This is not very newbee friendly and if included into a model-call nearly ufnreadable.

The function in the code box cut.at.n.tile() does the job.


cut.at.n.tile <- function(X , n = 4){ cut( X , breaks = quantile( X , probs = (0:n)/n , na.rm = TRUE ) , include.lowest = TRUE )}


In order to cut the continuous variable Creatinine in the dataset Patients into deciles (n=10) the syntax is:
cut.at.n.tile( Patients$Creatinine , n = 10 )

No big deal, but maybe useful…

• plot titles

  title( expression(...))

• axis anotations

  plot( ... , xlab = expression(...))

  or

• the plot panel itself

  text( x , y , expression(...))

An example:

plot( 0 , 0 , type = "n" , xlab = expression( "Nothing" * (mu*mol/l)) )
text( 0 , 0 , expression(beta>=0.2))
title( expression( "Only an expression() demo " * theta^(2*pi)))


It is even possible to update the plot anotation from a variable (found on the R forum)

[R] Expression in plot text

Roger Koenker roger at ysidro.econ.uiuc.edu
Wed Dec 6 20:22:05 CET 2000

expression(paste(hat(theta),'= ',that)))

We’ve been here before. To get an expression either use parse on your
pasted character string or substitute on an expression. There are worked
examples in the list achives. The neatest is

title(substitute(hat(theta) == that, list(that=that)))

(note it is == not =)

Violin plots are essentially pretty versions of box plots, where the width is set by the local density. For skewed distributions, you get things that look a bit like “violins”, hence the name.

The catch is that we need to install and load the package UsingR to get the violin plot function. [...]
library(UsingR)
simple.violinplot(Age ~ Gender, data=Santa, col = "red")

The covariates can be enclosed in other funtions:

• factors should be enclosed by factor()
• strata, which allow to adjust for a factor without getting an estimate, should be enclosed by strata()
• non-log linear continuous terms can be enclosed by
  pspline()

In the latter case the model might look like
model <- coxph (SurvivalObject ~ pspline(Covariate1) + Covariate2 + factor(Factor1) + strata(Factor2) , data = Dataset )

The functional form of the covariates (including the factors) can now be plotted with
termplot(model)

Though the termplot() function fails with plotting just one covariate and leaves no cusomization.

The function plotHR() plots the functional form of the desired term: plotHR(model)
plots the first term in the model by default but other terms can be accessed by calling their number (e.g. the second one):
plotHR(model , terms = 2)

In order to use the function you have to “source” it into R. It is the same procedure as calling a package, but using “source” instead of “library”.

Paste the function syntax into a textfile and safe it (as plot.HR.R) on your harddisk, remember the path and include
source("C:Path/to/plotHR_0.6.R")
before using the function.

Download plotHR()

Note: I have rewritten the function several times since I wrote the initial post … using version numbers now …

• V0.6 – removed the y.log option, since the scale should be logaritmic anyway. Later I will also rewrite the x.log option, since the feature is already incorporated in the plot.default() function. I also removed the dottet line at HR=1 level, since some complained about it overstating the importance of the log(HR) intercept. I included it, since it gives a hint about the significance of the smooth term, in case the confidence intervalls cross over the line… Those who miss it can add manually lines( h = 0 , type = 2 )

  I rewrote the “rugs” option. Try rugs = "density" It is still “beta”ish, but some like it.

• V0.5 – bug fix for the y-scale and slight adjustment of the default plotting colors (paler CI shade and stronger term-line)
• V0.4 – the y-scale should be logarithmic; a HR of 0.5 (50% reduced Hazard) should show the same distance from HR = 1 as a doubled Hazard (HR = 2); this is now default. The linear scale I used initially is biased in this concern (Hat-tip: Arve Ulvik, Eva Pedersen and Roy Nilsen). The option y.log allows both ways (linear and log-scale); the axis labels denote Hazard Ratio instead of log(HR).

