search for: gray40

...ttice plot in which I want to adjust the number of tick marks used, and I want to have the drawn grid reflect that change. Here is what I'm doing: bwplot(var1 ~ var2, data=df, scales=list(tick.number=10), panel=function(...) { panel.grid(h=0,v=-1,...); panel.stripplot(col="gray40", pch="|", cex=2, ...); panel.bwplot(...); }) Unfortunately this doesn't quite work. Although the bwplot's tick marks are indeed increased as requested, the panel.grid produces the same (3 line) grid as before, seemingly unaware of the changed # of ticks. Any sugge...

...cale. Further I have tried the function yaxt="n" without any results. Any help in this matter will be appreciated. The code is posted below: par(mfrow=c(1,1)) fit <- survfit(Surv(revreg$Tcuprev , revreg$Rcup_n_j_n)~revreg$RAceGrp_1Maz) fit survplot (fit, col=c("gray1","gray40"), lty=1, lwd = 1, col.fill=c("gray50","gray80"), mark.time= T, conf="bands" , conf.int=.95, add=F , xlim=c(0, 20) , time.inc = 4, ylim=c(0.5, 1), xlab="Years Postoperative" , ylab="Cumulative Survival Pr...

...ith 8 values cars <- c(1,1,1,1,1,1,1,1) # Define colors in each sector color1 <- c("grey80","grey80","grey80","grey80","gray80","grey30","grey20","gray10" ) ## gray10=<0.0001, gray20<0.001, gray30<0.01, gray40=<0.05, gray 80=>0.05 # Putting sector levels car_labels <- c(1,2,3,4,5,6,7,8) car_labels <- paste(car_labels,sep="") # Create a pie chart with defined heading and custom colors # and labels pie(cars, main="Sectors", col=color1, labels=" ", radius=0.05,bor...

...om_text(data=niveles, aes(x=-1.4, y=inicio[3:4]+(fin[3:4]-inicio[3:4])/2, label=Niveles[3:4], size=2)) + scale_x_discrete(labels=Jurisdiction)+ scale_y_continuous(breaks=seq(245, 305, 10))+ geom_errorbar(data=tabla, aes(y=Mean, ymin=Mean - 1.96*Mean.Error, ymax = Mean + 1.96*Mean.Error),col="gray40", width=0.3, size=1) + geom_point(data=tabla, aes(y=Mean), size = 3, colour ="gray30", pch=16) + labs(x="", y="")+ theme(panel.background=element_rect(fill="white", colour="#B4CDFF"), panel.grid.minor=element_blank(), panel.grid.major.y=element...

...m_text(data=niveles, aes(x=-1.4, y=inicio[3:4]+(fin[3:4]-inicio[3:4])/2, label=Niveles[3:4], size=2)) + scale_x_discrete(labels=tabla$Country)+ scale_y_continuous(breaks=seq(245, 305, 10))+ geom_errorbar(data=tabla, aes(y=Mean, ymin=Mean - 1.96*Mean.Error, ymax = Mean + 1.96*Mean.Error),col="gray40", width=0.3, size=1) + geom_point(data=tabla, aes(y=Mean), size = 3, colour ="gray30", pch=16) + labs(x="", y="")+ theme(panel.background=element_rect(fill="white", colour="#B4CDFF"), panel.grid.minor=element_blank(), panel.grid.major.y=eleme...

I would like to organize my data as follows: I have a table that contains various data, and the numbers represent a level of similarity between these data, eg RF00013 has 100% similarity with the data RF00014. I would leave my table as a heatmap where darker colors represent higher similarity, and the lighter colors represent less level of similarity. I'm using version 2.11 of R. these

...h(medians, panel.polygon(c(x, rev(x)), c(h1, rev(h2)), col = 'gray80', border = NA, alpha = 0.75)) # Basic bar and whisker plot panel.bwplot(..., subscripts = subscripts) # Add placebo median reference with(medians, panel.lines(x, median, col = 'gray40')) }, as.table = TRUE, ylab = '', scales = list( alternating = 1, tck = c(1, 0), x = list(rot = 90), y = list(relation = 'free', rot = 0) ), # Make correct room for the strips lattice.options = list( layout.heig...

...545454", "gray34", "#575757", "gray35", "#595959", "gray36", "#5C5C5C", "gray37", "#5E5E5E", "gray38", "#616161", "gray39", "#636363", "gray40", "#666666", "gray41", "#696969", "gray42", "#6B6B6B", "gray43", "#6E6E6E", "gray44", "#707070", "gray45", "#737373", "gray46", "#757575&q...

Hi, I want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same