Help on class RpyD2 in module rpyd2:

class RpyD2 | Methods defined here: |
| init(self, input, **kwargs) | input is your data, which can be in the following forms: | 1. LD (List of Dictionaries) | [ {'hair':'blonde','eyes':'blue'}, {'hair':'black','eyes':'green'}, ... ] | 2. DL (Dictionary of Lists) | { 'hair':['blonde','blue'], 'eyes':['blue','green] } | 3. Rpy2 DataFrame | 4. Another RpyD2 |
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| Keyword arguments will override the following default options: | self.cols=None # specify which columns to build from | self.rownamecol=None # specify a column name from which row names should be used | self.allcols=False # if False, columns limited to those shared among all rows; | if True, all columns are chosen; | if a positive integer N, columns limited to the 'top' N columns, | where columns are compared numerically by: | self.trimbyVariance=True # if trimbyVariance==True, sum of absolute value of Z-scores across column | otherwise, sum of scores across column |
| self.rank=True # if rank==True, append 'r'+ranknum to the top N columns | self.zero=0.0 # if allcols is True or an integer, what should empty cells be populated with? | self.z=False # if True, Z-score all quantitative columns | self.factor=True # if True, treat strings as factors | self.onlyQuant=False # if True, only build quantitative columns | self.onlyCat=False # if True, only build categorical (string) columns |
| self.toprint=True # if True, print R objects using R's summary() before returning them |
| repr(self) |
| str(self) |
| addCol(self, name, vals) |
| aov(self, formula, tukey=False, plot=False, fn=None, w=1100, h=800) |
| boxplot(self, fn=None, x=None, y=None, main=None, xlab=None, ylab=None, ggplot=False, w=1100, h=800) |
| ca(self, fn, cols=[]) |
| chisq(self, cols=[]) |
| cloud(self, fn=None, x='x', y='y', z='z', title=False, w=800, h=800) |
| col(self, colname) | Return column 'colname', where colname can be either a string name or an integer position (starting at 0). |
| cor(self, returnType='rpyd2') |
| cordist(self) |
| corrgram(self, fn=None, w=1600, h=1600) | API to corrgram package: |
| csv(self, fn='csv.txt', sep='\t') |
| dist(self, z=False) |
| distro(self, fn=None) |
| glm(self, ykey='y', family='gaussian', anovaTest='Chisq') | API to R's glm: | http://web.njit.edu/all_topics/Prog_Lang_Docs/html/library/base/html/glm.html |
| Family can be: | [ref: http://web.njit.edu/all_topics/Prog_Lang_Docs/html/library/base/html/family.html] |
| group(self, x=None, ys=[], yname='y', ytype='y_type') |
| hclust(self, cor=False, z=True, plot=True, fn=None, w=1100, h=900) |
| kclust(self, k=4, z=True, plot=True, fn=None, w=1100, h=800) | Currently set to return self.pam(k) for robust k-means clustering. |
| kmeans(self, k=4) | API to R's kmeans clustering function: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/kmeans.html |
| lm(self, formula, toprint=True) |
| loess(self, formula, toprint=True) |
| mclust(self, z=True, fn='mclust.png', w=1100, h=900) |
| mean_stdev(self, cols=[], rows=[]) |
| pam(self, k=4, z=True) | API to R's pam function: | http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/pam.html | A more robust version of k-means clustering, 'around medoids.' |
| pca(self, fn='pca.png', col=None, w=1200, h=1200) |
| plot(self, fn=None, x=None, y=None, col=None, group=None, w=1100, h=800, size=2, smooth=False, point=True, jitter=False, boxplot=False, boxplot2=False, title=False, flip=False, se=False, density=False, line=False, bar=False, xlab_size=14, ylab_size=14) |
| plot3d(self, fn=None, x='x', y='y', z='z', title=False, w=800, h=800) |
| plots(self, x=None, y=None, n=1) |
| points_3d(self, fn=None, x='x', y='y', z='z', title=False, w=800, h=800) |
| polyfit(self, x, y, deg=3, addCol=True, addDer=True) |
| polyfits(self, x, y, degs, addCol=True, fn=None, onlyBest=False) |
| polyplot(self, terms) |
| predict(self, y='', z=True, fn='predict.png', w=1100, h=800) | API to pamr.train and pamr.predict: | http://www-stat.stanford.edu/~tibs/PAM/Rdist/pamr.train.html | http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=pamr:pamr.predict |
| pvclust(self, z=True, fn='pvclust.png', w=1100, h=900) | API to R package pvclust: http://cran.r-project.org/web/packages/pvclust/index.html |
| q(self, z=False) | Return a version of self of only quantitative columns |
| rankcols(self, byVariance=False, returnSums=False) |
| removeCol(self, name) |
| row(self, rowname) | Return row 'rowname', where rowname can be either a string name or an integer position (starting at 0). |
| rows_where(self, qdict) |
| save(self, fn=None) |
| sub(self, cols=[], rows=[]) | Return an RpyD2 from self, with only those rows and/or columns as specified. |
| sub_where(self, rows={}) |
| summary(self, obj=None) |
| t(self) |
| toDL(self, cols=None, rows=None, rownamecol=False) | Return a dictionary of lists representation of self: | {'col0':[row0val,row1val,...], | 'col1':[row1val,row2val,...], | ...} |
| If rows is a non-empty list, return only these rows. | If cols is a non-empty list, return only these cols. | If both are non-empty, return only these rows and only these cols. |
| toVectors(self, xcol='x', ycol='y') |
| treepredict(self, y='', fn='treepredict.png', w=1100, h=800) |
| vioplot(self, fn=None, x=None, y=None, w=1100, h=800) | API to the 'vioplot' R package: http://cran.r-project.org/web/packages/vioplot/index.html |
| xtabs(self, cols=[])