similar to: XML: Reading transition matrices into R

I am trying to age standardize using the svystandardize package in R. I have successfully managed to hit my SUDAAN based targets for estimates by sex, but not the total. The total is only a little different, but I'd like some help knowing why it isn't exact. I've included the SUDAAN code that generates the targets and my R script (and output) that I have so far. I can't supply the

I'm an SPSS user trying to make the transition to R. Can someone help me translate the following SPSS code into R?: GLM Total_tp1 Total_tp2 WITH Age Sex   /WSFACTOR=Time 2 Repeated   /METHOD=SSTYPE(3)   /CRITERIA=ALPHA(.05)   /WSDESIGN= Time   /DESIGN= Age Sex Age*Sex. Also. can anyone recommend any resources to help SPSS users learn to things in R? Thanks, -kristi [[alternative HTML

[Delayed and reposted by list maintainer due to not passing through spam filter.. MM] Hello, I am looking for two R-functions that could generate an Output like SPSS. 1. An equivalent function for (SPSS) FREQUENCIES which generate something like this: Valid Cum Value Frequency Percent Percent Percent 1 1 5.3 5.6 5.6

Dear all, I did post this more or less identical mail in a follow up to another question I posted, but under another heading. I try again, but now under the correct header. upon running this code (from the Hmisc library-latex function) I believe the call to summary.formula is allright and produces wonderful tables, but the latex command results in a correct formatted table but where all the

Dear all, I noticed the following while playing around with fitting log-linear models to contingency tables using R 1.8.1, but the problem also exists under R 1.9.0. A reproducible example uses the following contingency table: > library(MASS) > data(quine) > tmp <- with(quine, expand.grid(Eth=levels(Eth), Sex=levels(Sex), + Lrn=levels(Lrn), Age=levels(Age)))

Dieter Menne pointed out that the (small) xml attachment didn't make it. Here is an in-line version (see end of message). Let's hope it works this time. I'm struggling with interpreting XML files created by ADODB as data.frames and I'm looking for advice. Note: This xlm contains a result set which comes from a rectangular data array. I've been trying to play with

Dear R-ers, my task is to simple: to assign cases to desired groupings based on the combined values on 2 variables. I can think of 3 methods of doing it. Method 1 seems to me pretty r-like, but it requires a lot of lines of code - onerous. Method 2 is a loop, so not very good - as it loops through all rows of mydata. Method 3 is a loop but loops through fewer lines, so it seems to me more

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)

Consider the following plot that shows separate regression lines ~ age for each subject in the Pothoff-Roy Orthodont data, with separate panels by Sex: library(nlme) #plot(Orthodont) xyplot(distance ~ age|Sex, data=Orthodont, type='r', groups=Subject, col=gray(.50), main="Individual linear regressions ~ age") I'd like to also show in each panel the pooled OLS

I am executing a Repeated Measures Analysis of Variance with 1 DV (LOCOMOTOR RESPONSE), 2 Within-Subjects Factors (AGE, ACOUSTIC CONDITION), and 1 Between-Subjects Factor (SEX). Does anyone know whether the between-subjects factor (SEX) belongs in the Error Term of the aov or not? And if it does belong, where in the Error Term does it go? The 3 possible scenarios are listed below: e.g., 1.

Hello, I am trying to convert a simple data.frame (it will always be a few equally long variables) into the XML format (which I don't understand too well but need as input for another program) and reverse the operation (from XML back into data.frame). I found some code which does the first and it works good enough for me (see below). Is there an easy way to reverse the operation? My XML files

Dear All, using the example from the help of summary.rms library(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) label(age) <- 'Age'

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e

> datafilename="E:/my documents/r/sex/bysex1.csv" > data.sex=read.table(datafilename,header=T) > data.sex y.sex.age.region.c.n 1 1980,F,A,N,-18.15,13.61 2 1980,F,A,N,-18.61,13.04 3 1980,F,A,N,-18.81,12.32 4 1990,F,A,N,-21.12,11.7 5 1990,F,A,N,-20.77,11.58 6 1990,F,A,N,-21.6,13.34 7 1990,F,A,N,-21.78,12.6 > model.anova<-aov(c~age*sex,data=data.sex)

Hi, just making some experiments with design library i get an error if i want plot(fit) - show below from onlineHelp !? ..perhaps is here another mask problem?, but label from xtable which was my first problem is now off ! Thanks for advance & regards, Christian $ n <- 1000 # define sample size $ set.seed(17) # so can reproduce the results $ age <- rnorm(n, 50, 10)

Hello, I am trying to plot age distribution data for a certain condition that runs in families. Below is a simplified view of the dataset, i.e. in this case there are four families, each line corresponding to one individual with age at diagnosis and sex. > famdata family age sex 1 fam1 2.1 f 2 fam1 2.3 f 3 fam1 1.0 m 4 fam2 7.3 f 5 fam2 4.1 f 6 fam2 1.2 f 7

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

For adjusted survival curves I took the sample code from here: https://rpubs.com/daspringate/survival and adapted for my date, but ... have a QUESTION. library(survival) library(survminer) df<-read.csv("base.csv", header = TRUE, sep = ";") head(df) ID start stop censor sex age stage treatment 1 1 0 66 0 2 1 3 1 2 2 0 18 0 1 2 4 2 3 3 0 43 1 2 3 3 1 4 4 0 47 1 2 3 NA 2 5 5

Dear R users, I noticed a problem in the anova command when applied on a single coxph object if there are missing observations in the data: This example code was run on R-2.6.1: > library(survival) > data(colon) > colondeath = colon[colon$etype==2, ] > m = coxph(Surv(time, status) ~ rx + sex + age + perfor, data=colondeath) > m Call: coxph(formula = Surv(time, status) ~ rx +

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively