statistics

问题 I am working on a regression script. I have a data.frame with roughly 130 columns, of which I need to do a regression for one column (lets call it X column) against all the other ~100 numeric columns. Before the regression is calculated, I need to group the data by 4 factors: myDat$Recipe , myDat$Step , myDat$Stage , and myDat$Prod while still keeping the other ~100 columns and row data attached for the regression. Then I need to do a regression of each column ~ X column and print out the R^2

问题 I've been working on generating Perlin noise for a map generator of mine. The problem I've run into is that the random noise is not distributed normally, and is more likely a normal distribution of kinds. Given two integers X and Y, and a seed value, I do the following: Use MurmurHash2 to generate a random number (-1,1). This is uniformly distributed. Interpolate points between integer values with cubic interpolation. Values now fall in the range (-2.25, 2.25) because the interpolation can

问题 I'd like to know what method is best suited for predicting event occurrences. For example, given a set of data from 5 years of malaria infection occurrences and several other factors that affect the occurrences, I'd like to predict the next five years for malaria infection occurrences. What I thought of doing was to derive a kind of occurrence factor using fuzzy logic rules, and then average the occurrences with the occurrence factor to get the first predicted occurrence, and then average all

问题 I am having trouble understanding the likelihood function for GDA given in Andrew Ng's CS229 notes. l(φ,µ0,µ1,Σ) = log (product from i to m) {p(x(i)|y(i);µ0,µ1,Σ)p(y(i);φ)} The link is http://cs229.stanford.edu/notes/cs229-notes2.pdf Page 5. For Linear regression the function was product from i to m p(y(i)|x(i);theta) which made sense to me. Why is there a change here saying it is given by p(x(i)|y(i) and that is multiplied by p(y(i);phi)? Thanks in advance 回答1: The starting formula on page 5

问题 I've created a new Robust HoltWinters function (based on the stats::Holt-Winters) method in R (per "Robust Forecasting with Exponential and Holt-Winters Smoothing" by Sarah Gelper1,, Roland Fried, Christophe Croux. September 26, 2008.) Why? Well...why not! But I digress... The core of the stats::Holt-Winters method is a C code called C_HoltWinters, which I've modified to be robust (See below) #include <stdlib.h> #include <string.h> // memcpy #include <math.h> #include <R.h> #include "ts.h"

问题 I work on calibration of probabilities. I'm using a probability mapping approach called generalized additive models. The algorithm I wrote is: probMapping = function(x, y, datax, datay) { if(length(x) < length(y))stop("train smaller than test") if(length(datax) < length(datay))stop("train smaller than test") datax$prob = x # trainset: data and raw probabilities datay$prob = y # testset: data and raw probabilities prob_map = gam(Target ~ prob, data = datax, familiy = binomial, trace = TRUE)

问题 I have a file with date/time data and its measured values for said date and time. The values were measured every ten minutes for the course of one month, and I am attempting to do a time series analysis eventually. Before that however, I wanted to aggregate the 10 minute intervals to hourly intervals by calculating the mean measurement of every 60 minutes. Here is a sample of my data(a total of 4319 observations): Date/Time Value 2013-01-01 00:00:00 31,439999 2013-01-01 00:10:00 33,439999

问题 I am trying to get an ANOVA table for my split-split plot design where it will use type III Sum sq instead of type I. This is what I have done so far; > Attach(Data) > library(car) > options(contrasts = c("contr.sum", "contr.poly")) > mod <- aov(Response ~ A*B*C + Error(Block/A/B/C)) > Anova(mod, type='III') Error in vcov.default(mod) : there is no vcov() method for models of class aovlist, listof I don't understand why I keep getting this error message, or what to do about it. Any help

问题 I am using scipy's stats module to try and determine values of a distribution at which the upper tail probability reaches some small value, but I am getting some very unrealistic results. For example: I fit a beta distribution to an array of the square of normalized correlation coefficients for a signal matching operation (correlation coefficient is always between -1 and 1 so its square is between 0 and 1). Using import scipy, numpy as np bd=scipy.beta.fit(np.square(data),floc=0,fscale=1)

问题 Using the following to estimate the coefficient of determination in MATLAB: load hospital y = hospital.BloodPressure(:,1); X = double(hospital(:,2:5)); X2 = X(:,3); mdl = fitlm(X2,y); Estimated Coefficients: Estimate SE tStat pValue ________ ________ ______ __________ (Intercept) 116.72 3.9389 29.633 1.0298e-50 x1 0.039357 0.025208 1.5613 0.12168 Number of observations: 100, Error degrees of freedom: 98 Root Mean Squared Error: 6.66 R-squared: 0.0243, Adjusted R-Squared 0.0143 F-statistic vs.