en,zh,source
Help,帮助,"global.R, radiant.R"
Keyboard shortcuts,键盘快捷键,global.R
User id:,用户 ID:,crs_ui.R
Product id:,产品 ID:,crs_ui.R
Choose products to recommend:,选择要推荐的产品:,crs_ui.R
Ratings variable:,评分变量:,crs_ui.R
Provide data name,请输入数据名称,crs_ui.R
Estimate model,估计模型,"crs_ui.R, crtree_ui.R, gbt_ui.R, logistic_ui.R"
Re-estimate model,重新估计模型,"crs_ui.R, crtree_ui.R, gbt_ui.R, logistic_ui.R"
,,crs_ui.R
Store,保存,"crs_ui.R, gbt_ui.R, logistic_ui.R"
Collaborative Filtering,协同过滤,crs_ui.R
Model > Recommend,模型 > 推荐,crs_ui.R
"This analysis requires a user id, a product id, and product ratings.
If these variables are not available please select another dataset.
","此分析需要用户 ID、产品 ID 和评分变量。
如果这些变量不存在，请选择另一个数据集。
",crs_ui.R
"A data filter or slice must be set to generate recommendations using
collaborative filtering. Add a filter or slice in the Data > View tab.
Note that the users in the training sample should not overlap
with the users in the test sample.","必须设置数据过滤或切片才能使用协同过滤生成推荐。
在“数据 > 查看”选项卡中添加过滤器或切片。
注意：训练集和测试集中的用户不应重叠。",crs_ui.R
"An invalid filter has been set for this dataset. Please
adjust the filter in the Data > View tab and try again","此数据集设置了无效的过滤条件。
请在“数据 > 查看”中调整过滤条件并重试。",crs_ui.R
Please select one or more products to generate recommendations,请选择一个或多个产品以生成推荐,crs_ui.R
Estimating model,正在估计模型,"crs_ui.R, crtree_ui.R"
** Press the Estimate button to generate recommendations **,** 请点击“估计模型”按钮以生成推荐 **,crs_ui.R
Generating plots,正在生成图形表,"crs_ui.R, crtree_ui.R, gbt_ui.R, logistic_ui.R"
No data selected to generate recommendations,未选择任何数据用于生成推荐,crs_ui.R
Data Stored,数据已保存,crs_ui.R
Dataset '{fixed}' was successfully added to the datasets dropdown. Add code to Report > Rmd or Report > R to (re)create the dataset by clicking the report icon on the bottom left of your screen.,数据集“{fixed}”已成功添加到数据下拉菜单中。要在报告中（重新）生成该数据集，请点击左下角的报告图标，并添加到“报告 > Rmd”或“报告 > R”。,crs_ui.R
OK,确定,"crs_ui.R, evalbin_ui.R"
No recommendations available,无推荐结果可用,crs_ui.R
Save collaborative filtering recommendations,保存协同过滤推荐结果,crs_ui.R
Save collaborative filtering plot,保存协同过滤图表,crs_ui.R
None,无,"crtree_ui.R, gbt_ui.R"
Prune,修剪,crtree_ui.R
Tree,决策树,crtree_ui.R
Permutation Importance,特征重要性,"crtree_ui.R, gbt_ui.R"
Prediction plots,预测图,"crtree_ui.R, gbt_ui.R"
Partial Dependence,部分依赖图,"crtree_ui.R, gbt_ui.R"
Dashboard,仪表盘,"crtree_ui.R, gbt_ui.R"
Acquiring variable information,获取变量信息,"crtree_ui.R, evalbin_ui.R, evalreg_ui.R, gbt_ui.R, logistic_ui.R"
Response variable:,因变量:,"crtree_ui.R, evalbin_ui.R, evalreg_ui.R, gbt_ui.R, logistic_ui.R"
Choose level:,选择水平:,"crtree_ui.R, evalbin_ui.R, logistic_ui.R"
Explanatory variables:,自变量:,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Explanatory variables to include:,包含的自变量:,"crtree_ui.R, logistic_ui.R"
2-way interactions to explore:,要探索的二阶交互项:,"crtree_ui.R, logistic_ui.R"
Weights:,权重:,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
classification,分类,"crtree_ui.R, gbt_ui.R"
regression,回归,"crtree_ui.R, gbt_ui.R"
Prior:,先验:,crtree_ui.R
