Verfügbarkeit: SAS and R :: THWS Bibkatalog

SAS and R: data management, statistical analysis, and graphics

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Bibliographische Detailangaben
Hauptverfasser:	Kleinman, Ken (VerfasserIn), Horton, Nicholas J. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton [u.a.] CRC Press 2014
Ausgabe:	2. ed.
Schlagworte:	SAS (Computer file) SAS (Computer program language) R (Computer program language) R > Programm SAS > Programm
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXV, 443 S. Ill., graph. Darst.
ISBN:	9781466584495

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Datensatz im Suchindex

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adam_text	Contents List of figures xvii List of tables xix Preface to the second edition xxi Preface to the first edition xxiii 1 Data input and output 1 1.1 Input...................................................................... 1 1.1.1 Native dataset..................................................... 1 1.1.2 Fixed format text files............................................ 2 1.1.3 Other fixed files.................................................. 3 1.1.4 Reading more complex text files.................................... 3 1.1.5 Comma separated value (CSV) files.................................. 4 1.1.6 Read sheets from an Excel file..................................... 5 1.1.7 Read data from R into SAS.......................................... 5 1.1.8 Read data from SAS into R.......................................... 6 1.1.9 Reading datasets in other formats.................................. 6 1.1.10 Reading data with a variable number of words in a field............ 7 1.1.11 Read a file byte by byte........................................... 8 1.1.12 Access data from a URL............................................. 9 1.1.13 Read an XML-formatted file......................................... 9 1.1.14 Manual data entry................................................. 10 1.2 Output ................................................................... 11 1.2.1 Displaying data................................................... 11 1.2.2 Number of digits to display....................................... 11 1.2.3 Save a native dataset............................................. 12 1.2.4 Creating datasets in text format ................................. 12 1.2.5 Creating Excel spreadsheets....................................... 12 1.2.6 Creating files for use by other packages.......................... 13 1.2.7 Creating HTML formatted output ................................... 14 1.2.8 Creating XML datasets and output.................................. 14 1.3 Further resources......................................................... 15 2 Data management 17 2.1 Structure and meta-data................................................... 17 2.1.1 Access variables from a dataset................................... 17 2.1.2 Names of variables and their types................................ 17 2.1.3 Values of variables in a dataset.................................. 18 VI CONTENTS 2.1.4 Label variables................................................... 2.1.5 Add comment to a dataset or variable.............................. 2.2 Derived variables and data manipulation................................... 2.2.1 Add derived variable to a dataset................................. 2.2.2 Rename variables in a dataset..................................... 2.2.3 Create string variables from numeric variables.................... 2.2.4 Create categorical variables from continuous variables ........... 2.2.5 Recode a categorical variable..................................... 2.2.6 Create a categorical variable using logic......................... 2.2.7 Create numeric variables from string variables.................... 2.2.8 Extract characters from string variables . ....................... 2.2.9 Length of string variables........................................ 2.2.10 Concatenate string variables...................................... 2.2.11 Set operations..................................................... 2.2.12 Find strings within string variables.............................. 2.2.13 Find approximate strings.......................................... 2.2.14 Replace strings within string variables........................... 2.2.15 Split strings into multiple strings............................... 2.2.16 Remove spaces around string variables............................. 2.2.17 Upper to lower case............................................... 2.2.18 Lagged variable................................................... 2.2.19 Formatting values of variables.................................... 2.2.20 Perl interface.................................................... 2.2.21 Accessing databases using SQL (structured query language) . . . . 2.3 Merging, combining, and subsetting datasets .............................. 2.3.1 Subsetting observations .......................................... 2.3.2 Drop or keep variables in a dataset............................... 2.3.3 Random sample of a dataset........................................ 2.3.4 Observation number................................................ 2.3.5 Keep unique values................................................ 2.3.6 Identify duplicated values........................................ 2.3.7 Convert from wide to long (tall) format........................... 2.3.8 Convert from long (tall) to wide format........................... 2.3.9 Concatenate and stack datasets.................................... 2.3.10 Sort datasets..................................................... 2.3.11 Merge datasets................................................ . . . 2.4 Date and time variables.................................................. 2.4.1 Create date variable.............................................. 2.4.2 Extract weekday................................................... 2.4.3 Extract month..................................................... 2.4.4 Extract year...................................................... 2.4.5 Extract quarter................................................... 2.4.6 Create time variable.............................................. 2.5 Further resources......................................................... 2.6 Examples.................................................................. 2.6.1 Data input and output............................................. 2.6.2 Data display...................................................... 2.6.3 Derived variables and data manipulation........................... 2.6.4 Sorting and subsetting datasets................................... 18 19 19 19 19 20 20 21 21 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 29 30 30 31 32 32 32 33 34 35 35 35 37 37 38 38 38 38 39 39 39 39 43 44 51 CONTENTS vii 3 Statistical and mathematical functions 53 3.1 Probability distributions and random number generation................... 53 3.1.1 Probability density function.................................... 53 3.1.2 Quantiles of a probability density function..................... 54 3.1.3 Setting the random number seed.................................. 55 3.1.4 Uniform random variables........................................ 55 3.1.5 Multinomial random variables.................................... 56 3.1.6 Normal random variables ........................................ 56 3.1.7 Multivariate normal random variables............................ 56 3.1.8 Truncated multivariate normal random variables.................. 58 3.1.9 Exponential random variables.................................... 58 3.1.10 Other random variables ......................................... 58 3.2 Mathematical functions................................................... 59 3.2.1 Basic functions................................................. 59 3.2.2 Trigonometric functions......................................... 60 3.2.3 Special functions............................................... 60 3.2.4 Integer functions............................................... 60 3.2.5 Comparisons of floating point variables......................... 61 3.2.6 Complex numbers................................................. 61 3.2.7 Derivatives .................................................... 62 3.2.8 Integration..................................................... 62 3.2.9 Optimization problems........................................... 62 3.3 Matrix operations ....................................................... 63 3.3.1 Create matrix from vector....................................... 63 3.3.2 Combine vectors or matrices..................................... 63 3.3.3 Matrix addition................................................. 64 3.3.4 Transpose matrix................................................ 64 3.3.5 Find the dimension of a matrix or dataset....................... 64 3.3.6 Matrix multiplication........................................... 65 3.3.7 Invert matrix................................................... 65 3.3.8 Component-wise multiplication................................... 66 3.3.9 Create submatrix............................................... 66 3.3.10 Create a diagonal matrix........................................ 66 3.3.11 Create a vector of diagonal elements ........................... 67 3.3.12 Create a vector from a matrix................................... 67 3.3.13 Calculate the determinant....................................... 67 3.3.14 Find eigenvalues and eigenvectors............................... 67 3.3.15 Find the singular value decomposition........................... 68 3.4 Examples............................................................... 68 3.4.1 Probability distributions...................................... 68 4 Programming and operating system interface 71 4.1 Control flow, programming, and data generation........................... 71 4.1.1 Looping......................................................... 71 4.1.2 Conditional execution .......................................... 72 4.1.3 Sequence of values or patterns.................................. 73 4.1.4 Referring to a range of variables............................... 74 4.1.5 Perform an action repeatedly over a set of variables............ 74 4.1.6 Grid of values.................................................. 75 4.1.7 Debugging....................................................... 76 4.1.8 Error recovery.................................................. 76 CONTENTS 4.2 Functions and macros....................................................... 77 4.2.1 SAS macros........................................................ 77 4.2.2 R functions....................................................... 78 4.3 Interactions with the operating system.................................. 78 4.3.1 Timing commands ................................................ 78 4.3.2 Suspend execution for a time interval............................. 79 4.3.3 Execute a command in the operating system......................... 79 4.3.4 Command history................................................... 80 4.3.5 Find working directory............................................ 80 4.3.6 Change working directory.......................................... 80 4.3.7 List and access files............................................. 81 5 Common statistical procedures 83 5.1 Summary statistics......................................................... 83 5.1.1 Means and other summary statistics................................ 83 5.1.2 Other moments..................................................... 84 5.1.3 Trimmed mean...................................................... 84 5.1.4 Quantiles ........................................................ 85 5.1.5 Centering, normalizing, and scaling............................... 85 5.1.6 Mean and 95% confidence interval ................................. 86 5.1.7 Proportion and 95% confidence interval ........................... 86 5.1.8 Maximum likelihood estimation of parameters....................... 86 5.2 Bivariate statistics....................................................... 87 5.2.1 Epidemiologic statistics ......................................... 