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(R) comparing several kernal density curves >library(plyr) > birthwt$smoke birthwt$smoke table(birthwt$smoke) NO smoke smoke 115 74 > ggplot(data=birthwt,aes(x=bwt))+geom_histogram(fill="white",colour="black")+facet_grid(vars(smoke)) > birthwt$race birthwt$race table(birthwt$race) black others white 26 67 96 > ggplot(data=birthwt,aes(x=bwt))+geom_histogram(fill="white",colour="black")+facet_grid(vars(race)) This form is not appropriate fo.. 2020. 9. 4.
(R) Histogram to Scale Histogram / Scale Histogram and Density curve/binwidth/adjust of geom_density() Scale Histogram>density curve: always above the horizontal x-axis. total area =1 histogram: 'counts' on the vertical y-axis the disadvantage of the histogram: disadvantage of highly depending on the number of observations. So it is not an appropriate method for comparing two histograms when the data that histograms express have a different numbers of observations. if you want to compare two grap.. 2020. 9. 4.
(R) Kernal density curve /geom_density()/geom_line(stat="density") > ggplot(data=trees,aes(x=Volume))+geom_density() > ggplot(data=trees,aes(x=Volume))+geom_density() > ggplot(data=trees,aes(x=Volume))+geom_line(stat="density") As you can see below, setting geom_density(adjust=1) means nothing. There is no difference between ggplot(data=trees,aes(x=Volume))+geom_desity() and ggplot(data=trees,aes(x=Volume))+geom_density(adjust=1) because basic set for adjust in.. 2020. 9. 3.
<MVUE>Cramer-Lao Lower bound/ Information Inequality /Fisher's Information/Information Inequality /Properties/Normal Approximation Let T(X) be the unbiased estimator of g(θ). Among available unbiased estimators what satisfy E(T(X))=g(θ), you can find the most favorable estimator with Mean Square Error. Mean square error, which is usually denoted as MSE is the expected value of squared error which is one of loss functions. The reason for computing the expected value of the loss function is that T(X) is the function of a rand.. 2020. 9. 3.
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