Min obs.:,最小观测数:,crtree_ui.R
Cost:,成本:,"crtree_ui.R, evalbin_ui.R"
Margin:,边际:,"crtree_ui.R, evalbin_ui.R"
Complexity:,复杂度:,crtree_ui.R
Max. nodes:,最大节点数:,crtree_ui.R
Prune compl.:,修剪复杂度:,crtree_ui.R
Seed:,随机种子:,"crtree_ui.R, gbt_ui.R"
Store residuals:,存储残差:,"crtree_ui.R, logistic_ui.R"
Provide variable name,提供变量名,crtree_ui.R
Number of data points plotted:,绘图数据点数:,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Classification and regression trees,分类与回归树,crtree_ui.R
Prediction input type:,预测输入类型:,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Prediction data:,预测数据:,"crtree_ui.R, gbt_ui.R"
Prediction command:,预测指令:,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
"Type a formula to set values for model variables (e.g., carat = 1; cut = 'Ideal') and press return",在此输入用于模型预测的变量值 (如 carat = 1; cut = 'Ideal') 并按回车键,"crtree_ui.R, gbt_ui.R, naivebayes_ui.R"
Plot predictions,绘制预测图,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Store predictions:,存储预测值:,"crtree_ui.R, gbt_ui.R"
Plots:,绘图选项:,"crtree_ui.R, evalbin_ui.R, gbt_ui.R, logistic_ui.R"
Plot direction:,绘图方向:,"crtree_ui.R, dtree_ui.R"
Left-right,左-右,"crtree_ui.R, dtree_ui.R"
Top-down,上-下,"crtree_ui.R, dtree-ui.R"
Right-left,右-左,crtree_ui.R
Bottom-Top,下-上,crtree_ui.R
Width:,宽度:,crtree_ui.R
Save crtree predictions,保存预测结果,crtree_ui.R
Save decision tree prediction plot,保存预测图,crtree_ui.R
Save decision tree plot,保存决策树图,crtree_ui.R
Generating predictions,正在生成预测,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Generating prediction plot,正在生成预测图,"crtree_ui.R, gbt_ui.R, logistic_ui.R"
Generating tree diagramm,正在生成树图,crtree_ui.R
Model > Estimate,模型 > 估计,"crtree_ui.R, logistic_ui.R"
** Press the Estimate button to estimate the model **,** 请点击“估计模型”按钮以生成推荐 **,"crtree_ui.R, gbt_ui.R"
Please select one or more explanatory variables.,请选择一个或多个自变量。,"crtree_ui.R, gbt_ui.R, nn_ui.R"
Max:,最大化:,dtree_ui.R
Min:,最小化:,dtree_ui.R
Max,最大化,dtree_ui.R
Min,最小化,dtree_ui.R
Remove,删除,dtree_ui.R
"No variables are available for sensitivity analysis. If the input file does contain a variables section, press the Calculate button to show the list of available variables.",没有可用于敏感性分析的变量。如果输入文件包含 variables 部分，请点击“计算树”按钮以显示可用变量列表。,dtree_ui.R
Sensitivity to changes in:,敏感性分析变量：,dtree_ui.R
Decisions to evaluate:,要评估的决策：,dtree_ui.R
Select decisions to evaluate,选择要评估的决策,dtree_ui.R
"","",dtree_ui.R
Step:,步长:,dtree_ui.R
Add variable,添加变量,"dtree_ui.R, simulater_ui.R"
Model,模型,dtree_ui.R
Decision analysis,决策分析,dtree_ui.R
,,dtree_ui.R
,,dtree_ui.R
Calculate tree,计算树,dtree_ui.R
Load input,加载输入,dtree_ui.R
Load decision tree input file (.yaml),加载决策树输入文件 (.yaml),dtree_ui.R
Save input,保存输入,dtree_ui.R
Save output,保存输出,dtree_ui.R
Provide structured input for a decision tree. Then click the 'Calculate tree' button to generate results. Click the ? icon on the top left of your screen for help and examples,为决策树提供结构化输入，然后点击“计算树”按钮生成结果。如需帮助和示例，请点击左上角的 ? 图标。,dtree_ui.R
Plot,图形,"dtree_ui.R, gbt_ui.R"
,,dtree_ui.R
