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@ -14,7 +14,7 @@ from plots_lpls import plot_corrloads
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# full smoker data
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# full smoker data
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DX = dataset.read_ftsv(open("../../data/smokers-full/Xfull.ftsv"))
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DX = dataset.read_ftsv(open("../../data/smokers-full/Xfull.ftsv"))
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DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
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DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
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Y = DY.asarray()
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Y = DY.asarray().astype('d')
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# select subset genes by SAM
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# select subset genes by SAM
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rpy.r.library("siggenes")
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rpy.r.library("siggenes")
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rpy.r.library("qvalue")
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rpy.r.library("qvalue")
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@ -88,7 +88,7 @@ Z, newind = rpy_go.genego_matrix(goterms, tmat, gene_ids, terms,func=mean)
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Z = Z.T
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Z = Z.T
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# update X matrix (no go-terms available)
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# update X matrix (no go-terms available)
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Xr = Xr[:,newind]
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Xr = Xr[:,newind]
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gene_ids = asarray(gene_ids)[newind]
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new_gene_ids = asarray(gene_ids)[newind]
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######## LPLSR ########
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######## LPLSR ########
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@ -104,6 +104,19 @@ dx,Ry,rssy = correlation_loadings(Y, T, Q)
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cadz,Rz,rssz = correlation_loadings(Z.T, W, L)
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cadz,Rz,rssz = correlation_loadings(Z.T, W, L)
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# Prediction error
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# Prediction error
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rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
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rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
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alpha_check=False
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if alpha_check:
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Alpha = arange(0.01, 1, .1)
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Rmsep,Yhat, CE = [],[],[]
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for a in Alpha:
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rmsep , yhat, ce = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
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Rmsep.append(rmsep)
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Yhat.append(yhat)
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CE.append(yhat)
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Rmsep = asarray(Rmsep)
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Yhat = asarray(Yhat)
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CE = asarray(CE)
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# Significance Hotellings T
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# Significance Hotellings T
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Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt)
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Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt)
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@ -120,9 +133,9 @@ m = Y.shape[1]
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for a in range(m):
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for a in range(m):
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bar(arange(a_max)+a*bar_w+.1, rmsep[:,a], width=bar_w, color=bar_col[a])
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bar(arange(a_max)+a*bar_w+.1, rmsep[:,a], width=bar_w, color=bar_col[a])
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ylim([rmsep.min()-.05, rmsep.max()+.05])
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ylim([rmsep.min()-.05, rmsep.max()+.05])
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title('RMSEP')
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figure(2) # Hypoid correlations
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figure(2) # Hypoid correlations
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title('RMSEP')
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plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
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plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
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ax = gca()
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ax = gca()
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ylabels = DY.get_identifiers('_cat', sorted=True)
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ylabels = DY.get_identifiers('_cat', sorted=True)
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