search for: weigthing

Here is my dataframe with 1000 rows: employee_id weigth p-value 100 150 101 200 102 300 103 180 ..... My question: how can I calculate the p-value in R for each employee? the distribution of the weigth will be established from the above 1000 samples. -- View this message in context:

Potential bug: I mistyped weights in the call ('weigths') and it did not produce any error= message. The coefs were exactly the same like without weights, so I was su= spicious and when weights(nls1) gave NULL, I saw my typo. Usually the function will say "Unused arguments", which shows you the error= , but not nls. Regards Stephen [[alternative HTML version deleted]]

Hello. I don't understand a particular output of portfolio.optim (tseries). I have 4 assets and the portfolio.optim returns an asset with weight equals to zero. If I do a portfolio.optim with 3 assets, without the asset with weight equals to zero, it returns a completely different result. That's I would expected the same weights as the run with 4 assets. Below the code. Thanks in

Hi everyone, I admit I am a bit of an R novice, and I was hoping someone could help me with this error message: Warning message: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments weigths are just disregarded. My equation is: lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1)) -- View this message in context:

Hello, I know this must be a very simple problem, but I can't work it out from the documentation that is available. I've got a list of data I would like to plot (the weights of a single neuron that was trained using the neural package). The problem I'm encountering is that this set of weights, are in the form of a list. > network$weigth[1] [[1]] [,1] [1,]

Dear R-friends, I have to simple questions. First I would like to exclude some columns from a dataframe and after I need select rows that satisfy some conditions. My data frame looks like Region Species Bodysize Weigth Age Africa Sp1 10.2 20 2 Africa Sp2 12.2 12 2 Africa Sp3 15.3 18 3 Africa Sp4 11.5 40 4 Brazil Sp1 10.2 40 3 Brazil Sp2 22.2 32 2 Brazil Sp3 12.3

Hi all I want to add a band of fluctuaci?n (exponential decreading) to a linear deacrecing values Imagine: I have a matrix like c(10,9,8,7,6,5,4,3,2,1) The thing is I want two new lines so that the m?ximum value of the new colum on the m?ximum is from 10% to 5% higher and the same lower for the m?nimum the final two matix will be something like c(10+0.10*10,9+0.089*9,8+0.075*8.....,1+0.05*1)

Hello R-helpers: I have data like this: sample replicate height weight age A 1.00 12.0 0.64 6.00 A 2.00 12.2 0.38 6.00 A 3.00 12.4 0.49 6.00 B 1.00 12.7 0.65 4.00 B 2.00 12.8 0.78 5.00 C 1.00 11.9 0.45 6.00 C 2.00 11.84 0.44 2.00 C 3.00 11.43 0.32 3.00 C 4.00 10.24 0.84 4.00 D

Hi there Sorry for what may be a naive or dumb question. I have the following data: > x <- c(1,2,3,4) # predictor vector > y <- c(2,4,6,8) # response vector. Notice that it is an exact, perfect straight line through the origin and slope equal to 2 > error <- c(0.3,0.3,0.3,0.3) # I have (equal) ``errors'', for instance, in the measured responses Of course the

---------- Forwarded message ---------- Hello, I am analyzing a weighted, stratified, clustered survey data set with approximately 1 million observations and 50 variables. I am new to R (I'm a Stata user), and so far couldn't find any documentation on how to handle survey data. In other words, is there a specific package to handle a combination of weigths, clusters and strata. I am also

Hi all, Again I have searched the net and so on, without finding an answer to this surely simple problem. A short bit of code would be appreciated. I have a object named `data' with the following column headings. Date, maxitemp, minitemp, admissions, d.o.w. Where d.o.w. is day of the week, written "Sun" "Mon" etc. I just need to scale the Monday admissions by

Hi, I have a data.frame in wide format which I would like to reshape into a long format: example (nonsense) data: > dput(perus2) structure(list(id = c(30L, 38L, 21L, 12L, 22L, 28L, 31L, 44L, 8L, 47L, 23L, 20L, 41L, 42L, 29L, 50L, 5L, 33L, 4L, 17L, 11L, 1L, 18L, 6L, 9L, 32L, 16L, 14L, 39L, 48L, 37L, 43L, 25L, 27L, 35L, 40L, 45L, 49L, 2L, 34L, 10L, 15L, 24L, 26L, 46L, 7L, 13L, 3L, 19L, 36L),

Hello! When I know the vector of the variance of the disturbances (i.e. the structure of heteroskedasticity), say Var(u_{i})=v_{i}, what is the weights I should use as argument to lm(): M <- lm(y~x,weigths=1/v) or M <- lm(y~x,weights=1/(v^0.5)) ??? In the help pages I did not find a clear answer to this question, so please could someone help me! Thanks, Wolfgang Koller

Dear list, how can I compute the inverse of the X'WX matrix ("inverse of the weighted sum of squares and crossproducts matrix") from an object of class "lm" from a weigthed linear regression? Thanks, Sven

Hi, I try to make a model using lmer, but the weigths is not accept. m1<-lmer(ocup/total~tempo+(tempo|estacao),family=binomial,weights=total) Erro em lmer(ocup/total ~ tempo + (tempo | estacao), family = binomial, : object `weights' of incorrect type I dont understand why this error, with glm this work. the total object is a vector. Any idea? Thanks Ronaldo -- God is subtle, but

Dear R users, I am currently analyzing a dataset using lme(). The model I use has the following structure: model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML") When I plot the residuals against the fitted values, I see a clear positive trend (meaning that the variance increases with the mean). I tried to solve this issue using weights=varPower(),

...Classes with a large number of observations (A,B,D,J), the other classes are never predicted by the model. I thougth that the option "weights" of the multinom function could be usefull but I am not sure how to use this in the above case. Is there someone with experience regarding such a weigthing approach in multinom? If someone could help me with suggestions it would be great! Nice day, Arne

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Jun 1, 2017 at 11:20 AM, Pedro p?ramo <percentil101 at gmail.com> wrote: > Hi all > > I want to add a band of fluctuaci?n (exponential decreading) to a linear > deacrecing

I explain better: I have this percentage z<-1/(n*365) where n=20 so z is 0,000136986 Imagine: a matrix with length(n*365) so that matrix result is resultc( 1, 1-z result previous row-z result previous row-z .... 0) If I plot this is a linear decreasing line I want to plot an upper line wich is 10% higher tan resultc on the first values and then decreasing so that last value will be 0.05.