,,dtree_ui.R
,,dtree_ui.R
Plot decision tree:,绘制决策树：,dtree_ui.R
Initial,初始,dtree_ui.R
Final,最终,dtree_ui.R
Decimals,小数位,dtree_ui.R
Symbol,符号,dtree_ui.R
Sensitivity,敏感性,dtree_ui.R
Evaluate sensitivity,评估敏感性,dtree_ui.R
At least one decision should be selected for evaluation,至少应选择一个决策进行评估,dtree_ui.R
No variables were specified for evaluation.\nClick the + icon to add variables for sensitivity evaluation,未指定任何变量用于评估。\n点击 + 图标添加要进行敏感性评估的变量,dtree_ui.R
Conducting sensitivity analysis,正在进行敏感性分析,dtree_ui.R
Making plot,正在生成图形,dtree_ui.R
Creating decision tree,正在创建决策树,dtree_ui.R
** Click the calculate button to generate results **,** 请点击计算按钮以生成结果 **,dtree_ui.R
Save decision tree output,保存决策树输出,dtree_ui.R
Save decision tree input,保存决策树输入,dtree_ui.R
Save decision tree sensitivity plot,保存敏感性分析图,dtree_ui.R
Lift,提升图,evalbin_ui.R
Gains,收益图,evalbin_ui.R
Profit,利润图,evalbin_ui.R
Expected profit,预期利润,evalbin_ui.R
ROME,投资回报率,evalbin_ui.R
All,全部,"evalbin_ui.R, evalreg_ui.R"
Training,训练集,"evalbin_ui.R, evalreg_ui.R"
Test,测试集,"evalbin_ui.R, evalreg_ui.R"
Both,训练集与测试集,"evalbin_ui.R, evalreg_ui.R"
Incremental uplift,增量提升,evalbin_ui.R
Uplift,提升,evalbin_ui.R
Incremental profit,增量利润,evalbin_ui.R
More than 50 levels. Please choose another response variable,超过 50 个水平。请选择其他响应变量,evalbin_ui.R
Treatment variable:,处理变量：,evalbin_ui.R
Select stored predictions:,选择已保存的预测：,"evalbin_ui.R, evalreg_ui.R"
Show results for:,显示结果：,"evalbin_ui.R, evalreg_ui.R"
Store uplift table as:,保存提升表为：,evalbin_ui.R
Provide a table name,请输入表名,evalbin_ui.R
Evaluate models,评估模型,"evalbin_ui.R, evalreg_ui.R"
Re-evaluate models,重新评估模型,"evalbin_ui.R, evalreg_ui.R"
# quantiles:,分位数数量：,evalbin_ui.R
Scale:,缩放因子：,evalbin_ui.R
Show model performance table,显示模型性能表,evalbin_ui.R
Show uplift table,显示提升表,evalbin_ui.R
Show plots,显示图形,"evalbin_ui.R, evalreg_ui.R"
Scale free,统一纵轴,evalbin_ui.R
Evaluate classification,评估分类模型,evalbin_ui.R
Confusion matrix,混淆矩阵,evalbin_ui.R
Evaluate uplift,评估提升效果,evalbin_ui.R
Uplift Table Stored,提升表已保存,evalbin_ui.R
The uplift table ',提升表 ',evalbin_ui.R
"' was successfully added to the
datasets dropdown. Add code to Report > Rmd or
Report > R to (re)create the results by clicking
the report icon on the bottom left of your screen.",' 已成功添加到数据集下拉菜单。可在 Report > Rmd 或 Report > R 中添加代码以（重新）生成结果，方法是点击屏幕左下角的报告图标。,evalbin_ui.R
Save model evaluations,保存模型评估结果,"evalbin_ui.R, evalreg_ui.R"
Save model performance metrics,保存模型性能指标,evalbin_ui.R
Save uplift evaluations,保存提升评估结果,evalbin_ui.R
Save model evaluation plot,保存模型评估图,"evalbin_ui.R, evalreg_ui.R"
Save confusion plots,保存混淆图,evalbin_ui.R
Save uplift plots,保存提升图,evalbin_ui.R
Evaluate,评估,evalbin_ui.R
Model > Evaluate,模型 > 评估,"evalbin_ui.R, evalreg_ui.R"
** Press the Evaluate button to evaluate models **,** 请点击“评估”按钮以评估模型 **,"evalbin_ui.R, evalreg_ui.R"
,,evalbin_ui.R
"This analysis requires a response variable of type factor and one or more
predictors of type numeric. If these variable types are not available please
select another dataset.
