search for: wtd

Dear R-users, I don't know if there is a problem in wtd.quantile (from library "Hmisc"): -------------------------------- x <- c(1,2,3,4,5) w <- c(0.5,0.4,0.3,0.2,0.1) wtd.quantile(x,weights=w) ------------------------------- The output is: 0% 25% 50% 75% 100% 3.00 3.25 3.50 3.75 4.00 The version of R I am using is: 2.1.0 Best,Ji...

...: http://n4.nabble.com/file/n1676330/US_Final_Values.txt US_Final_Values.txt and the following code i got the image at the end of this message: US.final.values<-read.table("c:/tmp/US_Final_Values.txt",header=T,sep=" ") US.nls.1<-nls(US.final.values$ECe~a*US.final.values$WTD^b+c,data=US.final.values,start=list(a=2.75,b=-0.95,c=0.731),trace=TRUE) f.US1<-function(x){coef(US.nls.1)["a"]*x^coef(US.nls.1)["b"]+coef(US.nls.1)["c"]} xvals.US1<-seq(min(US.final.values$WTD),max(US.final.values$WTD),length.out=75) yvals.US1<-f.US1(xvals.US1...

The function wtd.var(x,w) in Hmisc calculates the weighted variance of x where w are the weights. It appears to me that wtd.var(x,w) = var(x) if all of the weights are equal, but this does not appear to be the case. Can someone point out to me where I am going wrong here? Thanks. Tom La Bone [[alternative...

....00085 -2.00130 0.0450 Likelihood ratio test=164 on 5 df, p=0 n= 308, number of events= 143 (110 observations deleted due to missingness) > > plot(survfit(Surv(time, status>0) ~ trt, data=pbc)) > lines(survexp( ~ trt, ratetable=pfit, data=pbc), col='purple') > > pfit.wtd <- coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age + + + platelet, weights=albumin, data=pbc + ) > > pfit.wtd Call: coxph(formula = Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + +platelet, data = pbc, weights = albumin)...

...dt tf 1 2009-06-20 00:53:00 73 2 2009-06-20 01:08:00 73 3 2009-06-20 01:44:00 72 4 2009-06-20 01:53:00 71 5 2009-06-20 02:07:00 72 ... I need a subset of the rows where the minutes are 53. The hour is immaterial. I can not find a wildcard character to use to select the rows. as.character(wtd$dt) %in% "2009-06-21 ??:53:00" or as.character(wtd $dt) %in% "2009-06-21 **:53:00" does not work. What would you recommend? Thanks, Keith Jones, Ph.D. VTS Pumps

...NaN NaN 6.8 7.1 9 7.6 7.9 7.6 7.1 7.2 NaN NaN 7.9 8.6 7.7 NaN 7.9 NaN 7.2 7.1 10 8.2 8.2 8.1 8.0 7.5 NaN NaN 8.2 NaN 7.5 NaN NaN NaN 7.5 7.0 etc. For my current situation, I need to have this function use a weighted mean and I am trying to incorporate the 'wtd.mean' function from Hmisc. I have tried various scenarios relying on the documentation for 'wtd.mean' in the Hmisc (shown below), but to no avail. Any assistance would be much appreciated. set.seed(1) x <- runif(500) wts <- sample(1:6, 500, TRUE) xg <- cut2(x,g=4) # Here is...

...rite functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a weight var (wt). I use Rcmdr's plotMeans to obtain a line chart. The following code works: attach(nes) obj1 = ddply(nes, .(partyid3), summarise, var = wtd.mean(environ.therm, wt)) print(obj1) plotMeans(obj1$var, obj1$partyid3, error.bars="none")...

...nsert double quotes around the strings. Here's an example: location <- c("AL", "AK", "MA", "PA") v=2 test <- cbind( " <- cbind(df$DATE[df$LOCATION %in% c(", location[v], ")], df$WTD.AVG.PRICE[df$LOCATION %in% c(", location[v], ")])") test <- paste(test, collapse="") test Solution: [1] " <- cbind(df$DATE[df$LOCATION %in% c(AK)], df$WTD.AVG.PRICE[df$LOCATION %in% c(AK)])" This does not pr...

...period: library(Hmisc) stbl <- read.csv("data.csv") index <- lapply(strsplit(stbl$index, ":", fixed = TRUE), as.numeric) count <- lapply(strsplit(stbl$count, ":", fixed = TRUE), as.numeric) len <- length(index) for (i in 1:len) { v <- wtd.quantile(index[[i]], count[[i]], c(0, 0.2, 0.5, 0.8, 1)) stbl$q0[i] <- v[1] stbl$q2[i] <- v[2] stbl$q5[i] <- v[3] stbl$q8[i] <- v[4] stbl$q10[i] <- v[5] } It works fine for a small N, but it get quickly inefficient as N grows. The for-loop takes too long. How...

...t? Any clues would be very helpful in moving forward. The overall goal of the code below is to minimize the residuals between the blue circles and green squares by adjusting the limits of the secondary y-axis. Thanks, Eric library(zoo) temp.dat<-read.table(textConnection("Well Meas_Date WTD ECgw GW_Project_Area Region Avg.EM.Survey.Value Avg.Soil.Paste.EC SD_EM_Survey 1 4/12/1999 1.75 2.27 LOWER-UPSTREAM US NA NA NA 1 5/11/1999 1.24 5.04 LOWER-UPSTREAM US NA NA NA 1 5/27/1999 1.27 4.45 LOWER-UPSTREAM US NA NA NA 1 6/3/1999 1.27 4.09 LOWER-UPSTREAM US 3.347069 3.126667 0.6347013 1 6/11...

