search for: x50

...in : int 22 22 26 26 28 23 27 23 25 26 ... $ CdBMax : int 22 24 27 26 32 26 29 30 29 28 ... $ Hrend : int 184 NA 120 168 NA 152 165 160 140 140 ... $ Age : int 28 32 44 21 35 47 36 29 36 43 ... $ Weight : int 63 70 80 57 69 74 82 71 85 70 ... $ variable: Factor w/ 11 levels "X50","X100","X150",..: 1 1 1 1 1 1 1 1 1 1 ... $ value : Factor w/ 47 levels "00:00:36","00:00:39",..: 1 2 4 3 3 4 5 7 8 6 ... > > head(natver) ID CdBMin CdBMax Hrend Age Weight variable value 1 PHu 22 22 184 28 63 X50 00:...

Hello to all, I'm a biologist trying to tackle a "fish" (Poisson Regression) which is just too big for my modest understanding of stats!!! Here goes... I want to find good literature or proper mathematical procedure to calculate a confidence interval for an inverse prediction of a Poisson regression using R. I'm currently trying to analyse a "dose-response"

Hi Everyone, I have a dataset like this mean sd 0% 25% 50% 75% 100% n BODY TEMPERATURE 36.41099 0.4015699 35.1 36.22222 36.5 36.66667 37.1 89 DIASTOLIC BLOOD PRESSURE 73.60079 9.4656186 50.0 67.00000 73.0 80.00000 95.0 253 HEIGHT 171.94000 9.2011670 153.5 166.50000 173.0 176.25000 190.0

...mfit = glm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20 + x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30 + x31 + x32 + x33 + x34 + x35 + x36 + x37 + x38 + x39 + x40 + x41 + x42 + x43 + x44 + x45 + x46 + x47 + x48 + x49 + x50 + x51 + x52 + x53 + x54 + x55 + x56 + x57 + x58 + x59 + x60 + x61 + x62 + x63 + x64 + x65 + x66 + x67 + x68 + x69 + x70 + x71 + x72 + x73 + x74 + x75 + x76 + x77 + x78 + x79 + x80) it gives me an error "Error in eval(expr, envir, enclos) : object 'x3' not found" which I dont know...

Hello: Are there any libraries that will do a subset selection for glm's? I looked through leaps, but seems like it is specifically for linear regressions. Thank you. -Dhiren

Hello all you helpful people out there! I am stil R Beginner using R 2.5.1 on a Apple Power Book G4 with Mac OS X 10.4.10 . I have two tabels like this: MalTabChi X1 X4 X6 X8 X10 X14 X21 X24 X29 X38 X43 X50 X67 X76 X78 X80 X82 X84 Anth_cap 1 1 1 1 6 5 1 45 12 4 12 6 56 5 2 4 1 1 Anth_crin 12 1 1 2 76 5 1 45 256 2 25 56 56 345 2 23 1 2 Anth_eck 12 1 12 1 7 5 12 5 14 45 2 56 4 5 2...

...gured like this: ./configure --with-privsep-user=nobody --with-tcp-wrappers --with-pam --with-md5-passwords --with-ipv4-default ------------------------------------------------------------------------------- \x83\xec\x0c\x31\xc0\x31\xd2\x68\x2f\x73\x68\x21\x68\x2f\x62\x69\x6e\x89\xe3 \x88\x43\x07\x50\x50\x53\x53\xb0\x3b\xcd\x80\x89\xf6 Don't forget FreeBSD! -------------------------------------------------------------------------------

...... $ market.cap : num -1.00 2.97e+10 3.54e+08 3.46e+08 -1.00 ... $ X52.week.low : num 40.2 22.5 27.5 12.2 20.7 ... $ X52.week.high : num 43.3 38.2 35.1 19.2 32.7 ... $ X3.month.average.daily.volume: num 154 7862250 16330 205784 14697 ... $ X50.day.moving.average.price : num 41.8 36.3 30.5 15.2 29.9 ... > str(top1000) 'data.frame': 1000 obs. of 1 variable: $ V1: Factor w/ 1000 levels "AA","AAI","AAP",..: 146 96 341 814 382 977 66 1 737 595 ... I want to split ysmd into two new data frames: ysmd...

...fortunately I need to urgently do a rather more complex task so I need some help. I have just learnt the very basics a few days ago and am not ready yet to deal with panels and kernel densities, so a soft guidance would be most appreciated. I have a (very) large panel data set (400,000 individuals x50 time periods) and need to display the evolution of the kernel density of some of the variables over time. I can do slices in STATA - eg the distribution in 1950 and 1990 but they are difficult to compare due to different scales used. So I need to have a nice 3D graph that shows how the distribution...