For an example dataset go to Data > Manage, select 'examples' from the
'Load data of type' dropdown, and press the 'Load examples' button. Then
select the 'titanic' dataset.","此分析需要一个因变量（类别型）和一个或多个自变量（数值型）。如果这些变量类型不可用，请选择另一个数据集。
如需示例数据集，请前往“数据 > 管理”，在“加载数据类型”下拉菜单中选择“示例”，然后点击“加载示例”按钮。接着选择“titanic”数据集。",evalbin_ui.R
"This analysis requires a response variable of type factor and one or more
predictors of type numeric. If these variable types are not available please
select another dataset.
",此分析需要一个因变量（类别型）和一个或多个自变量（数值型）。如果这些变量类型不可用，请选择另一个数据集。,evalbin_ui.R
Evaluate Regression,回归评估,evalreg_ui.R
This analysis requires a numeric response variable and one or more\nnumeric predictors. If these variable types are not available please\nselect another dataset.\n\n,本分析要求一个数值型因变量和一个或多个数值型自变量。如果当前数据集中不包含这些类型的变量，请选择另一个数据集。\n\n,evalreg_ui.R
Choose first level:,选择第一个水平：,gbt_ui.R
Max depth:,最大深度：,gbt_ui.R
Learning rate:,学习率：,gbt_ui.R
Min split loss:,最小分裂损失：,gbt_ui.R
Min child weight:,最小子节点权重：,gbt_ui.R
Sub-sample:,子样本比例：,gbt_ui.R
# rounds:,迭代轮数：,gbt_ui.R
Early stopping:,提前停止：,gbt_ui.R
Gradient Boosted Trees,梯度提升树,gbt_ui.R
Model > Trees,模型 > 树模型,gbt_ui.R
** Select prediction input **,** 请选择预测输入 **,"gbt_ui.R, logistic_ui.R"
** Select data for prediction **,** 请选择用于预测的数据 **,"gbt_ui.R, logistic_ui.R"
** Enter prediction commands **,** 请输入预测命令 **,"gbt_ui.R, logistic_ui.R"
Please select a gradient boosted trees plot from the drop-down menu,请从下拉菜单中选择一个梯度提升树图表,gbt_ui.R
Storing predictions,正在保存预测结果,"gbt_ui.R, logistic_ui.R"
No output available. Press the Estimate button to generate results,无可用输出。请点击“估计模型”按钮生成结果,"gbt_ui.R, logistic_ui.R"
Save predictions,保存预测结果,"gbt_ui.R, logistic_ui.R"
Save gradient boosted trees prediction plot,保存梯度提升树预测图,gbt_ui.R
Save gradient boosted trees plot,保存梯度提升树图,gbt_ui.R
This analysis requires a response variable with two levels and one\nor more explanatory variables. If these variables are not available\nplease select another dataset.\n\n,此分析需要一个具有两个水平的响应变量和一个\n或多个解释变量。如果这些变量不可用\n请选择其他数据集。\n\n,gbt_ui.R
This analysis requires a response variable of type integer\nor numeric and one or more explanatory variables.\nIf these variables are not available please select another dataset.\n\n,此分析需要一个整数类型的响应变量\n或数值型，以及一个或多个解释变量。\n如果这些变量不可用，请选择其他数据集。\n\n,gbt_ui.R
Summary,摘要,gbt_ui.R
Predict,预测,gbt_ui.R
Storing residuals,存储残差,logistic_ui.R
Save coefficients,保存系数,logistic_ui.R
Save logistic prediction plot,保存逻辑回归预测图,logistic_ui.R
Save logistic plot,保存逻辑回归图,logistic_ui.R
Variables to test:,测试的变量：,logistic_ui.R
Interactions:,交互作用：,logistic_ui.R
