search for: formul

Dear All: I need to have some data frame objects. First aa object: pH Formulation time Subject [1]1.2 F 0 1 [2]7.4 S 1 2 [3] MF 2 3 [4] 3 4 [5] n i Then, I need to produce 2*3(pH*formulation) different tables. This table includes column of (pH, Formulation, time S1 S2 S3...

...erstand the use of the constraints in matrix form. I use them like: constr_mat<- -diag(2) constr_vec<- rep(-0.05,2) constr_mat<- rbind(constr_mat, diag(2)) constr_vec<- c(constr_vec, rep(-1, 2)) To get parameters in the interval [-1, 0.05]. (And this works so far) Now I would like to formulate a constraint like Sum(Abs(x_i))<=limit But I have no idea how I could get such a condition by using the matrix constraint formulation. I have already searched the help, web and all searchable R resources I could find, but got no helping result. Is constrOptim the right function to use? M...

Dear all: I am a new R-user and I have 2 questions about it. 1) I have a dataframe. Based on ?formulation? and ?subject?, a dataframe is split into 4 dataframes. The example is as follows. Moreover, I want to calculate ?test? value for these 4 dataframes. My question is that the ?test? values not correct and I do not know where the problem is. 2) There are 12 ?test? (y) values from 1). The...

HI, sorry but i don't understand how to make a function with as.function() formula<-"2+3*x" formu<-as.symbol(formula) > formu 2+3*x formul<-as.function(alist(x=,formu)) curve(formul,1,5,col="blue") Error in xy.coords(x, y, xlabel, ylabel, log) : x and y lengths differ > typeof(formul) [1] "closure" and not plot the curve fu...

this is a clearer (I hope) version of an earlier post - My problem is formulating the random = argument to give estimates of all 9 random components for this kind of setup where there are (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.5 Treatment T1: subject: 1 2 3 4 5 6 ... 13 14 15 Treatm...

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij + e_ijk where L ~ N(0...

...00 to 1700 nm. Instead of fitting with octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... I want to fit the data with: octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... + b[0]*NIR[0,i]*NIR[0,i] + b[1] * NIR[0,i]*NIR[1,i] + ... i.e. quadratic with interaction terms. But I don't know how to formulate this. May I have some help please? Thanks, Kelvin [[alternative HTML version deleted]]

...les gender and age response<-c(2.1,3.5,6.1,2.0,1.5) dataset<-data.frame(ID,gender,age,G1,G2,G3,response) GG<-c("G1","G2","G3") # here I construct a function that makes a basic fit, then updates with each variable from GG vector. trial_function(mydata,formule,expl,distr="binomial") { n_length(expl) fit.low_glm(formule, family = distr, data = mydata, na.action = na.exclude) for (j in 1:n) { fit_update(fit.low,~.+ mydata[,expl[j]]) print(mydata[,expl[j]]) } } result<- trial(mydata=dataset,formule=r...

...rst thought was the following: glmmPQL(count~site,data=dat,random=~day|site/transect, family="poisson") however, the random effects are not separated into day and site/transect. Instead, there is day|site and day|site %in% transect, which I realize makes sense in light of the model formulation. my second guess was glmmPQL(count~site,random=list(~day|site,~1|trans),family="poisson",data =dat2) which estimates a random effect on ~day|site and on ~1|trans%in%site..... which seems more appropriate, but does not give the same answers as I have for the genstat; nor does...

Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I write: update(model, . ~ . + factorX:matchingvariableY) then I get one extra level for the level that is contained in the intercept which is conditioned away, and...

...raction. op and fr are taken as random. For each batch, only 4 on the total 5 matrices are mesured (oven constraint), and op and meth are constant. I spent time trying every kind of aov models seeming sensible to me, read the according chapters in MASS2 and still didn't succeed writing the formula to obtain the results I expected (e.a. meth tested vs meth*op, mat vs mat*op, etc.). Can someone bring some light to my clouded mind ? Thanks in advance, Y. Brostaux ===================================================================== YVES BROSTAUX - Ing?nieur agronome Orientation Eaux &...

...he problem is a simple balanced parentheses problem in CFL-reachability, and it can be computed efficiently. The paper you mentioned is a very nice paper that proposed an efficient algorithm to solve Dyck-CFL-reachability problem in bidirectional graphs. However, precise pointer analysis cannot be formulated as a Dyck-CFL-reachability problem. The alias analysis presented in that paper is an over approximation. Hence the time bound from our paper still applies. Regards, Lian On Fri, Oct 18, 2013 at 4:39 PM, Daniel Berlin <dberlin at dberlin.org> wrote: > I notice you guys formulate y...

...exactly fits into the bidirectional graph problem, unless i'm missing something. > > The paper you mentioned is a very nice paper that proposed an > efficient algorithm to solve Dyck-CFL-reachability problem in > bidirectional graphs. Right, and your matching call sites can be formulated as one. However, precise pointer analysis cannot be > formulated as a Dyck-CFL-reachability problem. FWIW: This remains to be seen, and certainly also depends on your definitions of "precise". I agree you cannot currently formulate precise andersen style field sensitive pointer...

Dear list, we are a statistical/epidemiological departement that - after a few years of rapid growth - finally is getting around to formulate a general data storage and retention policy - mainly to ensure that we can reproduce results from published papers/theses easier in the future, but also with the hope that we get more synergy between related projects. We have formulated what we feel is a reasonable draft, requiring ba...

Hi, I'm trying to make a regression of the form : formula <- y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x) / scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2) )^(1/n2) ) ) ) ) which is a sum of the generalized logistic model proposed by richards. with data such as these: x <- c(88,113,128,143,157,172,184,198,210,22...

...any), + "chemical", "")) concept category 1 butan acid chemical 2 nano diamond particl 3 slurri composit 4 composit ph polis chemical 5 inorgan particl 6 grind liquid 7 liquid formul 8 nanoparticl 9 size abras particl 10 agent malic acid chemical Or, if you're wedded to magrittr: > sapply(chemical_df$chemical, + function(x) grepl(x, concept_df$concept)) %>% + apply(1, any) %>% + ifelse("chemical", &qu...

In the almost current development version (2009-05-22 r48594) and also in https://svn.r-project.org/R/trunk/src/library/base/man/factor.Rd ?factor contains (compare the formulations marked by ^^^^^^) \section{Warning}{ The interpretation of a factor depends on both the codes and the \code{"levels"} attribute. Be careful only to compare factors with the same set of levels (in the same order). ^^^^^^^^^^^^^^^^^ \section{Compar...

On 06/10/2010 08:16 PM, Chris Pearce wrote: > I have no plans to change the 'index' packet format further. I have proposed an alternative formulation of the index packet at http://github.com/bemasc/OggIndex/blob/master/Proposed-modified-spec.txt That repository also contains a working implementation of the alternative formulation. I have reviewed the details with Chris extensively. I believe that this formulation (which I have dubbed &q...

...ane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... >> I want to fit the data with: >> octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... + >> b[0]*NIR[0,i]*NIR[0,i] + b[1] * NIR[0,i]*NIR[1,i] + ... >> i.e. quadratic with interaction terms. >> But I don't know how to formulate this. >> May I have some help please? >> Thanks, >> Kelvin > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman...

...of fitting with octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... > I want to fit the data with: > octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... + > b[0]*NIR[0,i]*NIR[0,i] + b[1] * NIR[0,i]*NIR[1,i] + ... > i.e. quadratic with interaction terms. > But I don't know how to formulate this. > May I have some help please? > Thanks, > Kelvin [[alternative HTML version deleted]]