably very slow versus other methods. ddply

I am performing a binary classification using a classification tree. Ironically, the data themselves are 2483 tree (real biological ones) locations as described by a suite of environmental variables (slope, soil moisture, radiation load, etc). I want to separate them from an equal number of random points. Doing eda on the data shows that there is substantial difference between the tree and random

Hello, I have a challenge! I have a large dataset with three columns, "date","temp", "location". "date" is in the format %m/%d/%y %H:%M, with a "temp" recorded every 10 minutes. These temperatures of surface temperatures and so fluctuate during the day, heating up and then cooling down, so the data is a series of peaks and troughs. I would like

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y

Hi, I am trying to get R to return just the slope of a linear regression line, but it seems that R has to return both the slope and the name of the slope. For example, > a=coef(lm(y~miles)) > a (Intercept) miles 360.3778 -7.2875 > names(a) [1] "(Intercept)" "miles" > a[1] (Intercept) 360.3778 > a[2] miles -7.2875 I don't understand the

I would like to calculate the line of organic correlation, so that I can use it for record extension for a stream gauging station. Below is a description (1) and then my first attempt with R code (2). Thanks in advance for any help. Stephen Sefick The LOC minimizes the sum of the areas of right triangles formed by horizontal and vertical lines extending from observations to the fitted line

Hello people I am doing some thinking about how to analyse data on dimorphic animals - where different individuals of the same species have rather different morphology. An example of this is that some male beetles have large horns and small wings, and rely on beating the other guys up to get access to mates, whereas others have smaller horns and larger wings, and rely on mobility to

Hi all: I have a question about linear mixed model. my linear mixed model with randomized slope and intercept with interaction of time and group(g1,g2,g3): model<- glmmPQL(log10(CD4) ~ time + factor(group)+ time:factor(group), random = ~time|id) What I get is only the main and interaction of time and group.My question is: 1. How can I get the g1,g2,g3's slope respectively?In other

Hi, I'm trying to work out plotting effect confidence intervals for a mixed effects design. For example, when measuring heights over age one will get two kinds of confidence intervals from the resulting model (using intervals in lme), a broad inference interval from the random intercept, and a narrow inference interval about the fixed effect slope. I've been considering what