I need to perform Chi squared independence test on binary variable (v1) against multiple categorical ones (more than 200, v2-v20..) excluding id column. The aim is to obtain a sorted list of chi statistics that will show which variables are most correlated with v1 variable. Could you help me to write this simple SAS code?
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I am using SAS proc surveyfreq with jack-knife replicate weights to describe frequencies across variables in a survey that used address based sampling. Some of the variables are coded by individual selection - for example, a survey question asks respondents to pick three top choices, so the actual dataset made each individual choice a variable with a Yes/No 0/1 response. Which SAS procedure that incorporate jk weights should I use in this case to describe the frequency for the entire question for the three top choices?
I am implementing a logit model in a database of households using as dependent variable the classification of poor or not poor household (1 if it is poor, 0 if it is not):
proc logistic data=regression;
model poor(event="1") = variable1 variable2 variable3 variable4;
run;
Using the proc logistic in SAS, I obtained the table "Association of predicted probabilities and observed responses" that allows me to know the concordant percentage. However, I require detailed information of how many households are classified poor adequately, in this way:
I will appreciate your help with this issue.
Add the CTABLE option to your MODEL statement.
model poor(event="1") = variable1 variable2 variable3 variable4 / ctable;
CTABLE classifies the input binary response observations according to
whether the predicted event probabilities are above or below some
cutpoint value z in the range . An observation is predicted as an
event if the predicted event probability exceeds or equals z. You can
supply a list of cutpoints other than the default list by specifying
the PPROB= option. Also, you can compute positive and negative
predictive values as posterior probabilities by using Bayes’ theorem.
You can use the PEVENT= option to specify prior probabilities for
computing these statistics. The CTABLE option is ignored if the data
have more than two response levels. This option is not available with
the STRATA statement.
For more information, see the section Classification Table.
I am new to SAS, and would like to create a table of summary statistics that I compute (not only the usual mean, median, etc.)as shown below:
Statistic (Header Column 1), Value (Header Column2), Number of Scored Items ## (Row 1), Number of Examinees ## (Row 2), Mean ##.#%, Median ##.#%, Standard Deviation ##.#%, Minimum ##.#%
Maximum ##.#%, Reliability Estimate #.##, Standard Error of Measurement #.## (Row 9).
I have tried using proc means, but it only allows me to use the summary statistics that's built in the function. So for instance, I don't know how I can use a formula to calculate the Reliability Estimate, and then show it in a table along with other summary stats such as the number of unique observations, etc.
Background: I have a categorical variable, X, with four levels that I fit as separate dummy variables. Thus, there are three total dummy variables representing x=1, x=2, x=3 (x=0 is baseline).
Problem/issue: I want to be able to calculate the value of a linear combination (i.e. using SAS as a calculator) of these dummy variables. For example, 2*B1 + 2*B2 + B3.
In Stata, this can be done using the lincom command, which uses the stored beta estimates to calculate linear combinations of the parameters.
In SAS in a procedure such as PROC GLM, I think I should use the ESTIMATE statement, but I'm not sure how I would specify the "weights" for each variable in this case.
You are looking for PROC SCORE. This takes output regression or factor estimates and scores a new data set. See here for an example. http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_score_examples02.htm
FYI, PROC MODEL does allow this in the model statement, which may be less work than PROC SCORE. I know PROC MODEL can be used readily in place of PROC REG, but I'm not sure how advanced of modeling PROC MODEL does, so it may not be an option for more complex models. I was hoping for something with less coding, but given the nature of SAS, I think this and PROC SCORE are the best I'm going to get.
What if you add your linear combination as a variable in your input dataset?
data myDatasetWithLinCom;
set mydata;
LinComb=2*(x=1)+ 2*(x=2)+(x=3); /*equvilent to 2*B1 + 2*B2 + B3*/
run;
then you can specify LinComb as one of the explanatory variables and you can lookup the coefficient directly from the output.
I'm trying to create a simulation of drug concentration based on the dose of a drug given. I have some preliminary data and I used a random effects model to analyze the relationship between log(dose), predicting log(drug concentration), modelling subject as a random effect.
The results of that analysis are below. I want to take these results and simulate similar data in SAS, so I can look at the effect of changing doses on the resulting concentration of drug in the body. I know that when I simulate the data, I need to ensure the random slope is correlated with the random intercept, but I'm unsure exactly how to do that. Any example code would be appreciated.
Random effects:
Formula: ~LDOS | RANDID
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 0.15915378 (Intr)
LDOS 0.01783609 0.735
Residual 0.05790635
Fixed effects:
LCMX ~ LDOS
Value Std.Error DF t-value p-value
(Intercept) 3.340712 0.04319325 16 77.34339 0
LDOS 1.000386 0.01034409 11 96.71090 0
Correlation:
(Intr)
LDOS -0.047