similar to: Assessing standard errors of polynomial contrasts

#I have a data frame with a numeric and a character variable. x=c(1,2,3,2,0,2,-1,-2,-4) md=c(rep("Miller",3), rep("Richard",3),rep("Smith",3)) data1=data.frame(x,md) #I subset this data.frame in a way such that one level of the character variable does not appear in the new dataset. data2=data1[x>0,] data3=subset(data1,x>0) #However, when I check the levels

Hi, I want to extract individual names from a single string that contains all names. My problem is not the extraction itself, but the looping over the extraction start and end points, which I try to realize with apply. #Say, I have a string with names. authors=c("Schleyer T, Spallek H, Butler BS, Subramanian S, Weiss D, Poythress ML, Rattanathikun P, Mueller G") #Since I only want the

I have a quick statistical question and hoped somebody has a tip for me without me having to go to the local statistician on Monday. I assess 4 statements from 90 subjects. Each of the 4 statements receives one of three responses (say -1, 0, or 1). I can use Cramer's V or Spearman correlations to assess the correlation between each pair of statements, but I am looking for a measure of

Hello [R]-help I am trying to find > a package where you can do ANOVA based trend analysis on grouped data > using orthogonal polynomial contrasts coefficients, for unequally > spaced factor levels. The closest hit I've had is from this web site: >(http://webcache.googleusercontent.com/search?q=cache:xN4K_KGuYGcJ:www.datavis.ca/sasmac/orpoly.html+Orthogonal+polynomial >l but I

In S-Plus, I can obtain polynomial contrasts for an ordered factor with contr.poly(). The function also exists in R, however is limited to factors where the levels are equally spaced. In S-Plus, one can obtain the contrasts for a set of numeric values representing unequally spaced ordered factors. Has anyone implemented this in R? I see that the S-Plus function calls another function (poly.raw())

Dear all, I am quite new to R and I am having trouble writing the polynomial contrasts for an ordinal factor in MANOVA. # I have a model such as this fit<-manova(cbind(Y1,Y2,Y3)~Groups,data=Events) # where groups is an ordinal factor with 4 levels # how to set polynomial contrasts for the "Groups" factor ? Thank you very much in advance for any help! Best regards, Mauro -- Dr.

Forgive my resending this post. To data I have received only one response (thank you Bert Gunter), and I still do not have an answer to my question. Respectfully, John Windows XP R 2.12.1 contrast package. I am trying to understand how to create contrasts for a model that contatains an interaction. I can get contrasts to work for a model without interaction, but not after adding the

How do you remove one of the terms from an ordered polynomial contrast in your linear model. For example, I have significant terms for linear and cubic but not quadratic, how would i remove the quadratic term from lm(response~treatment) Cheers, Chris -- View this message in context: http://www.nabble.com/Orthogonal-polynomial-contrasts-tp16608353p16608353.html Sent from the R help mailing list

Dear all using the orthogonal polymial on a set of data, I get an approximate density which basically is in the form: exp(-polynomial), as you know, the parameters are the converged coeeficients. obviously, It is hard, if not impossible, to use the inverse CDF method to get a sample and then plot density. then how can I plot the approximated density in order to have a graphical comparision

On Mon, May 18, 2020 at 2:31 AM Nathan Chancellor <natechancellor at gmail.com> wrote: > > On Wed, Apr 08, 2020 at 01:53:23PM -0700, Nathan Chancellor wrote: > > When building with Clang + -Wtautological-compare and > > CONFIG_CPUMASK_OFFSTACK unset: > > > > arch/x86/mm/mmio-mod.c:375:6: warning: comparison of array 'downed_cpus' > > equal to a

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Clang warns when CONFIG_BALLOON_COMPACTION is unset: ../drivers/virtio/virtio_balloon.c:963:1: warning: unused label 'out_del_vqs' [-Wunused-label] out_del_vqs: ^~~~~~~~~~~~ 1 warning generated. Move the label within the preprocessor block since it is only used when CONFIG_BALLOON_COMPACTION is set. Fixes: 1ad6f58ea936 ("virtio_balloon: Fix memory leaks on errors in

Clang warns when CONFIG_BALLOON_COMPACTION is unset: ../drivers/virtio/virtio_balloon.c:963:1: warning: unused label 'out_del_vqs' [-Wunused-label] out_del_vqs: ^~~~~~~~~~~~ 1 warning generated. Move the label within the preprocessor block since it is only used when CONFIG_BALLOON_COMPACTION is set. Fixes: 1ad6f58ea936 ("virtio_balloon: Fix memory leaks on errors in

When building with Clang + -Wtautological-compare and CONFIG_CPUMASK_OFFSTACK unset: arch/x86/mm/mmio-mod.c:375:6: warning: comparison of array 'downed_cpus' equal to a null pointer is always false [-Wtautological-pointer-compare] if (downed_cpus == NULL && ^~~~~~~~~~~ ~~~~ arch/x86/mm/mmio-mod.c:405:6: warning: comparison of array 'downed_cpus'

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

On Sun, Feb 16, 2020 at 08:36:45AM +0100, David Hildenbrand wrote: > > > > Am 16.02.2020 um 01:41 schrieb Nathan Chancellor <natechancellor at gmail.com>: > > > > ?Clang warns when CONFIG_BALLOON_COMPACTION is unset: > > > > ../drivers/virtio/virtio_balloon.c:963:1: warning: unused label > > 'out_del_vqs' [-Wunused-label] > >

Clang warns: kernel/resource.c:1281:53: warning: operator '?:' has lower precedence than '|'; '|' will be evaluated first [-Wbitwise-conditional-parentheses] new_res = alloc_resource(GFP_KERNEL | alloc_nofail ? __GFP_NOFAIL : 0); ~~~~~~~~~~~~~~~~~~~~~~~~~ ^ kernel/resource.c:1281:53: note: place parentheses around the '|'

I am trying to trouble shoot one of my ISP's network and compare to my other ISPs offering. Although network 1 is reasonably fast and has low enough latency, voice quality is not good and the reason for this is not readily apparent using standard network tools. What tools can be used to assess the quality of the network in terms of it's suitability for voice? I am using ping, mtr,

I am in a situation where I have to fit a distrution, such as cauchy or normal, to an empirical dataset. Well and good, that is easy. But I wanted to assess just how good the fit is, using ks.test. I am concerned about the following note in the docs (about the example provided): "Note that the distribution theory is not valid here as we have estimated the parameters of the normal

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.