search for: sexes

Hello, from a software I have the following output in xml (see below): It is a series of matrices, for each age one. I have 3 categories (might vary in the application), hence, 3x3 matrices where each element gives the probability of transition from i to j. I would like read this data into R (preferably in a list, where each list element is one of the age specific matrices) and - after altering

...and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are differences between sexes and age classes (variable AGECAT2) and whether this differs with location. However, whether or not I get a significant sex:location interaction depends on which location is my reference level and I cannot understand why this is the case. It seems to be due to the fact that the standard error assoc...

Dear all, is there anyone who can help me extracting p-values from an Anova object from the car library? I can't seem to locate the p-values using str(result) or str(summary(result)) in the example below > A <- factor( rep(1:2,each=3) ) > B <- factor( rep(1:3,times=2) ) > idata <- data.frame(A,B) > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ? sex,

Hello. I asked before and it was great, cause as a beginner I learned a lot. But, if I have this in R (1 and 2 are codes for sex): > sex<-c(1,2,2,1,1,2,2,2) > sex [1] 1 2 2 1 1 2 2 2 I´d like to obtain the proportion according to sex.So I type: > prop.table(sex) [1] 0.07692308 0.15384615 0.15384615 0.07692308 0.07692308 0.15384615 0.15384615 [8] 0.15384615 The result is OK, but I

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)

anova() on three or more objects behaves inconsistently in R. In R anovalist.lm does a sequential ANOVA using pairwise F tests, ignoring all the other objects, so the larger of the two models provides the denominator. In S anova.lmlist uses the denominator from the largest model (smallest residual df) in the set, as does anova.glmlist in both. I suggest that R's anovalist.lm is wrong (that

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)))

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'm trying to do bar plot where 'sex' will be the category axis and 'occupation' will represent the bars and the clusters will represent the mean 'income'. sex occupation income 1 female j 12 2 male b 34 3 male j 22 4 female j 54 5 male b 33 6

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

Hi all: My data is in the attachment. I want to analysis the mean difference of y between 2 sex. My code: result_lm<-lm(y~factor(sex) + x1 + x2) summary(result_lm) The result of "factor(sex)m" 136.83, is the mean difference of y between 2 sex,and the corresponding p value is 0.07618. My question is: how to get the mean y of sex(m) and sex(f) respectively via lm function? Many

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Dear R community, I have data on beta&standard error (for the main effect of variable x), stratified by sex for my dataset. I wish to calculate the sex-interaction effect (as beta&se) from these two stratified datasets. Is there a package to do this? If not, any advice how to do it manually? Thank you very much and best regards, Georg. ************************ Georg Ehret, JHU,

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi all, Sent the following question yesterday, but haven't got any suggestions yet. So just trying again, can anyone comment on the problem that I have? Thank you! ------------- Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Hi All, I'm fiddling with an program to read a text file containing periods that SAS uses for missing values. I know that if I had the original SAS data set instead of a text file, R would handle this conversion for me. Data frames do not allow missing values in their indices but vectors do. Why is that? A search of the error message points out the problem and solution but not why they

Say I have a list of variables,  listVar <- list(age,sex) I am looking for a way to either 1- create a vector c("age","sex") from it, or 2- get the names one by one in a for loop such as these     a)  for (i in 1:length(listVar)) rownames(result)[i] <- ???     b)  for(i in listVar) print (variable's name) Any help much appreciated. [[alternative HTML version

Hello, I'm struggling with an elementary problem with R. I have a simple data frame such as this one giving the number of accidents subdivided by sex, age and region. sex age region no_of_accidents F young north 10 F young south 12 F old north 5 F old south 7 M young north 24 M