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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'm new to Stata and have a question about its command language. I want to use my ARIMA model to forecast, ie use x[t], x[t-1]... to produce an estimate xhat[t+1], and then roll forward one time step, to make the next forecast, rebuilding the model every N time steps.
i can duplicate code, something like the following code for T, T+1, T+2, etc.:
arima x if t<=T, arima(2,0,2)
predict xhat
to produce a series of xhats to compare with in-sample x observations. There must be a more natural way to do this in the command language. any suggestions, pointers would be very much appreciated.
Posting a working solution provided by Stata tech support:
webuse dfex
tsset month
generate int id = _n
capture program drop forecarima
program forecarima, rclass
syntax [if]
tempvar yhat
arima unemp `if', arima(1,1,0)
local T = e(tmax)
local T1 = `T' + 1
summarize id if month == `T1'
local h = r(max)
predict `yhat', y dynamic(`T')
return scalar y = unemp[`h']
return scalar yhat = `yhat'[`h']
end
rolling unemp = r(y) unemp_hat = r(yhat), window(400) recursive ///
saving(results,replace): forecarima
use results,clear
browse
this provides output with the prediction and observed both available. the dates are off by one step, but easier left to post-processing.
I am working with a data-set of patient information and trying to calculate the Propensity Score from the data using MATLAB. After removing features with many missing values, I am still left with several missing (NaN) values.
I get errors due to these missing values, as the values of my cost-function and gradient vector become NaN, when I try to perform logistic regression using the following Matlab code (from Andrew Ng's Coursera Machine Learning class) :
[m, n] = size(X);
X = [ones(m, 1) X];
initial_theta = ones(n+1, 1);
[cost, grad] = costFunction(initial_theta, X, y);
options = optimset('GradObj', 'on', 'MaxIter', 400);
[theta, cost] = ...
fminunc(#(t)(costFunction(t, X, y)), initial_theta, options);
Note: sigmoid and costfunction are working functions I created for overall ease of use.
The calculations can be performed smoothly if I replace all NaN values with 1 or 0. However I am not sure if that is the best way to deal with this issue, and I was also wondering what replacement value I should pick (in general) to get the best results for performing logistic regression with missing data. Are there any benefits/drawbacks to using a particular number (0 or 1 or something else) for replacing the said missing values in my data?
Note: I have also normalized all feature values to be in the range of 0-1.
Any insight on this issue will be highly appreciated. Thank you
As pointed out earlier, this is a generic problem people deal with regardless of the programming platform. It is called "missing data imputation".
Enforcing all missing values to a particular number certainly has drawbacks. Depending on the distribution of your data it can be drastic, for example, setting all missing values to 1 in a binary sparse data having more zeroes than ones.
Fortunately, MATLAB has a function called knnimpute that estimates a missing data point by its closest neighbor.
From my experience, I often found knnimpute useful. However, it may fall short when there are too many missing sites as in your data; the neighbors of a missing site may be incomplete as well, thereby leading to inaccurate estimation. Below, I figured out a walk-around solution to that; it begins with imputing the least incomplete columns, (optionally) imposing a safe predefined distance for the neighbors. I hope this helps.
function data = dnnimpute(data,distCutoff,option,distMetric)
% data = dnnimpute(data,distCutoff,option,distMetric)
%
% Distance-based nearest neighbor imputation that impose a distance
% cutoff to determine nearest neighbors, i.e., avoids those samples
% that are more distant than the distCutoff argument.
%
% Imputes missing data coded by "NaN" starting from the covarites
% (columns) with the least number of missing data. Then it continues by
% including more (complete) covariates in the calculation of pair-wise
% distances.
%
% option,
% 'median' - Median of the nearest neighboring values
% 'weighted' - Weighted average of the nearest neighboring values
% 'default' - Unweighted average of the nearest neighboring values
%
% distMetric,
% 'euclidean' - Euclidean distance (default)
% 'seuclidean' - Standardized Euclidean distance. Each coordinate
% difference between rows in X is scaled by dividing
% by the corresponding element of the standard
% deviation S=NANSTD(X). To specify another value for
% S, use D=pdist(X,'seuclidean',S).
