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Intermediate Statistical Methods. Authors: Marasinghe, Mervyn G., Koehler, Kenneth. Integrates SAS programming with complex data analysis applications.
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- SAS for Data Analysis: Intermediate Statistical Methods
Tell us about a web accessibility problem. About online privacy and data collection. All rights reserved. Terms and conditions. Toggle navigation. Mervyn G. Marasinghe, William J. Back 0 Marked Mark Options. Holdings In. UofT at Mississauga. Subjects subject. Mathematical statistics--Data processing. Mathematical statistics--Software. SAS Computer program language. SAS Computer file. More Details author. Marasinghe, Mervyn G. Kennedy, William J. William Jo , Toon meer Toon minder. Recensie s From the reviews: The authors have presented an exceptionally detailed and complete guide to using SAS to read and process data, make tables and plots, and fit linear models with fixed, random, or mixtures of fixed and random components.
All procedures are illustrated with numerous data examples, and both the SAS commands and the output are explained in meticulous detail. And as one would expect, all of the data and SAS code used in the book may be downloaded from a website Dirk F Moore, Journal of Biopharmaceutical Statistics JBS , Issue 5, The authors provide an easily readable introduction into the SAS language, some of its basic statistical methods, and many applications for statistical linear modeling Very helpful are the many exercises in each chapter which make this book valuable for teaching at universities and colleges As of today, almost all test examples and data sets are available from the Web page accompanying the book.
Wolfgang M. Hartman, Journal of Statistical Software, Vol. A ll procedures are illustrated with numerous data examples, and both the SAS commands and the output are explained in meticulous detail. And as one would expect, all of the data and SAS code used in the book may be downloaded from a website.
But for a student who needs to learn the details of using SAS to process data and fit classical linear models, this book would make an excellent choice. Dirk F. Moore, Journal of Biopharmaceutical Statistics, , Issue 5 Many universities offer graduate courses in applied statistics where the emphasis is on regression and design of experiments. These courses typically attache individuals from a wide array of disciplines with the purpose being to equip students with some of the essentials of data analysis. Typically students have been exposed to one or more point-and-click statistical software packages along the way in their statistical training but their exposure to programming-sophisticated packages such as R and SAS is at best, limited.
The authors of this text clearly have a great deal of experience teaching these types of applied statistics courses as they have put together a fine text. The text is carefully written and well organized. The introduction to the SAS language is one of the best that I am aware of. Note that the second statement is aconvenient shortened form to read data values into a sequence of ten variablesnamed Score1, Score2, List input can be used for reading data values for either numeric or char-acter variables.
SAS allocates character variablesdescribed in this way a maximum length of eight characters bytes by default. If a value is longer thaneight characters, it is truncated on the right to eight characters. Charactervariables expected to contain values of length more than eight characters canbe read using an informat in the formatted input method discussed below. For this reason, when using the list input method, if there areany missing data values, they must be indicated on the data line by enteringa period the SAS missing value indicator as described previously separatedfrom other data values by at least one blank on either side of the period,instead of leaving it blank.
Formatted InputFor many instream data sets, or those accessed from recording media such asdisks or CDs, list input may be inappropriate. This is because, in order tosave space, the data values contiguous to one another may have been preparedwith no spaces or, other characters such as commas, separating them.
In suchcases, SAS informats must be used to input the data. In general, informats can be used to read data lines present in almostany form. They provide information to SAS such as how many columns areoccupied by a data value, how to read the data value, and how to store thedata value in the SAS data set. The two most commonly used informatsare those available for the purpose of inputting numeric and character datavalues. To read a data value from a data line, the user must specify in whichcolumn the data value begins, how many columns to scan, whether the datavalue is numeric or character, and where, if needed, a decimal point shouldbe placed in the case of a numeric value.
For example, input 26 Store 45 Sales;tells SAS that a value for the variable Store is to be read beginning in col-umn 26 and a value for Sales beginning in column Here it is assumedthat the values in each data line are separated by blanks as when using thelist input style ; otherwise, informats are required to read these values, asdescribed below.
The w must be a positive integer. For example, input 25 Weight 3. For example, input 10 Price 6. Leadingand trailing blanks are removed. Some examples below illustrate theuse of informats in practice. Suppose a data line contains IAwhere is the I. Let Id, State, Fert,.
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Percent, and Members be the names assigned by the user to the correspondingvariables. An appropriate input statement would be input Id 4. Fert 5. Thus SASautomatically accesses the data value for the next variable beginning from thecolumn following the last value. The use of the pointer and pointer controls inreading data from an input data line is described in detail in Sect. Finally, the variable names and informats including pointer controls thatoccur on an input statement can be grouped into two separate lists enclosedin parentheses.
If the informat list is shorter than thenumber of variables present, then the entire informat list is reapplied to theremaining variables as required. Column INPUTColumn input is another alternative to list input when the data values arenot separated by blanks or other separators, but the user prefers not to useinformats. In this case, the values must occupy the same columns on all datalines, a requirement that is also necessary for using formatted input.
However,in the input statement, the variable name is followed by the range of columnsthat the data value occupies in the data line, instead of an informat. Blanks occurring both before and after the data value are ignored. Forexample, if the data line has the appearance IA 5.
The value forFert is read as the value exactly as it appears in columns 7 through 12, i. Thevalue for Percent will thus be read as 0. For example, as in the previous example, if the data line has the appearance IA 5. Fert Percent 2. As mentioned above and discussed later in Sect. Consequently, the informat 2. Then the value for Members is read using columninput again. Thus, a knowledge of how the pointer is handled by the threestyles of input is necessary to combine them correctly in a single statement.