Confidence level:,置信水平：,logistic_ui.R
"Type a formula to set values for model variables (e.g., class = '1st'; gender = 'male') and press return",输入公式设置模型变量的值（例如，class = '1st'; gender = 'male'），然后按回车,logistic_ui.R
Include intercept,包含截距,logistic_ui.R
Save,保存,logistic_ui.R
Logistic regression (GLM),逻辑回归（GLM）,logistic_ui.R
Logistic regression,逻辑回归,logistic_ui.R
3-way, "三项交互", "logistic_ui.R"
Data, "数据", "logistic_ui.R"
Command, "命令", "logistic_ui.R"
Data & Command, "数据和命令", "logistic_ui.R"
Standardize, "标准化", "logistic_ui.R"
Center, "居中", "logistic_ui.R"
Stepwise, "逐步回归", "logistic_ui.R"
Robust, "稳健", "logistic_ui.R"
VIF, "方差膨胀因子", "logistic_ui.R"
Confidence intervals, "置信区间", "logistic_ui.R"
Odds, "赔率", "logistic_ui.R"
Distribution, "分布", "logistic_ui.R"
Correlations, "相关性", "logistic_ui.R"
Scatter, "散点图", "logistic_ui.R"
Model fit, "模型拟合", "logistic_ui.R"
Coefficient (OR) plot, "系数（OR）图", "logistic_ui.R"
Influential observations, "影响观察值", "logistic_ui.R"
This analysis requires a response variable with two levels and one or more explanatory variables. If these variables are not available please select another dataset., "该分析需要一个具有两个级别的响应变量以及一个或多个解释变量。如果这些变量不可用，请选择另一个数据集。", "logistic_ui.R"
Drop intercept,去除截距项,mnl_ui.R
RRRs,相对风险比 (RRR),mnl_ui.R
Coefficient (RRR) plot,系数图（RRR）,mnl_ui.R
Multinomial logistic regression (MNL),多项式逻辑回归（MNL）,mnl_ui.R
Save mnl prediction plot,保存 MNL 预测图,mnl_ui.R
Save mnl plot,保存 MNL 图表,mnl_ui.R
Please select a mnl regression plot from the drop-down menu,请从下拉菜单中选择一个 MNL 回归图,mnl_ui.R
Choose base level:,选择基准水平：,mnl_ui.R
Variable importance,变量重要性,naivebayes_ui.R
Naive Bayes,朴素贝叶斯,naivebayes_ui.R
Laplace:,拉普拉斯修正：,naivebayes_ui.R
Save naive Bayes prediction plot,保存朴素贝叶斯预测图,naivebayes_ui.R
Save naive Bayes plot,保存朴素贝叶斯图,naivebayes_ui.R
Please select a naive Bayes plot from the drop-down menu,请从下拉菜单中选择一个朴素贝叶斯图,naivebayes_ui.R
All levels,所有水平,naivebayes_ui.R
Network,网络结构,nn_ui.R
Olden,节点权重贡献图（Olden 方法）,nn_ui.R
Garson,输入变量重要性图（Garson 方法）,nn_ui.R
Neural Network,神经网络,nn_ui.R
Regression,回归,nn_ui.R
Size:,大小：,nn_ui.R
Decay:,衰减：,nn_ui.R
Save neural network prediction plot,保存神经网络预测图,nn_ui.R
Save neural network plot,保存神经网络图,nn_ui.R
Please select a neural network plot from the drop-down menu,请从下拉菜单中选择一种神经网络图,nn_ui.R
RMSE,均方根误差,regress_ui.R
Sum of squares,平方和,regress_ui.R
Line,线性,regress_ui.R
Loess,局部加权回归（Loess）,regress_ui.R
Jitter,扰动点（Jitter）,regress_ui.R
Residual vs explanatory,残差对解释变量图,regress_ui.R
Coefficient plot,系数图,regress_ui.R
Linear regression (OLS),线性回归（最小二乘法）,regress_ui.R