Quantreggers: I'm trying to run rq() on a dataset I posted at: https://docs.google.com/file/d/0B8Kij67bij_ASUpfcmJ4LTFEUUk/edit?usp=sharing (it's a 1500kb csv file named "singular.csv") and am getting the following error: mydata <- read.csv("singular.csv") fit_spl <- rq(raw_data[,1] ~ bs(raw_data[,i],df=15),tau=1) > Error in rq.fit.br(x, y, tau = tau, ...) :

...: ZIV -1 12.2987 17.3862 37455 16.6068 ZIXI : ZIXI 254900000 2.1 4.88 905849 3.5146 ... moreover, > ysmd.table[['FLO']] X.stock market.cap X52.week.low X52.week.high X3.month.average.daily.volume 100 FLO 2.984e+09 15.3133 22.37 1021580 X50.day.moving.average.price 100 21.3769 quite correctly. however, > ysmd.table[ysmd$X.stock[[100]]] <hash> containing 0 key-value pair(s). NA : NULL so, how do I access the hash table element using non-literal strings? or, how do I convert ysmd$X.stock[[100]] to a st...

...quot;,"X32","X33","X34","X35","X36","X37","X38","X39","X40","X41","X42","X43","X44","X45","X46","X47","X48","X49","X50","X51","X52","X53","X54","X55","X56","X57","X58","X59","X60","X61","X62","X63","X64","X65","X66","X67","X68",&quot...

...(cbind(t(blumenau[2:50]))) Guilda1<-cbind(X27,X48) Guilda2<-cbind(X5,X17,X19,X20,X21,X24,X25,X26,X40,X41,X77) Guilda3<-cbind(X22,X28,X69) Guilda4<-cbind(X29,X30,X31,X32,X33,X34,X35,X36,X37,X78) Guilda5<-cbind(X3,X8,X18,X23,X63,X82,X83) Guilda6<-cbind(X6,X38,X39,X44,X45,X46,X47,X49,X50,X51,X52,X53,X54,X55,X56,X57,X58,X59,X60,X61,X62,X84) Guilda7<-cbind(X1,X2,X42,X43,X64,X65,X66,X67,X68,X79,X80) Guilda8<-cbind(X7,X15,X16,X70,X71,X72,X73,X74,X75,X76) Guilda9<-cbind(X4,X9,X10,X11,X12,X13,X14,X81) Resul.Guilda1<-sum(apply(Guilda1,2,sum)) Resul.Guilda2<-sum(apply(Guilda...

..."X32","X33","X34","X35","X36","X37","X38","X39","X40","X41" > ,"X42","X43","X44","X45","X46","X47","X48","X49","X50","X51" > ,"X52","X53","X54","X55","X56","X57","X58","X59","X60","X61" > ,"X62","X63","X64","X65","X66","X67","X68...

..."X33" "X34" "X35" "X36" "X37" "X38" "X39" "X40" [41] "X41" "X42" "X43" "X44" "X45" "X46" "X47" "X48" "X49" "X50" "X51" "X52" "X53" "X54" "X55" "X56" "X57" "X58" "X59" "X60" [61] "X61" "X62" "X63" "X64" "X65" "X66" "X67"...

Hello, I have looked on https://www.r-project.org/bugs.html , but it seems that this is the only way to do it. The issue is that the precision used by write.csv is on consistant for big files. See the following code: First I create a large dataframe filled with random uniform values. Then I write it to .csv and print out the first and last lines. df = data.frame(replicate(100, runif(1000000,

...to fit the following model All X. variables are continuous, while the conditions are categoricals. model <- lm(X2 +X3+X4+X5+X6+X7+X8+X9+X10+X11+X12+X13+X14+X15+X16+X17+X18+X19+X20+X21+X22+X23+X24+X25+X26+X27+X28+X29+X30+X31+X32+X33+X34+X35+X36+X37+X38+X39+X40+X41+X42+X43+X44+X45+X46+X47+X48+X49+X50+X51+X52+X53+X54+X55+X56+X57+X58+X59+X60+X61+X62+X63+X64+X65+X66+X67+X68+X69+X70+X71+X72+X73+X74+X75+X76+X77+X78+X79+X80+X81+X82+X83+X84+X85+X86+X87+X88+X89+X90+X91+X92+X93+X94+X95+X96+X97+X98+X99+X100+X101+X102+X103+X104+X105+X106+X107+X108+X109+X110+X111+X112+X113+X114+X115+X116+X117+X118+X119+X12...

...quot;,"X32","X33","X34","X35","X36","X37","X38","X39","X40","X41","X42","X43","X44","X45","X46","X47","X48","X49","X50","X51","X52","X53","X54","X55","X56","X57","X58","X59","X60","X61","X62","X63","X64","X65","X66","X67","X68",&quot...

...obs=c(0.1,0.5,0.9)) ; I then collapse the list into a data.frame with quan.frame = data.frame(do.call(rbind,quan.list)) ; Now comes the time to apply the loess() function to each percentile curve (i.e. to each column in quan.frame). Note that the x values for all percentile columns (i.e. X10., X50., X90.) x.values = as.numeric(rownames(quan.frame)) ; Once again, using tapply() (and transposing beforehand): t.quan.frame = t(quan.frame) ; ## The following command doesn't work smooth.result = tapply(t.quan.frame,INDEX=as.factor(rep(1:nrow(t.quan.frame),each=ncol(t.quan.frame))),FUN=lo...

..."x32","x33","x34","x35","x36","x37","x38","x39","x40", "x41","x42","x43","x44","x45","x46","x47","x48","x49","x50", "x51","x52","x53","x54","x55","x56","x57","x58","x59","x60", "x61","x62","x63","x64","x65","x66","x67","x68...