SAS programming statements are generally used to modify thedata during the process of creating a new SAS data set, either from raw dataor from data already available in a SAS data set; hence, they must followan input or a set, statement. The syntax and usage of several statementsavailable for SAS data step programming are discussed below. Assignment StatementsAssignment statements are used to create new variables and change the valuesof existing ones. This may involve combining arithmetic operators,SAS functions, and other arithmetic expressions enclosed in parentheses andassigning the value of that expression to a new variable name.
For example,in the SAS data step in Example 1. The value of Y1 for eachobservation in the data set, for example, will be the sum of the value of X1and the square of the value of X2 in that observation. The variable namein an assignment statement may be a new variable to be added to the dataset and assigned the value of the expression; or it may be a variable alreadypresent in the data set, in which case the original value of the variable isreplaced by the value resulting from evaluating the expression.
Thus, in the. Arithmetic expressions are normally evaluated beginning from the left andproceeding to the right, but applying the Rules 1, 2, and 3, given in Fig. The result of an arithmetic expressioncontaining a missing value is a missing value. The SAS system incorporates alarge number of mathematical functions that can be used in the expressions,as shown in the above example.
Some examples of the commonly used math-ematical functions are abs, log, and sqrt. The general form of a functioncall is function-name argument1,argument2,. Arithmetic expressions are evaluated according to a set of rules calledprecedence rules. These rules, summarized in Fig. It is good programming practiceto follow these rules when writing expressions. Some details on the use of theoperators in Fig. Rule 1. Rule 2. An operator in a higher ranking group below has higher priority and therefore is evaluated before an operator in lower ranking group.
Operators with the same priority same group are evaluated from left to right of the expression except for Group I opera- tors, which are evaluated right to left. The assignment statements used in Example 1. However, variable names may be combined using comparison op-erators to form logical expressions as described in the paragraph below.
The if-then and elsestatements allow the execution of SAS programming statements that dependon the value of an expression. A logical expres-sion consists of numerical or character comparisons made using comparisonoperators. The statement in the above syntax is any executable SAS statement; however,several SAS statements enclosed in a do-end group may be used in place of asingle SAS statement.
Example 1. If this is not so, then the currentvalue of Region will be determined by if-then statements appearing later inthe SAS data step, or otherwise will be left blank. This observation is not written to the SAS dataset being created, and control returns to the beginning of the data step toprocess the next line of data.
In this example, if the current value of thevariable Income is found to be a SAS missing value, then the observationis not written into the data set as a new observation. The result is that noobservation is created from the data line being processed. Sometimes it may be required to avoid executing or jump over a few SASprogram statements depending on the value of an expression.
For this purpose,SAS program statements could be labeled using the label: notation. Note that the if 6. Theabove example is a straightforward extension of Example 1. The pointer control symbol causes the input statementto be repeatedly executed for the data line. Thus, the data set named group1will have 14 observations, each with a single value for the variable Age. In the second data step 2 , the SAS data set group2 will be formed usingthe observations from group1 as input, with a new variable named AgeGroupbeing created. In the third data step 3 , the SAS data set group3 will be formed usingthe observations in group1 as in the previous step.
However, the values forthe new variable AgeGroup this time are determined simply by executing the. Note thatthe int function is a SAS function that truncates the result of execution of anumerical expression to the lower integer value. SAS Example A4: listing output.
The two proc print; statements constitute two proc steps that list twoof these data sets group2 and group3, which are identical in content. One ofthe two data sets is displayed in Fig. Repetitive ComputationRepetitive computation is achieved through the use of do loops or for loops,respectively, in commonly known low-level programming languages such asFortran or C.protmecgistssysduff.ga/el-pecado-de-los-jueves.php
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In the SAS data step language, several forms of do statements, inaddition to the do-end groups discussed earlier are available. The use of iterative do loops in the data step isillustrated in Examples 1. An iterative do loop, in general, is used to perform the same operation on asequence of variables. This is achieved by the use ofsubscripts. In Example 1. Within the do loop, the subscripts are assigned byusing an index variable, here named I, that is used as a counting variable inthe do statement.
During the execution of the loop i. The task performed by the do loop in Example 1.
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Then the current value of each of the variables H1—H7 is set to 12 times the value of each of the corresponding variables D1—D7,respectively. Note carefully that the second array statement assigns an arrayname to a set variables H1—H7 yet to be used in the data step. This technique is often used for generating fac-tor levels of combinations of factors or interactions in factorial experiments.
The output statement inside the loop forces a new observation containing cur-rent values of the variables A, B, and C to be written to the data set, each passthrough the loop.
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Thus at the end of the processing of the loop, the SAS dataset index will contain 12 observations corresponding to all 12 combinationsof the 4 values of A and the 3 values of B. The printed listing of this data set is. Creating indicesObs A B C 3 6 9 13 16 19 23 26 4 3 4 6 4 9 39 The do while statement repeatedly executes statements in a do looprepetitively as long as a condition, checked before each iteration, evaluatesto TRUE. The do until statement executes statements similarly but checksthe condition at the end of the loop.
SAS for Data Analysis: Intermediate Statistical Methods
Thisvariable automatically retains its current value until it is updated during theexecution of the loop. If the expression evaluates to a missing value, it istreated as zero. If the variable is not assigned an initial value, it is automat-ically initialized to zero before the DO loop begins note the variable Monthin this example. The printed listing of data set loan is. Loan AmortizationObs Balance Month 1 The discussion here is kept to a minimum technical level bymaking use of illustrations and examples.
These locationsare all initialized to SAS missing values when the data step processing begins.