search for: tmean

I've noticed a change in the way grep() behaves between the 1.9.0 release and a recent R-patched. On 1.9.0 I get the following output: > x <- dget(file = url("http://www.biostat.jhsph.edu/~rpeng/names.R")) > length(grep("^l\\w+tmean", x, perl = TRUE, value = TRUE)) [1] 84 And on R-patched (2004-06-11) I get > x <- dget(file = url("http://www.biostat.jhsph.edu/~rpeng/names.R")) > length(grep("^l\\w+tmean", x, perl = TRUE, value = TRUE)) [1] 13 I can't come up with a simpler example whi...

...quot;month", "week") setkeyv(temp_hist, mykey) setkeyv(humid_hist, mykey) # hist<- merge(temp_hist, humid_hist, by=mykey) # hist$X.x <- NULL hist$X.y <- NULL # # Max hist_max <- hist hist_max$FIPS <- hist_max$year <- hist_max$month <- hist_max$tmin <- hist_max$tmean <- hist_max$hmin <- hist_max$hmean <- NULL # # Adding Factors hist_max$citycode <- rep(101,nrow(hist_max)) hist_max$year <- rep(2010,nrow(hist_max)) hist_max$lnincome <- rep(10.262,nrow(hist_max)) # # Predictions pred_hist_max <- predict.gam(gam_max_count_wk,hist_max) # pred_hi...

...NA NA NA NA 13 195501 NA NA NA NA NA NA NA 14 195502 NA NA NA NA NA NA NA 15 195503 NA NA NA NA NA NA NA tmin.mean tmax.max tmax.mean tmean.mean 10 NA NA NA NA 11 NA NA NA NA 12 NA NA NA NA 13 NA NA NA NA 14 NA NA NA NA 15 NA NA NA NA > from this one: > mansNew[1...

Is there a package in R that can do a variability plot? A variability plot is a kind of categorized dot plot. (If there is a lot of data in each category, box plots are used rather than dot plots.) Usually, the categories are factor level combinations. All the dot plots appear in the same window; below the x-axis a hierarchy of factors shows which dot plot corresponds to which factor-level

Hi R-Helpers, I am having difficulty plotting a coxph model with two predictors. My predictors are "morder" (a factor with five levels where the mean of each level is plotted as a separate line) and tmean (continuous). When I run a model with just morder it is fine and the plot is fine. When I add tmean, the coxph model runs fine but this model will not plot and I receive an error message. Someone posting back in April 2009 received a similar error in R2.9.0 (which I am using also) when run...

Dear everyone, I'm encountering an unknown error message when using gamm function: > fitoutput <- gamm(cvd~as.factor(dow)+pm10+s(time,bs="cr",k=15,fx=TRUE)+s(tmean,bs="cr",k=7,fx=TRUE) + ,correlation=corAR1(form=~1|city),family=poisson,random=list(city=~pm10),data=mimp) Maximum number of PQL iterations: 20 iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Error in `*tmp*`[[k]] : attempt to select less than one element I ne...

...+ mans.met$year[i]<-years(mans.met$date[i]) + mans.met$month[i]<-months(mans.met$date[i]) + mans.met$yearmo[i]<-cut(mans.met$date[i], "months") + mans.met$day[i]<-days(mans.met$date[i]) + } > mans.met[1:10,] date year month yearmo day snow.new snow.dep prcp tmin tmax tmean 1 11/01/54 1 11 1 1 NA NA NA NA NA NA 2 11/02/54 1 11 1 2 NA NA NA NA NA NA 3 11/03/54 1 11 1 3 NA NA NA NA NA NA 4 11/04/54 1 11 1 4 NA NA NA NA NA NA 5 11/0...

...ear[i]<-years(mans.met$date[i]) : + mans.met$month[i]<-months(mans.met$date[i]) : + mans.met$yearmo[i]<-cut(mans.met$date[i], "months") : + mans.met$day[i]<-days(mans.met$date[i]) : + } : > mans.met[1:10,] : date year month yearmo day snow.new snow.dep prcp tmin tmax tmean : 1 11/01/54 1 11 1 1 NA NA NA NA NA NA : 2 11/02/54 1 11 1 2 NA NA NA NA NA NA : 3 11/03/54 1 11 1 3 NA NA NA NA NA NA : 4 11/04/54 1 11 1 4 NA NA NA NA NA NA...

...===================================== # I THINK THIS WAY IS CORRECT (n-1) #curve(dchisq(x, n-1), add = T, col = 'red') #=========================================== curve(dchisq(x, n), add = T, col = 'red') cat('\nPopulation:'); cat('\n') cat('\tMean =', Mpop); cat('\n') cat('\tVariance =', Vpop); cat('\n') cat('\tSize (N) =', N); cat('\n') cat('\nSample:'); cat('\n') cat('\tSize (n) =', n, '->', (n - 1), 'df'); cat('\n') cat('\nSam...

...A qs <- as.matrix(t1[,116:211]) colnames(qs) <- c(paste("q01",1:24,sep="."),paste("q02",1:24,sep="."), paste("q03",1:24,sep="."),paste("q04",1:24,sep=".")) times <- qs[,c(0:3*24+3,0:3*24+24)] tmean <- apply(times,1,mean,trim=.15) #tmean is time mean for each Ss and trims off top and bottom 15% plot(sort(tmean)) #each Ss has 6 times #throw out the longest and shortest 15% times for each Ss and then compute time means for each Ss #look at Ss mean for times (plot(sort(tmean)) and eliminate...