Usage:
plotHR( model , terms = 1 , se = TRUE , rug = "ticks" , x.log = FALSE , xlab = "" , ylab = "Hazard Ratio" , main = NULL , xlim = NULL , ylim = NULL, col.term = "#08519C", lwd.term = 3, col.se = "#DEEBF7", cex = 1 , bty = "n" , axes = TRUE )

• model – a coxph model
• terms – integer; the number of the term to plot
• se – logical TRUE/FALSE; plotting the CI
• rug – “ticks” or “density”; rug plot or density plot at x-axis. Any other value for “rug” will omit the rugplot.
• x.log – logical TRUE/FALSE; log-transformed exposure variable
• xlab – character; x-axis label
• ylab – character; y-axis label
• main – character; main plot title
• xlim – 2×1 column vector; x-range of plot
• ylim – 2×1 column vector; y-range of plot
• col.term – color of HR-curve
• lwd.term – line width of HR-curve
• col.se – color of CI (if plotted)
• cex – numeric; size factor of labels
• bty – specifies the boxtype around the plot. See ?plot.default

]]> http://rforge.org/2009/10/30/plot-function-for-additive-cox-proportional-hazard-regression/feed/ 13 rforge Exampleplot of Additive Proportional Hazard Model http://rforge.org/2009/08/21/matrix-operations-in-r/ http://rforge.org/2009/08/21/matrix-operations-in-r/#comments Fri, 21 Aug 2009 14:13:37 +0000 rforge http://rforge.wordpress.com/?p=420 ]]> R can do all kinds of matrix calculations, like multiplication, tranposing and calculating the inverse. The following manual was created by Phil Ender.

Note: R wants the data to be entered by columns starting with column one.

The Matrix

# the matrix function
# 1st arg: c(2,3,-2,1,2,2) the values of the elements filling the columns
# 2nd arg: 3 the number of rows
# 3rd arg: 2 the number of columns

> A <- matrix(c(2,3,-2,1,2,2),3,2)
> A

[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]   -2    2

Is Something a Matrix

> is.matrix(A)

[1] TRUE

> is.vector(A)

[1] FALSE

Multiplication by a Scalar

> c <- 3
> c*A

[,1] [,2]
[1,]    6    3
[2,]    9    6
[3,]   -6    6

Matrix Addition & Subtraction

> B <- matrix(c(1,4,-2,1,2,1),3,2)
> B

[,1] [,2]
[1,]    1    1
[2,]    4    2
[3,]   -2    1

> C <- A + B
> C

[,1] [,2]
[1,]    3    2
[2,]    7    4
[3,]   -4    3

> D <- A - B
> D

[,1] [,2]
[1,]    1    0
[2,]   -1    0
[3,]    0    1

Matrix Multiplication

> D <- matrix(c(2,-2,1,2,3,1),2,3)
> D

[,1] [,2] [,3]
[1,]    2    1    3
[2,]   -2    2    1

> C <- D %*% A
> C

[,1] [,2]
[1,]    1   10
[2,]    0    4

> C <- A %*% D
> C

[,1] [,2] [,3]
[1,]    2    4    7
[2,]    2    7   11
[3,]   -8    2   -4

> D <- matrix(c(2,1,3),1,3)
> D

[,1] [,2] [,3]
[1,]    2    1    3

> C <- D %*% A
> C

[,1] [,2]
[1,]    1   10

> C <- A %*% D

Error in A %*% D : non-conformable arguments

Transpose of a Matrix

> AT <- t(A)
> AT

[,1] [,2] [,3]
[1,]    2    3   -2
[2,]    1    2    2

> ATT <- t(AT)
>ATT

[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]   -2    2

Common Vectors

Unit Vector

> U <- matrix(1,3,1)
> U

[,1]
[1,]    1
[2,]    1
[3,]    1

Common Matrices

Unit Matrix

Using Stata

> U <- matrix(1,3,2)
> U

[,1] [,2]
[1,]    1    1
[2,]    1    1
[3,]    1    1

Diagonal Matrix

> S <- matrix(c(2,3,-2,1,2,2,4,2,3),3,3)
> S

[,1] [,2] [,3]
[1,]    2    1    4
[2,]    3    2    2
[3,]   -2    2    3

> D <- diag(S)
> D

[1] 2 2 3

> D <- diag(diag(S))
> D

[,1] [,2] [,3]
[1,]    2    0    0
[2,]    0    2    0
[3,]    0    0    3

Identity Matrix

> I <- diag(c(1,1,1))
> I

[,1] [,2] [,3]
[1,]    1    0    0
[2,]    0    1    0
[3,]    0    0    1

Symmetric Matrix

> C <- matrix(c(2,1,5,1,3,4,5,4,-2),3,3)
> C

[,1] [,2] [,3]
[1,]    2    1    5
[2,]    1    3    4
[3,]    5    4   -2

> CT <- t(C)
> CT

[,1] [,2] [,3]
[1,]    2    1    5
[2,]    1    3    4
[3,]    5    4   -2

Inverse of a Matrix

> A <- matrix(c(4,4,-2,2,6,2,2,8,4),3,3)
> A

[,1] [,2] [,3]
[1,]    4    2    2
[2,]    4    6    8
[3,]   -2    2    4

> # using MASS package

> library(MASS)

