consider the included example in the np-package for r,
page 21 of the Vignettes for np package.
npcdens returns a conditional density object and is able to plot 2d-pdf and 2d-cdf, as shown. I wanted to know if I can somehow extract the 1-D information (pdf / cdf) from the object if I were to specify one of the two parameters, like in a vector or something ?? I am new to R and was not able to find out the format of the object.
Thanks for the help.
-Egon.
Here is the code as requested:
require(np)
data("Italy")
attach(Italy)
bw <- npcdensbw(formula=gdp~ordered(year), tol=.1, ftol=.1)
fhat <- npcdens(bws=bw)
summary(fhat)
npplot(bws=bw)
npplot(bws=bw, cdf=TRUE)
detach(Italy)
The fhat object contains all the needed info plus a whole lot more. To see what all is in there, do a str( fhat ) to see the structure.
I believe the values you are interested in are xeval, yeval, and condens (PDF density).
There are lots of ways to get at the values but I tend to like data frames. I'd pop the three vectors in a single data frame:
denDf <- cbind( year=as.character( fhat$xeval[,1] ), fhat$yeval, fhat$condens )
## had to do a dance around the year variable because it's a factor
then I'd select the values I want with a subset():
subset( denDf, year==1951 & gdp > 8 & gdp < 8.2)
since gdp is a floating point value it's very hard to select with a == operator.
The method suggested by JD Long will only extract density for data points in the existing training set. If you want the density at other points (conditioning or conditional variables) you will need to use the predict()
function. The following code extracts and plots the 1-D density distribution conditioned on year ==1999, a value not contained in the original data set.
First construct a data frame with the same components as the Italy data set, with gdp regularly spaced and with "1999" an ordered factor.
yr1999<- rep("1999", 100)
gdpVals <-seq(1,35, length.out=100)
nD1999 <- data.frame(year = ordered(yr1999), gdp = gdpVals)
Next use the predict function to extract the densities.
gdpDens1999 <-predict(fhat,newdata = nD1999)
The following code plots the density.
plot(gdpVals, gdpDens1999, type='l', col='red', xlab='gdp', ylab = 'p(gdp|yr = 1999)')
Related
I am using the isomap-function from vegan package in R to analyse community data of epiphytic mosses and lichens. I started analysing the data using NMDS but due to the structure of the data ran into problems which is why I switched to ISOMAP which works perfectly well and returns very nice results. So far so good... However, the output of the function does not support plotting of species within the ISOMAP plot as species scores are not available. Anyway, I would really like to add species information to enhance the interpretability of the output.
Does anyone of you has a solution or hint to this problem? Is there a way to add species kind of post hoc to the plot as it can be done with environmental data?
I would greatly appreciate any help on this topic!
Thank you and best regards,
Inga
No, there is no function to add species scores to isomap. It would look like this:
`sppscores<-.isomap` <-
function(object, value)
{
value <- scale(value, center = TRUE, scale = FALSE)
v <- crossprod(value, object$points)
attr(v, "data") <- deparse(substitute(value))
object$species <- v
object
}
Or alternatively:
`sppscores<-.isomap` <-
function(object, value)
{
wa <- vegan::wascores(object$points, value, expand = TRUE)
attr(wa, "data") <- deparse(substitute(value))
object$species <- wa
object
}
If ord is your isomap result and comm are your community data, you can use these as:
sppscores(ord) <- comm # either alternative
I have no idea (yet) which of these alternatives is more correct. The first adds species scores as vectors of their linear increase, the second as their weighted averages in ordination space, but expanded so that we allow some species be more extreme than the site units where they occur.
These will add new element species to the result object ord. However, using these in vegan would need more coding, but you can extract the species scores with vegan::scores, but their scaling is based on the original scale of community data, and may be badly scaled with respect to points of site units, and working on this would require more work. However, you can plot them separately, or then multiply with a constant giving similar scaling as site unit scores.
sp <- scores(ord, display="species", choices=1:2)
plot(sp, type = "n", asp = 1) # does not allow plotting text
text(sp, labels = rownames(sp)) # so we must add text
I have some experience in using PCA, but this is the first time I am attempting to use PCA for spectral data...
I have a large data with spectra where I used prcomp command to calculated PCA for the whole dataset. My results show that 3 components explain 99% of the variance.
I would like to plot the contribution of each of the three PCA components at every wavelength (in steps of 4, 200-1000 nm) like the example of a plot 2 I found on this site:
https://learnche.org/pid/latent-variable-modelling/principal-component-analysis/pca-example-analysis-of-spectral-data
Does anyone have a code how I could do this in R?
Thank you
I believe the matrix of variable loadings is found in model.pca$rotation, see prcomp documentation.
So something like this should do (using the example on your linked website):
file <- 'http://openmv.net/file/tablet-spectra.csv'
spectra <- read.csv(file, header = FALSE)
n.comp <- 4
model.pca <- prcomp(spectra[,2:651],
center = TRUE,
scale =TRUE,
rank. = n.comp)
summary(model.pca)
par(mfrow=c(n.comp,1))
sapply(1:n.comp, function(comp){
plot(2:651, model.pca$rotation[,comp], type='l', lwd=2,
main=paste("Comp.", comp), xlab="Wavelength INDEX")
})
I don't have the wavelength values, so I used the indices of the array here ; output below.
