regression

问题 I need to speed up the calculation of the mean estimate of beta in a WLS in R - I was able to speed up the covariance calculation thanks to SO, and now I am wondering if there is another trick to also speed up the mean calculation (or if what I am doing is already efficient enough). n = 10000 y = rnorm(n, 3, 0.4) X = matrix(c(rnorm(n,1,2), sample(c(1,-1), n, replace = TRUE), rnorm(n,2,0.5)), nrow = n, ncol = 3) Q = diag(rnorm(n, 1.5, 0.3)) wls.cov.matrix = crossprod(X / sqrt(diag(Q))) Q.inv =

问题 I have yield data for a farm (independent variable) and various nutrients which serve as predictors. I have performed univariate (cubic) linear regression using lm(y ~ ploy(x,3)) . Then I plotted the predictor variable (P) against the yield, and added a best fit curve (Figure 1). How do I then find the local maxima of this curve, and add a point to my plot which includes the value of this fitted yield (Figure 2)? I have looked into the findPeaks() function from the quantmod package, but have

问题 I have yield data for a farm (independent variable) and various nutrients which serve as predictors. I have performed univariate (cubic) linear regression using lm(y ~ ploy(x,3)) . Then I plotted the predictor variable (P) against the yield, and added a best fit curve (Figure 1). How do I then find the local maxima of this curve, and add a point to my plot which includes the value of this fitted yield (Figure 2)? I have looked into the findPeaks() function from the quantmod package, but have

问题 The way I understand regression for neural networks is weights being added to each x-input from the dataset. I want something slightly different. I want weights added to the function that computes each x-input we'll call these s-inputs The function to compute the x-inputs is a summation function of all s-inputs I want each s-input to have its own weight So I say regression because I want the end result to be a beautiful continuous function between the mapping x -> y ...but that is

问题 The way I understand regression for neural networks is weights being added to each x-input from the dataset. I want something slightly different. I want weights added to the function that computes each x-input we'll call these s-inputs The function to compute the x-inputs is a summation function of all s-inputs I want each s-input to have its own weight So I say regression because I want the end result to be a beautiful continuous function between the mapping x -> y ...but that is

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.

问题 I am estimating Fama-Macbeth regression. I have taken the code from this site fpmg <- pmg(Mumbo~Jumbo, test, index=c("year","firmid")) summary(fpmg) Mean Groups model Call: pmg(formula = Mumbo ~ Jumbo, data = superfdf, index = c("day","Firm")) Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -0.142200 -0.006930 0.000000 0.000000 0.006093 0.142900 Coefficients Estimate Std. Error z-value Pr(>|z|) (Intercept) -3.0114e-03 3.7080e-03 -0.8121 0.4167 Jumbo 4.9434e-05 3.4309e-04 0.1441 0.8854 Total

问题 I'm trying to do a simple least-squares regression in R and have been getting errors constantly. This is really frustrating, can anyone point out what I am doing wrong? First I attach the dataset (17 variables, 440 observations, each observation on a single line, no column titles). Here, I get a "masked" error. From what I've read, the "masked" error happens when objects overlap. However here I am not using any packages but the default, and I loaded a new workspace image before this. Not sure

问题 I'm using predict.lm(fit, newdata=newdata, interval="prediction") to get predictions and their prediction intervals (PI) for new observations. Now I would like to aggregate (sum and mean) these predictions and their PI's based on an additional variable (i.e. a spatial aggregation on the zip code level of predictions for single households). I learned from StackExchange, that you cannot aggregate the prediction intervals of single predictions just by aggregating the limits of the prediction

问题 I am taking a course on Deep Learning in Python and I am stuck on the following lines of an example: regressor.compile(optimizer = 'adam', loss = 'mean_squared_error') regressor.fit(X_train, y_train, epochs = 100, batch_size = 32) From the definitions I know, 1 epoch = going through all training examples once to do one weight update. batch_size is used in optimizer that divide the training examples into mini batches. Each mini batch is of size batch_size . I am not familiar with adam