> AI <- ginv(A)
> AI

[,1] [,2] [,3]
[1,]  1.0 -0.5  0.5
[2,] -4.0  2.5 -3.0
[3,]  2.5 -1.5  2.0

> # using car package

> library(car)

> AI <- inv(A)
> AI

[,1] [,2] [,3]
[1,]  1.0 -0.5  0.5
[2,] -4.0  2.5 -3.0
[3,]  2.5 -1.5  2.0

> A %*% AI

[,1] [,2] [,3]
[1,]    1    0    0
[2,]    0    1    0
[3,]    0    0    1

> AI %*% A

[,1] [,2] [,3]
[1,]    1    0    0
[2,]    0    1    0
[3,]    0    0    1

Inverse & Determinant of a Matrix

> C <- matrix(c(2,1,6,1,3,4,6,4,-2),3,3)
> C

[,1] [,2] [,3]
[1,]    2    1    6
[2,]    1    3    4
[3,]    6    4   -2

> CI <- inv(C)
CI

[,1]        [,2]        [,3]
[1,]  0.2156863 -0.25490196  0.13725490
[2,] -0.2549020  0.39215686  0.01960784
[3,]  0.1372549  0.01960784 -0.04901961

> d <- det(C)
> d

[1] -102

Number of Rows & Columns

> X <- matrix(c(3,2,4,3,2,-2,6,1),4,2)
> X

[,1] [,2]
[1,]    3    2
[2,]    2   -2
[3,]    4    6
[4,]    3    1

> dim(X)

[1] 4 2

> r <- nrow(X)
> r

[1] 4

> c <- ncol(X)
> c

[1] 2

Computing Column & Row Sums

# note the uppercase S

> A <- matrix(c(2,3,-2,1,2,2),3,2)
> A

[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]   -2    2

> c <- colSums(A)
> c

[1] 3 5

> r <- rowSums(A)
> r

[1] 3 5 0

> a <- sum(A)
> a

[1] 8

Computing Column & Row Means

# note the uppercase M

> cm <- colMeans(A)
> cm

[1] 1.000000 1.666667

> rm <- rowMeans(A)
> rm

[1] 1.5 2.5 0.0

> m <- mean(A)
> m

[1] 1.333333

Horizontal Concatenation

> A
> A

[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]   -2    2

> B <- matrix(c(1,3,2,1,4,2),3,2)
> B

[,1] [,2]
[1,]    1    1
[2,]    3    4
[3,]    2    2

> C <- cbind(A,B)
> C

[,1] [,2] [,3] [,4]
[1,]    2    1    1    1
[2,]    3    2    3    4
[3,]   -2    2    2    2

Vertical Concatenation (Appending)

> C <- rbind(A,B)
> C

[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]   -2    2
[4,]    1    1
[5,]    3    4
[6,]    2    2


]]> http://rforge.org/2009/08/21/matrix-operations-in-r/feed/ 0 rforge http://rforge.org/2009/08/18/densityplot-variations/ http://rforge.org/2009/08/18/densityplot-variations/#comments Tue, 18 Aug 2009 08:31:13 +0000 rforge http://rforge.wordpress.com/?p=395 ]]> Variation I


Densityplot with filled area, quartiles and mean

Just playing around the other day to get the default plot.density() function a bit more like publishing quality. Above is my favorite so far. Further down are two more spartanic versions.

In this connection I also wrote my first function. When I get more customed to R I will package my ideas into an R library – but later…

There is the possibility to call R-functions without embedding the code into your analysis script, but I did not look that up yet, so embedding the following code into your script (at the beginning) and then using
densityplot(Dataset$Coviate)
will do the job, where Dataset and Covariate have to be replaced by the according values of course.

