similar to: Forcing best-fit lines to go through the origin

Dear all: I have a regression model that has collinearity problems (between three regressor variables). I need a F-test that will allow me to compare between full (with all variables) and partial models (minus 1=< variables). The general F-test formula I'm using is: F = {[SS(full model) - SS(reduced model)] / (#variables taken out)} / MSS(full model) Unfortunately, the ANOVA table

I need to create artificial datasets with specific correlation coefficients (i.e. a dataset that returns r = 0.30, etc.) as examples for a lab I am teaching this summer. Is there a way to do that in R? Thanks. Jim Milks Graduate Student Environmental Sciences Ph.D. Program 136 Biological Sciences Wright State University 3640 Colonel Glenn Hwy Dayton, OH 45435 [[alternative HTML version

I am running R 2.01 on Mac OS 10.3.7. Whenever I save graphs, they are saved as PDF files. Are there any other file formats to which I could save my graphs. I would like to directly export my graphs to MS Word, if possible. Thank you in advance. Sincerely, Jim Milks Graduate Student Environmental Sciences Ph.D. Program Wright State University 3640 Colonel Glenn Hwy Dayton, OH 45435

Hello, While exploring if rgl is along the lines of what I need, I checked out demo("rgl") and didn't find quite what I'm looking for, and am therefore seeking additional help/suggestions. The application is geared towards displaying a 3D rendering of a contaminant plume in the subsurface with the following highlights: Once the plume was rendered as a 3D object, a

I am attempting to fit a logistic regression plane to a 3-D scatterplot (which was generated using scatterplot3d). I've noticed that the help pages of scatterplot3d include a function titled "plane3d." However, when I attempt to use the function, I get the following message: Error: couldn't find function "plane3d" I've searched the archives and found no

I have data on number of vines per tree for ~550 trees. Over half of the trees did not have any vines and the data is fairly skewed (median = 0, mean = 1.158, 3rd qu. = 1.000). I am attempting to investigate whether plot location (four sites), species (I'm using only the four most common species), or tree dbh has a significant influence on the number of vines per tree. When I

I have a data frame with two columns, one of which is a factor (Species) and the other is numeric (BA, which stands for basal area). Here's a sample: Species BA ACSA 55.7632696 FRAM 122.9933524 ACSA 67.54424205 ACSA 89.22123136 ACSA 82.46680716 ACSA 22.46238747 ACSA 19.94911335 ACSA 20.42035225 ACSA 19.00663555 ACSA 21.67698931 ACSA 57.80530483 ACSA 30.31636911 Dead 43.98229715 Dead

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

hello, I'd like know how to sort a data frame in R for example how I should do to sort by Catholic with swiss data frame like below thanks Fertility Agriculture Examination Education Catholic Infant.Mortality Courtelary 80.2 17.0 15 12 9.96 22.2 Delemont 83.1 45.1 6 9 84.84 22.2

Dear R Gurus, Let's get the technical details out of the way first: Computer: 1.83 GHz MacBook R version 2.5.1 I have a data set that contains the following variables: site, species, total.vines. I need to partition the main data set by site, the further select only those species that occurred at each site. When I select by site (site.name<-subset(data frame,Site=="Site

R gurus, I'm working on data analysis for a small project. My response variable is total vines per tree (median = 0, mean = 1.65, min = 0, max = 24). My predictors are two categorical variables (four sites and four species) and one continuous (tree diameter at breast height (DBH)). The main question I'm attempting to answer is whether or not the species identity of a tree has

On Thu, Apr 04, 2019 at 11:52:46AM -0400, Michael S. Tsirkin wrote: > I simply love it that you have analysed the individual impact of > each patch! Great job! Thanks! I followed Stefan's suggestions! > > For comparison's sake, it could be IMHO benefitial to add a column > with virtio-net+vhost-net performance. > > This will both give us an idea about whether the

On Thu, Apr 04, 2019 at 11:52:46AM -0400, Michael S. Tsirkin wrote: > I simply love it that you have analysed the individual impact of > each patch! Great job! Thanks! I followed Stefan's suggestions! > > For comparison's sake, it could be IMHO benefitial to add a column > with virtio-net+vhost-net performance. > > This will both give us an idea about whether the

Hello all, I am trying to figure out an optimal linear model by using stepwise regression which requires partial f-test, I did some Googling on the Internet and realised that someone seemed to ask the question before: Jim Milks <jrclmilks at joimail.com> writes: > Dear all: > > I have a regression model that has collinearity problems (between > three regressor variables). I

R gurus, I have a data set that looks something like this: Site Species DBH #Vines G PLOC 45.9 4 G ACNE 23.3 1 G ACNE 12.0 0 G FRAM 35.9 5 G AEGL 11.2 2 N PLOC 77.3 12 N JUNI 78.6 7 N ACNE 18.9 1 N ACNE 15.7 3 N ACRU 35.5 4 H ACSA2 24.1 6 H ULAM 35.2 7 There are 730 individual trees (22 species) from four sites in the actual data set. I would like to create a second data frame that

Dear friends, when i use glm() to fit my data, i use glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, *family = Gamma(link = inverse),* data =a,)) It shows: error in eval(expr, envir, enclos) : *gamma distribution don't allow negative value*. But i use result<-glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, family = poisson, data =a) #this

Hi, I am very new to R and I've been trying to work through the R book to gain a better idea of the code (which is also completely new to me). Initially I imputed my data from a text file and that seemed to work ok, but I'm trying to examine linear relationships between gdist and gair, gdist and gsub, m6dist and m6air, etc. This didn't work and I think it might have something to do

Dear all- I have a dataset (see a sample below - but the whole dataset is June 2005 - June 2008). The "LST" format is "YYMMDDHHmm" and I would like to get the hourly average of the "mph" for the summer months (spanning all years). I have been trying to use "aggregate" but am not having much success at all! any thoughts would be greatly appreciated.

This series tries to increase the throughput of virtio-vsock with slight changes: - patch 1/4: reduces the number of credit update messages sent to the transmitter - patch 2/4: allows the host to split packets on multiple buffers, in this way, we can remove the packet size limit to VIRTIO_VSOCK_DEFAULT_RX_BUF_SIZE - patch 3/4: uses

This series tries to increase the throughput of virtio-vsock with slight changes: - patch 1/4: reduces the number of credit update messages sent to the transmitter - patch 2/4: allows the host to split packets on multiple buffers, in this way, we can remove the packet size limit to VIRTIO_VSOCK_DEFAULT_RX_BUF_SIZE - patch 3/4: uses