87 5.2.2 Test characteristics.............................................. 87 5.2.3 Correlation....................................................... 89 5.2.4 Kappa (agreement)................................................. 89 5.3 Contingency tables......................................................... 90 5.3.1 Display cross-classification table................................ 90 5.3.2 Displaying missing value categories in a table.................... 90 5.3.3 Pearson chi-square statistic ..................................... 91 5.3.4 Cochran-Mantel-Haenszel test ................................... 91 5.3.5 Cramer’s V........................................................ 91 5.3.6 Fisher’s exact test ............................................ 92 5.3.7 McNemar’s test.................................................... 92 5.4 Tests for continuous variables............................................. 92 5.4.1 Tests for normality............................................... 92 5.4.2 Student’s t test.................................................. 93 5.4.3 Test for equal variances ......................................... 93 5.4.4 Nonparametric tests.............................................. 94 5.4.5 Permutation test.................................................. 94 5.4.6 Logrank test ..................................................... 95 5.5 Analytic powrer and sample size calculations............................... 95 5.6 Further resources.......................................................... 97 5.7 Examples.................................................................. 97 5.7.1 Summary statistics and exploratory data analysis.................. 97 5.7.2 Bivariate relationships.......................................... 101 5.7.3 Contingency tables.............................................. 103 5.7.4 Two sample tests of continuous variables ....................... 107 5.7.5 Survival analysis: logrank test................................. Ill CONTENTS IX 6 Linear regression and ANOVA 113 6.1 Model fitting............................................................. 113 6.1.1 Linear regression................................................ 113 6.1.2 Linear regression with categorical covariates.................... 114 6.1.3 Changing the reference category.................................. 114 6.1.4 Parameterization of categorical covariates....................... 115 6.1.5 Linear regression with no intercept.............................. 116 6.1.6 Linear regression with interactions.............................. 117 6.1.7 One-way analysis of variance .................................... 117 6.1.8 Analysis of variance with two or more factors.................... 117 6.2 Tests, contrasts, and linear functions of parameters...................... 118 6.2.1 Joint null hypotheses: several parameters equal 0................ 118 6.2.2 Joint null hypotheses: sum of parameters......................... 118 6.2.3 Tests of equality of parameters.................................. 119 6.2.4 Multiple comparisons............................................. 119 6.2.5 Linear combinations of parameters................................ 120 6.3 Model diagnostics......................................................... 120 6.3.1 Predicted values................................................. 120 6.3.2 Residuals........................................................ 121 6.3.3 Standardized and Studentized residuals........................... 121 6.3.4 Leverage......................................................... 122 6.3.5 Cook’s D......................................................... 122 6.3.6 DFFITS........................................................... 123 6.3.7 Diagnostic plots................................................. 123 6.3.8 Heteroscedasticity tests......................................... 124 6.4 Model parameters and results............................................. 124 6.4.1 Parameter estimates.............................................. 124 6.4.2 Standardized regression coefficients............................. 124 6.4.3 Standard errors of parameter estimates......................... 125 6.4.4 Confidence interval for parameter estimates...................... 125 6.4.5 Confidence limits for the mean................................... 125 6.4.6 Prediction limits................................................ 126 6.4.7 R-squared...................................................... 127 6.4.8 Design and information matrix.................................... 127 6.4.9 Covariance matrix of parameter estimates......................... 127 6.4.10 Correlation matrix of parameter estimates........................ 128 6.5 Further resources......................................................... 128 6.6 Examples.................................................................. 128 6.6.1 Scatterplot with smooth fit ..................................... 129 6.6.2 Linear regression with interaction............................... 130 6.6.3 Regression diagnostics .......................................... 135 6.6.4 Fitting the regression model separately for each value of another variable......................................................... 138 6.6.5 Two-way ANOVA.................................................... 139 6.6.6 Multiple comparisons............................................. 144 6.6.7 Contrasts........................................................ 146 149 149 151 152 152 152 153 153 153 154 154 154 155 155 156 156 156 156 157 157 158 158 159 160 160 161 161 162 162 163 163 163 164 164 165 166 166 166 166 167 167 168 168 169 169 170 170 170 171 171 Regression generalizations and modeling 7.1 Generalized linear models................................ 7.1.1 Logistic regression model........................ 7.1.2 Conditional logistic regression model............ 7.1.3 Exact logistic regression........................ 7.1.4 Ordered logistic model........................... 7.1.5 Generalized logistic model....................... 7.1.6 Poisson model.................................... 