regression

问题 I have a set of points (in the form x1,y1,z1 ... xn,yn,zn ) obtained from a surface mesh. I want to find the best-fit 3D plane to these points by minimizing orthogonal distances. x,y,z coordinates are independent, that is I want to obtain the coefficient A, B, C, D for the plane equation Ax + By + Cz + D = 0. What would be the algorithm to obtain A, B, C, D? Note: in a previous post it was discussed the best-fit plane in a least squares sense, by considering the z coordinate a linear function

问题 I am trying to fit a quadratic function to some data, and I'm trying to do this without using numpy's polyfit function. Mathematically I tried to follow this website https://neutrium.net/mathematics/least-squares-fitting-of-a-polynomial/ but somehow I don't think that I'm doing it right. If anyone could assist me that would be great, or If you could suggest another way to do it that would also be awesome. What I've tried so far: import numpy as np import matplotlib.pyplot as plt import pandas

问题 When using stat_smooth() with geom_point is there a way to remove the shaded fit region, but only draw its outer bounds? I know I can remove the shaded region with something like: geom_point(aes(x=x, y=y)) + geom_stat(aes(x=x, y=y), alpha=0) but how can I make the outer bounds of it (outer curves) still visible as faint black lines? 回答1: You can also use geom_ribbon with fill = NA. gg <- ggplot(mtcars, aes(qsec, wt))+ geom_point() + stat_smooth( alpha=0,method='loess') rib_data <- ggplot

问题 I just joined the community and looking forward to get some help for the data analysis for my master thesis. At the moment I have the following problem: I plotted 42 varieties with ggplot by using facet_wrap: `ggplot(sumfvvar,aes(x=TemperaturCmean,y=Fv.Fm,col=treatment))+ geom_point(shape=1,size=1)+ geom_smooth(method=lm)+ scale_color_brewer(palette = "Set1")+ facet_wrap(.~Variety)` That works very well, but I would like to annotate the r squared values for the regression lines. I have two

问题 I just joined the community and looking forward to get some help for the data analysis for my master thesis. At the moment I have the following problem: I plotted 42 varieties with ggplot by using facet_wrap: `ggplot(sumfvvar,aes(x=TemperaturCmean,y=Fv.Fm,col=treatment))+ geom_point(shape=1,size=1)+ geom_smooth(method=lm)+ scale_color_brewer(palette = "Set1")+ facet_wrap(.~Variety)` That works very well, but I would like to annotate the r squared values for the regression lines. I have two

问题 When I perform a regression in R and use type factor it helps me avoid setting up the categorical variables in the data. But how do I remove a factor that is not significant from the regression to just show significant variables? For example: dependent <- c(1:10) independent1 <- as.factor(c('d','a','a','a','a','a','a','b','b','c')) independent2 <- c(-0.71,0.30,1.32,0.30,2.78,0.85,-0.25,-1.08,-0.94,1.33) output <- lm(dependent ~ independent1+independent2) summary(output) Which results in the

问题 Can anyone explain to me how I can apply non linear regression to this equation t find out K using the matlab command window. I = 10^-9(exp(38.68V/k)-1). Screenshot of Equation I have data values as follows: Voltage := [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: Current:= [0, 0, 0, 0, 0, 0, 0, 0.07, 0.92, 12.02, 158.29]: Screenshot of Equation [NEW]: Now I used FminSearch as an alternative another and another error message appeared. Matrix dimensions must agree. Error in @(k)sum((I

问题 Can anyone explain to me how I can apply non linear regression to this equation t find out K using the matlab command window. I = 10^-9(exp(38.68V/k)-1). Screenshot of Equation I have data values as follows: Voltage := [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: Current:= [0, 0, 0, 0, 0, 0, 0, 0.07, 0.92, 12.02, 158.29]: Screenshot of Equation [NEW]: Now I used FminSearch as an alternative another and another error message appeared. Matrix dimensions must agree. Error in @(k)sum((I

问题 I have created a simple neural network (Python, Theano) to estimate a persons age based on their spending history from a selection of different stores. Unfortunately, it is not particularly accurate. The accuracy might be hurt by the fact that the network has no knowledge of ordinality. For the network there is no relationship between the age classifications. It is currently selecting the age with the highest probability from the softmax output layer. I have considered changing the output

问题 I ran a multiple regression with several continuous predictors, a few of which came out significant, and I'd like to create a scatterplot or scatter-like plot of my DV against one of the predictors, including a "regression line". How can I do this? My plot looks like this D = my.data; plot( D$probCategorySame, D$posttestScore ) If it were simple regression, I could add a regression line like this: lmSimple <- lm( posttestScore ~ probCategorySame, data=D ) abline( lmSimple ) But my actual