Save regression predictions,保存回归预测结果,regress_ui.R
Save regression plot,保存回归图表,regress_ui.R
Please select one or more explanatory variables. Then press the Estimate\nbutton to estimate the model.,请选择一个或多个解释变量，然后点击“估计模型”按钮。,regress_ui.R
Save regression prediction plot,保存回归预测图,regress_ui.R
Please select a regression plot from the drop-down menu,请从下拉菜单中选择一个回归图,regress_ui.R
Random Forest,随机森林,rforest_ui.R
mtry:,mtry：特征子集数,rforest_ui.R
# trees:,树数量：,rforest_ui.R
Min node size:,最小节点样本数：,rforest_ui.R
Sample fraction:,样本抽样比例：,rforest_ui.R
Save random forest plot,保存随机森林图,rforest_ui.R
Binomial,二项分布,simulater_ui.R
Discrete,离散分布,simulater_ui.R
Log normal,对数正态分布,simulater_ui.R
Normal,正态分布,simulater_ui.R
Poisson,泊松分布,simulater_ui.R
Uniform,均匀分布,simulater_ui.R
Constant,常数,simulater_ui.R
Grid search,网格搜索,simulater_ui.R
Sequence,序列,simulater_ui.R
Run simulation,运行模拟,simulater_ui.R
Repeat simulation,重复模拟,simulater_ui.R
Simulate,模拟,simulater_ui.R
,,simulater_ui.R
"Use formulas to perform calculations on simulated variables
(e.g., demand = 5 * price). Press the Run simulation button
to run the simulation. Click the ? icon on the bottom left
of your screen for help and examples",使用公式对模拟变量进行计算（例如：demand = 5 * price）。点击“运行模拟”按钮开始模拟。点击左下角的问号图标查看帮助和示例。,simulater_ui.R
,,simulater_ui.R
"Create your own R functions (e.g., add = function(x, y) {x + y}).
Call these functions from the 'formula' input and press the Run
simulation button to run the simulation. Click the ? icon on the
bottom left of your screen for help and examples","创建你自己的 R 函数（例如：add = function(x, y) {x + y}）。在“公式”输入框中调用这些函数并点击“运行模拟”按钮。点击左下角的问号图标查看帮助和示例。",simulater_ui.R
Repeat,重复,simulater_ui.R
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"Press the Repeat simulation button to repeat the simulation specified in the
Simulate tab. Use formulas to perform additional calculations on the repeated
simulation data. Click the ? icon on the bottom left of your screen for help
and examples",点击“重复模拟”按钮，对“模拟”页中指定的模拟进行重复执行。你可以使用公式对重复模拟的数据执行额外计算。点击左下角的问号图标查看帮助和示例。,simulater_ui.R
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Model > Decide,建模 > 决策,simulater_ui.R
Name:,名称：,simulater_ui.R
n:,n：,simulater_ui.R
p:,p：,simulater_ui.R
Value:,数值：,simulater_ui.R
Values:,数值：,simulater_ui.R
Prob.:,概率：,simulater_ui.R
Mean:,均值：,simulater_ui.R
St.dev.:,标准差：,simulater_ui.R
Use exact specifications,使用精确指定,simulater_ui.R
Correlations:,相关性：,simulater_ui.R
Set random seed:,设置随机种子：,simulater_ui.R
# sims:,模拟次数：,simulater_ui.R
Simulated data:,模拟数据：,simulater_ui.R
Decimals:,小数位数：,simulater_ui.R
Add functions,添加函数,simulater_ui.R
