Error with plot function object not found - r
When I try and generate a plot with the below I receive an Object not found for the fertility rate.
I've used str and names to make sure I've got the name correct.
there is a file ("https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P2-Section5-Homework-Data.csv) that I have to use also.
From what I can see everything should run ok. But I've obviously done something wrong as I get:
Error in plot(data = New_Data1960, x = Fertility.Rate, y =
Life_Expectancy_At_Birth_1960, : object 'Fertility.Rate' not found
Can someone explain why I'm getting this error and how I can fix it?
#Execute below code to generate three new vectors
Country_Code <- c("ABW","AFG","AGO","ALB","ARE","ARG","ARM","ATG","AUS","AUT","AZE","BDI","BEL","BEN","BFA","BGD","BGR","BHR","BHS","BIH","BLR","BLZ","BOL","BRA","BRB","BRN","BTN","BWA","CAF","CAN","CHE","CHL","CHN","CIV","CMR","COG","COL","COM","CPV","CRI","CUB","CYP","CZE","DEU","DJI","DNK","DOM","DZA","ECU","EGY","ERI","ESP","EST","ETH","FIN","FJI","FRA","FSM","GAB","GBR","GEO","GHA","GIN","GMB","GNB","GNQ","GRC","GRD","GTM","GUM","GUY","HKG","HND","HRV","HTI","HUN","IDN","IND","IRL","IRN","IRQ","ISL","ITA","JAM","JOR","JPN","KAZ","KEN","KGZ","KHM","KIR","KOR","KWT","LAO","LBN","LBR","LBY","LCA","LKA","LSO","LTU","LUX","LVA","MAC","MAR","MDA","MDG","MDV","MEX","MKD","MLI","MLT","MMR","MNE","MNG","MOZ","MRT","MUS","MWI","MYS","NAM","NCL","NER","NGA","NIC","NLD","NOR","NPL","NZL","OMN","PAK","PAN","PER","PHL","PNG","POL","PRI","PRT","PRY","PYF","QAT","ROU","RUS","RWA","SAU","SDN","SEN","SGP","SLB","SLE","SLV","SOM","SSD","STP","SUR","SVK","SVN","SWE","SWZ","SYR","TCD","TGO","THA","TJK","TKM","TLS","TON","TTO","TUN","TUR","TZA","UGA","UKR","URY","USA","UZB","VCT","VEN","VIR","VNM","VUT","WSM","YEM","ZAF","COD","ZMB","ZWE")
Life_Expectancy_At_Birth_1960 <- c(65.5693658536586,32.328512195122,32.9848292682927,62.2543658536585,52.2432195121951,65.2155365853659,65.8634634146342,61.7827317073171,70.8170731707317,68.5856097560976,60.836243902439,41.2360487804878,69.7019512195122,37.2782682926829,34.4779024390244,45.8293170731707,69.2475609756098,52.0893658536585,62.7290487804878,60.2762195121951,67.7080975609756,59.9613658536585,42.1183170731707,54.2054634146342,60.7380487804878,62.5003658536585,32.3593658536585,50.5477317073171,36.4826341463415,71.1331707317073,71.3134146341463,57.4582926829268,43.4658048780488,36.8724146341463,41.523756097561,48.5816341463415,56.716756097561,41.4424390243903,48.8564146341463,60.5761951219512,63.9046585365854,69.5939268292683,70.3487804878049,69.3129512195122,44.0212682926829,72.1765853658537,51.8452682926829,46.1351219512195,53.215,48.0137073170732,37.3629024390244,69.1092682926829,67.9059756097561,38.4057073170732,68.819756097561,55.9584878048781,69.8682926829268,57.5865853658537,39.5701219512195,71.1268292682927,63.4318536585366,45.8314634146342,34.8863902439024,32.0422195121951,37.8404390243902,36.7330487804878,68.1639024390244,59.8159268292683,45.5316341463415,61.2263414634146,60.2787317073171,66.9997073170732,46.2883170731707,64.6086585365854,42.1000975609756,68.0031707317073,48.6403170731707,41.1719512195122,69.691756097561,44.945512195122,48.0306829268293,73.4286585365854,69.1239024390244,64.1918292682927,52.6852682926829,67.6660975609756,58.3675853658537,46.3624146341463,56.1280731707317,41.2320243902439,49.2159756097561,53.0013170731707,60.3479512195122,43.2044634146342,63.2801219512195,34.7831707317073,42.6411951219512,57.303756097561,59.7471463414634,46.5107073170732,69.8473170731707,68.4463902439024,69.7868292682927,64.6609268292683,48.4466341463415,61.8127804878049,39.9746829268293,37.2686341463415,57.0656341463415,60.6228048780488,28.2116097560976,67.6017804878049,42.7363902439024,63.7056097560976,48.3688048780488,35.0037073170732,43.4830975609756,58.7452195121951,37.7736341463415,59.4753414634146,46.8803902439024,58.6390243902439,35.5150487804878,37.1829512195122,46.9988292682927,73.3926829268293,73.549756097561,35.1708292682927,71.2365853658537,42.6670731707317,45.2904634146342,60.8817073170732,47.6915853658537,57.8119268292683,38.462243902439,67.6804878048781,68.7196097560976,62.8089268292683,63.7937073170732,56.3570487804878,61.2060731707317,65.6424390243903,66.0552926829268,42.2492926829268,45.6662682926829,48.1876341463415,38.206,65.6598292682927,49.3817073170732,30.3315365853659,49.9479268292683,36.9658780487805,31.6767073170732,50.4513658536585,59.6801219512195,69.9759268292683,68.9780487804878,73.0056097560976,44.2337804878049,52.768243902439,38.0161219512195,40.2728292682927,54.6993170731707,56.1535365853659,54.4586829268293,33.7271219512195,61.3645365853659,62.6575853658537,42.009756097561,45.3844146341463,43.6538780487805,43.9835609756098,68.2995365853659,67.8963902439025,69.7707317073171,58.8855365853659,57.7238780487805,59.2851219512195,63.7302195121951,59.0670243902439,46.4874878048781,49.969512195122,34.3638048780488,49.0362926829268,41.0180487804878,45.1098048780488,51.5424634146342)
Life_Expectancy_At_Birth_2013 <- c(75.3286585365854,60.0282682926829,51.8661707317073,77.537243902439,77.1956341463415,75.9860975609756,74.5613658536585,75.7786585365854,82.1975609756098,80.890243902439,70.6931463414634,56.2516097560976,80.3853658536585,59.3120243902439,58.2406341463415,71.245243902439,74.4658536585366,76.5459512195122,75.0735365853659,76.2769268292683,72.4707317073171,69.9820487804878,67.9134390243903,74.1224390243903,75.3339512195122,78.5466585365854,69.1029268292683,64.3608048780488,49.8798780487805,81.4011219512195,82.7487804878049,81.1979268292683,75.3530243902439,51.2084634146342,55.0418048780488,61.6663902439024,73.8097317073171,62.9321707317073,72.9723658536585,79.2252195121951,79.2563902439025,79.9497804878049,78.2780487804878,81.0439024390244,61.6864634146342,80.3024390243903,73.3199024390244,74.5689512195122,75.648512195122,70.9257804878049,63.1778780487805,82.4268292682927,76.4243902439025,63.4421951219512,80.8317073170732,69.9179268292683,81.9682926829268,68.9733902439024,63.8435853658537,80.9560975609756,74.079512195122,61.1420731707317,58.216487804878,59.9992682926829,54.8384146341464,57.2908292682927,80.6341463414634,73.1935609756098,71.4863902439024,78.872512195122,66.3100243902439,83.8317073170732,72.9428536585366,77.1268292682927,62.4011463414634,75.2682926829268,68.7046097560976,67.6604146341463,81.0439024390244,75.1259756097561,69.4716829268293,83.1170731707317,82.290243902439,73.4689268292683,73.9014146341463,83.3319512195122,70.45,60.9537804878049,70.2024390243902,67.7720487804878,65.7665853658537,81.459756097561,74.462756097561,65.687243902439,80.1288780487805,60.5203902439024,71.6576829268293,74.9127073170732,74.2402926829268,49.3314634146342,74.1634146341464,81.7975609756098,73.9804878048781,80.3391463414634,73.7090487804878,68.811512195122,64.6739024390244,76.6026097560976,76.5326585365854,75.1870487804878,57.5351951219512,80.7463414634146,65.6540975609756,74.7583658536585,69.0618048780488,54.641512195122,62.8027073170732,74.46,61.466,74.567512195122,64.3438780487805,77.1219512195122,60.8281463414634,52.4421463414634,74.514756097561,81.1048780487805,81.4512195121951,69.222,81.4073170731707,76.8410487804878,65.9636829268293,77.4192195121951,74.2838536585366,68.1315609756097,62.4491707317073,76.8487804878049,78.7111951219512,80.3731707317073,72.7991707317073,76.3340731707317,78.4184878048781,74.4634146341463,71.0731707317073,63.3948292682927,74.1776