similar to: Best and worst values for each date

Ira, obj_name<- load("arun.RData") Pred1<- get(obj_name[1]) Actual1<- get(obj_name[2]) dat2<- data.frame(S1=rep(Pred1[,1],ncol(Pred1)-1),variable=rep(colnames(Pred1)[-1],each=nrow(Pred1)),Predict=unlist(Pred1[,-1],use.names=FALSE),Actual=unlist(Actual1[,-1],use.names=FALSE),stringsAsFactors=FALSE) dat2New<- dat2[!(is.na(dat2$Predict)|is.na(dat2$Actual)),] ?dat3<-

Hi All, I have been able to modify the x-axis to start at zero by adding xlow and xhigh parameters; that was pretty simple. I have been unable to find the location of the code that would turn off the information weighting of the box size (I have smaller randomized trials getting less weight than a much larger non-randomized trial). The function is forestplot() from rmeta. Thanks for any

All, I am using the forestplot function in rmeta. I was able to modify the x axis range by commenting out one line and feeding it two new parameters (I wanted to set zero as the axis start point). #xrange <- c(max(min(lower, na.rm = TRUE), clip[1]), min(max(upper, na.rm = TRUE), clip[2])) #new line xrange <- c(xlow,xhigh) Now I am trying to modify the text font size for

my dataset looked like this in the beginning: >Daten V1 V2 V3 1 Dosis Gewicht Geschlecht 2 0 6.62 m 3 0 6.65 m 4 0 5.78 m 5 0 5.63 m I need box plots for V2 with all combination of V1 and V3, so I deleted the first row, and tried this: boxplot(Daten$V2[Daten$V3=="m"]) but it does not work and I

Hi everyone: This may be trivial but I just have not been able to figure it out. Imagine the following dataframe: a b c d TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE I would like to create a new dataframe, in which TRUE gets 0 but if false then add 1 to the cell to the left. So the results for the example above should be something like: a b c

Hi dear all, the following code is correct. but I want to use non-numeric x-axis, for example if I replace time <- seq(0,72,6) by month <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec","Pag") Ofcourse I use factor(month) instead of

could someone explain why the name of FPVAL gets " .value" concatenated onto it when the code below is run and temp is returned. I've been trying to figure this out for too long. It doesn't matter when I put the FPVAL in the return statement. It happens regardless of whether it's first or last. Thanks. f.lmmultenhanced <- function(response, pred1, pred2) {

Hello; I am working on porting a tool written for LLVM3.5 to LLVM10. There used to be a call instruction with the signature static CallInst * Create (Value *F, Value *Actual, const Twine &NameStr="", Instruction *InsertBefore=0) Can anyone please explain what it supposed to do? What was F and Actual? Thank you so much. -- Dr. Arnamoy Bhattacharyya R&D Compiler Engineer

Hi! I am looking to choose a condom based on its pleasure score. I received some summarised data from 10 individuals: structure(list(Ramses = c(4, 4, 5, 5, 6, 3, 4, 4, 3, 4), Sheiks = c(5, 5, 6, 4, 7, 6, 4, 5, 6, 3), Trojans = c(7, 8, 7, 9, 6, 3, 2, 2, 2, 3), Unnamed = c(2, 1, 1, 3, 3, 4, 5, 4, 4, 3)), .Names = c("Ramses", "Sheiks", "Trojans", "Unnamed"),

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

I'm new to R and am trying to extract the factors of a dataframe using numeric indices (e.g. df[1]) that are input to a function definition instead of the other types of references (e.g. df$out). df[1] is a list(?) whose class is "dataframe". These indexed lists can be printed successfuly but are not agreeable to the plot() and lm() functions shown below as are their df$out

I have a function that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and

Dear all, I tried to run as.formula("x") and got an error message "Error: C stack usage 7971120 is too close to the limit" whether x exists or not. This is not the case in as.formula("y"), where "object 'y' not found" is the error message if y not exists, or "invalid formula" error or a formula depending on y. Can anyone confirm this is

I'm fairly new to R, and I'm trying to write out a population model that satisfies the following; the system consists of s species, i= 1, 2,...,s network of interactions between species is specified by a (s x s) real matrix, C[i,j] x[i] being the relative population of the "ith" species (0 =< x[i] =< 1, sum(x[i]=1) the evolution rule being considered is as follows;

I am an R beginner and trying to run a market model using event study in R framework. First, I run a market model, that is lm(stock security~SP500 index, subset=Obs[197, 396]) ->result1 Then I get predict results for a new dataset using predict (result1, newdata=Obs[397,399]) ->pred1 Pred1 should have three numbers. Now I need to calculate abnormal return by the formula stock

Hi, Your question is still not clear. May be this helps: dat2<- read.table(text=" patient_id????? t???????? scores 1????????????????????? 0??????????????? 1.6 1????????????????????? 1??????????????? 2.6 1????????????????????? 2???????????????? 2.2 1????????????????????? 3???????????????? 1.8 2????????????????????? 0????????????????? 2.3 2?????????????????????? 2???????????????? 2.5