similar to: How to test a model with two unkown constants

I am wont to decode a speex 11kbps 8kHz 16bit to a raw data 8kHz 16bit LSB on a c5509. Trying to understand the "testenc-TI-C5x.c" exsample, but it looks to me wary complicated. Is there more documentation for the exsample or a decoder exsample available? Can somebody help? Peter -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi all, I don't understand the following output. I've created a data subset from a data frame by > p1.sub <- subset(p1.dat, vp!="p1") this is ok. But > attach(p1.sub) > vp [1] p1ab p1ab p1ab p1ab p1ab p1br p1br p1br p1br p1br p1kf p1kf p1kf p1kf p1kf [16] p1mg p1mg p1mg p1mg p1mg p1mw p1mw p1mw p1mw p1mw Levels: p1 p1ab p1br p1kf p1mg p1mw shows me that the

Thanks a lot for all of you that have reply me about opening and ending R workspaces in BATCH mode. However replies were a king general and I?m afraid I could not take the entire message from them. Therefore I chose to expose here a representative fraction of my work. I have 50 Rdata files (F1,F2,F3,F4, ,F50) with objects inside. I need to: open F1: - perform some simple operations

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

Hello, I have a rather complex problem... I will have to explain everything in detail because I cannot solve it by myself...i just ran out of ideas. So here is what I want to do: I take quotes of two indices - S&P500 and DJ. And my first aim is to estimate coefficients of the DCC-GARCH model for them. This is how I do it: library(tseries) p1 = get.hist.quote(instrument =

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

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 might be a trivial question, but I would appreciate if anybody could suggest an elegant way of plotting a function such as the following (a simple distribution function): F(x) = 0 if x<=0 =(x^2)/2 if 0<x<=1 =2x-((x^2)/2)-1 if 1<x<=2 =1 if x>2 This is just an example. In this case it is a continuous function. But how to do it in general in an elegant way.

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

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:

[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,

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 ->

[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)); }

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 <-

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

There's a StackOverflow question https://stackoverflow.com/q/22024082/2554330 that references this text from ?missing: "Currently missing can only be used in the immediate body of the function that defines the argument, not in the body of a nested function or a local call. This may change in the future." Someone pointed out (in https://stackoverflow.com/a/58169498/2554330)

Hello, I'd like to produce a ggplot where the order of factors within facets is based on the average of another variable. Here's a reproducible example. My problem is that the factors are ordered similarly in both facets. I would like to have, within each facet of `f1', boxplots for 'x' within each factor `f2', where the boxplots are ordered based on the average of x

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more