7.1.7 Negative binomial model.......................... 7.1.8 Log-linear model................................. 7.2 Further generalizations.................................. 7.2.1 Zero-inflated Poisson model...................... 7.2.2 Zero-inflated negative binomial model ........... 7.2.3 Generalized additive model ...................... 7.2.4 Nonlinear least squares model.................... 7.3 Robust methods .......................................... 7.3.1 Quantile regression model........................ 7.3.2 Robust regression model.......................... 7.3.3 Ridge regression model........................... 7.4 Models for correlated data............................... 7.4.1 Linear models with correlated outcomes........... 7.4.2 Linear mixed models with random intercepts . . . 7.4.3 Linear mixed models with random slopes............. 7.4.4 More complex random coefficient models........... 7.4.5 Multilevel models................................ 7.4.6 Generalized linear models with correlated outcomes 7.4.7 Generalized linear mixed models ................. 7.4.8 Generalized estimating equations................. 7.4.9 MANOVA........................................... 7.4.10 Time series model................................ 7.5 Survival analysis........................................ 7.5.1 Proportional hazards (Cox) regression model . . . 7.5.2 Proportional hazards (Cox) model with frailty . . . 7.5.3 Nelson-Aalen estimate of cumulative hazard .... 7.5.4 Testing the proportionality of the Cox model . . . 7.5.5 Cox model with time-varying predictors........... 7.6 Multivariate statistics and discriminant procedures...... 7.6.1 Cronbach’s a..................................... 7.6.2 Factor analysis.................................. 7.6.3 Recursive partitioning........................... 7.6.4 Linear discriminant analysis..................... 7.6.5 Latent class analysis............................ 7.6.6 Hierarchical clustering.......................... 7.7 Complex survey design.................................... 7.8 Model selection and assessment........................... 7.8.1 Compare two models............................... 7.8.2 Log-likelihood................................... 7.8.3 Akaike Information Criterion (AIC)............... 7.8.4 Bayesian Information Criterion (BIC) ............ 7.8.5 LASSO model...................................... 7.8.6 Hosmer-Lemeshow goodness of fit ................. CONTENTS xi 7.8.7 Goodness of fit for count models .................................. 171 7.9 Further resources.......................................................... 172 7.10 Examples................................................................... 172 7.10.1 Logistic regression ............................................... 172 7.10.2 Poisson regression................................................. 176 7.10.3 Zero-inflated Poisson regression................................... 178 7.10.4 Negative binomial regression....................................... 180 7.10.5 Quantile regression................................................ 181 7.10.6 Ordered logistic................................................... 182 7.10.7 Generalized logistic model......................................... 183 7.10.8 Generalized additive model ........................................ 185 7.10.9 Reshaping a dataset for longitudinal regression.................... 187 7.10.10 Linear model for correlated data .................................. 190 7.10.11 Linear mixed (random slope) model.................................. 193 7.10.12 Generalized estimating equations................................... 197 7.10.13 Generalized linear mixed model..................................... 199 7.10.14 Cox proportional hazards model..................................... 200 7.10.15 Cronbach’s a....................................................... 201 7.10.16 Factor analysis.................................................... 202 7.10.17 Recursive partitioning............................................. 205 7.10.18 Linear discriminant analysis....................................... 206 7.10.19 Hierarchical clustering............................................ 208 8 A graphical compendium 211 8.1 Univariate plots........................................................... 211 8.1.1 Barplot ........................................................... 211 8.1.2 Stem-and-leaf plot................................................. 212 8.1.3 Dotplot............................................................ 212 8.1.4 Histogram.......................................................... 213 8.1.5 Density plots...................................................... 213 8.1.6 Empirical cumulative probability density plot...................... 214 8.1.7 Boxplot............................................................ 214 8.1.8 Violin plots..................................................... 215 8.2 Univariate plots by grouping variable...................................... 215 8.2.1 Side-by-side histograms ........................................... 215 8.2.2 Side-by-side boxplots.............................................. 215 8.2.3 Overlaid density plots............................................. 216 8.2.4 Bar chart with error bars ......................................... 216 8.3 Bivariate plots.......................................................... 217 8.3.1 Scatterplot........................................................ 217 8.3.2 Scatterplot with multiple y values................................. 218 8.3.3 Scatterplot with binning........................................... 219 8.3.4 Transparent overplotting scatterplot............................... 219 8.3.5 Bivariate density plot............................................ 220 8.3.6 Scatterplot with marginal histograms............................... 220 8.4 Multivariate plots ........................................................ 221 8.4.1 Matrix of scatterplots .......................................... 