similar to: How to read in expressions as function parameters?

I'm trying to write some code that throws a few expressions around the place, and I've boiled down the problem to be equivalent to this. Consider the curve function which plots expressions in 'x': > curve(x^2) Now wrap that in the most naive wrapper function: > fc=function(m){curve(m)} and try it: > fc(x^2) Error in eval(expr, envir, enclos) : Object

Hello, I'm testing a simple delphi application that have 3 forms. >From FORM1 (the main form) I press a button to show FORM2, then from FORM2 I press a button to show FORM3, when I close FORM3, FORM1 becomes focused instead of FORM2. Every time you close a form or a dialog the application main form becomes the active one. This only happend in KDE, allowing windows manager to control the

Dear List Members,I am trying to set up a large system of equations and I am trying to find a simple way to set up the list command under the package system fit. Here is the example from system fit and what I am trying to do: EQ1 <- Y1 ~ X1 + X2 + X4EQ2 <- Y2 ~ X2 + X3EQ3 <- Y3 ~ X2 + X3 + X4EQ4 <- Y4 ~ X1 + X3 + X4eqSystem <-list(form1 = EQ1, form2 = EQ2, form3 = EQ3, form4 = EQ4)

Hello, I've written a small function that's supposed to save me some time, and it's ending up killing it- the intention is to iteratively subset a dataset fram on framevec, fit a model (either lm or nls depending on type) and return the r2 or AIC from the model, respectively. Although as far as I can tell in my code the plots are dependent on the fit of the model to the data and the

Each of the data sets contains monthly observations on price indices for 7 countries. I use the fitted values from reg1 in the reg2 model. The interior loop executes without error as long as I explicitly specify the data set, i.e. data=dat70. However the code fails to execute if I specify the model in the form of the commented line, i. e reg1 <-dynlm(form1,data=Dnames[j]) I get the following

Good afternoon list, I have the following character strings; one with spaces between the maths operators and variable names, and one without said spaces. form<-c('~ Sentence + LEGAL + Intro + Intro / Intro1 + Intro * LEGAL + benefit + benefit / benefit1 + product + action * mean + CTA + help + mean * product')

Here is a function I wrote. It runs no problem, but generate empty pdf files. I can't find what is the problem. create.pdf<- function(x, dir) { dir.create(dir, showWarnings = FALSE) plist<- c("a", "b" , "c", "d") for(j in plist) { filedir<- paste(dir, "/", j, ".pdf",

I'm trying to figure out how to plot basic utility maximization results with R, Ideally I'd like to plot the value of u through x1,x2 space, so you can graph income / substitution effects easily... also, it'd be nice if I could put a linear budget constraint on the graph here's an example with cobb douglas utility u <- function(x) { x1 <- x[1] x2 <- x[2]

My opinion is that security by RFC is not security, it's mommy medicine. Standards have had a terrible time keeping up with security realities. NITS's curves leak side channel information all over the place. I don't have details on what implementations are set to calculate the NIST curves in constant time, and that's not an easy feat to do anyway so I don't want to depend

# 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

Hello. I am trying to write an R function to plot the survival function (and associated hazard and density) for a Siler competing hazards model. This model is similar to the Gompertz-Makeham, with the addition of a juvenile component that includes two parameters---one that describes the initial infant mortality rate, and a negative exponential that describes typical mortality decline over the

Hi, I'm using a non linear model to fit experimental survival curves. This model describes the fraction of "still active" experiments as a function of time t as follows: f(t)=(1+exp(-etaD*cD)) / (1+exp(etaD(t-cD))) Moreover, when experiments are still active, they may change of state (from 0 to 1). But they may fall inactive before changing their state (their state still

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

Please help. I found the code in the archive. The author of this script says: "The first function (seROC) calculate the standard error of ROC curve, the second function (cROC) compare ROC curves." Can some one explain to me what are the na, nn and r parameters which are used as the input to the following two functions? Thanks much in advance. > From: Bernardo Rangel Tura >

Hi, The following two segments of code are actually the same, but llvm created different cfg for them. Form1: 1 #define N 10 2 int test(int A[N][N]) 3 { 4 int i, j; 5 int result =0; 6 for(j=0; j+2<N; ++j) { 7 //for(i=0; i<j && i+j+1<N; i++) { 8 for(i=0; i<j && i<N-j-1; i++) { 9 A[i+j+1][j] = A[j + 2][j-i] + i; 10 } 11 } 12 13 for (i=0; i<N-2; ++i) 14 for (j=0;

Though I have read quite a bit, and tried quite a bit, I have yet to find a nice way to overlay 2 or more curves in the same plot, with different ranges. Here is simplified sample code to demonstrate the question: > plot(2*(seq(1,5)), type="l", axes=FALSE) > curve(2*(seq(1,5)), type="b", add=TRUE) Error in curve(2 * (seq(1, 5)), type = "b", add = TRUE) :

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

Sebastian: This is somewhat arcane, perhaps even a bug (correction on this welcomed). The problem is that the "expr" argument to curve() must be an actual expression, not a call to parse that evaluates to an expression. If you look at the code of curve() you'll see why (substitute() does not evaluate expr in the code). Another simple workaround other than sticking in the eval()

Hi List, I would like to plot multiple curves (parametric density curves) in one plot. For example: # parameters for three normal density curves parms = data.frame(ID=c(1,2,3),mu=c(50,55,60),sigma=c(10,12,15)) # I can easily draw three normal density curves using curve(): curve(dnorm(x,mean=parms$mu[1],sd=parms$sigma[1]),from=0, to=150, ylab="density", col="red")

Typo: should be NULL not NUL of course An alternative approach closer to your original attempt is to use do.call() to explicitly evaluate the expr argument: w <- "1 + x^2" do.call(curve, list(expr = parse(text = w), ylab ="y")) Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus