similar to: Error in x[good, ] * w : non-conformable arrays

Hello, Consider this function for generalized ridge regression: gre <- function (X,y,D){ n <- dim(X)[1] p <- dim(X)[2] intercept <- rep(1, n) X <- cbind(intercept, X) X2D <- crossprod(X,X)+ D Xy <- crossprod(X,y) bth <- qr.solve(X2D, Xy) } # suppose X is an (nxp) design matrix and y is an (nx1) response vector p <- dim(x)[2] D<- diag(rep(1.5,p)) bt

Dear all, I wish to generate a lattice boxplot which skips an empty cell in a design. I have trawled r-help, scruitinized xyplot(lattice) help page, and merrily reproduced examples of using skip from a couple of previous r-help queries and the example given in Pinheiro & Bates. But I must be missing something... Here's an example (running R 1.9.1 on Win2k): # generate some data df1

#I want to "merge" or "join" 2 dataframes (df1 & df2) into a 3rd (mydf). I want the 3rd dataframe to contain 1 row for each row in df1 & df2, and all the columns in both df1 & df2. The solution should "work" even if the 2 dataframes are identical, and even if the 2 dataframes do not have the same column names. The rbind.fill function seems to work. For

# Dear R Community, # I have this data frame: df1 <- data.frame( F1 = factor( c( rep("D1",12),rep("D2",12),rep("D3",12) ) ), F2 = factor( rep( rep( paste("O",1:6,sep=""), rep(2,6) ), 3) ), F3 = factor( rep( c("V1","V2"), 18 ) ), S1 =

Greetings R-helpers, I am attempting to fit an lme() while specifying a correlation structure, but I'm getting into trouble long before I get to that point. I am receiving the error: Error in y[revOrder] - Fitted : non-conformable arrays It doesn't seem to matter how simple or complex the model I specify is, it always gives this same error message. This makes me suspect something is

Good Day, Included below is some code to generate data and to fit a mixed effects model to this fake data. The code works as expected when I call the function "lme" in Splus but not in R. The error message from calling lme in R is: "Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups" I installed the nlme package for R around 20 August

Hi, Two ways to create what should normally be the same data frame: > df1 <- data.frame(a=character(0), b=character(0))> df1 [1] a b <0 rows> (or 0-length row.names) > df2 <- as.data.frame(matrix(character(0), ncol=2, dimnames=list(NULL, letters[1:2]))) > df2 [1] a b <0 rows> (or 0-length row.names) unique() works as expected except that I

Hi Everyone, I'm running some simulations where eventually I need to table the results. The problem is, that while most simulations I have at least one predicted outcome for each of the six possible categories, sometimes the algorithm assigns all the outcomes and one category is left out. Thus when I try to add the misclassification matrices I get an error. Is there a simple way I can make

Hello, I wonder if I ran into a bug. If I do summary(df1$X1) -> df1.y df1.y a b c d e [1,] 50.74627 8.955224 17.91045 19.40299 2.985075 sort(df1.y) a b c d e [1,] 2.985075 8.955224 17.91045 19.40299 50.74627 my numbers are sorted but do not anymore correspond to the rownames. For me it is counterintuitive that solely the numbers are sorted and not the names. Is

Dear expeRts, What is the best approach to create a third data frame from two given ones, when the new/third data frame has last column computed from the last columns of the two given data frames? ## Okay, sounds complicated, so here is an example. Assume we have the two data frames: df1 <- data.frame(Year=rep(2001:2010, each=2), Group=c("Group 1","Group 2"), Value=1:20)

Hello! Data.frames have new rownames funcionality, however in use of colnames<- in R-devel "changes" this. Here is the example: > df1 <- data.frame(letters[1:5]) > attributes(df1) $names [1] "letters.1.5." $row.names [1] 1 2 3 4 5 $class [1] "data.frame" > colnames(df1) <- "bla" > attributes(df1) $names [1] "bla"

Dear all, I have partial data set with four colums. First column is "site" with three factors (i.e., A, B, and C). From second to fourth columns (v1 ~ v3) are my observations. In the observations of the data set, "." indicates missing value. I replaced "." with NA. To replace "." with NA, I used two steps. First, I replaced "." with NA, and

Folks, I'm checking the structure of a dataframe for duplicate parameters at a site station (i.e depth should be measured once, not twice), using aggregate to count each parameter within a site station. The fake data below has only 26000 rows, and takes roughly 14 seconds. My real data has 750000 rows and I had to stop execution after about an hour. The by() function is faster, but I

Thanks, Jim. The code works, but I don't understand why you use q1090 <- quantile(DF1$B, probs=c()), rather than DF1$A? Also, how to add a legend for both points DF1 and DF2? On Wed, Jul 12, 2017 at 8:25 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi lily, > Here is the first plot: > > plot(DF1$A,DF1$B,pch=19,col="red") > meanA<-mean(DF1$A) >

Hi, I have two dataframes: The first, df1, contains some missing data: cola colb colc cold cole 1 NA 5 9 NA 17 2 NA 6 NA 14 NA 3 3 NA 11 15 19 4 4 8 12 NA 20 The second, df2, contains the following: cola colb colc cold cole 1 1.4 0.8 0.02 1.6 0.6 I'm wanting all missing data in df1$cola to be replaced by the value of df2$cola.

Dear community, I'm working with the data.frame attached ( http://r.789695.n4.nabble.com/file/n4122926/df1.xls df1.xls ), let's call it df1. I typed: df1<- read.xls("C:/... dir .../df1.xls",colNames= TRUE, rowNames= TRUE) Then I splited randomly df1 using splitdf function (http://gettinggeneticsdone.blogspot.com/2011/03/splitting- dataset-revisited-keeping.html)

Hi, I have a data.frame that contains a variable act which records the duration (in seconds) of two states (wet-dry) for several individuals (identified by Ring) over a period of time. Since I want to work with daytime (i.e. from sunrise till sunset) and night time (i.e. from sunset till next sunrise), I have to split act from time[i] till sunset and from sunset until time[i+1], and from time[k]

Dear list, I am trying to make a contingency table with xtabs but I am getting a 0 where I expect a 'NA'. Here is a simple example: options(stringsAsFactors = FALSE) rn <- LETTERS[1:4] df1 <- data.frame(r07 = rep(rn, each=4), r08 = rep(rn, 4), value = 1:16) xtabs(value ~ r07 + r08, df1) # Delete the combination [A, C] df1 <- df1[-3,] # Set 'value'

Thank you Rui. I ran the following script df1 <- read.table("solar_hour", header = TRUE) df1$date <- as.Date(paste(df1$year, df1$hour), format = "%Y %j", origin = "2012-08-01-0") df2 <- df1[c("date", "IMF", "SWS", "SSN", "Dst", "f10")] head(df1) #To display all the rows print(df2). It gave me this

A<-c(1,2,NA,7,5) B<-c(3,4,1,4,1) C<-c(6,5,6,NA,9) D<-c(8,7,4,6,2) df1<-cbind(A,B,C,D) for(i in seq(1,ncol(df1)-1, by=2)) { ifelse(df1[,i]=="NA",df1[,i+1]=="NA",df1[,] ) } Tried several variations but none worked. I wish to find any NA's in column's 1 or 3 and change the numerical value to the right of the " NA"'s . In this case I