categorical-data

问题 I have a dataset with some categorical string columns and I want to represent them in double type. I used StringIndexer for this convertion and It works but when I tried it in another dataset that has NULL values it gave java.lang.NullPointerException error and did not work. For better understanding here is my code: for(col <- cols){ out_name = col ++ "_" var indexer = new StringIndexer().setInputCol(col).setOutputCol(out_name) var indexed = indexer.fit(df).transform(df) df = (indexed

问题 R's output for a multivariable regression model including one or more factor variable does not automatically include a likelihood ratio test (LRT) of the significance of the entire factor variable in the model. For example: fake = data.frame( x1=rnorm(100), x2=sample(LETTERS[1:4], size=100, replace=TRUE), y=rnorm(100) ) head(fake) x1 x2 y 1 0.6152511 A 0.7682467 2 -0.8215727 A -0.5389245 3 -1.3287208 A -0.1797851 4 0.5837217 D 0.9509888 5 -0.2828024 C -0.9829126 6 0.3971358 B -0.4895091 m =

问题 In machine learning, it is common to represent a categorical (specifically: nominal) feature with one-hot-encoding. I am trying to learn how to use tensorflow's embedding layer to represent a categorical feature in a classification problem. I have got tensorflow version 1.01 installed and I am using Python 3.6 . I am aware of the tensorflow tutorial for word2vec, but it is not very instructive for my case. While building the tf.Graph , it uses NCE-specific weights and tf.nn.nce_loss . I just

问题 I would like to use the raster attribute table information to create the legend of a raster such as the raster 1 and display the legend only for the class displayed in the raster. I build an example to explain what I would like to get. 1/ Build the raster r <- raster(ncol=10, nrow=10) values(r) <-sample(1:3,ncell(r),replace=T) 2/ Add the Raster Attribute Table r <- ratify(r) # build the Raster Attibute table rat <- levels(r)[[1]]#get the values of the unique cell frot the attribute table rat

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 years ago . I would like to make a boxplot that shows how time spent doing a behaviour(Alert) is affected by two variables (Period= Morning/Afternoon and Visitor Level= High/Low). Alert ~ Period + Vis.Level 'Alert' is a set of 12 numbers that show the amount of time spent awake with the other two as the

问题 Background : I have a categorical variable, X , with four levels that I fit as separate dummy variables. Thus, there are three total dummy variables representing x=1, x=2, x=3 (x=0 is baseline). Problem/issue : I want to be able to calculate the value of a linear combination (i.e. using SAS as a calculator) of these dummy variables. For example, 2*B1 + 2*B2 + B3. In Stata, this can be done using the lincom command, which uses the stored beta estimates to calculate linear combinations of the

问题 I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example: library(mice) library(Hmisc) # turn all the variables into factors fake = nhanes fake$age = as.factor(nhanes$age) fake$bmi = cut2(nhanes$bmi, g=3) fake$chl = cut2(nhanes$chl, g=3) head(fake) age bmi hyp chl 1 1 <NA> NA <NA> 2 2 [20.4,25.5) 1 [187,206) 3 1 <NA> 1 [187,206) 4 3 <NA> NA <NA> 5 1 [20.4,25.5) 1 [113,187) 6 3 <NA> NA [113,187) imput = mice(nhanes) # big model fit1 = glm

问题 I have a categorical array, race, and an array of yes/no, and I want to somehow create a stacked bar/histogram plot with each race having its own bar and each bar is broken up into two different colors - one for the respondents that said yes, and the others for the ones that said no. Is there any way to do this relatively simply in MATLAB? And is there a way at least create a table that shows for each race, how many said yes, how many said no? To clarify, there are 1250 rows in my data set,

问题 I am using the seaborn module to produce a plot similar to the example below. import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns location = "/global/scratch/umalmonj/WRF/juris/golden_hourly_manual_obs.csv" df = pd.read_csv(location,usecols= ["Year","Month","Day","Time","Weather"],parse_dates=[["Year","Month","Day","Time"]]) I have a df that looks like: Year_Month_Day_Time Weather 0 2010-01-01 00:00:00 NaN 1 2010-01-01 01:00:00 NaN 2 2010-01-01 02:00

问题 Like in this post I'm struggling with the notation of MCMCglmm , especially what is meant by trait . My code ist the following library("MCMCglmm") set.seed(123) y <- sample(letters[1:3], size = 100, replace = TRUE) x <- rnorm(100) id <- rep(1:10, each = 10) dat <- data.frame(y, x, id) mod <- MCMCglmm(fixed = y ~ x, random = ~us(x):id, data = dat, family = "categorical") Which gives me the error message For error structures involving catgeorical data with more than 2 categories pleasue use