I want to perform a PCA analysis in adegenet starting from a genepop file without defined populations.
I imported the data like this:
datapop <- read.genepop('tous.gen', ncode=3, quiet = FALSE)
it works, and I can perform a PCA after scaling the data.
But I would like to plot the results / individuals on the PCA axis according to their population of origin using s.class. I have a vcf file with a three lettre code for each individual. I imported it in R:
pops_list <- read.csv('liste_pops.csv', header=FALSE)
but now how can I use it to define population levels in the genind object datapop?
I tried something likes this:
setPop(datapop, formula = NULL)
setPop(datapop) <- pops_list
but it doesn't work; even the first line doesn't work: I get this message:
"Erreur : formula must be a valid formula object."
And then how should I use it in s.class?
thanks
Didier
Without a working example it is kind of hard to tell but perhaps you can find the solution to your problem here: How to add strata information to a genind
Either way from your examples and given how the setPop method works, your line setPop(datapop, formula = NULL) would not work because you would not be defining anything. You would actually have to do:
setPop(datapop) <- pops_list
while also guaranteeing that pops_list is a factor with the appropriate format
I know this is a bit late, but the way to do this is to add pops_list as the strata and then use setPop() to select a certain column:
strata(datapop) <- pops_list
setPop(datapop) <- ~myPop # set the population to the column called "myPop" in the data frame
Is there are any R package that can produce cube plots for 2 factors? I want something similar to the first plot at the end of this page
http://www.processma.com/resource/factorial_plots.htm
It is possible to obtain such plots in Minitab.
In the package FrF2 there is the command cubeplot but only for 3 factors.
Of course I can use 2 identical factors, but want images with nice squares(instead of cubes).
You can use cubePlot from FrF2 package. It produces a cube plot for the combined effect of three factors. Here an example :
data(BM93.e3.data) #from BsMD
iMdat <- BM93.e3.data[1:16,2:10] #only original experiment
colnames(iMdat) <- c("MoldTemp","Moisture","HoldPress","CavityThick","BoostPress",
"CycleTime","GateSize","ScrewSpeed", "y")
iM.lm <- lm(y ~ (.)^2, data = iMdat)
cubePlot(iM.lm, "MoldTemp", "HoldPress", "BoostPress")
I use the following R code to generate a dendrogram (see attached picture) with labels based on TraMineR sequences:
library(TraMineR)
library(cluster)
clusterward <- agnes(twitter.om, diss = TRUE, method = "ward")
plot(clusterward, which.plots = 2, labels=colnames(twitter_sequences))
The full code (including dataset) can be found here.
As informative as the dendrogram is graphically, it would be handy to get the same information in text and/or table format. If I call any of the aspects of the object clusterward (created by agnes), such as "order" or "merge" I get everything labeled using numbers rather than the names I get from colnames(twitter_sequences). Also, I don't see how I can output the groupings represented graphically in the dendrogram.
To summarize: How can I get the cluster output in text/table format with the labels properly displayed using R and ideally the traminer/cluster libraries?
The question concerns the cluster package. The help page for the agnes.object returned by agnes
(See http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/agnes.object.html ) states that this object contains an order.lab component "similar to order, but containing observation labels instead of observation numbers. This component is only available if the original observations were labelled."
The dissimilarity matrix (twitter.om in your case) produced by TraMineR does currently not retain the sequence labels as row and column names. To get the order.lab component you have to manually assign sequence labels as both the rownames and colnames of your twitter.om matrix. I illustrate here with the mvad data provided by the TraMineR package.
library(TraMineR)
data(mvad)
## attaching row labels
rownames(mvad) <- paste("seq",rownames(mvad),sep="")
mvad.seq <- seqdef(mvad[17:86])
## computing the dissimilarity matrix
dist.om <- seqdist(mvad.seq, method = "OM", indel = 1, sm = "TRATE")
## assigning row and column labels
rownames(dist.om) <- rownames(mvad)
colnames(dist.om) <- rownames(mvad)
dist.om[1:6,1:6]
## Hierarchical cluster with agnes library(cluster)
cward <- agnes(dist.om, diss = TRUE, method = "ward")
## here we can see that cward has an order.lab component
attributes(cward)
That is for getting order with sequence labels rather than numbers. But now it is not clear to me which cluster outcome you want in text/table form. From the dendrogram you decide of where you want to cut it, i.e., the number of groups you want and cut the dendrogram with cutree, e.g. cl.4 <- cutree(clusterward1, k = 4). The result cl.4 is a vector with the cluster membership for each sequence and you get the list of the members of group 1, for example, with rownames(mvad.seq)[cl.4==1].
Alternatively, you can use the identify method (see ?identify.hclust) to select the groups interactively from the plot, but need to pass the argument as as.hclust(cward). Here is the code for the example
## plot the dendrogram
plot(cward, which.plot = 2, labels=FALSE)
## and select the groups manually from the plot
x <- identify(as.hclust(cward)) ## Terminate with second mouse button
## number of groups selected
length(x)
## list of members of the first group
x[[1]]
Hope this helps.