search for: av2

...do this one by one, but with that many days to calculate the average for, it will get very long. I'm sure there is a faster way to do this, I just don't know how. What i have so far is: av1 <- subset(ER9r, Day == 98, select = c (Depth1j:Depth0.75j)) av1 <- mean(av1 [av1>0]) av2 <- subset(ER9r, Day == 99, select = c (Depth1j:Depth0.75j)) av2 <- mean(av2 [av2>0]) av3 <- subset(ER9r, Day == 100, select = c (Depth1j:Depth0.75j)) av3 <- mean(av3 [av3>0]) av4 <- subset(ER9r, Day == 101, select = c (Depth1j:Depth0.75j)) av4 <- mean(av4 [av4>0]) av5...

...#auxiliary variable df = data.frame(df, aux) attach(df) par(mfrow = c(2, 1)) interaction.plot(r, e, y, col = 'blue', ylab = 'y', xlab = 'r') interaction.plot(e, r, y, col = 'blue', ylab = 'y', xlab = 'r') av1 = aov(y ~ r*e) av2 = aov(y ~ r/e) efR_E = summary(av2, split = list('r:e' = list( 'e1 vs e2/r1' = 1, 'e1 vs e2/r2' = 2, 'e1 vs e2/r3' = 3))) av3 = aov(y ~ e/r) efE_R = summary(av3, split = list('e...

..., 18.8, 19.2, 19.8, 21.4, 22.8, 21.3) df = data.frame(r, e, y) attach(df) par(mfrow=c(2,1)) interaction.plot(r, e, y, col = 'blue', ylab = 'y', xlab = 'r') interaction.plot(e, r, y, col = 'blue', ylab = 'y', xlab = 'r') av1 = aov(y ~ r*e) av2 = aov(y ~ r/e) efR_E = summary(av2, split = list('r:e' = list('e1 vs e2/r1' = 1, 'e1 vs e2/r2' = 2, 'e1 vs e2/r3' = 3))) av3 = aov(y ~ e/r) efE_R = summary(av3, split = list('e:r' = list('r/e1' = c(1,3), 'r/e2' = c(2,4)))) # ---------...

..., 18.8, 19.2, 19.8, 21.4, 22.8, 21.3) df = data.frame(r, e, y) attach(df) par(mfrow=c(2,1)) interaction.plot(r, e, y, col = 'blue', ylab = 'y', xlab = 'r') interaction.plot(e, r, y, col = 'blue', ylab = 'y', xlab = 'r') av1 = aov(y ~ r*e) av2 = aov(y ~ r/e) efR_E = summary(av2, split = list('r:e' = list('e1 vs e2/r1' = 1, 'e1 vs e2/r2' = 2, 'e1 vs e2/r3' = 3))) av3 = aov(y ~ e/r) efE_R = summary(av3, split = list('e:r' = list('r/e1' = c(1,3), 'r/e2' = c(2,4)))) mds = mod...

..., 18.8, 19.2, 19.8, 21.4, 22.8, 21.3) df = data.frame(r, e, y) attach(df) par(mfrow=c(2,1)) interaction.plot(r, e, y, col = 'blue', ylab = 'y', xlab = 'r') interaction.plot(e, r, y, col = 'blue', ylab = 'y', xlab = 'r') av1 = aov(y ~ r*e) av2 = aov(y ~ r/e) efR_E = summary(av2, split = list('r:e' = list('e1 vs e2/r1' = 1, 'e1 vs e2/r2' = 2, 'e1 vs e2/r3' = 3))) av3 = aov(y ~ e/r) efE_R = summary(av3, split = list('e:r' = list('r/e1' = c(1,3), 'r/e2' = c(2,4)))) mds = mod...

...$e, dF$y, col = 'blue', ylab = 'Y', xlab = 'R') interaction.plot(dF$e, dF$r, dF$y, col = 'blue', ylab = 'Y', xlab = 'R') # ANOVAs av0 = aov(y ~ tra, data=dF) summary(av0) av1 = aov(y ~ r*e, data=dF) summary(av1) av2 = aov(y ~ r/e, data=dF) e_r = summary(av2, split = list('r:e' = list( 'e1 vs e2/r1' = 1, 'e1 vs e2/r2' = 2, 'e1 vs e2/r3' = 3))) e_r av3 = aov(y ~ e/r, data=dF) r_e = summary(av3, split = list('e:r' = list( 'r/e1' = c(1,3...

...par(mfrow=c(2,1)) interaction.plot(dF$a, dF$b, dF$y, ylab = 'y', xlab = 'a') interaction.plot(dF$b, dF$a, dF$y, ylab = 'y', xlab = 'b') # ANOVAs av0 = aov(y ~ tra, data=dF) summary(av0) av1 = aov(y ~ a*b, data=dF) summary(av1) av2 = aov(y ~ a/b, data=dF) b_a = summary(av2, split = list('a:b' = list( 'b1 vs b2/a1' = 1, 'b1 vs b2/a2' = 2, 'b1 vs b2/a3' = 3))) b_a av3 = aov(y ~ b/a, data=dF) a_b = summary(av3, split = list('b:a' = list( 'a/b1' = c(1,3...

Hi, I would know how to plot two variograms on a same graph. I can plot one by one but I would draw both on the same one. Is it possible? Do i need any special package? Thanks! Cordialement Damien Landais

...ating PACKAGES 2) a bug in read.dcf(): not looking if PACKAGES.gz is present 3) a bug in available.packages(). Debugging install.packages() indicates that the error occurs at the line below with type <- "win.binary", repos <- getOption("repos") and missing(method) == TRUE av2 <- available.packages(contriburl = contrib.url(repos, type2), method = method) It would be nice if available.packages() handled an error returned by read.dcf() more gracefully. An error with one repository now completely blocks install.packages() sessionInfo("drat") R version 3.2.3 (...

...ES | 2) a bug in read.dcf(): not looking if PACKAGES.gz is present | 3) a bug in available.packages(). Debugging install.packages() | indicates that the error occurs at the line below with type <- | "win.binary", repos <- getOption("repos") and missing(method) == TRUE | | av2 <- available.packages(contriburl = contrib.url(repos, type2), | method = method) | | It would be nice if available.packages() handled an error returned by | read.dcf() more gracefully. An error with one repository now | completely blocks install.packages() I also found the file semantics over...