similar to: Call the Standard Error and t-test probability in linear regression

Hello all, I would appreciate your thoughts on a seemingly simple problem. I have a database, where each row represent a single record. I want to aggregate this database so I use the aggregate command : D<-read.csv("C:\\Users\\test.csv") attach(D) by1<-factor(Class) by2<-factor(X) W<-aggregate(x=Count,by=list(by1,by2),FUN="sum") The results I

Hello everyone, Thank you for this. Nonetheless it is not exactly want i need. I need mydata[[1]] to provide the values for all 3 variables (Y, X1 and X2) of the first imputation only. As it stands it returns the whole database. Any ideas? Best, ioanna ________________________________ From: Bert Gunter <bgunter.4567 at gmail.com> Sent: 24 May 2018 16:04 To: Ioanna Ioannou Cc:

Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 =

This is one of those instances where a less superficial knowledge of R's technical details comes in really handy. What you need to do is convert the data frame to a single (numeric) vector for, e.g. a matrix() call. This can be easily done by noting that a data frame is also a list and using do.call(): ## imp is the data frame: do.call(c,imp) X11 X12 X13 X14 X15 X16 X17 X18 X19

Hello all, I have data in the form of a table: X Y1 Y2 0.1 3 2 0.2 2 1 And I would like to transform in the form: X Y 0.1 Y1 0.1 Y1 0.1 Y1 0.1 Y2 0.1 Y2 0.2 Y1 0.2 Y1 0.2 Y2 Any ideas how? Thanks in advance, IOanna [[alternative HTML version deleted]]

Hello all, I would like to use the Nadaraya-Watson estimator assuming a Gaussian kernel: So far I sued the library(sm) library(sm) x<-runif(5000) y<-rnorm(5000) plot(x,y,col='black') h1<-h.select(x,y,method='aicc') lines(ksmooth(x,y,bandwidth=h1)) which works fine. What if my data were clustered requiring a bandwidth that varies with x? How can I do that? Thanks in

Hi, Is there any chance you will merge the suggested patch? Thanks, Jo On Thu, Apr 18, 2019 at 2:01 PM Richard W.M. Jones <rjones@redhat.com> wrote: > On Thu, Apr 18, 2019 at 12:17:18PM +0100, Ioanna Alifieraki wrote: > > I had tried this approach, to add > /etc/dhcp/dhclient-enter-hooks.d/resolved > > to excludefiles, however it didn't work. > > As far as I

Hello, I am trying to perform a logistic regression using counts. For example: cedegren <- read.table("http://www.cloudstat.org/index.php?do=/attachment/download/id_95 /", header=T) attach(cedegren) ced.del <- cbind(sDel, sNoDel) ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=binomial("logit")) This works. However, if I change the family to

Hello, A very simple question but I am stuck. I have an excel file each row is an address. However, I cannot make geocode read each line and come up with the latitude longitude. Could you please correct my code? library(ggmap) X<-c (2 Afxentiou Ampelokipi Thessaloniki Greece, 2 Afxentiou Ampelokipi Thessaloniki Greece, 4 Afxentiou Ampelokipi Thessaloniki Greece, 55 Agathonos

I had tried this approach, to add /etc/dhcp/dhclient-enter-hooks.d/resolved to excludefiles, however it didn't work. As far as I know, this hook is included in the base image and according to [1], the current implementation does not apply excludefiles to the base image. Jo [1] https://github.com/libguestfs/supermin/blob/b2401285cd3e3d42006fc164ef1f046cc35a50c4/src/mode_build.ml#L137 On

Workaround for Ubuntu which uses this script to try to start a systemd service. That won't work because systemd is not used inside the appliance. See: https://bugs.launchpad.net/ubuntu/+source/supermin/+bug/1824236 Thanks: Ioanna Alifieraki --- appliance/init | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/appliance/init b/appliance/init index 4f2b55822..b1c4d09ea

Hi, I am facing the following bug while runnign guestfish on Ubuntu Bionic onward, where the network cannot be configured : https://bugs.launchpad.net/ubuntu/+source/supermin/+bug/1824236 The actual problem is the combination of (a) supermin and the fact that it does chroot and (b) a dhclient hook present in Bionic (/etc/dhcp/dhclient-eneter-hooks.d/resolved) that overwrites the make_resolv_conf

I am trying to find a confidence band for a fitted non-linear curve. I see that the predict.nls function has an interval argument, but a previous post indicates that this argument has not been implemented. Is this still true? I have tried various ways to extract the interval information from the model object without success. My code is: Model.predict <- predict(My.nls.model,

dear sir, my data larger than this example but is of the following format: y x Age 30 0.0323 O 24 0.0389 Y 158 0.058 Y 120 0.0581 O 100 0.0471 Y 102 0.0615 Y 160 0.0546 O i ma making a scatter plot of y~x and want to specify different coloured and filled shaped for the points according the the third categorical variable A. the code i have managed is : plot(y~x,pch=as.numeric(factor(Age)))

Hello, I have a statistical problem that I am using R for, but I am not making sense of the results. I am trying to use multiple regression to explore which variables (weather conditions) have the greater effect on a local atmospheric variable. The data is taken from a database that has 20391 data points (Z1). A simplified version of the data I'm looking at is given below, but I have a

Dear All, I was wondering if it is possible to generate a regression summary (it does not matter at this stage if from an lm or for example a glm estimate) in which to obtain the joint significance of a set of regressors? Examples could be looking at the joint significance level of a polynomial, or of a set of exogenous variables of which is of interest the linear combination suggested by the

Hi everyone, I'm sorry if this turns out to be more a statistical question than one specifically about R - but would greatly appreciate your advice anyway. I've been using a logistic regression model to look at the relationship between a binary outcome (say, the odds of picking n white balls from a bag containing m balls in total) and a variety of other binary parameters:

Hi, we have a validated program to do our calculations, but sometime I want to use R to do some quick statistical calculations. But for our linearity test, I can't reproduce in R. Suppose the following data set: dat <- structure(list(Level = structure(c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("A", "B", "C", "D",

I have a large dataset grouped by a factor and I want to perform a regression on each data subset based on this factor. There are many ways to do this, posted here and elsewhere. I have tried several. However I found one method posted on the R wiki which works exactly as I want, and I like the elegance and simplicity of the solution, but I don't understand how it works. Its all in the formula

I have a file: x y z 0.025 0.025 1.65775 0.025 0.050 1.62602 0.025 0.075 1.63683 0.025 0.100 1.91847 0.025 0.125 2.00913 0.025 0.150 1.82222 0.025 0.175 1.70901 0.025 0.200 1.39759 0.025 0.225 1.39089 0.025 0.250 1.04762 If I read the file like this: data<-read.table("file.dat") How do I access the vectors x,y,z that are inside the dataframe data? I studied Venables and