Pareto plot (coefficient plot) for a factorial design modelKevin Dunn
void CoefficientPlot( Matrix Decomposition, Matrix ThreshOpt, int StartLevel, int EndLevel, String HowScaled, String Header )ParametersMatrix Decomposition A wavelet decomposition produced by the WAVFT subroutine.Matrix ThreshOpt A numeric vector with four elements that specifies the thresholding to be ...
plot(NULL,#createemptyplot xlim=c(-2,2),#setxlimbyguessing ylim=c(.7,length(var.names)+.3),#setylimbythenumberof variables axes=F,xlab=NA,ylab=NA)#turnoffaxesandlabels #addthedata est<-coef(m)[-1] #convenientlystoretheestimates(minustheconstant) se<-sqrt(diag(vcov(m)))[-1] #co...
This plot is based on first using PCA on the predicted Y-values and then regressing the different X-variables onto the scores from the PCA. One then obtains Y-loadings and Y-scores directly from the PCA and a separate X-loadings plot from the regression coefficients. The PCP plot is ...
plt.stackplot(x_values, y_values, diagonal) plt.xlabel('Cumulative Share of Predictions') plt.ylabel('Cumulative Share of Actual Values') plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 4.计算橙色区域面积 ...
The silhouette plot shows how close each point in a cluster is to points in the neighboring clusters and thus provides a way to visually determine parameters such as the number of clusters. This measure has a range of [-1, 1]. Silhouette coefficients close to + 1 imply that the sample ...
Generate a dendrogram plot of the hierarchical cluster tree. dendrogram(Z) Compute the inconsistency coefficient for each link in the cluster tree Z to depth 3. W = inconsistent(Z,3) W =9×40.1313 0 1.0000 0 0.1386 0 1.0000 0 0.1463 0.0109 2.0000 0.7071 0.2391 0 1.0000 0 0.1951 0.0568 4....
Make the forest plot fp.forestplot(df,# the dataframe with results dataestimate="r",# col containing estimated effect sizell="ll",hl="hl",# columns containing conf. int. lower and higher limitsvarlabel="label",# column containing variable labelylabel="Confidence interval",# y-label titlexla...
设置图形大小plt.subplot(1,2,1)# 定义子图布局plt.plot(k_values,sse,'bo-')# 绘制SSE值曲线图plt.title('Elbow Method For Optimal k')# 添加标题plt.xlabel('Number of clusters (k)')# x轴标签plt.ylabel('SSE')# y轴标签# 绘制不同k值的轮廓分数图plt.subplot(1,2,2)# 定义第二个子图布局...
The results are illustrated in Figure 9.16, plotting the coefficient of friction values against the dimensionless group Δhσfm/Pr; in the plot the coefficients of friction from the laboratory mill were corrected to allow for the effect of geometry according to the square root of the ratio of ...