Now the function code:
# function densityplot
densityplot <- function(x , digits = 1 , xlab = "" , ylab = "" , main = "Density" , col = "blue"){
dens <- density(x , na.rm = T)
bins <- as.numeric(cut(dens$x , breaks = fivenum(x)))
i1 <- bins == 1 & !is.na(bins)
i2 <- bins == 2 & !is.na(bins)
i3 <- bins == 3 & !is.na(bins)
i4 <- bins == 4 & !is.na(bins)
x.p1 <- dens$x[i1]; x.p1 <- c(x.p1,max(x.p1),min(x.p1))
x.p2 <- dens$x[i2]; x.p2 <- c(x.p2,max(x.p2),min(x.p2))
x.p3 <- dens$x[i3]; x.p3 <- c(x.p3,max(x.p3),min(x.p3))
x.p4 <- dens$x[i4]; x.p4 <- c(x.p4,max(x.p4),min(x.p4))
y.p1 <- dens$y[i1]; y.p1 <- c(y.p1,0,0)
y.p2 <- dens$y[i2]; y.p2 <- c(y.p2,0,0)
y.p3 <- dens$y[i3]; y.p3 <- c(y.p3,0,0)
y.p4 <- dens$y[i4]; y.p4 <- c(y.p4,0,0)
plot(dens , type = "n" , axes = F, main = main, xlab = xlab , ylab = ylab)
polygon(x.p1,y.p1 , border = F , col = col)
polygon(x.p2,y.p2 , border = F , col = col)
polygon(x.p3,y.p3 , border = F , col = col)
polygon(x.p4,y.p4 , border = F , col = col)
axis(side = 1 , line = 0 , at = fivenum(x, na.rm = T) , label = c("Minimum","Quartile 1", "Median", "Quartile 3", "Maximum"), lwd = 0, cex.axis = 0.6)
axis(side = 1 , line = 1 , at = fivenum(x, na.rm = T))
axis(side = 1 , line = 1 , at = round(mean(x , na.rm = T) , digits = digits) , tcl = 0.4 , label = F)
axis(side = 1 , line = -1.5 , at = round(mean(x , na.rm = T) , digits = digits) , tick = F , cex.axis = 0.6)
axis(side = 1 , line = -2.0 , at = round(mean(x , na.rm = T) , digits = digits) , label = "Mean" , tick = F , cex.axis = 0.6)
}

Variation II

Some might find the colored area to much, although IMHO it puts the focus on the fact that one is looking at areas when ploting a density. But then something similar without the color fill. Not a function just a few lines of code to embed and adjust to the script:




Densityplot with quartiles and mean

… and the source…
plot(density(angio$PE_ALDER , na.rm = T), axes = F, main = "Basic densityplot", xlab = "" , ylab = "")
# Add Quartiles
axis(side = 1 , line = 1 , at = fivenum(angio$PE_ALDER, na.rm = T) , label = c("Minimum","Quartile 1", "Median", "Quartile 3", "Maximum"), lwd = 0, cex.axis = 0.6)
axis(side = 1 , line = 2 , at = fivenum(angio$PE_ALDER, na.rm = T))
abline(v = fivenum(angio$PE_ALDER, na.rm = T)[2:4] , lty = 3)
# Mean
axis(side = 1 , line = 2 , at = round(mean(angio$PE_ALDER , na.rm = T) , digits = 2) , tcl = 0.4 , label = F)
axis(side = 1 , line = -0.5 , at = round(mean(angio$PE_ALDER , na.rm = T) , digits = 2) , tick = F)
axis(side = 1 , line = -1.4 , at = round(mean(angio$PE_ALDER , na.rm = T) , digits = 2) , label = "Mean" , tick = F , cex.axis = 0.6)
abline(v = mean(angio$PE_ALDER , na.rm = T) , lty = 4)

Variation III

… finally an even more stripped down version without the mean:

which was achieved by:
plot(density(angio$PE_ALDER , na.rm = T), axes = F, main = "Basic densityplot", xlab = "Age")
abline(v = fivenum(angio$PE_ALDER, na.rm = T)[2:4] , lty = 3)
axis(side = 1 , line = -1 , at = fivenum(angio$PE_ALDER, na.rm = T) , label = c("Minimum","Quartile 1", "Median", "Quartile 3", "Maximum"), lwd = 0, cex.axis = 0.6)
axis(side = 1 , at = fivenum(angio$PE_ALDER, na.rm = T))


]]> http://rforge.org/2009/08/18/densityplot-variations/feed/ 0 rforge Densityplot3 Densityplot2 Densityplot1 http://rforge.org/2009/08/11/gam-plot-with-95-confidence-shade/ http://rforge.org/2009/08/11/gam-plot-with-95-confidence-shade/#comments Tue, 11 Aug 2009 13:11:49 +0000 rforge http://rforge.wordpress.com/?p=345 ]]>
The lightblue shade denoting the 95% pointwise confidence limits of the GAM estimate is a polygon() object in R.

