search for: pm10

Hi Gustav, Try this: lapply(1:length(models),function(i) lapply(models[[i]],function(x) summary(x)$coef[2,]))[[1]] #1st list component [[1]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) # pm10 #5.999185e-04 1.486195e-04 4.036606e+00 5.423004e-05 #[[2]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) #ozone #0.0010117294 0.0003792739 2.6675428048 0.0076408155 #[[3]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) #so2 #0.0026595441 0.0009352046 2.8438097399 0.004457765...

dear R users, my data looks like this PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 2 6.381684 2.250446 nein Sommer Sonntag nein ja Süd 3 62.586512 66.304869 ja Sommer Sonntag nein nein Ost 4 5.590101 8.526152 ja Sommer Donnerstag nein nein Nord 5...

...that there is a way of using "exact", that wouldn't go berserk due to the many ties (see #Berserk script at bottom). e. The prize to pay for using a case-crossover analysis? It should perhaps be noted that, because of the absence of individual data, the exposure to air-pollution (pm10) is assumed to be common to all individuals on a certain day. I'd be most grateful for any help and ideas on this matter. Best regards, Fredrik Nilsson, PhD PS. I am aware of the limitations that Whitaker et al. presented in Environmetrics 2007; 18: 157-171, but tried to use th...

...glm overly precise due to the positive autocorrelation (changed to -0.9 which made the time-stratifed be more precise, but not the glm). This note was not in the HTML-version. Sorry for that. It should perhaps be noted that, because of the absence of individual data, the exposure to air-pollution (pm10) is assumed to be common to all individuals on a certain day. I'd be most grateful for any help and ideas on this matter. Best regards, Fredrik Nilsson, PhD PS. I am aware of the limitations that Whitaker et al. presented in Environmetrics 2007; 18: 157-171, but tried to use the time-strat...

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]]...

Hello, i would like to import this txt file: Giorno;PM10 2006-01-01 10:10;10.3 2006-02-02 20:22;50.3 2006-03-03 23:33;20.1 ......... As it's an irregular time series i use zoo as follow: require(zoo) z <- read.table("c:\\1.csv", sep=";", na.strings="-999", header=TRUE) q <- zoo(z$PM10, strptime(as.character(z$Gi...

...panels but changing the grouping variable each time so that each panel highlights a different feature of the data set. The following code does exactly that with a simple and fabricated air quality data set. dataSet <- data.frame("Pollutant"=c(rep("Black Carbon",5),rep("PM10",5)), "Detector"=c(1:5,1:5), "Value"=c(seq(50,10,-10),seq(100,60,-10)), "Class"="Mass") xyplot( Value ~ Detector | Pollutant, data=dataSet, aspect = 1.0, subscripts=TRUE, panel = function(x,y,subscripts,...) { if(panel.number() == 1) panel...

I am analyzing particulate matter data (PM10) on a small data set (147 observations). I fitted a semi-parametric model and am worried about overfitting. How can one check for model fit in GAM? Jean G. Orelien

i read a book from WOOD, there's an example which is talking about the pollutant. library(gamair) library(mgcv) y<-gam(death~s(time,bs="cr",k=200)+s(pm10median,bs="cr")+s(so2median,bs="cr")+s(o3median,bs="cr")+s(tmpd,bs="cr"),data=chicago,family=Possion) lag.sum<-function(a,10,11) {n<-length(a) b<-rep(0,n-11) for(i in 0:(11-10)) b<-b+a[(i+1):(n-11+i)] b} death<-chicago$death[4:5114] time<-...

...cases = sample(x=100:500,size=10), conc = sample(x=20:125,size=10) ) ## Two of the published dose-response relationships adult.CP.mortality = list(end.point = "Cardiopulmanory mortality in adults over 30", pollutant = "PM10", relationship = "log-linear", beta = c(0.0562,0.1551,0.2541), Xpop = c("Xbabies","Xkids","Xteens","Xadults","Xaged")[4:5]) adult.LC.mortality = list(end.p...

Hi - I am trying to find and describe missing data in a time series. For instance, in the library openair, there is a data frame called "mydata": library(openair) head(mydata) date ws wd nox no2 o3 pm10 so2 co pm25 1 1998-01-01 00:00:00 0.60 280 285 39 1 29 4.7225 3.3725 NA 2 1998-01-01 01:00:00 2.16 230 NA NA NA 37 NA NA NA 3 1998-01-01 02:00:00 2.76 190 NA NA 3 34 6.8300 9.6025 NA 4 1998-01-01 03:00:00 2.16 170 493 52 3 35 7.6625 10.2175 NA 5 1998-01-0...

my data looks like this: PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 2 6.381684 2.250446 nein Sommer Sonntag nein ja Süd 3 62.586512 66.304869 ja Sommer Sonntag nein nein Ost 4 5.590101 8.526152 ja Sommer Donnerstag nein nein Nord 5...

Hi R users,I am a beginner in the use of R. I need urgent help for my thesis study. <https://stats.stackexchange.com/posts/633206/timeline> I have daily air pollution parameters PM10, PM2.5 CO, NO2, SO2, and O3. I also have daily hospital admission numbers. Taking into account the effect of weekends and holidays, I would like to used generalised additive model (GAM) to explore the relationship between daily patients admissions, and air pollution parameters. I would like tu use...

Hi R people, I want to write some functions to automate the plotting of some expressions that use some of the plotmath capabilities. An example is a string supplied to a plot call such as: plot(1, 1, ylab = "pm10 (ug/m3)") This should actually appear like: plot(1, 1, ylab = expression("PM"[10] * " (" * mu * "g m" ^-3 * ")")) i.e. pollutant name, units What I would like to do is write a function that will automatically detect certain strings, reformat them, a...

...A NA NA ... $ NOx.tunn : num NA NA NA NA NA NA NA NA NA NA ... $ NOx.diff : num NA NA NA NA NA NA NA NA NA NA ... $ THC.fresh : num NA NA NA NA NA NA NA NA NA NA ... $ THC.tunn : num NA NA NA NA NA NA NA NA NA NA ... $ THC.diff : num NA NA NA NA NA NA NA NA NA NA ... $ PM10.fresh : num NA NA NA NA NA NA NA NA NA NA ... $ PM10.tunn : num NA NA NA NA NA NA NA NA NA NA ... $ PM10.diff : num NA NA NA NA NA NA NA NA NA NA ... $ O3.fresh : num NA NA NA NA NA NA NA NA NA NA ... $ CO2.fresh : num NA NA NA NA NA NA NA NA NA NA ... $ CH4.tunn : num...