similar to: ggplot2: proper use of facet_grid inside a function

Displaying 20 results from an estimated 1000 matches similar to: "ggplot2: proper use of facet_grid inside a function"

2009 Oct 06
1
ggplot2: mapping categorical variable to color aesthetic with faceting
Hello Again... I?m making a faceted plot of a response on two categorical variables using ggplot2 and having troubles with the coloring. Here is a sample that produces the desired plot: compareCats <- function(data, res, fac1, fac2, colors) { require(ggplot2) p <- ggplot(data, aes(fac1, res)) + facet_grid(. ~ fac2) jit <- position_jitter(width = 0.1) p <- p +
2008 Sep 09
1
How do I compute interactions with anova.mlm ?
Hi, I wish to compute multivariate test statistics for a within-subjects repeated measures design with anova.mlm. This works great if I only have two factors, but I don't know how to compute interactions with more than two factors. I suspect, I have to create a new "grouping" factor and then test with this factor to get these interactions (as it is hinted in R News 2007/2), but
2002 Oct 17
1
manova with Error?
Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives
2010 Oct 13
1
interaction contrasts
hello list, i'd very much appreciate help with setting up the contrast for a 2-factorial crossed design. here is a toy example: library(multcomp) dat<-data.frame(fac1=gl(4,8,labels=LETTERS[1:4]), fac2=rep(c("I","II"),16),y=rnorm(32,1,1)) mod<-lm(y~fac1*fac2,data=dat) ## the contrasts i'm interressted in: c1<-rbind("fac2-effect in
2009 May 22
1
regrouping factor levels
Hi all, I had some trouble in?regrouping factor levels for a variable. After some experiments, I have figured out how I can recode to modify the factor levels. I would now like some help to understand why some methods work and others don't. Here's my code : rm(list=ls()) ###some trials in recoding factor levels char<-letters[1:10] fac<-factor(char) levels(fac) print(fac) ##first
2007 Oct 30
2
flexible processing
Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =
2011 Oct 03
1
function recode within sapply
Dear List, I am using function recode, from package car, within sapply, as follows: L3 <- LETTERS[1:3] (d <- data.frame(cbind(x = 1, y = 1:10), fac1 = sample(L3, 10, replace=TRUE), fac2 = sample(L3, 10, replace=TRUE), fac3 = sample(L3, 10, replace=TRUE))) str(d) d[, c("fac1", "fac2")] <- sapply(d[, c("fac1", "fac2")], recode, "c('A',
2007 Oct 17
1
passing arguments to functions within functions
Dear R Users, I am trying to write a wrapper around summarize and xYplot from Hmisc and am having trouble understanding how to pass arguments from the function I am writing to the nested functions. There must be a way, but I have not been able to figure it out. An example is below. Any advice would be greatly appreciated. Thanks, Dan # some example data df=expand.grid(rep=1:4,
2011 Jun 17
1
question about split
Dear R-users I seem to be stumped on something simple. I want to split a data frame by factor levels given in one or more columns e.g. given dat <- data.frame(x = runif(100), fac1 = rep(c("a", "b", "c", "d"), each = 25), fac2 = rep(c("A", "B"), 50)) I know I can split it by fac1, fac2 by:
2010 Nov 27
1
d.f. in F test of nested glm models
Dear all, I am fitting a glm to count data using poison errors with the log link. My goal is to test for the significance of model terms by calling the anova function on two nested models following the recommendation in Michael Crawley's guide to Statistical Computing. Without going into too much detail, essentially, I have a small overdispersion problem (errors do not fit the poisson
2002 Dec 13
1
Problem with lattice bwplot
I've come across the following error when using free scales with bwplot (I use a small example data set just to illustrate the problem): > d <- data.frame( x=c(34.4, 12.4, NA, 65.3, NA, 12.0, 45.0, 645.0, 644.0,323.0), fac1=c('a','a','b','a','b','a','a','c','c','c'),
2002 Jun 04
2
Scaling on a data.frame
Hey, hopefully there is an easy way to solve my problem. All that i think off is lengthy and clumsy. Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. Let FAC1 be something like experiment number, so that there are exactly the same number of rows for each level of FAC1 in the data.frame. Now i would like to scale all values according to the center of its experiment. So i can apply s
2001 Aug 30
1
lattice
Hello. I know that lattice is still in beta beta but. . . Nick Ellis wrote to S-news with an example of 'trellis' with several lines in each panel. df<-expand.grid(fac1=letters[1:2],x=seq(0,1,0.1),fac2=LETTERS[1:4]) df$y<-df$x*codes(df$fac1)+codes(df$fac2)*df$x^2+rnorm(nrow(df))/3 xyplot(y ~ x | fac2, groups=fac1, data=df,
2004 Mar 18
1
help with aov
Hi all, Suppose the following data and the simple model y<-1:12+rnorm(12) fac1<-c(rep("A",4),rep("B",4),rep("C",4)) fac2<-rep(c("D","C"),6) dat<-data.frame(y,fac1,fac2) tmp<-aov(y~fac1+fac2,dat) the command tmp$coeff gives the fllowing results : (Intercept) fac1B fac1C fac2D 3.307888 2.898187 7.409010
2010 Apr 21
5
Bugs? when dealing with contrasts
R version 2.10.1 (2009-12-14) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with
2004 Sep 01
1
obtaining exact p-values in mixed effects model
Hello, Using a fixed effects linear model (with lm), I can get exact p-values out of the AVOVA table, even if they are very small, eg. 1.0e-200. Using lme (linear mixed effects) from the nlme library, it appears that there is rounding of the p-values to zero, if the p-value is less than about 1.0e-16. Is there a way we can obtain the exact p-values from lme without rounding? used commands:
2008 Dec 20
1
How test contrasts/coefficients of Repeated-Measures ANOVA?
Hi all, I'm doing a Repeated-Measures ANOVA, but I don't know how to test its contrasts or where to find the p-values of its coefficients. I know how to find the coefficient estimates of a contrast, but not how to test these estimates. First I do something like: y.aov <- aov(y ~ fac1 * fac2 + Error(subj/(fac1 * fac2)), data=data) Then, with coef(y.aov) I get the coefficients
2012 Nov 29
5
bootstrapped cox regression (rms package)
Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the
2006 Apr 25
1
summary.lme: argument "adjustSigma"
Dear R-list I have a question concerning the argument "adjustSigma" in the function "lme" of the package "nlme". The help page says: "the residual standard error is multiplied by sqrt(nobs/(nobs - npar)), converting it to a REML-like estimate." Having a look into the code I found: stdFixed <- sqrt(diag(as.matrix(object$varFix))) if (object$method
2006 Feb 13
2
?bug? strange factors produced by chron
Hallo all Please help me. I am lost and do not know what is the problem. I have a factor called kvartaly. > attributes(kvartaly) $levels [1] "1Q.04" "2Q.04" "3Q.04" "4Q.04" "1Q.05" "2Q.05" "3Q.05" "4Q.05" $class [1] "factor" > mode(kvartaly) [1] "numeric" > str(kvartaly) Factor w/ 8