search for: indep

Hello, Is there a better way to use paste such as: a = paste(colnames(list.indep)[1],colnames(list.indep)[2],colnames(list.indep)[3],colnames(list.indep)[4],colnames(list.indep)[5],sep="+") > a [1] "aa+dummy1+dummy2+bb+cc" I tried a = paste(colnames(list.indep)[1:5],sep="+") > a [1] "aa" "dummy1"...

.....+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1 <- ts(data.df[selection,c("RET1")]) indep.ret2 <- ts(data.df[selection,c("RET2")]) indep.ret3 <- ts(data.df[selection,c("RET3")]) indep.ret4 <- ts(data.df[selection,c("RET4")]) indep.ret5 <- ts(data.df[selection,c(&quot...

...e to aggregate up my data frame and run glm(..., family="binomial",weights=wts) and get the same coefficients (maybe this is my incorrect assumption, but I can't see why it would be). Anyways, here's a minimum working example below: > d = data.frame( RESP=c(rep(1,5),rep(0,5)), INDEP=c(1,1,1,1,0,0,0,0,0,0) ) > glm( RESP ~ INDEP, family="binomial", data=d ) Call: glm(formula = RESP ~ INDEP, family = "binomial", data = d) Coefficients: (Intercept) INDEP -1.609 21.176 Degrees of Freedom: 9 Total (i.e. Null); 8 Residual Null Deviance:...

...9;t already.) hope this helps. spencer graves RenE J.V. Bertin wrote: > Hello, > > I'm trying to understand how to interpret the differences in results between two versions of a 2-factor ANOVA with (slightly?) different models, of an observable y, a within-subject factor 'indep' and a grouping factor 'cond' (and a subject 'factor' Snr): > > >>summary( aov( y~cond + indep + Error(Snr/indep) ) ) > > # example results: > Error: Snr > Df Sum Sq Mean Sq F value Pr(>F) > cond 1 103.1 103.1 1.425 0.248 &g...

...is not so, please suggest another function that can be used (and no need to read further). But if this is usable, then the numbers are not making sense for me. What I am seeing is best explained through an example: #Setting up the data: >c(1,1,0,0,1,1,0,1,1,0,0,0,0,1,1,0,0,0,0,1)->resp >indep<-rep(2:3,10) #Running the logistic regression: >glm(formula = resp~as.factor(indep) , family = binomial(link = "logit"),na.action = na.pass) #Gives me: Coefficients: (Intercept) as.factor(indep)3 -1.692e-16 -4.055e-01 >table(indep,resp) resp indep 0 1...

...llowing issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) ---> not what I want to see lm(formula = dyn(Y ~ lag(Y, -1) + XVARY)) -----> this is what I want to see (see simplified version of the code and output below) Thanks in advance for any help!! options (scipen=999, digits=7) library(Hmisc) library(DAAG) library(car) library(MASS) l...

...only* if a glm object was passed as an argument, not a call to glm() producing that object. How can I distinguish these two cases? For example, I can use the following to get the name of the argument: foo.glm <- function(object) { oname <- as.character(sys.call())[2] oname } > indep <- glm(Freq ~ mental + ses, family = poisson, data = Mental) > foo.glm(indep) [1] "indep" But in foo.glm() I want to avoid using this as oname: > foo.glm(glm(formula = Freq ~ mental + ses, family = poisson, data = Mental)) [1] "glm(formula = Freq ~ mental + ses, family =...

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2) cat("\n") cat("PRESS =",press,"\n") } On the command R window I define - Ignored: >...

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the fo...

I wonder if anyone out there has written a routine to solve the simple linear calibration problem? - fit regression of y vs x - estimate the value x0 (with 95% CI) that gives y0 Thanks for any help. Bill -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Could anyone tell me what am I doing wrong? > pro<-function(indep,dep){ + d<-data.frame(indep) + form<-formula(lm(dep~.,data=d)) + forward<-step(lm(dep~X1,data=d),scope=form,trace=0,direction='f') + return(forward) + } > pro(m,q) Error in inherits(x, "data.frame") : Object "d" not found Where q is a vector with the de...