% 'cityblock' - City Block distance
% 'minkowski' - Minkowski distance. The default exponent is 2. To
% specify a different exponent, use
% D = pdist(X,'minkowski',P), where the exponent P is
% a scalar positive value.
% 'chebychev' - Chebychev distance (maximum coordinate difference)
% 'mahalanobis' - Mahalanobis distance, using the sample covariance
% of X as computed by NANCOV. To compute the distance
% with a different covariance, use
% D = pdist(X,'mahalanobis',C), where the matrix C
% is symmetric and positive definite.
% 'cosine' - One minus the cosine of the included angle
% between observations (treated as vectors)
% 'correlation' - One minus the sample linear correlation between
% observations (treated as sequences of values).
% 'spearman' - One minus the sample Spearman's rank correlation
% between observations (treated as sequences of values).
% 'hamming' - Hamming distance, percentage of coordinates
% that differ
% 'jaccard' - One minus the Jaccard coefficient, the
% percentage of nonzero coordinates that differ
% function - A distance function specified using #, for
% example #DISTFUN.
%
if nargin < 3
option = 'mean';
end
if nargin < 4
distMetric = 'euclidean';
end
nanVals = isnan(data);
nanValsPerCov = sum(nanVals,1);
noNansCov = nanValsPerCov == 0;
if isempty(find(noNansCov, 1))
[~,leastNans] = min(nanValsPerCov);
noNansCov(leastNans) = true;
first = data(nanVals(:,noNansCov),:);
nanRows = find(nanVals(:,noNansCov)==true); i = 1;
for row = first'
data(nanRows(i),noNansCov) = mean(row(~isnan(row)));
i = i+1;
end
end
nSamples = size(data,1);
if nargin < 2
dataNoNans = data(:,noNansCov);
distances = pdist(dataNoNans);
distCutoff = min(distances);
end
[stdCovMissDat,idxCovMissDat] = sort(nanValsPerCov,'ascend');
imputeCols = idxCovMissDat(stdCovMissDat>0);
% Impute starting from the cols (covariates) with the least number of
% missing data.
for c = reshape(imputeCols,1,length(imputeCols))
imputeRows = 1:nSamples;
imputeRows = imputeRows(nanVals(:,c));
for r = reshape(imputeRows,1,length(imputeRows))
% Calculate distances
distR = inf(nSamples,1);
%
noNansCov_r = find(isnan(data(r,:))==0);
noNansCov_r = noNansCov_r(sum(isnan(data(nanVals(:,c)'==false,~isnan(data(r,:)))),1)==0);
%
for i = find(nanVals(:,c)'==false)
distR(i) = pdist([data(r,noNansCov_r); data(i,noNansCov_r)],distMetric);
end
tmp = min(distR(distR>0));
% Impute the missing data at sample r of covariate c
switch option
case 'weighted'
data(r,c) = (1./distR(distR<=max(distCutoff,tmp)))' * data(distR<=max(distCutoff,tmp),c) / sum(1./distR(distR<=max(distCutoff,tmp)));
case 'median'
data(r,c) = median(data(distR<=max(distCutoff,tmp),c),1);
case 'mean'
data(r,c) = mean(data(distR<=max(distCutoff,tmp),c),1);
end
% The missing data in sample r is imputed. Update the sample
% indices of c which are imputed.
nanVals(r,c) = false;
end
fprintf('%u/%u of the covariates are imputed.\n',find(c==imputeCols),length(imputeCols));
end
To deal with missing data you can use one of the following three options:
If there are not many instances with missing values, you can just delete the ones with missing values.
If you have many features and it is affordable to lose some information, delete the entire feature with missing values.
The best method is to fill some value (mean, median) in place of missing value. You can calculate the mean of the rest of the training examples for that feature and fill all the missing values with the mean. This works out pretty well as the mean value stays in the distribution of your data.
Note: When you replace the missing values with the mean, calculate the mean only using training set. Also, store that value and use it to change the missing values in the test set also.