Hi, I am doing double for loops to calculate SDs with some weights and wondering if I can get rid of the outer for loop as well. I made a simple examples which is essentially what I am doing. Thanks for your help! -Young #------------------------------------------------------ # wtd.var is Hmisc package # you can replace the 3 lines inside for loop as # sdx[i,] = apply(x[(i-4):i,],2,var,na.rm=T) # ------------------------------------------------------- x = matrix(rnorm(200),10,20) sdx = matrix(0,10,20) for(i in 5:nrow(x)){ wts = ( 0.5 )^(i-c(1:i))...

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr message in the red): >so <- summary(wtd.mixed) > so$df[1:6] NULL Warning message: $...

...only weighted quintiles; but what I need is conditional weighted quintiles. The below example illustrates what I mean. x <- data.frame("id"=c(1:5),"income"=c(10,10,20,30,50), "education"=c(0,5,5,0,0),"weight"=c(3,2,3,1,1)) x library(Hmisc) wtd.quantile(x$income,weights=x$weight) wtd.quantile(x$education,weights=x$weight) I would like to see the expenditure of each quintile conditional on income, i.e. the education expenditures of the 5th quintile equal to zero. Thanks in advance, -- ozan bakis --__--__--

Hi Holger, > Note that I did specify "-g", but not any "-Ox" switches. That > made the size of all *.o files together being 143 MB. LLVM represents debug info as explicit calls to intrinsics. This approach has many advantages, but a possible disadvantage is that it can significantly increase the size of the bitcode. I don't know if that explains your observations.

...stuff the code that follows the plot commands into a loop that adjusts ylim_2 so as to minimize "min_this"? I'm guessing there is a function to do this? Thanks, Eric library(zoo) #example data and plotting commands follow temp.dat<-read.table(textConnection("Well Meas_Date WTD ECgw GW_Project_Area Region Avg.EM.Survey.Value Avg.Soil.Paste.EC SD_EM_Survey 1 4/12/1999 1.75 2.27 LOWER-UPSTREAM US NA NA NA 1 5/11/1999 1.24 5.04 LOWER-UPSTREAM US NA NA NA 1 5/27/1999 1.27 4.45 LOWER-UPSTREAM US NA NA NA 1 6/3/1999 1.27 4.09 LOWER-UPSTREAM US 3.347069 3.126667 0.6347013 1 6/11...

...advantages, but a possible disadvantage > is that it can significantly increase the size of the bitcode. > I don't know if that explains your observations. I'm curious > to know how gcc stores debug info... GCC stores debug info in ELF sections of the .o file, e.g.: $ objdump -h wtd.o wtd.o: file format elf32-i386 Sections: Idx Name Size VMA LMA File off Algn 0 .text 00000ef4 00000000 00000000 00000034 2**2 CONTENTS, ALLOC, LOAD, RELOC, READONLY, CODE 1 .data 00000000 00000000 00000000 00000f28 2**2...

I' m trying to compute weighted mean on different groups but it only returns NA. If I use the following data.frame truc: x y w 1 1 1 1 2 2 1 3 1 1 4 2 0 2 1 0 3 2 0 4 1 0 5 1 where x is a factor, and then use the command : tapply(truc$y,list(truc$x),wtd.mean, weights=truc$w) I just get NA. What's the problem ? What can I do ?

...p; trips$DUTYPE == hom & trips$PURPOSE == pur, select=c("HOUSEID","WTTRDFIN", "WTHHFIN")) cat(length(tmp$HOUSEID)) cat(",") tmp$tr <- (tmp$WTTRDFIN/365)/tmp$WTHHFIN w.m <- wtd.mean(tmp$tr,weights=tmp$WTHHFIN) w.sd <- sqrt(wtd.var(tmp$tr, weights=tmp$WTHHFIN)) cat(w.m) cat(",") cat(w.sd) cat("\n") } sink() [[alternative HTML version deleted]]

Hi Duncan I tried Ecdf and/or wtd.quantile from Hmisc and it is working (probably). Ecdf(x, q=.5) Ecdf(x, weights=xw,col=2, add=T, q=.5) wtd.quantile(x) 0% 25% 50% 75% 100% 10 10 10 100 300 wtd.quantile(x, weights=xw, type="i/n") 0% 25% 50% 75% 100% 10.0000 138.8667 192.5778 246.2889...

...???????? 1.436647351? 0.2540660? 5.65462216 0.02988003 #X5?????????? -0.005406684? 0.2906822 -0.01859998 0.98684896 #X6?????????? -0.050101169? 0.1486149 -0.33712071 0.76811703 #X7??????????? 0.563284987? 0.1204945? 4.67477901 0.04284010 #X8?????????? -1.901918462? 0.2697166 -7.05154460 0.01952387 Wtd<-row.names(res[res[,"Pr(>|t|)"]<0.05,]) ?Wtd1<-Wtd[!grepl("\\(.*\\)",Wtd)] fit1<-lm(as.formula(paste("Y~",paste(Wtd1,collapse="+"))),data=dat1) ?coef(summary(fit1)) #?????????????? Estimate? Std. Error?? t value???? Pr(>|t|) #(Intercept) 146...