221 8.4.2 Conditioning plot.................................................. 221 8.4.3 Contour plots.................................................... 222 8.4.4 3-D plots.......................................................... 222 8.5 Special purpose plots...................................................... 223 Xii CONTENTS 8.5.1 Choropleth maps.................................................. 223 8.5.2 interaction plots ............................................... 223 8.5.3 Plots for categorical data....................................... 224 8.5.4 Circular plot..................................................... 224 8.5.5 Plot an arbitrary function....................................... 224 8.5.6 Normal quantile-quantile plot.................................... 225 8.5.7 Receiver operating characteristic (ROC) curve..................... 225 8.5.8 Plot confidence intervals for the mean ........................... 226 8.5.9 Plot prediction limits from a simple linear regression............ 226 8.5.10 Plot predicted lines for each value of a variable................ 226 8.5.11 Kaplan-Meier plot................................................. 227 8.5.12 Hazard function plotting.......................................... 228 8.5.13 Mean-difference plots............................................. 228 8.6 Further resources........................................................ 230 8.7 Examples.................................................................. 230 8.7.1 Scatterplot with multiple axes.................................... 230 8.7.2 Conditioning plot................................................ 232 8.7.3 Scatterplot with marginal histograms.............................. 232 8.7.4 Kaplan-Meier plot................................................. 234 8.7.5 ROC curve ........................................................ 235 8.7.6 Pairs plot........................................................ 236 8.7.7 Visualize correlation matrix...................................... 238 9 Graphical options and configuration 241 9.1 Adding elements......................................................... 241 9.1.1 Arbitrary straight line........................................... 242 9.1.2 Plot symbols ..................................................... 242 9.1.3 Add points to an existing graphic................................. 243 9.1.4 Jitter points..................................................... 243 9.1.5 Regression line fit to points..................................... 244 9.1.6 Smoothed line..................................................... 244 9.1.7 Normal density.................................................... 245 9.1.8 Marginal rug plot................................................. 245 9.1.9 Titles............................................................ 246 9.1.10 Footnotes......................................................... 246 9.1.11 Text.............................................................. 246 9.1.12 Mathematical symbols............................................ 247 9.1.13 Arrows and shapes............................................... 247 9.1.14 Add grid.......................................................... 248 9.1.15 Legend........................................................... 248 9.1.16 Identifying and locating points................................. 249 9.2 Options and parameters.................................................. 250 9.2.1 Graph size ....................................................... 250 9.2.2 Grid of plots per page............................ .............. 250 9.2.3 More general page layouts....................................... 251 9.2.4 Fonts.......................................................... 252 9.2.5 Point and text size............................................... 252 9.2.6 Box around plots................................................. 252 9.2.7 Size of margins................................................. 253 9.2.8 Graphical settings ............................................. 253 9.2.9 Axis range and style............................................ 253 CONTENTS xiii 9.2.10 Axis labels, values, and tick marks................................ 254 9.2.11 Line styles........................................................ 254 9.2.12 Line widths........................................................ 255 9.2.13 Colors............................................................. 255 9.2.14 Log scale.......................................................... 255 9.2.15 Omit axes.......................................................... 256 9.3 Saving graphs.............................................................. 256 9.3.1 PDF................................................................ 256 9.3.2 Postscript......................................................... 256 9.3.3 RTF................................................................ 257 9.3.4 JPEG............................................................... 258 9.3.5 Windows Metafile (WMF)............................................. 258 9.3.6 Bitmap image file (BMP) ........................................... 258 9.3.7 Tagged image file format (TIFF).................................... 259 9.3.8 Portable Network Graphics (PNG).................................... 259 9.3.9 Closing a graphic device......................................... . 260 10 Simulation 261 10.1 Generating data ........................................................... 261 10.1.1 Generate categorical data.......................................... 261 10.1.2 Generate data from a logistic regression........................... 263 10.1.3 Generate data from a generalized linear mixed model................ 264 10.1.4 Generate correlated binary data.................................. 267 10.1.5 Generate data from a Cox model..................................... 269 10.1.6 Sampling from a challenging distribution........................... 271 10.2 Simulation applications ................................................... 274 10.2.1 Simulation study of Student’s t test.............................. 274 10.2.2 Diploma (or hat-check) problem..................................... 