Usually one would plot the GAM model with the default termplot() function and specifiy se=T to get the confidence limits as lines. I showed in a recent post how to plot the fitted GAM smooth and the confidence limits manually.

The same approach can be used to have the confidence limits as a shade. In order to achieve this:

1. Fit the model

library(mgcv)
model <- gam( MyResponse ~ MyCovariate , data = MyDataset)

2. get the estimated values of the fitted smoothing spline

fit <- predict( model , se = TRUE )$fit

3. and pointwise standard errors

se <- predict( model , se = TRUE)$se.fit

4. calculate the values of the upper and lower 95%-confidence limits

lcl <- fit - 1.96 * se
ucl <- fit + 1.96 * se

5. plot an empty coordinate system with right scaling, labels , titles and so on. I prefer to include axes = FALSE and add the axes manually. See here for an example.

plot( 0 , type = "n" , bty = "n" , xlab = "The x-axis lable" , ylab = "The y-axis lable" , main = "The Title" , xlim = c(0,5) , ylim = c(0,3) )

6. No it gets a bit tricky with sorting the coordinates in the right order. R provides a very effective way to achieve this, though it is not easily understandable at once. We create two indices on the dataset i.for and i.back which number the dataset according to the independend variable on the x-axis – forward and backward respectively:

i.for <- order( MyDataset$MyCovariate )
i.back <- order( MyDataset$MyCovariate , decreasing = TRUE )

7. The points of the shade polygon follow here first the upper confidence line (ucl) forward and then the lower confidence line backward. The coordinates of the shade-polygon are

x.polygon <- c( MyDataset$MyCovariate[i.for] , MyDataset$MyCovariate[i.back] )
y.polygon <- c( ucl[i.for] , lcl[i.back] )

8. First plot the polygon with the confidence shade – otherwise the GAM plot vanishes behind it.

polygon( x.polygon , y.polygon , col = "#A6CEE3" , border = NA )

9. now the mainplot afterwards

lines( MyDataset$MyCovariate[i.for] , fit[i.for], col = "#1F78B4" , lwd = 3 )

10. add a horizontal line marking the intercept ( the mean of the covariate) – note: univariate only. I will post a remark on this later…

abline( h = mean(MyDataset$MyCovariate) , lty = 2 )

Hope no typos. Comment if you find some.


]]> http://rforge.org/2009/08/11/gam-plot-with-95-confidence-shade/feed/ 7 rforge GAM plot with conficence "shade" and customized rugs http://rforge.org/2009/08/10/fancy-rugs-in-regression-plots/ http://rforge.org/2009/08/10/fancy-rugs-in-regression-plots/#comments Mon, 10 Aug 2009 08:44:09 +0000 rforge http://rforge.wordpress.com/?p=269 ]]>

Additive Regression Model with Customized Rug

Rugplots along the axes show the distribution of the underlying data in regression model plots. This is particulary useful in connection with additive (nonparametric) models where the plotted smooth function is the exclusive representation of the model in order to assess how much data contributed to the model fit at the different values of the exlanatory variable.

The custom plot.gam() function includes the possibility of such rugs and pointwise conficence intervalls by default.

Adding quartiles to the rugs requires some customization, though. I included the complete code to produce the above plot underneath.

The example for this GAM is borrowed from the excellent book of Alain Zuur et. al. Mixed Effects Models and Extensions in Ecology with R p.55ff. The ISIT data to run the code above is included in the R package AED which can be downloaded from the books website.

Note: the package AED is needed for the example dataset only. It is NOT necessary to use the example code on ones own dataset.