If you use 0 or 1 to replace all the missing values then the data may get skewed so it is better to replace the missing values by an average of all the other values.
I would like to predict time series values X using another time series Y and the past value of X.In detail, I would like to predict X at time t (Xt) using (Xt-p,...,Xt-1) and (Yt-p,...,Yt-1,Yt) with p the dimension of the "look back".
So, my problem is that I do not have the same length for my 2 predictors.
Let's use a exemple to be clearer.
If I use a timestep of 2, I would have for one observation :
[(Xt-p,Yt-p),...,(Xt-1,Yt-1),(??,Yt)] as input and Xt as output. I do not know what to use instead of the ??
I understand that mathematically speaking I need to have the same length for my predictors, so I am looking for a value to replace the missing value.
I really do not know if there is a good solution here and if I could to something so any help would be greatly appreciated.
Cheers !
PS : you could see my problem as if I wanted to predict the number of ice cream sell one day in advance in a city using the forcast of weather for the next day. X would be the number of ice cream and Y could be the temperature.
You could e.g. do the following:
input_x = Input(shape=input_shape_x)
input_y = Input(shape=input_shape_y)
lstm_for_x = LSTM(50, return_sequences=False)(input_x)
lstm_for_y = LSTM(50, return_sequences=False)(input_y)
merged = merge([lstm_for_x, lstm_for_y], mode="concat") # for keras < 2.0
merged = Concatenate([lstm_for_x, lstm_for_y])
output = Dense(1)(merged)
model = Model([x_input, y_input], output)
model.compile(..)
model.fit([X, Y], X_next)
Where X is an array of sequences, X_forward is X p-steps ahead and Y is an array of sequences of Ys.
I have a bunch of already human-classified documents in some groups.
Is there a modified version of lda which I can use to train a model and then later classify unknown documents with it?
For what it's worth, LDA as a classifier is going to be fairly weak because it's a generative model, and classification is a discriminative problem. There is a variant of LDA called supervised LDA which uses a more discriminative criterion to form the topics (you can get source for this in various places), and there's also a paper with a max margin formulation that I don't know the status of source-code-wise. I would avoid the Labelled LDA formulation unless you're sure that's what you want, because it makes a strong assumption about the correspondence between topics and categories in the classification problem.
However, it's worth pointing out that none of these methods use the topic model directly to do the classification. Instead, they take documents, and instead of using word-based features use the posterior over the topics (the vector that results from inference for the document) as its feature representation before feeding it to a classifier, usually a Linear SVM. This gets you a topic model based dimensionality reduction, followed by a strong discriminative classifier, which is probably what you're after. This pipeline is available
in most languages using popular toolkits.
You can implement supervised LDA with PyMC that uses Metropolis sampler to learn the latent variables in the following graphical model:
The training corpus consists of 10 movie reviews (5 positive and 5 negative) along with the associated star rating for each document. The star rating is known as a response variable which is a quantity of interest associated with each document. The documents and response variables are modeled jointly in order to find latent topics that will best predict the response variables for future unlabeled documents. For more information, check out the original paper.
