I keep running into an error while trying to run the BIOMOD_FormatingData()-function.
I have checked through all arguments and removed any NA-values, the explanatory variables are the same for both the testing and training datasets (independent datasets), and I've generated pseudo-absence data for the evaluation dataset (included in eval.resp.var).
Has anyone run into this error before? and if so, what was the issue related to? This is my first time using Biomod2 for ensemble modelling and I've run out of ideas as to what could be causing this error!
Here is my script and the subsequent error:
library(biomod2)
geranium_data <-
BIOMOD_FormatingData(
resp.var = SG.occ.train['Geranium.lucidum'],
resp.xy = SG.occ.train[, c('Longitude', 'Latitude')],
expl.var = SG.variables,
resp.name = "geranium_data",
eval.resp.var = SG.test.data['Geranium.lucidum'],
eval.expl.var = SG.variables,
eval.resp.xy = SG.test.data[, c('Longitude', 'Latitude')],
PA.nb.rep = 10,
PA.nb.absences = 4650,
PA.strategy = 'random',
na.rm = TRUE
)
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= geranium_data Data Formating -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Response variable name was converted into geranium.data
> Pseudo Absences Selection checkings...
> random pseudo absences selection
> Pseudo absences are selected in explanatory variablesError in `names<-`(`*tmp*`, value = c("calibration", "validation")) : incorrect number of layer names
Related
I've recently been attempting to evaluate output from k-modes (a cluster label), relative to a so-called True cluster label (labelled 'class' below).
In other words: I've been attempting to external validate the clustering output. However, when I tried external validation measures from the 'fpc' package, I was unsuccessful (error term posted below script).
I've attached my code for the mushroom dataset. I would appreciate if anyone could show me how to successful execute these external validation measures in the context of categorical data.
Any help appreciated.
# LIBRARIES
install.packages('klaR')
install.packages('fpc')
library(klaR)
library(fpc)
#MUSHROOM DATA
mushrooms <- read.csv(file = "https://raw.githubusercontent.com/miachen410/Mushrooms/master/mushrooms.csv", header = FALSE)
names(mushrooms) <- c("edibility", "cap-shape", "cap-surface", "cap-color",
"bruises", "odor", "gill-attachment", "gill-spacing",
"gill-size", "gill-color", "stalk-shape", "stalk-root",
"stalk-surface-above-ring", "stalk-surface-below-ring",
"stalk-color-above-ring", "stalk-color-below-ring", "veil-type",
"veil-color", "ring-number", "ring-type", "spore-print-color",
"population", "habitat")
names(mushrooms)[names(mushrooms)=="edibility"] <- "class"
indexes <- apply(mushrooms, 2, function(x) any(is.na(x) | is.infinite(x)))
colnames(mushrooms)[indexes]
table(mushrooms$class)
str(mushrooms)
#REMOVING CLASS VARIABLE
mushroom.df <- subset(mushrooms, select = -c(class))
#KMODES ANALYSIS
result.kmode <- kmodes(mushroom.df, 2, iter.max = 50, weighted = FALSE)
#EXTERNAL VALIDATION ATTEMPT
mushrooms$class <- as.factor(mushrooms$class)
class <- as.numeric(mushrooms$class))
clust_stats <- cluster.stats(d = dist(mushroom.df),
class, result.kmode$cluster)
#ERROR TERM
Error in silhouette.default(clustering, dmatrix = dmat) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In dist(mushroom.df) : NAs introduced by coercion
I am looking for some help in resolving an error using the partial least squares path modeling package ('plspm').
I can get results running a basic PLS-PM analysis but run into issues when using the grouping function, receiving the error message:
Error in if (w_dif < specs$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$Distance, method = "permutation")
The data is contained here:
https://www.dropbox.com/s/8vewuupywpi1jkt/farmwood_groups%28distance%29.csv?dl=0
Any help would be appreciated. Thank you in advance
I am using WarbleR in R to do some acoustic analyses. As freq_range couldn't detect all the bottom frequencies very well, I have created a data frame manually with all the right bottom frequencies, loaded this into R and turned it into a selection table. Traq_freq_contour and compare.methods and freq_DTW all work fine (although freq_DTW does give a warning message:
Warning message: In (0:(n - 1)) * f : NAs produced by integer overflow
However. If I try to do the function cross_correlation, I get the following error:
Error in if (ncol(spc1$amp) > ncol(spc2$amp)) { :
argument is of length zero
I do not get this error with a selection table with the bottom and top frequency added with the freq_range function in R instead of manually. What could be the issue here? The selection tables both look similar:
This is the selection table partly made by R through freq_range:
And this is the one with the bottom frequencies added manually (which has more sound files than the one before):
This is part of the code I use:
#Comparing methods for quantitative analysis of signal structure
compare.methods(X = stnew, flim = c(0.6,2.5), bp = c(0.6,2.5), methods = c("XCORR", "dfDTW"))
#Measure acoustic parameters with spectro_analysis
paramsnew <- spectro_analysis(stnew, bp = c(0.6,2), threshold = 20)
write.csv(paramsnew, "new_acoustic_parameters.csv", row.names = FALSE)
#Remove parameters derived from fundamental frequency
paramsnew <- paramsnew[, grep("fun|peakf", colnames(paramsnew), invert = TRUE)]
#Dynamic time warping
dm <- freq_DTW(stnew, length.out = 30, flim = c(0.6,2), bp = c(0.6,2), wl = 300, img = TRUE)
str(dm)
#Spectrographic cross-correlation
xcnew <- cross_correlation(stnew, wl = 300, na.rm = FALSE)
str(xc)
Any idea what I'm doing wrong?
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.
I'm trying to use cor.ci to obtain polychoric correlations with significance tests, but it keeps giving me an error message. Here is the code:
install.packages("Hmisc")
library(Hmisc)
mydata <- spss.get("S-IAT for R.sav", use.value.labels=TRUE)
install.packages('psych')
library(psych)
poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
poly.example
print(corr.test(poly.example$rho), short=FALSE)
Here is the error message it gives:
> library(psych)
> poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
Error in cor.ci(mydata(nvar = 10, n = 100)$items, n.iter = 10, poly = TRUE) :
could not find function "mydata"
> poly.example
Error: object 'poly.example' not found
> print(corr.test(poly.example$rho), short=FALSE)
Error in is.data.frame(x) : object 'poly.example' not found
How can I make it recognize mydata and/or select certain variables from this dataset for the analysis? I got the above code from here:
Polychoric correlation matrix with significance in R
Thanks!
You have several problems.
1) As previously commented upon, you are treating mydata as a function, but you need to treat it as a data.frame. Thus the call should be
poly.example <- cor.ci(mydata,n.iter = 10,poly = TRUE)
If you are trying to just get the first 100 cases and the first 10 variables, then
poly.example <- cor.ci(mydata[1:10,1:100],n.iter = 10,poly = TRUE)
2) Then, you do not want to run corr.test on the resulting correlation matrix. corr.test should be run on the data.
print(corr.test(mydata[1:10,1:100],short=FALSE)
Note that corr.test is testing the Pearson correlation.