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yikeshu0611
cutoff:Seek the Significant Cutoff Value
Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Missing values will be deleted by na.omit() function before analysis.
Maintained by Jing Zhang. Last updated 5 years ago.
1 stars 3.67 score 31 scripts 1 dependentsyikeshu0611
nomogramFormula:Calculate Total Points and Probabilities for Nomogram
A nomogram, which can be carried out in 'rms' package, provides a graphical explanation of a prediction process. However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complexed condition, interaction or restricted cubic spine, TotalPoints.rms() can be used.
Maintained by Jing Zhang. Last updated 5 years ago.
3.13 score 15 scripts 1 dependents
cran
ggrisk:Risk Score Plot for Cox Regression
The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.
Maintained by Jing Zhang. Last updated 4 years ago.
2 stars 2.08 score