similar to: optimize calculations

Hi, I'm having trouble with the confidence interval of the nls function. I did my home work, and searched acros the support list until I came up with following solution of Peter Dalgaard: example(predict.nls) se.fit <- sqrt(apply(attr(predict(fm,list(Time = tt)),"gradient"),1, function(x) sum(vcov(fm)*outer(x,x)))) matplot(tt,

Hi, I'm a database admin for a database which manage chromatographic results of products during stability studies. I use R for the reporting of the results in MS Word through R2wd. But now I think I need your help: suppose we have the following data frame: ID rrt Mnd Result 1 0.45 0 0.10 1 0.48 0 0.30 1 1.24 0 0.50 2 0.45 3 0.20 2 0.48 3 0.60 2 1.22 3 0.40 3

Is it the tick labels that you want to change? -----Original Message----- From: "Elisabeth Bjerke Rastad" <ebr024 at post.uit.no> To: "r-help at r-project.org" <r-help at r-project.org> Sent: 5/10/10 11:20 AM Subject: [R] [Fwd: Re: Plotting log-axis with the exponential base to a plot with the default logarithm base 10] Hello! Thank you for answering! What I am

Hello! Thank you for answering! What I am trying to do is to plot my raw values (biomass of different species) on a logaritmic y-axis with the base of e. When I type "log="y"", the axis transforms into a logaritmic axis with the base of 10. Best regards, Elisabeth > Dear Elisabeth, > > I'm not sure if I have understood your question -- are you trying > to

Hello! I have a problem which I have tried to solve for several days now.. I have plottet a lineplot.CI in the library "sciplot", and I am trying to plot it with a logaritmic y-axis (with exponential base). The problem is that; when I type "log "y"", the axis transforms into the logaritmic of base 10. I wonder if someeone could tell me how to specify that I would

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

In one particular situation predict.coxph gives an error message. Namely: stratified data, predict='expected', new data, se=TRUE. I think I found the error but I'll leave that to you to decide. Thanks, Chris ######## CODE library(survival) set.seed(20121221) nn <- 10 # sample size in each group lambda0 <- 0.1 # event rate in group 0 lambda1 <- 0.2 # event rate in group 1

Hello Ted! Thank you a lot for your reply!!! I will try to explain again; what I want is a logarithmic scaled y-axis with the base of e (not 10). And the values I would like to use in the plot are the raw values (not transformed in any way). Do you still think that the base of log does not matter in this case (wheather it is e or 10 as a base)? Elisabeth > On 09-May-10 18:10:27, Elisabeth

Hi All, I'm conducting a meta-analysis and have taken a data.frame with multiple rows per study (for each effect size) and performed a weighted average of effect size for each study. This results in a reduced # of rows. I am particularly interested in simply reducing the additional variables in the data.frame to the first row of the corresponding id variable. For example:

I'm trying to get predicted probabilities out of a regression model, but am having trouble with the "newdata" option in the predict() function. Suppose I have a model with two independent variables, like this: y=rbinom(100, 1, .3) x1=rbinom(100, 1, .5) x2=rnorm(100, 3, 2) mod=glm(y ~ x1 + x2, family=binomial) I can then get the predicted probabilities for the two values of

Hi , I know this question has been asked twice in the past but to my knowldege, it still hasn't been solved. I am doing a zero inflated binomial model using the VGAM package, I need to obtain p values for my Tvalues in the vglm output. code is as follows > mod2=vglm(dmat~Season+Diel+Tidal.phase+Tidal.cycle,zibinomial, data=mp1) > summary(mod2) Call: vglm(formula = dmat ~ Season +

Hi, from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small! Other tests for normality are available in package: nortest compare the log-transformation of your ecdf

Hi All, I am interested in printing column names without quotes and am struggling to do it properly. The tough part is that I am interested in using these column names for a function within a function (e.g., lm() within a wrapper function). Therefore, cat() doesnt seem appropriate and print() is not what I need. Ideas? # sample data mod1 <- rnorm(20, 10, 2) mod2 <- rnorm(20, 5, 1) dat

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Dear list: I am trying to merge two files together from output I get based on the coef() command. Here is what I am running into. I have two simple linear mixed models > mod1.lme<-lme(math~year, data=sample, random=~year|childid/schoolid) > mod2.lme<-lme(math~year, data=sample, random=~year|childid) I then call the coefficients and store them in the following objects using >

Hi, I have processed measurements of a rough surface to a heigh-height correlation plot. What the meaning of this exactly is, is not important. Only that it is a plot that had two (almost ) linear parts when plotted on a logaritmic scale. In this plot, I want to draw the best fitting lines for these linear parts but I just can't get it done. It is easy when the scales are linear but as you

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

?s 17:38 de 30/11/2023, Robert Baer escreveu: > I am having trouble using back ticks with the R extractor function > 'predict' and an lm() model.? I'm trying too construct some nice vectors > that can be used for plotting the two types of regression intervals.? I > think it works with normal column heading names but it fails when I have > "special"

I am using the effects package to construct some probability graphs showing the predicted probabilities from a logistic regression model. However, I get an odd error message and don't know what the issue is. When I attempt to generate the plots, I get the following error: dat$won_ping = as.factor(dat$won_ping) mod2 = glm(won_ping ~ our_bid + age_of_oldest_driver2 + credit_type2 +

Dear Colleagues, I run this model: mod1 <- lmer(x~category+subcomp+category*subcomp+(1|id),data=impchiefsrm) obtain this summary result: Linear mixed-effects model fit by REML Formula: x ~ category + subcomp + category * subcomp + (1 | id) Data: impchiefsrm AIC BIC logLik MLdeviance REMLdeviance 4102 4670 -1954 3665 3908 Random effects: Groups Name Variance