linear-regression

问题 As the title describes, I'm simply trying to create a shiny application that allows the user to generate linear regression plots based on an imported csv file. After importing the file the dropdown for the variables of interest should be dynamically updated. As the code below shows, I'm able to accomplish that with mtcars but I'm not able to do the same with an imported files that would have different dependent and independent variables . Thank you for your help data(mtcars) cols <- sort

问题 Hi I’m starting to use r and am stuck on analyzing my data. I have a dataframe that has 80 columns. Column 1 is the dependent variable and from column 2 to 80 they are the independent variables. I want to perform 78 multiple linear regressions leaving the first independent variable of the model fixed (column 2) and create a list where I can to save all regressions to later be able to compare the models using AIC scores. how can i do it? Here is my loop data.frame for(i in 2:80) { Regressions

问题 I am trying to run an R application, but I receive the following first error : Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases Calls: lm -> lm.fit The code which generates the error is : pppb = lm(Exchange.rate.change ~ Inflation.difference) I am new to R and is really hard for me to find the mistake, so any help it is really appreciated. This is a minimal data set: Country Inflation.difference Exchange.rate.change Developed Australia -1.235100000e

问题 My goal is to add 5 fit lines to the exchange index, which is a time series data. Below is what I want to achieve: There should be a plotted (&log2 transformed) index, a best fit line (in yellow), and 4 other parallel lines where it covers 95%, 75%, 25% and 5% of the index respectively. The x-axis is omitted in the picture, but it should be dates. So my question is how to add these 5 lines using Plotly? With my current code, I was able to plot the index without the 5 parallel lines but I

问题 My goal is to add 5 fit lines to the exchange index, which is a time series data. Below is what I want to achieve: There should be a plotted (&log2 transformed) index, a best fit line (in yellow), and 4 other parallel lines where it covers 95%, 75%, 25% and 5% of the index respectively. The x-axis is omitted in the picture, but it should be dates. So my question is how to add these 5 lines using Plotly? With my current code, I was able to plot the index without the 5 parallel lines but I

问题 I have some data about the percentages of temperature for different time periods and I want to create a barplot showing those percentages and then add a linear regression line showing the trend. Although i manage to get the first graph, I fail to add a straight linear regression line Basically I try to make a barplot with these tx_1 data tx_1<-c(0.055,0.051,0.057,0.049,0.061,0.045) mypath<-file.path("C:\\tx5\\1.jpeg") jpeg(file = mypath,width = 1200, height = 600) plot.dim<-barplot(get(name),

问题 So, I can get sklearn.linear_model.LinearRegression to process my data - at least to run the script without raising any exceptions or warnings. The only issue is, that I am not trying to plot the results with matplotlib, but instead I want to see the estimators and diagnostic statistics for the model. How can I get a model summary such as the slope and intercept (B0,B1), R squared adjusted, etc to display in the console or populate into a variable instead of plotting this? This is a generic

问题 I have created a scatterplot3d with a linear model applied. Unfortunately the results of the LM are subtle and need to be emphasised, my question is how can I extend the LM grid outside of the 'cube'. Plot: Code: Plot1 <-scatterplot3d( d$MEI, d$YYYYMM, d$AOELog10, pch=20, grid = FALSE, color = "black", xlab="MEI", ylab="Date", zlab="AOE Log(10)" ) fit <- lm(d$AOELog10 ~ d$MEI+d$Rank) Plot1$plane3d(fit) Now I guess it might be a variable within lm(), but I cant find anything.... 回答1: To see a

问题 I'm working with a data frame containing a lot of indicator variables that I made from categorical variables using dummy() . When using regsubsets (from the leaps package), is there a way to make it include these indicators by group, not individually? In other words, can I get it to test e.g. models that contain DF$Cat1.A , DF$Cat1.B , and DF$Cat1.C , but not models containing DF$Cat1.A without DF$Cat1.B or DF$Cat1.C ? 来源： https://stackoverflow.com/questions/40267516/r-can-i-get-regsubsets-to

问题 I have panel data and numerous variables are missing observations before certain years. The years vary across variables. What is an efficient way to extrapolate for missing data points across multiple columns? I'm thinking of something as simple as extrapolation from a linear trend, but I'm hoping to find an efficient way to apply the prediction to multiple columns. Below is a sample data set with missingness similar to what I'm dealing with. In this example, I'm hoping to fill in the NA