similar to: Saving fits (glm, nls) without data

Hi I have the problem with fitting curve to data with lm and glm. When I use polynominal dependiency, fitted values from model are OK, but I cannot recive proper values when I use coefficents to caltulate this. Let me present simple example: I have simple data.frame: (dd) a: 1 2 3 4 5 6 b: 3 5 6 7 9 10 I try to fit it to model: model=glm(b~poly(a,3),data=dd) I have following data

Hi all, I'm trying to make an integer-backed quarter (as in fraction of year) class, but I can't quite it to work. I want integer-backed so I don't have to worry about floating-point effects when doing math, and so that I can use it as in data.table. First of all, is there a good reference for this anywhere? All of the S4 tutorials that I've found have been too high-level, and

Is there any way to get the current memory used by R without running gc()? I'd like to include the memory usage in logging output, but calling gc() to get that information takes long enough to be noticeable (~ 6 s with ~ 20 GB of ram in use), which goes against the point of logging. Thanks, Johann

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### Warning messages: 1: algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ... 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y,

Hi there, I just don't find the solution on the following problem. :( Suppose I have a dataframe with two predictor variables (x1,x2) and one depend binary variable (y). How is it possible to define a glm object (family="binomial") with a user defined logistic function like p(y) = exp(a + c1*x1 + c2*x2) where c1,c2 are the coefficents which I define. So I would like to do no

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call:

Thanks to the help of many on this list, I am now an R user and have been able to write some functioning code to do matching estimation. I have two for loops (i in 1:3, and j in 0:2). Within the loops, I had been creating matrices of relevant estimation coefficents in order to make lots of LaTeX tables. Well, now I want to be able to combine the results of many different estimations from within

How for the love of god can I prevent the lm() function from padding on to my factor variables? I start out with 2 tables: Table1 123123 124351 ... 626773 Table2 Count,IS_DEAD,IS_BURNING 1231,T,F 4521,F,T ... 3321,T,T Everything looks fine when I import the data. then we get a oh_crap <- lm(table1 ~ Count + IS_DEAD + IS_BURNING, table2) Magically when I look at my oh_crap coefficents

What is the current best package for manipulating HDF5 data files? I tried "hdf5" a long time ago, but I ran into memory problems. "h5r" is on CRAN now, and "rhdf5" is part of bioconductor. Ideally, I'd like to read simple vectors or tables, either the entire thing or a subset of rows. I don't need much writing support, but it would be nice. Compression is a

I have formulae with coefficents that I would like to update. However, I get some strange results. For example, see the following: For the formula y ~ d+ 3*r+t I want to add a variable p, so > update(y~d+0*r+t, .~.+p) produces y ~ d + t + p - 1 If the coefficient is not 0, but rather, something else - say, 3, I get the following: > update(y~d+3*r+t, .~.+p) Error in

Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use. The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the

# I need to generate some data. I'm modeling some time series that follow a # negative exponential decay (mostly). I have 20 samples that can easily be fit with cubic splines. # What I want to do is generate many thousands of similar samples using the parameters from the splines # For instance one data sample looks not unlike this: foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5

I'm using R 2.10.0, with zoo 1.5-8. The release notes for zoo 1.5-8 claim a bug with unique for yearmon objects has been fixed, but I'm still having problems. Browse[1]> tmp2 [1] "Dec 1996" "Dec 1996" Browse[1]> unique(tmp2) [1] "Dec 1996" "Dec 1996" Browse[1]> unique(unique(tmp2)) [1] "Dec 1996" Browse[1]> as.numeric(tmp2) -

Hi everyone. I have a quick question regarding the look of my legend in my plot. As you can see in the next figure, I have 3 series. http://r.789695.n4.nabble.com/file/n3067466/legend.png However, I find rather difficult to differentiate the series 1 and 3 according to their line type (lty). I would like to know if it was possible to make the line type in the legend to appear more clearly.

Hi, I'm dealing with a datamining analysis: I've a lot of categories of product sold per week (n. week =26, n. categories about 50. my dataframe is like this: Settimana ALIMENTI..ALTRI. ALIMENTI.APROTEICI 1 1 3 19 2 2 2 0 3 3 1 22 4 4 2

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Hello- Such a strange problem, can't figure it out at all. Using binomial glm models, and the step() function, so the call looks like this: sectionmodel = glm(formula = Target3 ~ S1Q12_NUM.1 + S1Q9_NUM.1 + S1Q5_NUM.1 + S1Q7_NUM.1 + S1Q8_NUM.1 + S1Q6_NUM.1 + S1Q10_NUM.1 + S1Q12_BURG.1 + S1Q12_CD.1 + S1Q4.1 + S1Q12_OTHVIOL.1 + S1Q8.1 + S1Q12_GBH.1 + S1Q11.1 + S1Q7.1 + S1Q12_THEFT.1

> I am trying to produce a heatmap of pairwise correlations, but since the matrix is > symmetric, I only need either the upper or the lower triangle. I have scoured the > web and R documentation, but I have not been able to find a way to produce such a > figure. Is there a simple way to produce a heat map with only the part above or > below the diagonal? You might want to check

Hi Folks, Just a quick message saying that, after much demand, I wrote a quick write-up for the RoR + XUL thing I did yesterday: http://www.zedshaw.com/blog/programming/ruby_xul.html It lays out the few simple things I did, has a bit of rant on an unrelated topic, and other stuff but it''s short still. On a related note, I''d like to thank "xal" on IRC for reminding

Hi all, I'm trying to plot multiple histograms in one plot (cross-validation values of model parameters), but I cannot seem to reduce the margins enough to fit as many of them in as I would like. I'm using split.screen to divide the window into a 5x4 grid, then plotting with hist. I've tried explicitly reducing the margins with par(mar=c(1,1,1,1)), but it doesn't seem to have