I was trying to analyze my RR intervals using the RHRV package.
I have used:
hrv.data = CreateHRVData()
hrv.data = SetVerbose(hrv.data, TRUE)
hrv.data = LoadBeatRR(hrv.data, "dataset.txt", RecordPath = "#the path of dataset.txt here", datetime = "1/1/1900 0:0:0", verbose = NULL)
Unfortunately, when I do this I get the warning message :
"NAs introduced by coercion".
And all my following analyses do not work.
I do not see what the problem could be.
You should make sure the time, hr and rr variables are named as such: "Time", "niHR", and "RR" respectively.
A work-around would to just to manually create the beat object.
hrv.data$Beat <- readr::read_delim("txt file") %>%
dplyr::rename("Time" = time, "niHR" = hr, "RR" = rr) %>%
dplyr::select(Time, niHR, RR)
hrv.data <- RHRV::BuildNIHR(hrv.data)
Related
I am looking for some help in resolving an error using the partial least squares path modeling package ('plspm').
I can get results running a basic PLS-PM analysis but run into issues when using the grouping function, receiving the error message:
Error in if (w_dif < specs$tol || iter == specs$maxiter) break : missing value where TRUE/FALSE needed
I have no missing values and all variables have the proper classification. Elsewhere I read that there is a problem with processing observations with the exact same values across all variables, I have deleted those and still face this issue. I seem to be facing the issue only when I run the groups using the "bootstrap" method as well.
farmwood = read.csv("farmwood_groups(distance).csv", header = TRUE) %>%
slice(-c(119:123))
Control = c(0,0,0,0,0,0)
Normative = c(0,0,0,0,0,0)
B_beliefs = c(0,0,0,0,0,0)
P_control = c(1,0,0,0,0,0)
S_norm = c(0,1,0,0,0,0)
Behavior = c(0,0,1,1,1,0)
farmwood_path = rbind(Control, Normative, B_beliefs, P_control, S_norm, Behavior)
colnames(farmwood_path) = rownames(farmwood_path)
farmwood_blocks = list(14:18,20:23,8:13,24:27,19,4:7)
farmwood_modes = rep("A", 6)
farmwood_pls = plspm(farmwood, farmwood_path, farmwood_blocks, modes = farmwood_modes)
ames(farmwood)[names(farmwood) == "QB3"] <- "Distance"
farmwood$Distance <- as.factor(farmwood$Distance)
distance_boot = plspm.groups(farmwood_pls, farmwood$Distance, method = "bootstrap")
distance_perm = plspm.groups(farmwood_pls, farmwood$Distance, method = "permutation")
The data is contained here:
https://www.dropbox.com/s/8vewuupywpi1jkt/farmwood_groups%28distance%29.csv?dl=0
Any help would be appreciated. Thank you in advance
I am currently developing a database integrated forecasting tool for a costumer. I am using mainly R and TM1 Persepctives. To connect R with tm1 I use tm1r. For Data import from tm1 to r it works fine.
However, when I am trying to write back the calculated forecast from R to tm1, I run into problems.
I will give you some reprex data, so you can have a look at the output. If I use "tm2_send_data" instead of "tm1_send_dataset" it works fine too.
The latter function gives me the Error :
"Error in if (is.character(txt) && length(txt) == 1 && nchar(txt, type = "bytes") < :
missing value where TRUE/FALSE needed"
I have no clue, what this is supposed to mean! I tried some formatting of the data types, without any effect.