341463415,63.1670487804878,65.878756097561,82.3463414634146,67.7189268292683,50.3631219512195,72.4981463414634,55.0230243902439,55.2209024390244,66.259512195122,70.99,76.2609756097561,80.2780487804878,81.7048780487805,48.9379268292683,74.7157804878049,51.1914878048781,59.1323658536585,74.2469268292683,69.4001707317073,65.4565609756098,67.5223658536585,72.6403414634147,70.3052926829268,73.6463414634147,75.1759512195122,64.2918292682927,57.7676829268293,71.159512195122,76.8361951219512,78.8414634146341,68.2275853658537,72.8108780487805,74.0744146341464,79.6243902439024,75.756487804878,71.669243902439,73.2503902439024,63.583512195122,56.7365853658537,58.2719268292683,59.2373658536585,55.633)
#(c) Kirill Eremenko, www.superdatascience.com
P2.Section5.Homework.Data <- read.csv("H:/Program Files/RStudio/P2-Section5-Homework-Data.csv")
View(P2.Section5.Homework.Data)
head(P2.Section5.Homework.Data)
#generate a data.frame from loaded values
yr1960 <- data.frame(Country_Code, Life_Expectancy_At_Birth_1960)
yr2013 <- data.frame(Country_Code, Life_Expectancy_At_Birth_2013)
#filter master data to only ncessary periods
P2_1960 <- P2.Section5.Homework.Data[P2.Section5.Homework.Data$Year == 1960,]
P2_2013 <- P2.Section5.Homework.Data[P2.Section5.Homework.Data$Year == 2013,]
head(P2_1960)
#merge data into one frame
New_Data1960 <- merge(P2_1960,yr1960, by.x = "Country.Code", by.y = "Country_Code")
head(New_Data1960)
names(New_Data1960)
New_Data2013 <- merge(P2_2013,yr2013, by.x = "Country.Code", by.y = "Country_Code")
head(New_Data1960)
#create scatter plot 1960
# plot(data=stats, x= Internet.users,y=Birth.rate,
# colour=Income.Group,size=I(4))
plot(data=New_Data1960, x= Fertility.Rate, y= Life_Expectancy_At_Birth_1960, colour = Region)
Try:
//To convert the Fertility rate to numeric for showing continuous x-scale on graph
New_Data1960$Fertility.Rate <- as.numeric(New_Data1960$Fertility.Rate)
ggplot(New_Data1960, aes(Fertility.Rate, Life_Expectancy_At_Birth_1960, group=Region, color=factor(Region))) +
geom_point(aes(color=Region))
The first line tells gives details about the plot
and the second line actually plots it into the plot.
Output is as follows:
Related
Weird characters appearing in the plot legend when using DoHeatmap
I was using Seurat to analyse single cell RNA-seq data and I managed to draw a heatmap plot with DoHeatmap() after clustering and marker selection, but got a bunch of random characters appearing in the legend. They are random characters as they will change every time you run the code. I was worrying over it's something related to my own dataset, so I then tried the test Seurat object 'ifnb' but still got the same issue (see the red oval in the example plot).
example plot
I also tried importing the Seurat object in R in the terminal (via readRDS) and ran the plotting function, but got the same issue there, so it's not a Rstudio thing.
Here are the codes I ran:
'''
library(Seurat)
library(SeuratData)
library(patchwork)
InstallData("ifnb")
LoadData("ifnb")
ifnb.list <- SplitObject(ifnb, split.by = "stim")
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
features <- SelectIntegrationFeatures(object.list = ifnb.list)
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
immune.combined <- IntegrateData(anchorset = immune.anchors)
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
DefaultAssay(immune.combined) <- 'RNA'
immune_markers <- FindAllMarkers(immune.combined, latent.vars = "stim", test.use = "MAST", assay = 'RNA')
immune_markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10_immune
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA')
'''
Does anyone have any idea how to solve this issue other than reinstalling everything?