Consider the following code:
import pymc as pm
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
train_corpus = ["exploitative and largely devoid of the depth or sophistication ",
"simplistic silly and tedious",
"it's so laddish and juvenile only teenage boys could possibly find it funny",
"it shows that some studios firmly believe that people have lost the ability to think",
"our culture is headed down the toilet with the ferocity of a frozen burrito",
"offers that rare combination of entertainment and education",
"the film provides some great insight",
"this is a film well worth seeing",
"a masterpiece four years in the making",
"offers a breath of the fresh air of true sophistication"]
test_corpus = ["this is a really positive review, great film"]
train_response = np.array([3, 1, 3, 2, 1, 5, 4, 4, 5, 5]) - 3
#LDA parameters
num_features = 1000 #vocabulary size
num_topics = 4 #fixed for LDA
tfidf = TfidfVectorizer(max_features = num_features, max_df=0.95, min_df=0, stop_words = 'english')
#generate tf-idf term-document matrix
A_tfidf_sp = tfidf.fit_transform(train_corpus) #size D x V
print "number of docs: %d" %A_tfidf_sp.shape[0]
print "dictionary size: %d" %A_tfidf_sp.shape[1]
#tf-idf dictionary
tfidf_dict = tfidf.get_feature_names()
K = num_topics # number of topics
V = A_tfidf_sp.shape[1] # number of words
D = A_tfidf_sp.shape[0] # number of documents
data = A_tfidf_sp.toarray()
#Supervised LDA Graphical Model
Wd = [len(doc) for doc in data]
alpha = np.ones(K)
beta = np.ones(V)
theta = pm.Container([pm.CompletedDirichlet("theta_%s" % i, pm.Dirichlet("ptheta_%s" % i, theta=alpha)) for i in range(D)])
phi = pm.Container([pm.CompletedDirichlet("phi_%s" % k, pm.Dirichlet("pphi_%s" % k, theta=beta)) for k in range(K)])
z = pm.Container([pm.Categorical('z_%s' % d, p = theta[d], size=Wd[d], value=np.random.randint(K, size=Wd[d])) for d in range(D)])
#pm.deterministic
def zbar(z=z):
zbar_list = []
for i in range(len(z)):
hist, bin_edges = np.histogram(z[i], bins=K)
zbar_list.append(hist / float(np.sum(hist)))
return pm.Container(zbar_list)
eta = pm.Container([pm.Normal("eta_%s" % k, mu=0, tau=1.0/10**2) for k in range(K)])
y_tau = pm.Gamma("tau", alpha=0.1, beta=0.1)
#pm.deterministic
def y_mu(eta=eta, zbar=zbar):
y_mu_list = []
for i in range(len(zbar)):
y_mu_list.append(np.dot(eta, zbar[i]))
return pm.Container(y_mu_list)
#response likelihood
y = pm.Container([pm.Normal("y_%s" % d, mu=y_mu[d], tau=y_tau, value=train_response[d], observed=True) for d in range(D)])
# cannot use p=phi[z[d][i]] here since phi is an ordinary list while z[d][i] is stochastic
w = pm.Container([pm.Categorical("w_%i_%i" % (d,i), p = pm.Lambda('phi_z_%i_%i' % (d,i), lambda z=z[d][i], phi=phi: phi[z]),
value=data[d][i], observed=True) for d in range(D) for i in range(Wd[d])])
model = pm.Model([theta, phi, z, eta, y, w])
mcmc = pm.MCMC(model)
mcmc.sample(iter=1000, burn=100, thin=2)
#visualize topics
phi0_samples = np.squeeze(mcmc.trace('phi_0')[:])
phi1_samples = np.squeeze(mcmc.trace('phi_1')[:])
phi2_samples = np.squeeze(mcmc.trace('phi_2')[:])
phi3_samples = np.squeeze(mcmc.trace('phi_3')[:])
ax = plt.subplot(221)
plt.bar(np.arange(V), phi0_samples[-1,:])
ax = plt.subplot(222)
plt.bar(np.arange(V), phi1_samples[-1,:])
ax = plt.subplot(223)
plt.bar(np.arange(V), phi2_samples[-1,:])
ax = plt.subplot(224)
plt.bar(np.arange(V), phi3_samples[-1,:])
plt.show()
Given the training data (observed words and response variables), we can learn the global topics (beta) and regression coefficients (eta) for predicting the response variable (Y) in addition to topic proportions for each document (theta).
In order to make predictions of Y given the learned beta and eta, we can define a new model where we do not observe Y and use the previously learned beta and eta to obtain the following result:
Here we predicted a positive review (approx 2 given review rating range of -2 to 2) for the test corpus consisting of one sentence: "this is a really positive review, great film" as shown by the mode of the posterior histogram on the right.
See ipython notebook for a complete implementation.
Yes you can try the Labelled LDA in the stanford parser at
http://nlp.stanford.edu/software/tmt/tmt-0.4/