lm

问题 The function poly() in R is used in order to produce orthogonal vectors and can be helpful to interpret coefficient significance. However, I don't see the point of using it for prediction. To my view, the two following model (model_1 and model_2) should produce the same predictions. q=1:11 v=c(3,5,7,9.2,14,20,26,34,50,59,80) model_1=lm(v~poly(q,2)) model_2=lm(v~1+q+q^2) predict(model_1) predict(model_2) But it doesn't. Why? 回答1: Because they are not the same model. Your second one has one

问题 I'm sure this can be fixed with few bytes, but I've spent hours on this simple thing and can't get out of it. I don't use R often. I have 5 asciigrid files that represent 5 raster images. Some pixels do have values, other do have NAs. For example, the first image might be something like: NA NA NA NA NA NA NA 2 3 NA NA 0.2 0.3 1 NA NA NA 4 NA NA and the second might be: NA NA NA NA NA NA NA 5 1 NA NA 0.1 12 12 NA NA NA 6 NA NA As you can see, NA position is always the same and I'm 100% sure

问题 I'm sure this can be fixed with few bytes, but I've spent hours on this simple thing and can't get out of it. I don't use R often. I have 5 asciigrid files that represent 5 raster images. Some pixels do have values, other do have NAs. For example, the first image might be something like: NA NA NA NA NA NA NA 2 3 NA NA 0.2 0.3 1 NA NA NA 4 NA NA and the second might be: NA NA NA NA NA NA NA 5 1 NA NA 0.1 12 12 NA NA NA 6 NA NA As you can see, NA position is always the same and I'm 100% sure

问题 The standard summary(lm(Height~Weight)) will output results for the hypothesis test H0: Beta1=0, but if I am interested in testing the hypothesis H0: B1=1 is there a package that will produce that p-value? I know I can calculate it by hand and I know I can "flip the confidence interval" for a two tailed test (test a 95% hypothesis by seeing if the 95% confint contains the point of interest), but I am looking for an easy way to generate the p-values for a simulation study. 回答1: You can use

问题 The standard summary(lm(Height~Weight)) will output results for the hypothesis test H0: Beta1=0, but if I am interested in testing the hypothesis H0: B1=1 is there a package that will produce that p-value? I know I can calculate it by hand and I know I can "flip the confidence interval" for a two tailed test (test a 95% hypothesis by seeing if the 95% confint contains the point of interest), but I am looking for an easy way to generate the p-values for a simulation study. 回答1: You can use

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem