Data are presented as means ± SE. Where error bars are not visible in a graph, they are smaller than the plotted symbols. Data were tested for normality and homogeneity of variance. Thereafter, data were examined using regression analyses, analysis of variance and pairwise t-tests (R version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria) or Tukey’s studentised test (package multcomp 1.3–1, procedure glht, R version 3.6.3; R Foundation for Statistical Computing).
R version 3
Manufactured by R Project for Statistical Computing
Sourced in United States, Austria, Japan, Belgium, New Zealand, United Kingdom, Germany, Denmark, Australia, France
R version 3.6.1 is a statistical computing and graphics software package. It is an open-source implementation of the S programming language and environment. R version 3.6.1 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.
Automatically generated - may contain errors
Lab products found in correlation
Sas version 9, by SAS Institute (463 mentions)
Sas 9, by SAS Institute (92 mentions)
Spss version 22, by IBM (30 mentions)
Stata version 14, by StataCorp (29 mentions)
Prism 6, by GraphPad (27 mentions)
Spss version 25, by IBM (27 mentions)
Spss version 23, by IBM (27 mentions)
Spss version 24, by IBM (25 mentions)
Prism 8, by GraphPad (22 mentions)
Rstudio version 1, by Posit (21 mentions)
Prism 7, by GraphPad (20 mentions)
Stata version 15, by StataCorp (19 mentions)
Spss version 26, by IBM (17 mentions)
Stata version 13, by StataCorp (16 mentions)
Sas enterprise guide 7, by SAS Institute (15 mentions)
Spss version 20, by IBM (15 mentions)
Stata 15, by StataCorp (13 mentions)
Spss 23, by IBM (12 mentions)
Nonmem version 7, by ICON Development Solutions (11 mentions)
Spss statistics 22, by IBM (11 mentions)
4 892 protocols using r version 3
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of Research Protocols
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For continuous variable values, the two-tailed t-test, unpaired, one-way ANOVA, or Mann–Whitney test were used for comparison. For categorical variables, the χ2 test or Fisher's exact test was used to compare. Standardization of the image features was applied by transforming the data of each feature into new scores with a mean of zero and a standard deviation of 1 (z-score transformation) (29 (link), 30 (link)). Most of the statistical tests were examined by using SPSS version 21.0 (IBM) and R version 3.5.3 (http://www.r-project.org ), and the nomograms and calibration plots were conducted by using the R version 3.5.3 with rms package. For all tests, P < 0.05 was thought to be statistically significant.
3
Statistical Analysis of Biological Experiments
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For osmotic avoidance assays comparisons were performed using the Student’s t-test. If the data was not normally distributed, the Mann-Whitney Ranked Sum was used instead. These tests were performed using SigmaStat3.1 software package (Systat Software IC).
Dye-filling experiments were analyzed using a 2x2 chi-square test in R (R foundation for statistical computing, 2014). An FDR postHoc test was completed to account for multiple comparisons. For all tests, p values less than 0.05 were considered significant.
Kymograph experiments were analyzed using a two-way ANOVA with a Tukey’s Post-Hoc test. These tests were performed using R version 3.1.2 software package (R foundation for statistical computing, 2014). For all tests, p values less than 0.05 were considered significant.
Cilia Length experiments were analyzed using a one-way ANOVA with a Tukey’s Post-Hoc test. These tests were performed using R version 3.1.2 software package (R foundation for statistical computing, 2014). For all tests, p values less than 0.05 were considered significant.
Dye-filling experiments were analyzed using a 2x2 chi-square test in R (R foundation for statistical computing, 2014). An FDR postHoc test was completed to account for multiple comparisons. For all tests, p values less than 0.05 were considered significant.
Kymograph experiments were analyzed using a two-way ANOVA with a Tukey’s Post-Hoc test. These tests were performed using R version 3.1.2 software package (R foundation for statistical computing, 2014). For all tests, p values less than 0.05 were considered significant.
Cilia Length experiments were analyzed using a one-way ANOVA with a Tukey’s Post-Hoc test. These tests were performed using R version 3.1.2 software package (R foundation for statistical computing, 2014). For all tests, p values less than 0.05 were considered significant.
4
Comparison of Data Entry Methods
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The data of elapsed time did not follow a normal distribution, so we conducted nonparametric analyses. To determine whether there were differences in the types of data entry times, we used the Wilcoxon rank-sum test. We also used the Kruskal-Wallis test and the Wilcoxon rank-sum test to compare the elapsed time between the first year of SDE, SDE in 2017, and free-text. For statistical analysis, we used the software program R, version 3.6.1 (The R Foundation).
