R help - Oct 2010 - problem with abline

Hi All,

I am running a scatter plot and trying to add a best fit line.  I use an abline
function, but get no line drawn over the points.  I also get no error.  I arm
using V 2.10.0 on Windows 7.

Here is my code, including the SAS transport file import:

require (foreign)
require (chron)
require (Hmisc)
require (lattice)

clin  <- sasxport.get("y:\\temp\\subset.xpt")
attach(clin)


plot.new()
xyplot(jitter(b.lvef)~jitter(log.fgf), 
       main="Scatter Plot of Baseline Ejection Fraction\nby Log10
FGF-23",
       ylim=c(10,90),
       ylab="Ejection Fraction", xlab="Log10 FGF-23")

abline(lm(b.lvef~log.fgf))

Any suggestions appreciated.

Thanks,

Gerard



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On Oct 6, 2010, at 5:56 PM, Gerard Smits wrote:
> Hi All,
>
> I am running a scatter plot and trying to add a best fit line.  I  
> use an abline function, but get no line drawn over the points.
xyplot is Lattice
abline is base graphics
>  I also get no error.  I arm using V 2.10.0 on Windows 7.
>
> Here is my code, including the SAS transport file import:
Nope. Read the Posting Guide about attachments to the list. Nice try,  
though.
>
> require (foreign)
> require (chron)
> require (Hmisc)
> require (lattice)
>
> clin  <- sasxport.get("y:\\temp\\subset.xpt")
> attach(clin)
>
>
> plot.new()
> xyplot(jitter(b.lvef)~jitter(log.fgf),
>       main="Scatter Plot of Baseline Ejection Fraction\nby Log10  
> FGF-23",
>       ylim=c(10,90),
>       ylab="Ejection Fraction", xlab="Log10 FGF-23")
Instead consider:
?panel.lmline

Perhaps (untested in absence of data):

plot.new()
xyplot(jitter(b.lvef)~jitter(log.fgf), panel = function(x, y) {
           panel.xyplot(x, y)
            panel.lmline(x,y)
        },
       main="Scatter Plot of Baseline Ejection Fraction\nby Log10  
FGF-23",
       ylim=c(10,90),
       ylab="Ejection Fraction", xlab="Log10 FGF-23")

>
> abline(lm(b.lvef~log.fgf))
>
> Any suggestions appreciated.
>
> Thanks,
>
> Gerard
> 	[[alternative HTML version deleted]]
>
David Winsemius, MD
West Hartford, CT

Hi:

See below.

On Wed, Oct 6, 2010 at 2:56 PM, Gerard Smits <g_smits@verizon.net> wrote:
> Hi All,
>
> I am running a scatter plot and trying to add a best fit line.  I use an
> abline function, but get no line drawn over the points.  I also get no
> error.  I arm using V 2.10.0 on Windows 7.
>
> Here is my code, including the SAS transport file import:
>
> require (foreign)
> require (chron)
> require (Hmisc)
> require (lattice)
>
> clin  <- sasxport.get("y:\\temp\\subset.xpt")
> attach(clin)
>
>
> plot.new()
> xyplot(jitter(b.lvef)~jitter(log.fgf),
>       main="Scatter Plot of Baseline Ejection Fraction\nby Log10
FGF-23",
>       ylim=c(10,90),
>       ylab="Ejection Fraction", xlab="Log10 FGF-23")
>
> abline(lm(b.lvef~log.fgf))
>
> Try this:
 xyplot(jitter(b.lvef)~jitter(log.fgf), type = c('p', 'r'),
      main="Scatter Plot of Baseline Ejection Fraction\nby Log10
FGF-23",
      ylim=c(10,90),
      ylab="Ejection Fraction", xlab="Log10 FGF-23")

Type 'p' plots the points, 'r' the least-squares regression
line. The set of
allowable types is found on p. 75 of the Lattice book.

HTH,
Dennis

Any suggestions appreciated.
>
> Thanks,
>
> Gerard
>
>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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Hi All,

I am using the package, cmprisk, to plot competing risks.  In my case, I have
four lines showing risk of going on dialysis (by a lab test [fgf-23] in
quartiles), where the 4 lower lines are for the competing risk of death.

I am trying to edit the function to just plot the upper curves (and not show the
lower (competing) risk death curves.   I am not especially facile in editing
this code, so would appreciate any help as to where I should be making changes.

Hope OK to include the function code.

Thanks.

