I am trying to generate a correlogram using the rcorr function (hmisc package) and corrplot
library("Hmisc")
library("corrplot")
res2<-rcorr(as.matrix(A), type = "spearman")
corrplot(res2$r, order="AOE",
p.mat = res2$P, sig.level = 0.05, insig = "blank")
I get the correlogram, but with an error message:
"error in data.frame(..., check.names = FALSE) :
arguments imply differing number of rows: 169, 156"
I can`t see anything wrong with the rcorr output. Can anyone help me?
Thanks!
In the vignette for package 'corrplot' it says "We can get p-value matrix...by cor.mtest()...". The output of that function produces a p-values matrix with zeroes on the diagonal. The output of rcorr produces a matrix of p-values with NA on the diagonal. Maybe you can just replace your NA diagonal values with zeroes. If you switch to cor.mtest() you'll need to be sure and understand all of its parameters.
Related
I'm trying to get the coefficients of the taxa that generate the most differences between the groups in my permutest of types of supplies for the animals.
I paste the results of the permutest:
permutest(betadispersionAlimentación, pairwise = TRUE)
enter image description here
When I try to get the coefficients, Studio answer me with this:
coefficients(betadispersionAlimentación)["C",]
NULL
The object to get the coefficients is the type of:
Permanova?
Betadisper?
Permutest?
i don't know how can I get the taxa (genus, Family, Specie...) that contribute to the differences.
can anybody help me, please?
Thanks a lot!
For anybody who have the same problem.
From the day that I posted the question I have worked about that.
To get de genus or the specie that more contribute to the dissimilarity I used the vegan library.
Here I used the Vegan Distance to create de distance object
DIST_ABUNDANCES <- vegdist(t(OTU_ABUNDANCES))
Here I took the anova function to create all the values.
anova(betadisper(DIST_ABUNDANCES, META_ABUNDANDES$Sexo))
Here I made the permutest to compare all the values of the variable Sexo
permutest(betadisper(DIST_ABUNDANCES, META_ABUNDANDES$Sexo), pairwise = TRUE)
Here I took the correlation coefficients of the anova to seek the objetive
COEF_ABUNDANCES <- coefficients(PERMANOVA_ABUNDANCES)["Sexo1",]
I created the top coeficients object
TOP.COEF_ABUNDANCES <- COEF_ABUNDANCES[rev(order(abs(COEF_ABUNDANCES)))[1:20]]
Here I designed the plot of the coefficients
par(mar = c(3, 14, 2, 1))
barplot(sort(TOP.COEF_ABUNDANCES), horiz = T, las = 1, main = "Top taxa")
I followed this tutorial: https://mibwurrepo.github.io/Microbial-bioinformatics-introductory-course-Material-2018/multivariate-comparisons-of-microbial-community-composition.html
I am getting the error "dtw() got an unexpected keyword argument 'dist'" while I'm trying to calculate the dtw of 2 wav files. I can't figure out why or what to do to fix it. I am attaching the code below.
import librosa
import librosa.display
y1, sr1 = librosa.load('sample_data/Abir_Arshad_22.wav')
y2, sr2 = librosa.load('sample_data/Abir_Arshad_22.wav')
%pylab inline
subplot(1, 2, 1)
mfcc1 = librosa.feature.mfcc(y1, sr1)
librosa.display.specshow(mfcc1)
subplot(1, 2, 2)
mfcc2 = librosa.feature.mfcc(y2, sr2)
librosa.display.specshow(mfcc2)
from dtw import dtw
from numpy.linalg import norm
dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))
print ('Normalized distance between the two sounds:', dist)
the error is occurring in the 2nd last line.
The error message is straight forward. Lets read the docs of the method you are calling:
https://dynamictimewarping.github.io/py-api/html/api/dtw.dtw.html#dtw.dtw
The dtw function has the following parameters:
Parameters x – query vector or local cost matrix
y – reference vector, unused if x given as cost matrix
dist_method – pointwise (local) distance function to use.
step_pattern – a stepPattern object describing the local warping steps
allowed with their cost (see [stepPattern()])
window_type – windowing function. Character: “none”, “itakura”,
“sakoechiba”, “slantedband”, or a function (see details).
open_begin,open_end – perform open-ended alignments
keep_internals – preserve the cumulative cost matrix, inputs, and
other internal structures
distance_only – only compute distance (no backtrack, faster)
You try to pass an argument named dist and that argument simply is not known.