I have been having the same issue myself and while I have solved it by not needing the legend, I think you could use this approach and use a similar solution:
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA') +
scale_color_manual(
values = my_colors,
limits = c('CTRL', 'STIM'))
Let me know if this works! It doesn't solve the source of the odd text values but it does the job! If you haven't already, I would recommend creating a forum question on the Seurat forums to see where these characters are coming from!
When I use seurat4.0, I met the same problem. While I loaded 4.1, it disappeared
Add labels in dendogram in R
I am trying to apply Hierarchical Clustering for Time Series in order to identify the states with similar behaviors in the time series for residential_percent_change_from_baseline. I get the dendrogram but the index i get in the x axis are just numbers and I want the states names.
my data looks like this:
Data
And this is some part of my code
data <- dataset
#Convert to factor
cols <- c("country_region_code", "country_region", "sub_region_1", "iso_3166_2_code")
data[cols] <- lapply(data[cols], factor)
sapply(data, class)
data$date <- as.Date(data$date)
summary(data)
#Data preparation
n <- 10
s <- sample(1:100, n)
i <- c(s,0+s, 279+s, 556+s, 833+s, 1110+s, 1387+s, 1664+s, 1941+s, 2218+s, 2495+s, 2772+s, 3049+s, 3326+s, 3603+s, 3880+s, 4157+s, 4434+s, 4711+s, 4988+s, 5265+s, 5542+s, 5819+s, 6096+s, 6373+s, 6650+s, 6927+s, 7204+s, 7481+s, 7758+s, 8035+s, 8312+s, 8589+s, 8866+s)
d <- data[i,3:4]
d$residential <- data[i,11]
d[,2] =NULL
str(d)
pattern <- c(rep('Mexico', n),
rep('Aguascalientes', n),
rep('Baja California',n),
rep('Baja California Sur',n),
rep('Campeche',n),
rep('Coahuila',n),
rep('Colima',n),
rep('Chiapas',n),
rep('Chihuahua',n),
rep('Durango',n),
rep('Guanajuato',n),
rep('Guerrero',n),
rep('Hidalgo',n),
rep('Jalisco',n),
rep('México City',n),
rep('Michoacan',n),
rep('Morelos',n),
rep('Nayarit',n),
rep('Nuevo León',n),
rep('Oaxaca',n),
rep('Puebla',n),
rep('Querétaro',n),
rep('Quintana Roo',n),
rep('San Luis Potosí',n),
rep('Sinaloa',n),
rep('Sonora',n),
rep('Tabasco',n),
rep('Tamaulipas',n),
rep('Tlaxcala',n),
rep('Veracruz',n),
rep('Yucatán',n),
rep('Zacatecas.',n))
d <- data.matrix(d)
distance <- dist(d, method = 'euclidean')
hc <- hclust(distance, method="ward.D")
plot(hc, cex=.7, hang = -1, col='blue', labels=pattern)
I get this dendrogram when I don't specify labels
dendrogram with numeric labels
But when I do I get this error
Error in graphics:::plotHclust(n1, merge, height, order(x$order), hang, : invalid dendrogram input
I hope somebody can help me, I am little bit tired of this
Maybe it will work with an alternative to the base r plot function. Try ggdendroplot. It should display the labels on the axis. You will need ggplot2 for this.
devtools::install("nicolash2/ggdendroplot")
library(ggdendroplot)
library(ggplot2)
ggplot() + geom_dendro(hc)
If you want to modify it (turn it, color it, etc.) check out the github page: https://github.com/NicolasH2/ggdendroplot
R baseline package saving plots in a loop
I'm trying to optimize the parameters for baseline in the R baseline package by changing each parameters in a loop and comparing plots to determine which parameters give me the best baseline.
