similar to: analogue of group option of SAS MIXED/random in R

Dear R users, A co-worker and I are writing a function to facilitate graph plotting in R. The function makes use of a lot of lists in its defaults. However, we discovered that R does not necessarily preserve the defaults if we were to input them in the form of list() when initializing the function. For example, if you feed the function codes below into R: myfunction=function( list1=list

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

Hello, I'm using SPSS at work but really would like to switch to R. Right now I'm trying to learn R in reproducing calculations I did with SPSS but am stuck with something that is quite simple and comprehensible in SPSS-Syntax: IF (variable1.fac = 0 AND variable2.num = 0) variable3=1. IF (variable1.fac = 0 AND variable2.num >= 1) variable3=2. IF (variable1.fac = 1 AND variable2.num =

Hi there, I have a huge dataframe containing 70,000 observations. I have filtered this dataframe (let it's name be "transformed_dataframe") as I wanted to select only those observations which are greater than or equal to 60,001 regarding the very first identity column. So I have a transformed dataframe now including 10,000 obeservations (from 60,001 - to 70,000) and if you send

Hi guys I seem to be having a problem, I don't know if it's a bug or whether I'm just doing it incorrectly. I want to set about 3 channel variables when I originate a call via AMI. All the documentation I have found says to do it like this: Variable: variable1=value|variable2=value|variable3=value However when I do this it runs them all together and I end up with:

Dear list, I have a set of 100+ variables. I would like to have one by one crosstables for each variable. I started with table(variable1, variable2) table(variable1, variable3) table(variable1, variable4) ... table(variable2, variable3) table(variable2, variable4) ... It seems rather tedious. Any better ideas around? Thanks for any help! Gerit -- NUR NOCH BIS 31.01.! GMX FreeDSL -

Hello, How do you print a variable name in a for loop? I'm trying to construct a csv file that looks like this: Hello, variable1, value_of_variable1, World, Hello, variable2, value_of_variable2, World, Hello, variable3, value_of_variable3, World, Using this: for (variable in list(variable1, variable2, variable3)){ cat("Hello,", ???variable???, variable, ",

Hello, I am looking for references about mixed models built on rank transformed data. Did anybody ever consider this topic? Thank you, Bruno ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Bruno L. Giordano, Ph.D. CIRMMT Schulich School of Music, McGill University 555 Sherbrooke Street West Montr?al, QC H3A 1E3 Canada http://www.music.mcgill.ca/~bruno/

I have a text file that has semi-colon separated values. The table is nearly 10,000 by 585. The files looks as follows: ******************************************* First line: Skip this line Second line: skip this line Third line: skip this line variable1 Variable2 Variable3 Variable4 Unit1 Unit2 Unit3 10 0.1 0.01 0.001 20

Hello R-Helpers, I am not sure if it is a very simple question but I would like to use two (...) in a function, for example, this is a script where I would like to input the variable names (in one of the (...)) and the variances associated to those variables which are not calculated in the script because there is a specific software to calculate it (the other (...)) data <- function

Listers, I have a question regarding correlation matrices. It is fairly straight forward to build a correlation matrix of an entire data frame. I simply use the command cor(MyDataFrame). However, what I would like to do is construct a smaller correlation matrix using just three of the variable out of my data set. When I run this: cor(MyDataFrame$variable1,

Hi everyone, I’m looking for a clever bit of code to replace NA’s with a specific score depending on an indicator variable. I can see how to do it using lots of if statements but I’m sure there most be a neater, better way of doing it. Any ideas at all will be much appreciated, I’m dreading coding up all those if statements!!!!! My problem is as follows: I have a data set with

Hello, I built 4 mixed models using different data sets and standardized variables as predictors. In all the models each of the fixed effects has an associated random effect (same predictor). What I find is that fixed effects with larger (absolute) standardized parameter estimates have also a higher estimate of the related random effect. In other words, the higher the average of the absolute

Hello I wrote a bootstrap program in C language that is called and run by R. When I tried it, it is slow and I'm trying to write and run the whole thing in C. But I cannot use handy functions in R and need to figure out how to write those functions by myself. Is there any way that I can get the actual codes that implement functions in R so that I can translate them into other languages? For

Hi Rers, I have a poission time series model with 5 parameters. I just wanted to remove two of the lag on response in the model and put it as a system model. I am not sure about the codes to combine these two on R. If anybody has any R example (code), please post it. My original model: log(Y(t))~constant+b1*Y(t-1)+b2*Y(t-2)+b3*(variable1)+b4*(variable2)+e I would like to construct a

Hello, I am using logistic discriminant analysis to check whether a known classification Yobs can be predicted by few continuous variables X. What I do is to predict class probabilities with multinom() in nnet(), obtaining a predicted classification Ypred and then compute the percentage P(obs) of objects classified the same in Yobs and Ypred. My problem now is to figure out whether P(obs) is

Hello, I looked for R packages which focused on order-restricted statistical inference, but I could find only the isoreg() function. I would need to test whether the means in my (repeated measures) data follow a given order, e.g. A<B=C<D. I took a look at the monograph by Barlow et al. (1972) on this topic and found that for my case the null hypothesis is always A=B=C=D. This might be

In clinical trial data preparation and many other data situations, the statistical programmer needs to merge and re-merge multiple input files countless times. A syntax for merging files that is clear and concise is very important for the statistical programmer's productivity. Here is how Vilno does it: inlist dataset1 dataset2 dataset3 ; joinby variable1 variable2 where ( var3<=var4 ) ;

Hello, I'd like to ask you something again. I have a database and it has a column which looks like this one here: small_factor <- factor(c("d_variable1","d_variable2","d_variable3")) small_factor Now the thing is that I would like to convert each element of this factorized column. Basically I want to rewrite the words in it without the first two characters:

Hola a todos Estoy cargando una base de datos a R llamada "Base", desde excel. A la hora de utilizar funciones como: >pairs(Base) Error in pairs.default(Base) : non-numeric argument to 'pairs' Una forma de arreglar este problema es utilizando la funcion as.numeric(), pero me toca hacerlo variable por variable: >Base$Variable1<-as.numeric( Base$Variable1)