276 10.2.3 Monty Hall problem................................................. 278 10.3 Further resources......................................................... 280 11 Special topics 281 11.1 Processing by group ....................................................... 281 11.2 Simulation-based power calculations........................................ 284 11.3 Reproducible analysis and output........................................... 287 11.4 Advanced statistical methods............................................... 290 11.4.1 Bayesian methods................................................. 290 11.4.2 Propensity scores ................................................. 296 11.4.3 Bootstrapping...................................................... 303 11.4.4 Missing data....................................................... 304 11.4.5 Finite mixture models with concomitant variables .................. 311 11.5 Further resources......................................................... 313 12 Case studies 315 12.1 Data management and related tasks........................................ 315 i ; ? , 12.1.1 Finding two closest values in a vector............................. 315 12.1.2 Tabulate binomial probabilities.................................... 317 12.1.3 Calculate and plot a running average............................... 318 12.1.4 Create a Fibonacci sequence........................................ 320 12.2 Read variable format files ............................................... 321 12.3 Plotting maps.............................................................. 324 XIV CONTENTS 12.3.1 Massachusetts counties, continued................................. 324 12.3.2 Bike ride plot.................................................... 325 12.3.3 Choropleth maps................................................... 327 12.4 Data scraping and visualization......................................... 329 12.4.1 Scraping data from HTML files..................................... 330 12.4.2 Reading data with two lines per observation....................... 331 12.4.3 Plotting time series data......................................... 333 12.4.4 URL APIs and truly random numbers................................. 334 12.5 Manipulating bigger datasets............................................ 336 12.6 Constrained optimization: the knapsack problem.......................... 337 A Introduction to SAS 341 A.l Installation............................................................ 341 A.2 Running SAS and a sample session........................................ 341 A.3 Learning SAS and getting help........................................... 346 A.4 Fundamental elements of SAS syntax...................................... 347 A.5 Work process: The cognitive style of SAS ............................... 349 A.6 Useful SAS background................................................... 349 A.6.1 Dataset options.................................................. 349 A.6.2 Subsetting....................................................... 350 A.6.3 Formats and informats............................................ 350 A.7 Output Delivery System.................................................. 351 A.7.1 Saving output as datasets and controlling output................. 351 A.7.2 Output file types and ODS destinations........................... 355 A.8 SAS macro variables................................................... 355 A.9 Miscellanea............................................................. 356 B Introduction to R and RStudio 357 B.l Installation............................................................. 358 B.l.l Installation under Windows...................................... 358 B.l.2 Installation under Mac OS X...................................... 359 B.l.3 RStudio........................................................... 359 B.1.4 Other graphical interfaces........................................ 359 B.2 Running R and sample session............................................. 360 B.2.I Replicating examples from the book and sourcing commands . . . 361 B.2.2 Batch mode........................................................ 362 B.3 Learning R and getting help.............................................. 362 B.4 Fundamental structures and objects....................................... 365 B.4.1 Objects and vectors............................................... 365 B.4.2 Indexing.......................................................... 365 B.4.3 Operators......................................................... 366 B.4.4 Lists............................................................. 366 B.4.5 Matrices.......................................................... 367 B.4.6 Dataframes........................................................ 367 B.4.7 Attributes and classes............................................ 369 B.4.8 Options......................................................... 369 B.5 Functions................................................................ 369 B.5.1 Calling functions................................................. 369 B.5.2 The apply family of functions .................................... 370 B.6 Add-ons: packages.........................................-..............371 B.6.1 Introduction to packages.......................................... 371 CONTENTS xv B.6.2 CRAN task views................................................ 372 B.6.3 Installed libraries and packages............................... 373 B.6.4 Packages referenced in this book .............................. 374 B.6.5 Datasets available with R.................................. 377 B. 7 Support and bugs....................................................... 377 C The HELP study dataset 379 C. l Background on the HELP study ......................................... 379 C.2 Roadmap to analyses of the HELP dataset ............................... 379 C.3 Detailed description of the dataset................................. 381 References 385 Subject index 399 SAS index 419 R index 431
any_adam_object	1
author	Kleinman, Ken Horton, Nicholas J.