library(tm1r)
# data
values <- data.frame(fake_values =
c(105,147,159,232,312,337,285,188,257,10,98,27)
)
date_stamps <- c("2021001","2021002","2021003","2021004","2021005","2021006","2021007","2021008","2021009","2021010","2021011","2021012")
rownames(values) = date_stamps
# Send dataset to TM1
con_obj <- tm1_connection("localhost", "8840", "test_admin", "")
tm1_send_dataset(
con_obj,
valueset = values, cube = "pvl_FORECAST_HILFSWÜRFEL",
rowdim = "PVL_test_Zeit", coldim = "pvl_Produkt",
titledim1 = "DATENART", titleel1 = "FC",
titledim2 = "Version", titleel2 = 'Version_Bearbeitung',
titledim3 = "FC-Scheibe", titleel3 = "ML_FC_2021",
titledim4 = "PVL_test_Kunde", titleel4 = "MGR_domestic_D",
titledim5 = "PVL_test_Measure", titleel5 = "Menge_EA"
)
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
I am trying to do a spectral density analysis with ozone data but have run into the error:
Error in ts(tser, frequency = x.fsamp, start = x.start) : 'ts'
object must have one or more observations
I am not sure what the problem is because it sounds like it is having a problem with the number of entries in the file but the file is certainly not empty. What could be causing this problem? The code is as follows:
library(openair)
library(psd)
filedir <-"C:/Users/dfmcg/Documents/Thesis files/ALL_GPMP_O3_Met"
myfiles <-c(list.files(path = filedir))
paste(filedir,myfiles,sep = '/')
npsfiles<-c(paste(filedir,myfiles,sep = '/'))
for (i in npsfiles[1:3]){
x <- substr(i,55,61)
y<-paste(paste('C:/Users/dfmcg/Documents/Thesis files/nps images',x,sep='/'), '.png', sep='')
png(filename = y)
timeozone<-import(i,date="DATE",date.format = "%m/%d/%Y %H",header=TRUE,na.strings="-999")
psdcore(timeozone, ntaper = ,
na.action = "-999", plot = TRUE,
refresh = TRUE)
dev()
}
I'm trying to apply the combinef and aggts functions from the R hts package to a time series matrix in order to obtain an optimized set of forecasts across a hierarchy. I've run the same code every month without issue, and am now seeing errors after upgrading to hts package v4.5.
Reproducible example (I can share data file offline if needed)
#Read in forecast data for all levels of hierarchy#
fcast<-read.csv("SampleHierarchyForecast.csv", header = TRUE, check.names = FALSE)
#Convert to time series#
fcast<-ts(fcast, start = as.numeric(2010.25) + (64)/12, end = as.numeric(2010.25) + (75)/12, f= 12)
#Create time series of only the bottom level of the hierarchy#
index<-c()
fcastBottom<-fcast
for (i in 1:length(fcastBottom [1,]))
{
if(nchar(colnames(fcastBottom)[i])!=28)
index[i]<-i
else
index[i]<-0
}
fcastBottom<-fcastBottom[,-index]
#Create grouped time series from the bottom level forecast #
GtsForecast <- gts(fcastBottom, characters = list(c(12,12), c(4)), gnames = c("Category", "Item", "Customer", "Category-Customer"))
#Use combinef function to optimally combine the full hierarchy forecast using the groups from the full hierarchy gts#
combo <- combinef(fcast, groups = GtsForecast$groups)
*Warning message:
In mapply(rep, as.list(gnames), times, SIMPLIFY = FALSE) :
longer argument not a multiple of length of shorter*
traceback()
2: stop("Argument fcasts requires all the forecasts.")
1: combinef(fcast, groups = GtsForecast$groups)
There's a little bug when comebinef() function calls gts(). Now I've fixed it on github. So you can run your own code above without any trouble after updating the development version.
Alternatively, you need to tweak your code a bit if you don't want to install the newest version.
combo <- combinef(fcast, groups = GtsForecast$groups, keep = "bottom")
combo <- ts(combo, start = as.numeric(2010.25) + (64)/12,
end = as.numeric(2010.25) + (75)/12, f = 12)
colnames(combo) <- colnames(fcastBottom)
newGtsForecast <- gts(combo, characters = list(c(12,12), c(4)),
gnames = c("Category", "Item", "Customer",
"Category-Customer"))
Aggregate <- aggts(newGtsForecast)
Hope it helps.