After applying SDEs, we surveyed three groups on different topics. All the questionnaires were different between the groups, so the comparison of scores between the groups was not meaningful. However, we conducted parametric analyses because the data extraction team survey results between 2013 and 2017 did follow a normal distribution. We used two-sample t tests using R, version 3.6.1 (The R Foundation).
After applying SDEs, we surveyed three groups on different topics. All the questionnaires were different between the groups, so the comparison of scores between the groups was not meaningful. However, we conducted parametric analyses because the data extraction team survey results between 2013 and 2017 did follow a normal distribution. We used two-sample t tests using R, version 3.6.1 (The R Foundation).
5
Pubertal Timing and Bone Density
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Of those who had a DXA or pQCT scan, 75% also had reports of pubertal timing. R version 3.2 (www.R-project.org ) was used to fit the SITAR model and generate the SITAR random effects. For all other analyses, Stata v10.1 was used. Regression models used natural logarithms for all bone variables for comparative purposes. The coefficients from these models are presented as the percentage difference in the bone outcome by category, or per unit increase.
6
Epitope-based Ebola Vaccine Design
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Selection of known T-cell epitopes of Ebola viruses was carried out based on the IEDB—Immune Epitope Database (http://iedb.org ) [24 (link)]. Prediction of T-cell epitopes was conducted using TEpredict software [25 (link)]. Design of polyepitope antigens was performed with PolyCTLDesigner [26 (link)]. Genes encoding target immunogens were developed using GeneDesigner software [27 (link)]; a compound of codons was optimized to achieve high expression of genes in human cells. Analysis of amino acid sequences of peptides, evaluating their conservatism, statistical analysis of obtained findings, and graph plotting were executed in statistical analysis environment R (version 3.2; https://www.R-project.org/ , Vienna, Austria) [28 ].
7
Bone Density Analysis via DXA and pQCT
8
COVID-19 Hospitalization Mortality Risk
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Categorical and continuous variables were described using frequencies with percentages and medians with interquartile ranges (IQRs), respectively. Mortality risk following COVID-19-related hospitalisation was estimated as hazard ratios (HRs) along with 95% confidence intervals (CIs), using a mixed-effect Cox proportional hazard model, comparing ACEIs, ARBs, and other antihypertensives versus CCBs. In an additional analysis, risk was estimated considering non-use of any antihypertensive as the reference category. We modelled catchment area as a random effect. Potential confounders were included in the multivariable model using a stepwise Akaike information criterion (AIC) method. Proportional hazard assumptions were verified on the basis of Schoenfeld residuals, and significance was set at a p value < 0.05. All statistical analyses were performed using STATA version 16 (StataCorp LLC, College Station, TX, USA) and R version 3.6 (The R Foundation for Statistical Computing, Vienna, Austria).
9
Temporal Trends in Prevalence Rates
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In addition to the descriptive analyses described previously, a trends analysis was conducted to estimate temporal changes in annual PR among the overall study sample and stratified by sex (female and male), geographic region (Northeast, Midwest, South, and West), and age group. Specifically, the annual PR during each subsequent calendar year was compared with the annual PR in 2016 using weighted logistic regression models, and changes in annual IRs from 2016 onward were estimated using weighted Poisson regression models. Significance was assessed at an α level of P < .05. All statistical analyses were performed using SAS, version 9.4 (SAS Institute), and R, version 3.6 (R Foundation).
10
Soybean and Wheat Seed Morphology Analysis
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All statistical analyses were carried out with R version 3.6 (R Foundation for Statistical Computing, Vienna, Austria). Pairwise correlation analysis among eight morphological traits of soybean seeds extracted by 3DPheno-Seed&Fruit software were computed using Pearson correlation coefficients. To compare the difference between the 3DPheno-Seed&Fruit and manual measurements for soybean and wheat seeds, we fitted the data to a simple linear regression using the CT measurement as y-axis and the manual measurement as x-axis. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were calculated with the following equations:
Where n is the total number of measurements; xi is the manual measurement results; yi is the CT measurement results, and is the mean of the CT measurements.
Where n is the total number of measurements; xi is the manual measurement results; yi is the CT measurement results, and is the mean of the CT measurements.
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