author_GND	(DE-588)130212288 (DE-588)1034622552
author_facet	Kleinman, Ken Horton, Nicholas J.
author_role	aut aut
author_sort	Kleinman, Ken
author_variant	k k kk n j h nj njh
building	Verbundindex
bvnumber	BV042383678
classification_rvk	QH 234 SK 850 ST 250 ST 601
ctrlnum	(OCoLC)889990375 (DE-599)BVBBV042383678
dewey-full	005.3
dewey-hundreds	000 - Computer science, information, general works
dewey-ones	005 - Computer programming, programs, data, security
dewey-raw	005.3
dewey-search	005.3
dewey-sort	15.3
dewey-tens	000 - Computer science, information, general works
discipline	Informatik Mathematik Wirtschaftswissenschaften
edition	2. ed.
format	Book
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id	DE-604.BV042383678
illustrated	Illustrated
indexdate	2024-07-10T01:20:05Z
institution	BVB
isbn	9781466584495
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-027819706
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physical	XXV, 443 S. Ill., graph. Darst.
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publisher	CRC Press
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spelling	Kleinman, Ken Verfasser (DE-588)130212288 aut SAS and R data management, statistical analysis, and graphics Ken Kleinman ; Nicholas J. Horton 2. ed. Boca Raton [u.a.] CRC Press 2014 XXV, 443 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier SAS (Computer file) SAS (Computer program language) R (Computer program language) R Programm (DE-588)4705956-4 gnd rswk-swf SAS Programm (DE-588)4195685-0 gnd rswk-swf SAS Programm (DE-588)4195685-0 s DE-604 R Programm (DE-588)4705956-4 s Horton, Nicholas J. Verfasser (DE-588)1034622552 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027819706&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Kleinman, Ken Horton, Nicholas J. SAS and R data management, statistical analysis, and graphics SAS (Computer file) SAS (Computer program language) R (Computer program language) R Programm (DE-588)4705956-4 gnd SAS Programm (DE-588)4195685-0 gnd
subject_GND	(DE-588)4705956-4 (DE-588)4195685-0
title	SAS and R data management, statistical analysis, and graphics
title_auth	SAS and R data management, statistical analysis, and graphics
title_exact_search	SAS and R data management, statistical analysis, and graphics
title_full	SAS and R data management, statistical analysis, and graphics Ken Kleinman ; Nicholas J. Horton
title_fullStr	SAS and R data management, statistical analysis, and graphics Ken Kleinman ; Nicholas J. Horton
title_full_unstemmed	SAS and R data management, statistical analysis, and graphics Ken Kleinman ; Nicholas J. Horton
title_short	SAS and R
title_sort	sas and r data management statistical analysis and graphics
title_sub	data management, statistical analysis, and graphics
topic	SAS (Computer file) SAS (Computer program language) R (Computer program language) R Programm (DE-588)4705956-4 gnd SAS Programm (DE-588)4195685-0 gnd
topic_facet	SAS (Computer file) SAS (Computer program language) R (Computer program language) R Programm SAS Programm
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027819706&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT kleinmanken sasandrdatamanagementstatisticalanalysisandgraphics AT hortonnicholasj sasandrdatamanagementstatisticalanalysisandgraphics

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