I currently have the code written so that the loop produces each plot, but I'm having trouble with getting the plot saved as the class of each object I'm creating is a baseline package-specific (which I'm suspecting is the problem here).
foo <- data.frame(Date=seq.Date(as.Date("1957-01-01"), by = "day",
length.out = ncol(milk$spectra)),
Visits=milk$spectra[1,],
Old_baseline_visits=milk$spectra[1,], row.names = NULL)
foo.t <- t(foo$Visits)
#the lines above were copied from https://stackoverflow.com/questions/37346967/r-packagebaseline-application-to-sample-dataset to make a reproducible dataset
df <- expand.grid(lambda=seq(1,10,1), p=seq(0.01,0.1,0.01))
baselinediff <- list()
for(i in 1:nrow(df)){
thislambda <- df[i,]$lambda
thisp <- df[i,]$p
thisplot <- baseline(foo.t, lambda=thislambda, p=thisp, maxit=20, method='als')
print(paste0("lambda = ", thislambda))
print(paste0("p = ", thisp))
print(paste0("index = ", i))
baselinediff[[i]] <- plot(thisplot)
jpeg(file = paste(baselinediff[[i]], '.jpeg', sep = ''))
dev.off()
}
I know that I would be able to extract corrected spectra using baseline.als but I just want to save the plot images with the red baseline so that I can see how well the baselines are getting drawn. Any baseline users out there that can help?
I suggest you change your loop in the following way:
for(i in 1:nrow(df)){
thislambda <- df[i,]$lambda
thisp <- df[i,]$p
thisplot <- baseline(foo.t, lambda=thislambda, p=thisp, maxit=20, method='als')
print(paste0("lambda = ", thislambda))
print(paste0("p = ", thisp))
print(paste0("index = ", i))
baselinediff[[i]] <- thisplot
jpeg(file = paste('baseline', i, '.jpeg', sep = ''))
plot(baselinediff[[i]])
dev.off()
}
Note that this does not try to capture the already plotted element (thisplot) inside of the list. Instead, the plotting is done after you call the jpeg command. This solves your export issue. Another problem was the naming of the file. If you call baselinediff[[i]] inside of paste, you apparently end up with an error. So I switched it to a simpler name. To plot your resulting list, call:
lapply(baselinediff, plot)
If you are determined on storing the already plotted element, the capture.plotfunction from the imager package might be a good start.
Use math symbols in panel titles for stratigraphic plot
I want to include math symbols in the panel titles for this stratigraphic plot:
library(analogue)
data(V12.122)
Depths <- as.numeric(rownames(V12.122))
names(V12.122)
(plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
data = V12.122,
type = c("h","l","g"),
zones = 400))
plt
For example, I want to have this text in place of "O.univ" etc.:
I used this code to make that text:
plot(1, type="n", axes=FALSE, ann=FALSE)
title(line = -1, main = expression(phantom()^14*C~years~BP))
title(line = -3, main = expression(delta^18*O))
title(line = -5, main = expression(paste("TP ", mu,"g l"^-1)))
title(line = -10, main = expression("very long title \n with \n line breaks"))
But if I try to update the colnames of the data frame passed to Stratiplot, the code is not parsed, and we do not get the correct text formatting:
V12.122 <- V12.122[, 1:4]
names(V12.122)[1] <- expression(phantom()^14*C~years~BP)
names(V12.122)[2] <- expression(delta^18*O)
names(V12.122)[3] <- expression(paste("TP ", mu,"g l"^-1))
(plt <- Stratiplot(Depths ~ .,
data = V12.122,
type = c("h","l","g"),
zones = 400))
plt
How can I get Stratiplot to parse the expressions in the colnames and format them correctly in the plot?
I've tried looking through str(plt) to see where the panel titles are stored, but no success:
text <- expression(phantom()^14*C~years~BP)
plt$condlevels$ind[1] <- text
names(plt$packet.sizes)[1] <- text
names(plt$par.settings$layout.widths$panel)[1] <- text
You can't actually do this in the current release of analogue; the function is doing too much messing around with data for the expressions to remain unevaluated prior to plotting. I could probably figure this out to allow expressions as the names of the data argument object, but it is easier to just allow users to pass a vector of labels that they want for the variables.
This is now implemented in the development version of the package on github, and I'll push this to CRAN early next week.
This change implements a new argument labelValues which takes a vector of labels for use in labelling the top axis. This can be a vector of expressions.
Here is an illustration of the usage:
library("analogue")
set.seed(1)
df <- setNames(data.frame(matrix(rnorm(200 * 3), ncol = 3)),
c("d13C", "d15N", "d18O"))
df <- transform(df, Age = 1:200)
exprs <- expression(delta^{13}*C, # label for 1st variable
delta^{15}*N, # label for 2nd variable
delta^{18}*O) # label for 3rd variable
Stratiplot(Age ~ ., data = df, labelValues = exprs, varTypes = "absolute", type = "h")
which produces
Note that this is just a first pass; I'm pretty sure I haven't accounted for any reordering that goes on with sort and svar etc. if they are used.