The main points concerning the rugs and quantile lables on the x-axis are:

1. Plot the coordinate system without lables

plot( ... , axes = FALSE )

2. Plot the x-axis with x-lables

axis(side = 1 , line = 0.3 , at = 0:5*1000 , tick = TRUE)

3. Plot rugs – the jitter() is necessary since a lot of datapoints sit on the same values of SampleDepth – so shake them a bit.

axis(side = 1 , line = -0.9 , at = jitter(ISIT$SampleDepth) , labels = F , tick = T , tcl = 0.8 , lwd.ticks = 0.1 , lwd = 0)

4. Print the lables “1Q”, “Median” and “3Q” or whatever you like to call them on the right position. The line and padj parameter set the position of the text and cex.axis the textsize.

axis(side = 1 , line = -0.8 , at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = F , labels = c("1Q","median","3Q"), cex.axis = 0.7, col.axis = "black" , padj = -2.8)

5. Plot thick tickmarks crossing through the rug cloud at 1Q, median and 3Q

axis(side = 1 , line = -0.8 , at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = T, tcl = 1.1 , lwd.ticks = 1 , col.ticks = "black", labels = FALSE)

6. and finally short thick tickmarks under the text touching the x-axis

axis(side = 1 , line = 0.3, at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = T, tcl = 0.2 , lwd.ticks = 1 , col.ticks = "black", labels = FALSE)

Here goes the complete code:
library(mgcv)
library(AED)
data(ISIT)
#
# Fit a univariate GAM model
model <- gam(Sources ~ s(SampleDepth) , data = ISIT)
fit <- predict(model, se = T)$fit
se <- predict(model, se = T)$se.fit
lcl <- fit - 1.96 * se
ucl <- fit + 1.96 * se
#
# open a jpeg
jpeg("FancyRugs.jpg" , width=400, height=400)
#
# set plotting options: 1 plot per page, horizontal labels and textsize
par(mfrow = c(1,1) , las = 1 , cex = 1)
#
# plot coordinatesystem and labels
plot(0 , bty = "n" , type = "n" , xlim = c(0,5000) , ylim = c(-10,50) , xlab = "Depth (m)" , ylab = expression(paste("Number of sources (" , m^-3 , ")")) , axes = FALSE)
#
title(main="Association between number of sources of\nbioluminescent organisms and ocean depth" , cex.main = 0.8)
#
## _____ X-AXIS ______
# x-axis values
axis(side = 1 , line = 0.3 , at = 0:5*1000 , tick = TRUE)
#
# rugs at datapoints
axis(side = 1 , line = -0.9 , at = jitter(ISIT$SampleDepth) , labels = F , tick = T , tcl = 0.8 , lwd.ticks = 0.1 , lwd = 0)
#
# labels at 1Q, median and 3Q
axis(side = 1 , line = -0.8 , at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = F , labels = c("1Q","median","3Q"), cex.axis = 0.7, col.axis = "black" , padj = -2.8)
#
# tick marks at 1Q, median and 3Q
axis(side = 1 , line = 0.3, at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = T, tcl = 0.2 , lwd.ticks = 1 , col.ticks = "black", labels = FALSE)
#
axis(side = 1 , line = -0.8 , at = fivenum(ISIT$SampleDepth)[2:4], lwd = 0 , tick = T, tcl = 1.1 , lwd.ticks = 1 , col.ticks = "black", labels = FALSE)
#
## _____ Y-AXIS ______
# y-axis values
axis(side = 2 , at = 0:5*10)
#
# rugs at datapoints
axis(side = 2 , line = -0.9 , at = jitter(ISIT$Sources) , labels = F , tick = T , tcl = 0.8 , lwd.ticks = 0.1 , lwd = 0)
#
# labels at 1Q, median and 3Q
axis(side = 2 , line = -0.7 , at = fivenum(ISIT$Sources)[2:4], lwd = 0 , tick = F , labels = c("1Q","median","3Q"), cex.axis = 0.7, col.axis = "black")
#
# thicker tick marks at 1Q, median and 3Q
axis(side = 2 , line = 0.3, at = fivenum(ISIT$Sources)[2:4], lwd = 0 , tick = T, tcl = 0.3 , lwd.ticks = 1 , col.ticks = "black", labels = FALSE , padj = -2)
axis(side = 2 , line = -0.7 , at = fivenum(ISIT$Sources)[2:4], lwd = 0 , tick = T, tcl = 1.1 , lwd.ticks = 1 , col.ticks = "black" , labels = FALSE)
#
# horizontal line marking the intercept = mean(Sources) (for univariate model only)
abline(h=mean(ISIT$Sources), lty=3)
#
# Scatterplot
lines(ISIT$SampleDepth , ISIT$Source , type = "p" , cex = 0.4 , lwd = 0.2 , col = "grey")
#
# plot main figure
lines(ISIT$SampleDepth[order(ISIT$SampleDepth)] , fit[order(ISIT$SampleDepth)] , col = "black" , lwd = 2)
#
# plot lower confidence limit (lcl)
lines(ISIT$SampleDepth[order(ISIT$SampleDepth)] , lcl[order(ISIT$SampleDepth)] , col = "grey" , lwd = 1)
#
# plot upper confidence limit (ucl)
lines(ISIT$SampleDepth[order(ISIT$SampleDepth)] , ucl[order(ISIT$SampleDepth)] , col = "grey" , lwd = 1)
#
# closing the jpg file
dev.off()