...E USED TO GET THE COEFFICIENTS: #Abbr = Subject #PDSI = covariate #PDSI2 = PDSI^2 (To fit a polynomial) #Set the Level order i.levels<-c("Mixed","Short","Tall","NoType") #I am not interested in this level for my comparison data.sub <- data[which(data$indep != "NoType"),] #Convert the data into factors data$indep<-factor(data$indep, levels = i.levels) #Run the model F <- lme(response~indep+PDSI+PDSI2+indep:PDSI+indep:PDSI2, random = ~PDSI|Abbr, data = data.sub, method = "ML") #Get the coefficients for the fixed effects s...

...foreign) library(Hmisc) library(prettyR) library(DAAG) library(lmtest) library(car) library(MASS) library(nlme) library(dyn) library(wle) library(lasso2) library(zoo) tdata <- ts(read.table("C:/tmp/data.csv" ,sep = ",",header=TRUE)) print(tdata) res <- function(dep, indep, fn, env = parent.frame()) { depnm <- deparse(substitute(dep)) indepnm <- deparse(substitute(indep)) fo <- sprintf("%s ~ lag(%s, -1) + %s", depnm, depnm, indepnm) fo <- as.formula(fo, env = env) mod <- do.call(dyn$lm, list(fo)) su...

...uot;glm")) stop("requires a glm object") if (!is.discrete.glm(model)) stop("only factors are allowed") ... } A small example, for count data, a poisson glm: GSS <- data.frame( expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")), count=c(279,165,73,47,225,191)) mod.glm <- glm(count ~ sex + party, family = poisson, data = GSS) So, the model terms are sex and party, both factors. Peeking inside mod.glm, I can find > mod.glm$xlevels $sex [1] "female" "male" $party [...

...nergy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of nonparametric energy tests of multivariate independence) These functions implement the new methods introduced in our recent article: G. J. Szekely, M. L. Rizzo and N. K. Bakirov (2007) Measuring and Testing Independence by Correlation of Distances, Annals of Statist...

...nergy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of nonparametric energy tests of multivariate independence) These functions implement the new methods introduced in our recent article: G. J. Szekely, M. L. Rizzo and N. K. Bakirov (2007) Measuring and Testing Independence by Correlation of Distances, Annals of Statist...

...t; > attach(data) ; aov.SS1(y=Obs) ; detach(data) > > Or is that just wishful thinking?? The latter. On Thu, 7 Oct 2004, RenE J.V. Bertin wrote: > Hello, > > I'm having a little argument with the 'with' function. I have > > > aov.SS1 <- function( y, indep=speed, fSnr=Subject, ... ) > { > indep <- factor(speed) > fSnr <- factor(fSnr) > ## .... > } > > and a dataframe containing speed, Subject, and a bunch of other columns. If I now do > > > with( data, aov.SS1( y=Obs ) ) > > I get a message > >...

hello, my problem is like this: now after processing the varibles, the remaining 160 varibles(independent) and a dependent y. when I used PLS method, with 10 components, the good r2 can be obtained. but I donot know how can I express my equation with the less varibles and the y. It is better to use less indepent varibles. that is how can I select my indepent varibles. Maybe GA is good method,...

Hello world, I'm trying to do an anova on data in data.set, dependent variable is a column named "dep.var", grouping variable is in a column called "indep.var", and is.factor(indep.var) is TRUE... why can't I just do aov(dep.var ~ indep.var, data = data.set)? What have I done to deserve this?! What gives? Am I missing something totlly obvious? R-base-1.0.0-1, linux RH 6.1. --------------------------------------------------------------...

...data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp = modulePreservation(multiExpr,multiColor,referenceNetworks=1,verbose=3,networkType="signed",nPermutations=2,maxGoldModuleSize=100,maxModuleSize=400) ..checking data for excessive amounts of missing data.. Flagging genes and samples with too m...