Never used lattice plots, but I thought a chance to learn something should be worth while. Took too long to figure out.
text <- "c( expression(phantom()^14*C~years~BP),expression(delta^18*O))"
strip = strip.custom(factor.levels=eval(parse(text=text)))
plt <- Stratiplot(Depths ~ .,
data = V12.122[, 1:4],
type = c("h","l","g"),
zones = 400,
strip = strip)
Hope this gets you started.
floating.pie error while using nodelables from ape package
I get an error while using the ARD model of the ace function in R. The error is
Error in floating.pie.asp(XX[i], YY[i], pie[i, ], radius = xrad[i], col = piecol) :
floating.pie: x values must be non-negative
library(ape)
library(phylobase)
tree <- read.nexus("data1.nexus")
plot(tree)
data <- read.csv("phagy_species.csv")
clade.full <- extract.clade(tree, node=91)
plot(clade.full)
clade.1 <- drop.tip(clade.full, "Bar_bre")
clade.2<- drop.tip(clade.1, "Par_pho")
clade.3<- drop.tip(clade.2, "Par_iph")
clade.4<- drop.tip(clade.3, "Eur_ser")
clade.5<- drop.tip(clade.4, "Opo_sym")
clade.6<- drop.tip(clade.5, "Mor_pel")
clade.7<- drop.tip(clade.6, "Aph_hyp")
clade.8<- drop.tip(clade.7, "Ere_oem")
clade.9<- drop.tip(clade.8, "Cal_bud")
clade.10<- drop.tip(clade.9, "Lim_red")
clade.11<- drop.tip(clade.10, "Act_str")
clade.12<- drop.tip(clade.11, "Hel_hec")
clade.13<- drop.tip(clade.12,"Col_dir")
clade.14<- drop.tip(clade.13, "Hyp_pau")
clade.15<- drop.tip(clade.14, "Nym_pol")
clade.16<- drop.tip(clade.15, "Mel_cin")
clade.17<- drop.tip(clade.16,"Apa_iri")
clade.18<- drop.tip(clade.17, "Bib_hyp")
clade.19<- drop.tip(clade.18, "Mar_ors")
clade.20<- drop.tip(clade.19, "Apo_cra")
clade.21<- drop.tip(clade.20, "Pse_par")
clade.22 <- drop.tip(clade.21, "Lep_sin")
clade.23<- drop.tip(clade.22, "Dis_spi")
plot(clade.23)
data2 <- as.numeric(data[,2])
model2 <- ace(data2, clade.23, type="discrete", method="ML", model="ARD")
summary(model2)
d <-logLik(model2)
deviance(model2)
AIC(model2)
plot(clade.23, type="phylogram", cex=0.8, font=3, label.offset = 0.004)
co <- c("red", "blue", "green", "black")
nodelabels(pie = model2$lik.anc, piecol = co, cex = 0.5)
And that is when I get the error. There is no error if I use the original tree without trimming. But, when i trim them to my requirements, it goes in the negative.
Here is the data
tree file
data file
The matrix you are using for the proportions of the pie has complex numbers in it. To see this, try: class(model2$lik.anc[1,1]) The rows of that matrix define the proportions of the pies, and they need to sum to 1. Your code produces a plot with pies if I replace the pie matrix in the nodelabels function like this: nodelabels(pie = matrix(0.25, 64, 4), piecol = co, cex = 0.5) because now there is a legitimate matrix for the pie argument with rows that sum to 1. As for why you have complex numbers in that matrix, I am not sure. It is probably related to all the warnings produced by the ace in your example. But that is a completely different issue.
I had the same problem with my data. I put my data into the matrix (like Slow Ioris suggested) and then unlisted the matrix. x <- matrix(data=c(model2$lik.anc[,1],model2$lik.anc[,2],model2$lik.anc[,3],model2$lik.anc[,4])) plotTree(tree,ftype="i",label.offset = 0.02) nodelabels(pie = unlist(x))
For other people having the same problem also after purging imaginable parts of their data: The nodelabels function gives the same error when you provide a data.frame instead of a matrix to pie.