similar to: Levels in returned data.frame after subset

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

The anti_join from the package dplyr might also be handy. install.package("dplyr") library(dplyr) anti_join (x1, x2) You can get help on the different functions by ?function.name(), so ?anti_join() will bring you help - and examples - on the anti_join function. It might be worth testing your approach on a small subset of the data. That makes it easier for you to follow what happens

Dear All: It was saved, but there was a space somewhere. So it works for me now. I do have another similar problem. I saved an R data file save(datahs0csv,file=" F:\Fall_2017\5-STA574\2-Notes\1-R\1-R_new\chapter4-Entering_Data/datahs0csv2 .rda") *The new R data file "*datahs0csv2.rda*" is in the directory.* I tried to load the file "" to R, but I got an error

I have been trying to get the lme4 package installed on Mac OS X... with no success. The Mac OS binary is not available on any CRAN, and I also can¹t install the package from old source. Has anyone found a solution to this problem? I am happy to use nlme for now, but I tend to prefer to do my mixed model analyses with lmer(). Thanks, Nicholas Griffin Center for Genome Sciences Washington

Hello, I'm having troubles defining a list where names are variables (of type character). Like this, which gives "foo" instead of "world" (the way I meant it is that "world" is the value of the variable foo). Any hint? > f <- function(foo, bar) { list(foo = bar) } > x <- f("hello", "world") > names(x) [1] "foo"

The diffobj package (https://cran.r-project.org/package=diffobj) is really helpful here. It provides "diff" functions diffPrint(), diffStr(), and diffChr() to compare two object 'x' and 'y' and provide neat colorized summary output. Example: > iris2 <- iris > iris2[122:125,4] <- iris2[122:125,4] + 0.1 > diffobj::diffPrint(iris2, iris) < iris2 >

Thanks, I think I've found the most succinct expression of differences in two data.frames... length(which( rowSums( x1 != x2 ) > 0)) gives a count of the # of records in two data.frames that do not match. // ________________________________________ From: Henrik Bengtsson [henrik.bengtsson at gmail.com] Sent: Sunday, January 28, 2018 11:12 AM To: Ulrik Stervbo Cc: Marsh Hardy ARA/RISK;

If your two objects have class "data.frame" (look at class(objectName)) and they both have the same number of columns and the same order of columns and the column types match closely enough (use all.equal(x1, x2) for that), then you can try which( rowSums( x1 != x2 ) > 0) E.g., > x1 <- data.frame(X=1:5, Y=rep(c("A","B"),c(3,2))) > x2 <-

Hi, I am new to R, so I am unsure of the formula to set up this analysis. I would like to run a linear model with a continuous dependent variable (brain volume) and a continuous independent variable (age) while controlling for a categorical nuisance variable (gender). Age and brain volume are correlated. There are no gender differences in age but there are significant gender differences in brain

The object you load has the same name as the object you saved. In this case datahs0csv and not the name of the file sans .rda On Di., 12. Sep. 2017, 21:26 AbouEl-Makarim Aboueissa < abouelmakarim1962 at gmail.com> wrote: > Dear All: > > > It was saved, but there was a space somewhere. So it works for me now. > > I do have another similar problem. > > I saved an R

Please keep the list in cc. Sorry, it didn't work as expected. Maybe someone else have an appropriate solution. Best, Ulrik On Sa., 26. Aug. 2017, 12:57 niharika singhal <niharikasinghal1990 at gmail.com> wrote: > Hi > > Thanks for you mail, > I really appreciate your time on my problem > > I have posted this problem on > > >

Hi! I'm trying to implement in R in an easy way the negative reflection method described in Silverman (1986) on p.31, ie I have a non-negative dataset and would like to estimate the density by applying a reflection method where the reflected points have weight -1. I thought there should be a way to evaluate the wanted kernel at the required points, in other words to calculate formula (2.16):

Well, something like this would work (there may be slicker solutions): > z <- data.frame(a=1:3,b = letters[1:3]) > i <- seq_len(nrow(z)) *2 > z <-rbind(z,z) > z[i, ] <- matrix(NA, nr=nrow(z),nc=ncol(z)) > z a b 1 1 a 2 NA <NA> 3 3 c 4 NA <NA> 5 2 b 6 NA <NA> But I agree with you that there is probably a way to handle the underlying

Using Ulrik?s example data (and assuming I understand what is wanted), here is what I would do: ex.dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") tst <- data.frame(x = ex.dat, stringsAsFactors=FALSE) sp <- strsplit(tst$x, ':', fixed=TRUE) chk <-

I asked the moderators about it. This is the reply "Other moderators have looked into this a bit and may be able to shed more light on it. This is a "new" tactic where the spammers appear to reply to the r-help post. They are not, however, going through the r-help server. It also seems that this does not happen to everyone. I am not sure how you can automatically block the

Hello Thomas, Ulrik, thanks for your suggestions. Giovanni On Fri, Aug 4, 2017 at 12:13 PM, Thomas Mailund <thomas.mailund at gmail.com> wrote: > Do you mean like this? > > >> f <- function(foo, bar) { > + result <- list(bar) > + names(result) <- foo > + result > + } > >> (x <- f("hello", "world")) > $hello >

Also, it will be easier to provide helpful information if you'd describe what in your data you want to compare and what you hope to get out of the comparison. Best wishes, Ulrik Eric Berger <ericjberger at gmail.com> schrieb am Sa., 27. Jan. 2018, 08:18: > Hi Marsh, > An RDS is not a data structure such as a data.frame. It can be anything. > For example if I want to save my

Hi, How can I generate data from multivariate gamma distribution & multivariate beta distribution? I only found command for multivariate normal only. Many thanks in advance :) Regards Rofizah [[alternative HTML version deleted]]

Hi R Users, I have two vectors, x and y, of equal length representing two types of data from two studies. I would like to test if they are similar enough to use them interchangeably. No assumptions about distributions can be made (initial tests clearly show that they are not normal). Here some result: Two-sample Kolmogorov-Smirnov test data: x and y D = 0.1091, p-value < 2.2e-16 alternative

Dear R experts, I'm beginner. My question about ANOVA for unequal sample sized data should be obsolete but I can not clarify it. I have a dataset from 23 males and 18 females. I measured one condition('cond') with 4 levels. So I'd like to see main effect of gender, cond and gender by cond interaction and also postHoc test. (In fact, I have to do anova 90 times) * 1. Question