search for: formulation

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 ?K.....

...;- 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? Maybe you can give me a hint or can point me to a good description of constraint handling. Greetings and thanks in advance, Benjamin Benjam...

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). Then, I...

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 function, Why?

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 Treatment T2: subject: 16 17

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,s_L)...

Dear all, I am using the pls package of R to perform partial least square on a set of multivariate data. Instead of fitting a linear model, I want to fit my data with a quadratic function with interaction terms. But I am not sure how. I will use an example to illustrate my problem: Following the example in the PLS manual: ## Read data data(gasoline) gasTrain <- gasoline[1:50,] ## Perform

I need a little piece of advice concerning passing data frames into the functions. As I do a lot of similar fits at a time, I'd like to write a small function doing the fits for all relevant variables automatically. However, I usually get error messages of the following kind: (I present here a part of a test code). ##################################################### # Data set:

...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 it es...

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

Hello, I'm trying to analyse data from an incomplete design with four factor : - fr : number of the batch - op : ID of operator - meth : method used - mat : nature of the material used and one variable - mv : mesure - trmv : transformed mesure str(matvol) `data.frame': 120 obs. of 6 variables: $ fr : Factor w/ 30 levels "1","2","3","4",..:

...CFL-based > pointer analysis is generally more expensive as it has O(L^3 N^3) > complexity [29], where L is the size of the grammar and N is the > size of the graph." > > should now been reduced to O(n + m log m) time, assuming you are > using a standard balanced parentheses formulation. > > > > > On Thu, Oct 17, 2013 at 9:51 PM, lian li <lianli at gmail.com> wrote: >> Hi Hal, >> >> Thanks for your interest. >> >> We tested with the following existing compiler optimizations in LLVM >> with SPECINT2006 benchmarks: >> -d...

...alysis is generally more expensive as it has O(L^3 N^3) > > complexity [29], where L is the size of the grammar and N is the > > size of the graph." > > > > should now been reduced to O(n + m log m) time, assuming you are > > using a standard balanced parentheses formulation. > > > > > > > > > > On Thu, Oct 17, 2013 at 9:51 PM, lian li <lianli at gmail.com> wrote: > >> Hi Hal, > >> > >> Thanks for your interest. > >> > >> We tested with the following existing compiler optimizations in LLVM...

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

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(88,113,128,143,157,172,184,198,210,226,240,249,263,284,302,340) y <-

Dear ?, I'm sure that there are many ways to do what you want; here's one: > cbind(concept_df, category= + ifelse(apply( + sapply(chemical_df$chemical, + function(x) grepl(x, concept_df$concept)), + 1, any), + "chemical", "")) concept category 1 butan

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{Comparison...

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 "S...

Below. -- Bert Bert Gunter On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: > I have two ideas about it. > > 1- > i) Entering variables in quadratic form is done with the command I > (variable ^ 2) - > plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = > "LOO" > You could also use a new variable

I have two ideas about it. 1- i) Entering variables in quadratic form is done with the command I (variable ^ 2) - plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = "LOO" You could also use a new variable NIR_sq <- (NIR) ^ 2 ii) To insert a square variable, use syntax I (x ^ 2) - it is very important to insert I before the parentheses. iii) If you want to