To compare the two matched groups, the Wilcoxon signed-rank test was performed using the wilcox.test() function in R. For linear correlations, Pearson correlation analysis was performed using the ggscatter() function in R. Survival analysis was performed in R, with optimal cut-point analysis performed using the surv_cutpoint() function in the R-package Survminer (ver. 0.4.8) [32 ]. All statistical analyses were performed using R-project 4.0.2 for Mac OS. p-values < 0.05 were considered significant.
R project 4
Manufactured by R Project for Statistical Computing
R is an open-source software environment for statistical computing and graphics. R version 4.0.2 is the latest stable release, providing a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R is designed to be highly extensible, with a large and active user community that contributes thousands of user-submitted packages to extend its functionality.
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Lab products found in correlation
5 protocols using r project 4
1
Comparative Statistical Analysis of Experimental Groups
2
Diagnostic Model Development using Machine Learning
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The statistical software SPSS 23.0 and GraphPad Prism 8.0.2 were utilized for all data analysis. The independent samples t-test was applied to analyze differences in continuous variables between the two groups if the data met a normal distribution; otherwise, the Mann-Whitney U test was performed. LASSO and logistic regression were used to screen the parameters for the construction of the diagnostic model. To assess the model’s capability, the receiver operating characteristic (ROC), calibration, and decision curves were plotted using R Project 4.0.2. A two-tailed p value of < 0.05 was considered statistically significant. Machine learning methods such as random forest, decision tree, and SVM were also utilized to construct discriminative diagnostic models.
3
Dietary Patterns and Mortality Risk
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The analyses (except principal component analysis [PCA] and partial least squares discriminant analysis [PLS‐DA]) incorporated sample weights, stratification, clustering, and domains to account for the complex survey design according to the NHANES analytic guidelines. Demographic characteristics, dietary nutrients intake, and anthropometric measurements were presented as mean (SD) for continuous variables and number (percentage) for categorical variables. General linear models adjusting for age and χ2 tests were used to compare baseline characteristics by mortality status, and they were also used to compare the differences for the food groups by dietary patterns. The PCA and Cox proportional hazards model were performed in the R‐project 4.0.2 using "Ade4" package and "Survival" package, respectively. The PLS‐DA was performed in SIMCA‐P 13.0. The 2‐sided P<0.05 was considered to be statistically significant.
4
Network Measures and Language Outcomes
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All statistical tests were performed utilizing the R-Project 4.1.2.2 Factors that were potentially associated with network measures were first screened using univariate analysis, and the significant ones were taken as covariates into the multiple regression to analyze the correlations between network measures and gestational age (GA) at birth, and postmenstrual age (PMA) at scan. Pearson’s correlation analysis was conducted between network measures obtained from UNC and M-CRIB parcelation to evaluate the effect of the parcelation scheme.
For assessing the correlation between network properties and language outcomes, univariate analysis was used to identify potential factors that were significantly associated with the Chinese CDI scores, which were included as covariates for subsequent multiple analysis. It was noted that the observed network measures were first regressed against PMA at scan and bodyweight at scan, and then, the residuals were used as PMA-corrected indicators of network for subsequent language correlation analysis. The p-values were adjusted by the false discovery rate method. The significance level for all analyses was set at 0.05.
For assessing the correlation between network properties and language outcomes, univariate analysis was used to identify potential factors that were significantly associated with the Chinese CDI scores, which were included as covariates for subsequent multiple analysis. It was noted that the observed network measures were first regressed against PMA at scan and bodyweight at scan, and then, the residuals were used as PMA-corrected indicators of network for subsequent language correlation analysis. The p-values were adjusted by the false discovery rate method. The significance level for all analyses was set at 0.05.
5
Pathological Factors in IgG4-Related Disorders
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The analyses of the pathological counts of IgG4+ and IgG+ plasmacytes and GCs among the three groups were conducted using one-way ANOVA and LSD tests. The logarithm of the serum IgG4 level was introduced in a parametric test for the concentration following the lognormal distribution in IgG4ROD. The difference in the histopathological and serological findings between the groups with and without extraophthalmic involvement was analyzed using the unpaired Student’s t test. Linear regression analysis was performed to analyze the association between histopathological factors and serum IgG4 levels. Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC) to identify diagnostic values of the counts of IgG4+ plasmacytes and serum IgG4 levels for IgG4ROD. Univariate and multivariate Cox regression analyses (enter method) were performed to identify the potential clinicopathological risk factors for relapse and their hazard ratios (HRs). Variables with p < 0.1 in the univariate Cox regression were included in the multivariate analysis. Kaplan–Meier survival curves and log-rank tests were performed to compare the effect of different grades of IgG4+ plasmacyte infiltration on IgG4ROD relapse. p < 0.05 was considered statistically significant. All statistical analyses were performed with SPSS statistics v.25 and R-project 4.1.2.
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