]]> http://rforge.org/2009/08/10/fancy-rugs-in-regression-plots/feed/ 0 rforge FancyRugs http://rforge.org/2009/08/06/correlation-matrices/ http://rforge.org/2009/08/06/correlation-matrices/#comments Thu, 06 Aug 2009 14:00:43 +0000 kjetil http://rforge.wordpress.com/?p=292 ]]> Correlation matrices are a great way to visualize covariation in large datasets. Thanks to Sarkar (2008) and Friendly (2002), we can borrow these examples. First we need to load a few packages:

library(lattice)
library(ellipse)

We then go on to importing our data:

MyData <- read.table("/FILE/LOCATION/FILE.txt", header=TRUE,sep='\t',quote='')

Then on for some tedious work. We’ll have to manually type in the name (as it named i the dataset!) of each of the variables we’re interested in (unless off course you’ll want to analyze the whole shebang – which often isn’t desirable).

var <- c("var1","var2","var3")

We then proceed to make a correlation matrix with the selected variables vector “var”:

cor.MyData <- cor(MyData[,var],use="pair")

And here’s the money shot; setting the order of the variables in the matrix based on the result of a cluster analysis. This is for easier visual interpretation.

ord <- order.dendrogram(as.dendrogram(hclust(dist(cor.MyData))))

We then can make our Corrgrams. First an easy one:

print(levelplot(cor.MyData[ord,ord],xlab=NULL,ylab=NULL,
at=do.breaks(c(-1.01,1.01),101),scales=list(x=list(rot=90)),
colorkey=list(space="top"),
col.regions=colorRampPalette(c("red","white","blue"))))



Then on to the more elaborate ones, first a informative one using the ellipse package. We’ll have to write our own panel function here (again, thanks to Sarkar 2008), but this is completly generic, so cut ‘n’ paste should suffice.

panel.corrgram<-function(x,y,z,subscripts,at,
level=0.9,label=FALSE,...
{require('ellipse',quietly=TRUE)
x<-as.numeric(x)[subscripts]
y<-as.numeric(y)[subscripts]
z<-as.numeric(z)[subscripts]
zcol<-level.colors(z,at=at,...)
for(i in seq(along=z)){
ell<-ellipse(z[i],level=level,npoints=50,
scale=c(.2,.2),centre=c(x[i],y[i]))
panel.polygon(ell,col=zcol[i],border=zcol[i],...)
}
if (label)
panel.text(x=x,y=y,lab=100*round(z,2),
cex=0.8,col=ifelse(z<0,'white','black'))
}

To create a *.pdf of your output we’ll run following code (remember your working directory!):

pdf('corrgram_ellipse.pdf',10,10)
levelplot(cor.MyData[ord,ord],
at=do.breaks(c(-1.01,1.01),20),xlab=NULL,
ylab=NULL,colorkey=list(space='top'),
scales=list(x=list(rot=90)),
panel=panel.corrgram,label=TRUE)
dev.off()



