similar to: linear model: Test difference between coefficients and given values (t.test?)

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

hi all, after fitting a multiple linear regression model <- lm(y ~ a + b+ c+d) i wanted to plot diagnostics plot(model) but get the error message Error in object$coefficients : $ operator is invalid for atomic vectors. which does not make a lot of sense, since there is no "$" - i am working with an attached dataset. can anyone help, please?? thanks a lot, kat

Hi! I am currently using Sweave for writing my bachelor thesis - and I have a problem: I am using a LaTeX style (report) with quite big margin spaces. The Sweave generated LaTeX code "floats" into the margin - and it looks ugly. The text is blocked and fine... then there comes some flattering code running over the margin... and blocked text again. Considering the LaTeX output, I guess

Hi! I have a question that is probably very basic, but I cannot figure out how to do it. I simply need to compare the significance of a regression slope against a slope of 1, instead of the default of zero. I know this topic has been posted before, and I have tried to use the advice given to others to fix my problem. I tried the offset command based on one of these advice threads as follows:

Have you tried web searching on " R constrained linear regression" or similar. There seemed to be resources related to your issues when I looked. You might also search on rseek.org . There are apparently several packages that do regression with constraints, but I don't know if they fit your situation. Cheers, Bert Bert Gunter "The trouble with having an open mind is that

If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should consult a local statistician to help you formulate your problem appropriately. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people

Ok, and if I would just like to force my slope coefficients to be inside an interval, let's say, between 0 and 1? Is there a way in R to formulate such a constraint regression? Thanks in advance and kind regards, Aljosa Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandrovic at man.com Tel +41 55 417 7603 Man Investments (CH) AG Huobstrasse 3 | 8808

Hi everyone, is it possible to write a certain value beside a point in a plot? I'm plotting the following: plot(coefficient$intercept ~ coefficient$average_BM_leaves_needles, main="intercepts ::: BM_leaves_needles", ylab="intercepts", xlab="average BM_leaves_needles per site [kg]") and I want to have the value of coefficient$site_no written beside each

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a nonlinear least squares solver and non-negative bound constraints. The constraint that the coefficients sum

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

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

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

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Hi Everyone, I am uncertain that I am writing the contrast statements correctly. Basically, I'm unsure when to use a -1 and a 1 when writing the contrasts. Specifically I am interested in comparing the slopes between different temperature regimes. Temperature is therefore a factor. Time and percent are numerical. Using the gmodels package I made the following model:

The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon) <Aljosa.Aleksandrovic at man.com> wrote: > Hi Gabor, > > Thanks a lot for your help! > > I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

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

Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

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

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of