Gerard



require(cmprsk)

#############################################################################
#                                                                           #
#                 CUMULATIVE INCIDENCE CURVES IN R                          #
#                                                                           #
# Written by Luca Scrucca                                                   #
#                                                                           #
# Reference:                                                                #
# Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis using  #
#   R: an easy guide for clinicians. Bone Marrow Transplantation, 40,       #
#   381--387.                                                               #
#############################################################################
# ver. 1.1 Feb 2008
#          - allow group to be missing
#          - if t is provided both computation and plots use t as time points
#          - allow col, lwd to be used for curves with confidence bands
#          - fix some bugs in the legend
#          - added help on source code
# ver. 1.0 May 2007
#          - Version appearing in the BMT paper
#############################################################################
#
# Usage:
# 
#   CumIncidence(ftime, fstatus, group, t, strata, rho = 0, cencode = 0,
#             	 subset, na.action = na.omit, level, 
#                xlab = "Time", ylab = "Probability", 
#                col, lty, lwd, digits = 4)
# 
# Arguments:
# 
# ftime   = failure time variable.
# fstatus = variable with distinct codes for different causes of 
#           failure and also a distinct code for censored observations.
# group	  = estimates will be calculated within groups given by distinct
#           values of this variable. Tests will compare these groups. If 
#           missing then treated as all one group (no test statistics).
# t =       a vector of time points where the cumulative incidence function 
#           should be evaluated.
# strata =  stratification variable. Has no effect on estimates. Tests 
#           will be stratified on this variable. (all data in 1 stratum,
#           if missing).
# rho =     power of the weight function used in the tests. By default is
#           set to 0.
# cencode = value of fstatus variable which indicates the failure time
#           is censored.
# subset =  a logical vector specifying a subset of cases to include in 
#           the analysis.
# na.action=a function specifying the action to take for any cases 
#           missing any of ftime, fstatus, group, strata, or subset. 
#           By default missing cases are omitted.
# level =   a value in the range [0,1] specifying the level for pointwise
#           confidence bands.
# xlab =    text for the x-axis label.
# ylab =    text for the y-axis label.
# col =     color(s) used for plotting curves (see plot.default).
# lty =     line type(s) used for plotting curves (see plot.default).
# lwd =     line width(s) used for plotting curves (see plot.default).
# digits =  number of significant digits used for printing values. By 
#           default set at 4.
# 
#############################################################################

"CumIncidence" <- function(ftime, fstatus, group, t, strata, rho =
0,
	                         cencode = 0, subset, na.action = na.omit, level,
	                         xlab = "Time", ylab =
"Probability",
	                         col, lty, lwd, digits = 4)
{
  # check for the required package
  if(!require("cmprsk"))
    { stop("Package `cmprsk' is required and must be installed.\n 
           See help(install.packages) or write the following command at prompt
           and then follow the instructions:\n
           > install.packages(\"cmprsk\")") } 
  # collect data
  mf  <- match.call(expand.dots = FALSE)
  mf[[1]] <- as.name("list")
  mf$t <- mf$digits <- mf$col <- mf$lty <- mf$lwd <- mf$level
<-
  mf$xlab <- mf$ylab <- NULL
  mf <- eval(mf, parent.frame())
  g <- max(1, length(unique(mf$group)))
  s <- length(unique(mf$fstatus))
  if(missing(t)) 
    { time <- pretty(c(0, max(mf$ftime)), 6)
      ttime <- time <- time[time < max(mf$ftime)] }
  else { ttime <- time <- t }
  # fit model and estimates at time points
  fit   <- do.call("cuminc", mf)
  tfit <- timepoints(fit, time)
  # print result
  cat("\n+", paste(rep("-", 67), collapse=""),
"+", sep ="")
  cat("\n| Cumulative incidence function estimates from competing risks
data |")
  cat("\n+", paste(rep("-", 67), collapse=""),
"+\n", sep ="")
  tests <- NULL
  if(g > 1)
    { tests <- fit$Tests
	    colnames(tests) <- c("Statistic", "p-value",
"df")
      cat("Test equality across groups:\n")
      print(tests, digits = digits) }
  cat("\nEstimates at time points:\n")
  print(tfit$est, digits = digits)
  cat("\nStandard errors:\n")
  print(sqrt(tfit$var), digits = digits)
  #
  if(missing(level))
    { # plot cumulative incidence functions
      if(missing(t))
        { time <- sort(unique(c(ftime, time)))
          x <- timepoints(fit, time) }
      else x <- tfit
      col <- if(missing(col)) rep(1:(s-1), rep(g,(s-1))) else col
      lty <- if(missing(lty)) rep(1:g, s-1) else lty
      lwd <- if(missing(lwd)) rep(1, g*(s-1)) else lwd      
      matplot(time, base::t(x$est), type="s", ylim = c(0,1), 
              xlab = xlab, ylab = ylab, xaxs="i", yaxs="i", 
              col = col, lty = lty, lwd = lwd)
      legend("topleft", legend =  rownames(x$est), x.intersp = 2, 
             bty = "n", xjust = 1, col = col, lty = lty, lwd = lwd,
cex=.75)
      out <- list(test = tests, est = tfit$est, se = sqrt(tfit$var))
    }
  else
    { if(level < 0 | level > 1) 
        error("level must be a value in the range [0,1]")
      # compute pointwise confidence intervals
      oldpar <- par(ask=TRUE)
      on.exit(par(oldpar))
      if(missing(t))
        { time <- sort(unique(c(ftime, time)))
          x <- timepoints(fit, time) }
      else x <- tfit
      z <- qnorm(1-(1-level)/2)
      lower <- x$est ^ exp(-z*sqrt(x$var)/(x$est*log(x$est)))
      upper <- x$est ^ exp(z*sqrt(x$var)/(x$est*log(x$est)))
      col <- if(missing(col)) rep(1:(s-1), rep(g,(s-1))) 
             else             rep(col, g*(s-1))
      lwd <- if(missing(lwd)) rep(1, g*(s-1)) 
             else             rep(lwd, g*(s-1))      
      # plot pointwise confidence intervals
      for(j in 1:nrow(x$est))
         { matplot(time, cbind(x$est[j,], lower[j,], upper[j,]),
type="s",
                   xlab = xlab, ylab = ylab, xaxs="i",
yaxs="i",
                   ylim = c(0,1), col = col[j], lwd = lwd[j], lty = c(1,3,3))
           legend("topleft", legend =  rownames(x$est)[j], bty =
"n", xjust = 1) }
      # print pointwise confidence intervals
      i <- match(ttime, time)
      ci <- array(NA, c(2, length(i), nrow(lower)))
      ci[1,,] <- base::t(lower[,i])
      ci[2,,] <- base::t(upper[,i])
      dimnames(ci) <- list(c("lower", "upper"), ttime,
rownames(lower))
      cat(paste("\n", level*100, "% pointwise confidence
intervals:\n\n", sep=""))
      print(ci, digits = digits)
      out <- list(test = tests, est = x$est, se = sqrt(tfit$var), ci = ci)
    }
  # return results
  invisible(out)
}




