similar to: function call

Hi Lars I am not very sure what you really want. However, I am suggesting the following code that enables (1) to obtain the full summary of your data and (2) retrieve only mean of X values as function of factors f1 and f2. library(tidyverse) library(psych) df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 = gl(3, 10, labels

Hi Lars, Two comments: 1. You can achieve what you want with a slight modification of your definition of s(), using the hint from the error message that you need an argument '.': s <- function(.) { dplyr::summarise(., x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) } 2. You have not given a great test case in

... can be used to represent unknown number of parameters passed into a function. For example, I write a function g. g calls another function f1. For example f1 could be different random number generation function. when f1=rnorm(), it has 3 parameters n, mean and standard deviation. when f1=rexp(), it has 2 parameters n and rate. g can be defined as g <- function(f1, ...) { f1(...) }

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

This is just my curiousity working. Suppose I write: f1 <- function(x) x + 1 f2 <- function(x) 2 * f1(x) f2(10) # 22 f1 <- function(x) x - 1 f2(10) # 18 This is quite obvious. But is there any way to define f2 in such a way that we "freeze" the definition of f1? f1 <- function(x) x + 1 f2 <- function(x) # put something here 2 * f1(x) # probably put something else here

Hi. I have defined a function, f1, that calls another function, f2. Inside f1 an intermediate variable called nm1 is created; it is a matrix. f2 takes a matrix argument, and I defined f2 (schematically) as follows: f2<-function(nmArg1=nm1,...){nC<-ncol(nmArg1); ... } so that it expects nm1 as the default value of its argument. f1 is defined (schematically) as:

Hi. I have tried to use Package "SEM". As a learning, I try to convert a program running well of EQS which is as follows to SEM: ### EQS ### /SPECIFICATION CAS=100; VAR=5 MAT=COR; ANA=COR; /EQUATIONS V1=*F1+E1; V2=*F1+E2; V3=*F1+*F2+E3; V4=**F1+*F2*E4; V5=*F2+E5; /VAR E1 TO E5=*; F1*1.0; F2=1.0; /COV E1,E2=*; F1,F2=*: /PRINT FIT ALL; /MATRIX ...... /END This is the converted SEM

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->

Code like df <- data.frame(x=1:10) y <- 20:29 eval(quote(x+y), env=df) does what you might expect: it looks for x and y in the data.frame, and when it doesn't find y there, it looks in the parent environment. However, sometimes I'd like to construct a single environment out of df, so that I can pass it to nested functions and get the same behaviour. Right now, I get the wrong

Code like df <- data.frame(x=1:10) y <- 20:29 eval(quote(x+y), env=df) does what you might expect: it looks for x and y in the data.frame, and when it doesn't find y there, it looks in the parent environment. However, sometimes I'd like to construct a single environment out of df, so that I can pass it to nested functions and get the same behaviour. Right now, I get the wrong

[AMD Public Use] Hi Oliver, I get rid of the warnings by explicitly type-casting it to struct*, and still get similar results. ####################################################### struct P { float f1; float f2; float f3[3]; float f4; }; void foo(struct P* p1, struct P* p2) { p1->f2 = 1.2; p2->f1 = 3.7; } int callFoo() { struct P p; foo(&p,

It seems odd/inconvenient to me that the "ignore.environment" argument of identical() only applies to closures (which I read as 'functions' -- someone can enlighten me about the technical differences between functions and closures if they like -- see below for consequences of my confusion). This is certainly not a bug, it's clearly documented, but it seems like a design

Dear R-list, I would like to use the lattice library to show several groups on the same graph. Here's my example : ## the data f1 <- factor(c("mod1","mod2","mod3"),levels=c("mod1","mod2","mod3")) f1 <- rep(f1,3) f2 <-

Hello, FYI, this is a bug http://llvm.org/bugs/show_bug.cgi?id=13324 When compiling the following code for sm_20, func params are by some reason given with .align 0, which is invalid. Problem does not occur if compiled for sm_10. > cat test.ll ; ModuleID = '__kernelgen_main_module' target datalayout = "e-p:64:64-i64:64:64-f64:64:64-n1:8:16:32:64" target triple =

Hi, I'm using R version 1.8.0 on Windows NT. When fitting a glm with Poisson random component and a log link, I frequently need to include an offset. Typically I use xtabs or table to get the counts for the contingency table, and then I use as.data.frame.table to create a data frame that I can use in the glm function. I have not found an option that allows me to total the offset variable to

Dear R-List, (I am not sure whether this list is the right place for my question...) I have a dataframe df.cfa

[AMD Official Use Only - Internal Distribution Only] Hi, Following issue is observed with Type Based Alias Analysis(TBAA). ####################################################### struct P { float f1; float f2; float f3[3]; float f4; }; void foo(struct P* p1, struct P* p2) { p1->f2 = 1.2; p2->f1 = 3.7; } int callFoo() { struct P p; foo(&p, &(p.f2)); }

Hi all, suppose I've got a vector y with some data (from a repeated measure design) observed given the conditions in f1 and f2. I've got a model with two unknown fix constants a and b which tries to predict y with respect to the values in f1 and f2. Here is an exsample # "data" y <- c(runif(10, -1,0), runif(10,0,1)) # f1 f1 <- rep(c(-1.4, 1.4), rep(10,2)) # f2 f2 <-

Dear all, I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and f2(p1,p2,p3) respectively. The goal is to maximize f1(p1, p2, p3) subject to two constraints: (1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known constants (2) p4 = f2(p1, p2, p3) In addition, each parameter