Curve fitting in R curve fitting with least squares fitting

Curve fitting: (Linear regression method: LM)1, x order 2, the linear regression equation and give a new variable Z=LM (y~x+i (x^2) + ...) 3, Plot (x,y) #做y对x的散点图 4, lines (x,fitted (z)) #添加拟合值对x的散点图并连线
curve Fitting: (NLS)LM is to line up the curve and do regression, the NLS is a direct fitting curve. Three conditions are required: The curve equation, the data position, and the estimated value of the coefficients. If the curve equation is more complex, you can first name a custom function.     Example: F=function (x1, x2, A, b) {a+x1+x2^b};         Result=nls (X$y~f (x$x1, x$x2, A, b), Data=x, Start=list (A=1, b=2));    #x可以是数据框或列表, but can not be a matrix #对系数的估计要尽量接近真实值, if the difference is too far will be an error: "Singular gradient" summary (result); #结果包含对系数的估计和p值 the lines curve can be used directly on the scatter chart according to the estimated coefficients.
curve fitting: (partial regression)Lowess (x, y=null, F = 2/3, iter = 3) #可以只包含x, you can also use X, y two variables #f为窗宽参数, the larger the smoother #iter为迭代次数, the greater the calculation the slower loess (y~x, data, span =0.75, degree=2) #data为包含x, y data sets, span for window width parameters #degree默认为二次回归 #该方法计算1000个数据点约占10M内存 Examples: x=seq (0, 10, 0.1);    Y=sin (x) +rnorm (a) #x的值必须排序 plot (x,y);    #做散点图 lines (lowess (x,y));    #利用lowess做回归曲线 lines (X,predict (loess (y~x)); #利用loess做回归曲线, the Predict is z=loess (y~x) with regression prediction value.    Lines (x, Z$fit); #利用loess做回归曲线的另一种做法

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