Instead, removing that argument would solve the issue, such as
dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T)
I'm trying to obtain a numerical solution to the following integral:
1
The correct answer is -0.324 + 0.382i but as seen below I am not getting a numerical answer and would appreciate help with the Maxima syntax.
2
Perhaps related to why I am not getting a numerical output are two specific questions:
I read that e and i in Maxima need to be preceded by % in input but should these also appear as %e and %i as seen in the Maxima output?
Why is dy missing at the end of the integral in the Maxima output?
Thank you!
Looks to me like your input is okay, however, the function to compute approximations to integrals is named quad_qags. (There are actually several related functions. See ?? quad_ for more info.) Also, a wrinkle here is that the integrand is a complex-valued function (of a real variable), and quad_qags can only work on real-valued integrands, so we'll have to work around it. Here's how I would arrange it.
myintegrand: exp(%i*(1 + %i*y))/(1 + %i*y + 1/(1 + %i*y));
result_realpart: quad_qags (realpart (myintegrand), y, 0, 6);
result_imagpart: quad_qags (imagpart (myintegrand), y, 0, 6);
result: result_realpart[1] + %i*result_imagpart[1];
I get 0.3243496676292901*%i + 0.3820529930785175 as the final result. That's a little different from what you said; maybe a minus sign went missing? or there's a missing or extra factor of %i?
A quick approximation
0.1 * lsum (x, x, float (rectform (makelist (ev (myintegrand, y = k/10), k, 0, 60))));
seems to show the result from quad_qags is reasonable.
for my thesis I have to calculate the number of workers at risk of substitution by machines. I have calculated the probability of substitution (X) and the number of employee at risk (Y) for each occupation category. I have a dataset like this:
X Y
1 0.1300 0
2 0.1000 0
3 0.0841 1513
4 0.0221 287
5 0.1175 3641
....
700 0.9875 4000
I tried to plot a histogram with this command:
hist(dataset1$X,dataset1$Y,xlim=c(0,1),ylim=c(0,30000),breaks=100,main="Distribution",xlab="Probability",ylab="Number of employee")
But I get this error:
In if (freq) x$counts else x$density
length > 1 and only the first element will be used
Can someone tell me what is the problem and write me the right command?
Thank you!
It is worth pointing out that the message displayed is a Warning message, and should not prevent the results being plotted. However, it does indicate there are some issues with the data.
Without the full dataset, it is not 100% obvious what may be the problem. I believe it is caused by the data not being in the correct format, with two potential issues. Firstly, some values have a value of 0, and these won't be plotted on the histogram. Secondly, the observations appear to be inconsistently spaced.
Histograms are best built from one of two datasets:
A dataframe which has been aggregated grouped into consistently sized bins.
A list of values X which in the data
I prefer the second technique. As originally shown here The expandRows() function in the package splitstackshape can be used to repeat the number of rows in the dataframe by the number of observations:
set.seed(123)
dataset1 <- data.frame(X = runif(900, 0, 1), Y = runif(900, 0, 1000))
library(splitstackshape)
dataset2 <- expandRows(dataset1, "Y")
hist(dataset2$X, xlim=c(0,1))
dataset1$bins <- cut(dataset1$X, breaks = seq(0,1,0.01), labels = FALSE)
I have a question about OpenCV's example on Basic Thresholding as provided in the link below:
http://docs.opencv.org/2.4/doc/tutorials/imgproc/threshold/threshold.html#goal
I am slowly beginning to understand the code and have tried out an example too. However I am confused about a part of the code regarding thresholding operations. How does the thresholding function know which threshold operation to use?
This is where it is called:
threshold( src_gray, dst, threshold_value, max_BINARY_value,threshold_type);
I get that the last parameter "threshold_type is how it knows which threshold operation to use(eg. binary, binary inverted, truncated etc.) However in the code, this is all that is assigned to threshold_type:
int threshold_type = 3
As it is only assigned an int value of 3. How does the Threshold function know what operation to give it? Could someone explain it to me?
You should avoid using numeric literals to call the method of OpenCV instead use the constant variable defined in the opencv namespace, However it won't create any difference in output, but it makes the code more readable, So deciphered set of inputs to the cv::threshold() method are:
THRESH_BINARY = 0,
THRESH_BINARY_INV = 1,
THRESH_TRUNC = 2,
THRESH_TOZERO = 3,
THRESH_TOZERO_INV = 4,
THRESH_MASK = 7,
THRESH_OTSU = 8,
THRESH_TRIANGLE = 16
According to this table you are using thresholdType == THRESH_TOZERO