** Press the Run simulation button to simulate data **,** 请点击“运行模拟”按钮以生成数据 **,simulater_ui.R
Select types,选择类型,simulater_ui.R
Select types:,选择类型：,simulater_ui.R
Save simulation plots,保存模拟图表,simulater_ui.R
** Press the Repeat simulation button **,** 请点击“重复模拟”按钮 **,simulater_ui.R
Select group-by variable,选择分组变量,simulater_ui.R
Group by:,分组变量：,simulater_ui.R
sum,求和,simulater_ui.R
mean,均值,simulater_ui.R
median,中位数,simulater_ui.R
min,最小值,simulater_ui.R
max,最大值,simulater_ui.R
sd,标准差,simulater_ui.R
var,方差,simulater_ui.R
sdprop,标准差比例,simulater_ui.R
varprop,方差比例,simulater_ui.R
p01,第1百分位数,simulater_ui.R
p025,第2.5百分位数,simulater_ui.R
p05,第5百分位数,simulater_ui.R
p10,第10百分位数,simulater_ui.R
p25,第25百分位数,simulater_ui.R
p75,第75百分位数,simulater_ui.R
p90,第90百分位数,simulater_ui.R
p95,第95百分位数,simulater_ui.R
p975,第97.5百分位数,simulater_ui.R
p99,第99百分位数,simulater_ui.R
first,第一个值,simulater_ui.R
last,最后一个值,simulater_ui.R
Apply function:,应用函数：,simulater_ui.R
Provide values in the input boxes above and then press the + symbol,请在上方输入框中填写数值，然后点击加号按钮,simulater_ui.R
Lambda:,λ：,simulater_ui.R
# reps:,重复次数：,simulater_ui.R
Repeat data:,重复模拟数据：,simulater_ui.R
No formulas or simulated variables specified,未指定任何公式或模拟变量,simulater_ui.R
Running simulation,正在运行模拟,simulater_ui.R
Generating simulation plots,正在生成模拟图表,simulater_ui.R
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Re-run simulation,重新运行模拟,simulater_ui.R
Simulation,模拟,simulater_ui.R
Binomial variables:, "二项变量：", "simulater_ui.R"
Grid search:, "网格搜索：", "simulater_ui.R"
Remove variable, "删除变量", "simulater_ui.R"
Save repeated simulation plots, "保存重复模拟图", "simulater_ui.R"
Inputs required, "需要输入", "simulater_ui.R"
Select at least one Output variable, "请至少选择一个输出变量", "simulater_ui.R"
Constant variables,常量变量,simulater_ui.R
Discrete variables,离散变量,simulater_ui.R
Log-normal variables,对数正态变量,simulater_ui.R
Normal variables,正态变量,simulater_ui.R
Poisson variables,泊松变量,simulater_ui.R
Uniform variables,均匀变量,simulater_ui.R
Sequence variables,序列变量,simulater_ui.R
Model,模型,init.R
Estimate,估计,init.R
Linear regression (OLS),线性回归（普通最小二乘法）,init.R
Logistic regression (GLM),逻辑回归（广义线性模型）,init.R
Multinomial logistic regression (MNL),多项逻辑回归,init.R
Naive Bayes,朴素贝叶斯,init.R
Neural Network,神经网络,init.R
Trees,树模型,init.R
Classification and regression trees,分类与回归树,init.R
Random Forest,随机森林,init.R
Gradient Boosted Trees,梯度提升树,init.R
Evaluate,评估,init.R
Evaluate regression,回归模型评估,init.R
Evaluate classification,分类模型评估,init.R
Recommend,推荐,init.R
Collaborative Filtering,协同过滤,init.R
Decide,决策,init.R
Decision analysis,决策分析,init.R
Simulate,模拟,init.R