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Hi All,

I have a number of plots to run and was hoping to use a function instead of bock
copying my code and retyping my parameters.

I am using R 3.0.0 on a mac.

My code is as follows:

library (car)
cres<-read.csv("//users//smits//r_work//cres.csv", header = TRUE)
attach(cres);

run_plots <- functon()
{
  scatterplot(y~x|z,
    jitter=list(x=1, y=1),
    grid=F,
    smooth=F,
    las=1,
    pch=c(1,2),
    col=c('red','blue'),
    main="Baseline Clinical CRP by RAMRIS Synovitis at Day 113",
    xlab="Baseline CRP", 
    ylab="Day 113 Synovitis",
    legend.plot=F)
  legend("bottomright",
     box.lty=0, # line type to surround the legend box (0 for none)
     legend=c("Active+MTX","Placebo+MTX"),
     col=c('red','blue'),
     pch=c(1,2),
     pt.cex=c(1,1,1,1))
}


then I was hoping to execute the function by passing 3 parameters: 
run_plots(b_crp, t_synos113, treatment)

This approach follows the way I usually do it with a SAS macro, were I can
repeat the innovation over and over using different variables, etc.

Right off the  bat, you can see that I have a problem finding the function
"function."  Then it does not recognize the first parameter it comes
across.

Any help appreciated.

Thanks,

Gerard

>     
> cres<-read.csv("//users//smits//r_work//cres.csv", header =
TRUE)
> attach(cres);
> 
> run_plots <- functon()
Error: could not find function "functon"
> {
+   scatterplot(y~x|z,
+     jitter=list(x=1, y=1),
+     grid=F,
+     smooth=F,
+     las=1,
+     pch=c(1,2),
+     col=c('red','blue'),
+     main="Baseline Clinical CRP by RAMRIS Synovitis at Day 113",
+     xlab="Baseline CRP", 
+     ylab="Day 113 Synovitis",
+     legend.plot=F)
+   legend("bottomright",
+      box.lty=0, # line type to surround the legend box (0 for none)
+      legend=c("Active+MTX","Placebo+MTX"),
+      col=c('red','blue'),
+      pch=c(1,2),
+      pt.cex=c(1,1,1,1))
+ }
Error in eval(expr, envir, enclos) : object 'y' not
found
>