However, if you’re more in the pacman thing, this next one might be a better choice. We’ll start by writing a new panel function:

panel.corrgram.2<-function(x,y,z,
subscripts,at=pretty(z),scale=0.8,...)
{
require('grid',quietly=TRUE)
x<-as.numeric(x)[subscripts]
y<-as.numeric(y)[subscripts]
z<-as.numeric(z)[subscripts]
zcol<-level.colors(z,at=at,...)
for(i in seq(along=z))
{
lims<-range(0,z[i])
tval<-2*base::pi*
seq(from=lims[1],to=lims[2],by=0.01)
grid.polygon(x=x[i]+.5*scale*c(0,sin(tval)),
y=y[i]+.5*scale*c(0,cos(tval)),
default.units='native',
gp=gpar(fill=zcol[i]))
grid.circle(x=x[i],y=y[i],r=.5*scale,
default.units='native')
}
}

Which we’ll export:

pdf('corrgram_pacman.pdf',10,10)
levelplot(cor.MyData[ord,ord],
at=do.breaks(c(-1.01,1.01),101),xlab=NULL,
ylab=NULL,colorkey=list(space='top'),
scales=list(x=list(rot=90)),
panel=panel.corrgram.2,
col.regions=colorRampPalette(c('red','white','blue')))
dev.off()



Of course, changing the output size (and type) is easy. Changing pdf –> png (or the format of your choosing). Size were in these examples 10 by 10 inches.


]]> http://rforge.org/2009/08/06/correlation-matrices/feed/ 2 kjetil Simple correlation matrix Correlation matrix - ellipse Correlation matrix - pacman http://rforge.org/2009/06/16/how2plot-nicer-gam-curves/ http://rforge.org/2009/06/16/how2plot-nicer-gam-curves/#comments Tue, 16 Jun 2009 20:35:32 +0000 rforge http://rforge.wordpress.com/?p=31 ]]> Generalized additive models are an established tool to model correlations for nonlinear covariates without too much hazzle with the form of the assosiation between predictor and response. It is a great straightforward tool, which leaves most of the work to the computer.

The default plotting method produces clean plots for all covariates in the model (or a selection) but: They do not have presentation quality by any means! To achieve this one needs customization (colors, understandable axes-labels, scaling).

This example shows a customization variant for a additive regression model with one covariate. The goal was to display the absolute value of the response variable on the y-axis and not the “difference from intercept” which is default.

This is only meaningful for a single covariate in the model.

1 The default plot.gam() method

MyGAM1<- with(MyData[MyData$Strata==1,], gam(Y ~ s(Covariate)))
MyGAM0<- with(MyData[MyData$Strata==0,], gam(Y ~ s(Covariate)))

par( mfcol=c(1,2))
plot(MyGAM0)
plot(MyGAM1)

This is the resulting plot:



Stratified Additive Regression Model

2 The fancy way

1. Extract the values of the model response from the GAM object:

response1 <- predict(MyGAM1, type="response", se.fit=T)
response0 <- predict(MyGAM0, type="response", se.fit=T)

2. Print the response values against the covariate (note: this works just with one covariate)

par(mfcol=c(1,1))

plot(0, type="n", bty="n", main="Fancy GAM plot", xlab="MyCovariate", ylab="MyResponse", lwd=3,ylim=c(0,60), xlim=c(0,200))
legend("bottomright", bty="n", lwd=5, col=c("green","red"), legend=c("Strata = 0", "Strata = 1"))

lines(sm.spline(MyGAM1$model$Covariate , response1$fit) , lwd = 3 , col = "red")
lines(sm.spline(MyGAM1$model$Covariate , response1$fit+1.96*response1$se) , lty = 3 , lwd = 2 , col = "red")
lines(sm.spline(MyGAM1$model$Covariate , response1$fit-1.96*response1$se) , lty = 3 , lwd = 2 , col = "red")

lines(sm.spline(MyGAM0$model$Covariate , response0$fit) , lwd = 3 , col = "green")
lines(sm.spline(MyGAM0$model$Covariate, response0$fit + 1.96 * response0$se) , lty = 3 , lwd = 2, col = "green")
lines(sm.spline(MyGAM0$model$Covariate, response0$fit - 1.96 * response0$se) , lty = 3 , lwd = 2 , col = "green")

abline(h=gam.dm1$coefficients[1], lty=2, lwd=1, col="red")
abline(h=gam.dm0$coefficients[1], lty=2, lwd=1, col="green")



Stratified Additive Regression Model on Response Scale


]]> http://rforge.org/2009/06/16/how2plot-nicer-gam-curves/feed/ 1 rforge plot.gam Fancy Plot