similar to: Linear Regression with lm Forcing the Slope to Equal 1

Dear forum members, How can I force a negative slope in a linear regression even though the slope might be positive? I will need it for the purpose of determining the trend due reasons other than biological because the biological (genetic) trend is not positive for these data. Thanks. Julia Example of the data: [1] 1.254 1.235 1.261 0.952 1.202 1.152 0.801 0.424 0.330 0.251 0.229

Hi, what I would need are 2 vector pairs (x,y) and (x1,y1). x and x1 must have the same sd. y and y1 should also exhibit the same sd's but different ones as x and x1. Plotting x,y and x1,y1 should produce a plot with 2 vectors having a different slope. Plotting both vector pairs in one plot with fixed axes should reveal the different slope. many thanks syrvn -- View this message in

Hello, I'm currently using the segmented package of M.R. Muggeo to fit a two-slope segmented regression. I would like to constrain a null-left-slope, but I cannot make it. I followed the explanations of the package (http://dssm.unipa.it/vmuggeo/segmentedRnews.pdf) to write the following code : fit.glm <- glm(y~x) fit.seg <- segmented(fit.glm, seg.Z=~x,psi=0.3) fit.glm

Hey, all. I'm working on a data set with a broken stick linear regression where I know one of the two slopes. It is a negative linear function until the line intersects with the x-axis, at which point it becomes 0. It is not a nonlinear asymptotic function, and, indeed, using negative exponential or logistic types of fits as an approximation has tended to lead to an under or

Does anyone know of a literature reference, or a piece of code that can help me calculate the amount of variation explained (R2 value), in a regression constrained to have a slope of 1 and an intercept of 0? Thanks! Sebastian J. Sebastián Tello Department of Biological Sciences 285 Life Sciences Building Louisiana State University Baton Rouge, LA, 70803 (225) 578-4284 (office and lab.)

Hi there, am trying to run a linear regression with a slope of 0. I have a dataset as follows t d 1 303 2 302 3 304 4 306 5 307 6 303 I would like to test the significance that these points would lie on a horizontal straight line. The standard regression lm(d~t) doesn't seem to allow the slope to be set. Any help very welcome. ed

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, 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

Hi, Is there a neat way of working out the slope of a flat surface in R? Given (x,y,z) co-ordinates of the four corners of a square, is there a function which will allow me to calculate the "mean" slope of the surface in a given direction? Thanks in advance.. Laura Laura Quinn Institute of Atmospheric Science School of Earth and Environment University of Leeds Leeds LS2 9JT tel: +44

Dear R-Users, I am new to R, so please excuse the ignorance. I have data: x (2.14, 2.41, 1.09, 0.17, 8.18) y (3.81, 5.13, 0.63, 0.75, 6.35) I would like to use simple linear regression and test 2 things: 1) slope of line of best fit is statistically different from 1 2) y-intercept is statically different from 0 Could anyone provide me with the R terminology to do this? Thanks so much, Dan

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

Hello, I have a linear model with different slopes for different treatment groups. I need to pairwise compare the different slope estimates for the different treatment groups. Is there a package that does pairwise comparisons of slope coefficients, making the appropriate adjustments in the P values? Thanks, John. -- ========================================== John Janmaat Department of

Hi for some data I working on I am merely plotting time against temperature for a variable named filmclip. So for example, I have volunteers who watched various film clips and have used infared camera to monitor the temperature on their face at every second of the clip. The variable names I have used are Normalised ( for the temperature) and Frame (for the time in seconds). So I have fitted a

Dear All, First of all I would like to say I do not have much knowledge about this subject, so most of you can find it really easy. I am doing a linear regression and I want to test if the slope of the curve is 0. R gives the summary statistics: Call: lm(formula = x ~ s) Residuals: Min 1Q Median 3Q Max -0.025096 -0.020316 -0.001203 0.011658 0.044970 Coefficients:

Is there an easy way to do a linear model with an a priori known slope? In essence, I want to minimize the residuals around a line of known slope. -R -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

I want to fit a linear model with fixed slope e.g. y = x + b (instead of general: y = a*x + b) Is it possible to do with lm()? Regards, Ryszard -------------------------------------------------------------------------- Confidentiality Notice: This message is private and may ...{{dropped:11}}

Hi, I was hoping to get some advice on how to derive estimates of slopes from four parameter logistic models fit with SSfpl. I fit the model using: model<-nls(temp~SSfpl(time,a,b,c,d)) summary(model) I am interested in the values of the lower and upper asymptotes (parameters a and b), but also in the gradient of the line at the inflection point (c) which I assume tells me my rate of

Hi all, I try to test a linear slope using offset. I have: > m2 <- glm(Y~X*V) > summary(m2) Call: glm(formula = Y ~ X * V) Deviance Residuals: Min 1Q Median 3Q Max -2.01688 -0.56028 0.05224 0.53213 3.60216 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.3673 0.8476 1.613 0.119788 X

Dear R Community, I am using the package DRC ( to fit a boltzman model to my data. I can fit the model and extract the lower limit, upper limit, and ED50 (aka V50), but I cannot figure out how to get the slope of the curve at ED50. Is there a simple way to do this? I've searched the mailing list and looked through the package documentation, but could not find anything. I am new to r, and