similar to: Lattice to ggplot2: Reference graphics across facets

Hello, Inline. ?s 19:03 de 12/06/2024, Yuan Chun Ding via R-help escreveu: > I am sorry that I know I should provide a dataset that allows to replicate my problem. > > It is a research dataset and quite large, so I can not share. > > Both Bert and Tim guessed my problem correctly. I also thought about the conflicting issue between different packages and function masking. > I

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )

Hi Rui, Thank you very much! Yes, I verified using real data, it worked correctly as expected after adding tidyr:: to the pivot_longer function and dplyr:: to the group_by and summarize Function. I did not know how to assign the tidyr and dplyr to the three functions because I do not really understand well the three functions and just got the code from a google search. I also tried your

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

Dear all, my aim is to estimate the efficacy over time of a treatment for headache prevention. Data consist of long sequences of repeated binary outcomes (1 if the subject has at least 1 episode of headache , 0 otherwise) on subjects randomized to placebo or treatment. I have fit a logistic regression model with Huber-White cluster sandwich covariance estimator. I have put in the model the

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

Hello List, I'm trying to do a paired t-test, and I'm wondering if it's consistent with equations. I have a dataset that has a response and two treatments (here's an example): ID trt order resp 17 1 0 1 0.0037513592 18 2 0 1 0.0118723051 19 4 0 1 0.0002610251 20 5 0 1 -0.0077951450 21 6 0 1 0.0022339952 22 7 0 2

Hi R users, The following code worked well to summarize four data groups in a dataframe for three variables (t_depth, t_alt_count, t_alt_ratio), 12 columns of summary, see attached. However, after running another 2000 lines of R codes using functions from more than 10 other R libraries, then it only generated one column of summary. Do you know why? Thank you, Yuan Chun Ding summary_anno1148ft

I am sorry that I know I should provide a dataset that allows to replicate my problem. It is a research dataset and quite large, so I can not share. Both Bert and Tim guessed my problem correctly. I also thought about the conflicting issue between different packages and function masking. I just hope to that someone has similar experience, so providing me suggestion. For conflicting issue,

Hello, I have a problem with creating an identity matrix for glmnet by using the contrasts function. I have a factor with 4 levels. When I create dummy variables I think there should be n-1 variables (in this case 3) - so that the contrasts would be against the baseline level. This is also what is written in the help file for 'contrasts'. The problem is that the function

I sometimes think people on this list are quite rude to posters. I'm afraid I'm likely to join in with some rudeness? 1. "Here is some code that works but also doesn't" is probably not going to get you an answer 2. I provide no information about the data it works on or doesn't 3. I tell you I'm using a load of dependencies, but don't tell you what 4. I refer to

Hi , I have a data set with recurrence time (up to four) of myocardial infarction (MI). Part of the file is showing below: Num1 Trt Sex Time T1 T2 T3 T4 1011 1 1 9 1211 0 1 59 3020 1 2 14 3 1245 0 1 18 12 16 3069 1 2 26 6 12 13 2051 0 1 53 3 15 46 51 The data consist of the following eight variables: Num1 , patient number Trt, treatment group (1=placebo and 2=drug) Sex,

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized

Hello List. I have many files of ECG, each one with 7 column and I need only the second column and the name of each ECG. I am doing this but althought I have various days trying this i haven't gotten, so I ask help with this, if somebody cans help me I'll be so thankfully. --------------------------------------------------------------------------------------------------------

Hi, I have recently been playing with the grid package in an attempt to create some pages containing multiple lattice plots on the same page. However, when I specify a grid layout with different widths, such as: pushViewport(viewport(layout = grid.layout(1, 2, unit(c(2, 1), "null")))) the individual graphs do not end up as the same height - which is a feature I would prefer to have.

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about

I have a following problem: The call qplot(wg, v.realtime, data=df.best.medians$gv1, colour=sp, geom="boxplot") works nice: for each value of the wg factor I get two box-plots (two levels in the sp factor) in different colours, side-by-side, centered at the wg x-axis. However, I want to separate the data belonging to different levels of the n factor, so I add the facets option:

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Hello, I'm using the "sem" package to do a confirmatory factor analysis on data collected with a questionnaire. In the model, there is a unique factor G and 23 items. I would like to calculate the standardized residual variance of the observed variables. "Sem" only gives the residual variance with the "summary" function, or the standardized loadings with the