search for: mean_cl_norm

...t;100)*100 The following displays mean score by cohort with 95% CL, facetted by subject, and includes a (blue, dashed) reference line (using geom_abline). library(ggplot2) library(Hmisc) ggp <- ggplot(dt,aes(x=cohort, y=score)) + ylim(0,100) ggp <- ggp + stat_summary(fun.data="mean_cl_normal") ggp <- ggp + geom_abline(aes(slope=0,intercept=mean(score)),color="blue",linetype="dashed") ggp <- ggp + facet_grid(subject~.) ggp The problem is that the reference line (from geom_abline) is the same in all facets (= the grand average score for all student...

...n = mean_price - se_price)? a<-ggplot(diamonds_df, aes(x = cut, y = mean_price, fill=color)) +? ?geom_bar(position=position_stack(width=0.9))? a? Then attempting to add error bars:? a+ geom_errorbar(limits, position=position_stack(width=0.9), width=0.9)? I have tried: ?? r + stat_sum_df("mean_cl_normal", geom = "errorbar"), ?using the aggregation function in?http://www.stanford.edu/~paunesku/, using ddplyr but haven't managed to work out a way of specifying the error bars to R. ?In my case I would like to use an error bar analogous to each value of "cut" in the data...

Dear list, How can I add the mean and standard deviation to each of the boxplots using the example provided in the boxplot function? boxplot(len ~ dose, data = ToothGrowth, boxwex = 0.25, at = 1:3 - 0.2, subset = supp == "VC", col = "yellow", main = "Guinea Pigs' Tooth Growth", xlab = "Vitamin C dose mg",

..."422", "423", "424", "425", "426", "427", "428")) library(ggplot2) fishplot <- qplot(PondName,BodyWeight.g.,data=fish_ByMuestreo,colour=Muestreo,position="jitter") + stat_summary(aes(group=Muestreo),fun.data="mean_cl_normal",colour="green",geom="smooth",fill=NA) + opts(title="Average weight(grs) by Pond") print(fishplot) Felipe D. Carrillo Supervisory Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA

Hi, I'm really struggling with barplot I have a data.frame with 3 columns. The first column represents an "incident" type The second column represents a "month" The third column represents a "time" Code for a sample data.frame incidents <- rep(c('a','b','d','e'), each =25) months <- rep(c(1,2), each =10) times

Hello, I need to plot multiple confidence intervals for the same model parameter e.g. so for the same value of the parameter in point x_1 I would like to see four different confidence intervals so that I can compare the accuracy e.g. boot basic vs normal vs my own vs classic lm CI etc. I like very very much the plotCI implemented here: http://cran.r-project.org/web/packages/plotrix/index.html