Hpgenselect logistic regression. For these data, drug and x are explanatory variables.

Hpgenselect logistic regression Especially check DFBETAS diagnostic . One syntax difference is that HPGENSELECT supports a separate SELECTION statement instead of overloading the MODEL statement. This analytics guide is available as a downloadable PDF. Feb 3, 2025 · The sigmoid function is a mathematical function used to map the predicted values to probabilities. Any idea what I did wrong? Thank you. The response-options determine how the HPGENSELECT procedure models probabilities for binary and multinomial data. It does not include deploying and monitoring the model in production. This will result in different parameter estimates. Multinomial and Ordinal Logistic Regression The multinomial logistic regression model, which can be considered as an extension of the binomial logistic regression model, is a proper model to be used when the dependent variable has more than two nominal, yet unordered, categories. For these models this paper discusses the variable selection procedures that are offered by PROC LOGISTIC, PROC HPLOGISTIC, and PROC HPGENSELECT. •(HLS) Hosmer D. Bob Rodriguez in the Statistical Applications Department at SAS introduces you a new procedure in SAS/STAT called HPGENSELECT. The models can be very different. > Thank for all your help. I don't think he meant to imply that the procedure has an option for user-controlled ridge regression (the way that PROC REG does). May 6, 2021 · I am new to LASSO regression and I am running it to select variables and then run a binary (1) and multinomial regression (2) using the variables selected to see how well they predict the outcomes of interest. For a reference to this trick see Hastie Tibshirani Friedman-Elements of statistical learning 2nd ed -2009 page 661 "Lasso regression can be applied to a two-class classifcation problem by coding the outcome +-1, and applying a cutoff (usually 0) to the predictions. The HPGENSELECT procedure provides both model fitting and model building for generalized linear models. Following your suggestion, I checked and found many contents of the book are out of date. My two questions Mar 22, 2017 · I interpret @SteveDenham's response in the previous thread to mean that PROC HPGENSELECT supports the LASSO method for variable selection. The cumulative logit model is one form of the ordinal logistic model. 4 (released this summer), you can run the new HPGENSELECT for model selection with exponential-family distributions. I think your last statement is backwards? Logistic works on binary or categorical variables, while GLM can be continuous. Furhter more, there seems to be no statements or options that would save the model, such as outmodel in proc logistic. Table of Contents. Mar 27, 2023 · Hello, calculated a logistic regression with HPGENSELECT as I needed stepwise to calculate the model. Feb 1, 2023 · I am conducting a logistic regression on variables that were selected via LASSO (hpgenselect). The model-options control other aspects of model formation and inference. while also stating that: NOT In marketing or credit risk a model with binary target is often fitted by logistic regression. LASSO (least absolute shrinkage and selection operator) selection arises from a constrained form of ordinary least squares regression in which the sum of the absolute values of the regression coefficients is constrained to be smaller than a specified parameter. A significance level of 0. 7 4 44 51 1. If I ever manage to fit the hpgenselect model, I dont want to do it again very Linear Regression Model Suppose data arise from a a normal distribution with the following statistical model: Y = f(x) + In linear regression f(x) = 0 + 1x 1 + 2x 2 ++ px p Least squares is the most popular estimation method which picks the coe cients = ( 0; 1;:::; p) that minimize the residual sum of squares: RSS( ) = XN i=1 0 @y i 0 + Xp j=1 The model degrees of freedom that PROC HPGENSELECT uses at any step of the LASSO are simply the number of nonzero regression coefficients in the model at that step. com Jun 16, 2014 · Lasso variable selection is available for logistic regression in the latest version of the HPGENSELECT procedure (SAS/STAT 13. 0 3 13 7 1. The FITSTAT and OUTROC= options in the SCORE statement enable you to evaluate the model applied to the new data set. Thank you, The HPLOGISTIC procedure estimates the parameters of a logistic regression model by using maximum likelihood techniques. Thus, you can do logistic regression. Oct 30, 2017 · I have run a huge logistic regression with about 900 independant variables in the model. Mar 25, 2025 · The HPGENSELECT procedure provides both model fitting and model building for generalized linear models. Subsections: 8. Getting Started; Community Memo; All Things Community; SAS Customer Recognition Awards (2023) This example shows how you can use PROC HPGENSELECT to perform model selection among Poisson regression models by using the LASSO method in single-machine and distributed modes. Here it is specified as log instead of logit: Code the outcome as -1 and 1, and run glmselect, and apply a cutoff of zero to the prediction. 2® User’s Guide Introduction to Regression Procedures SAS® Documentation November 06, 2020 Apr 9, 2024 · Specifically, it does not delve into data cleaning and verification, assumption validation, model diagnostics, potential follow-up analyses, or any other possible approaches for performing these frequency and binary logistic regression procedures. A logistic regression for these data is a generalized linear model with response equal to the binomial proportion r/n. This method is similar to the FORWARD method except that effects already in the model do not necessarily stay there. SAS 9. provides model-building syntax in the CLASS statement and the effect-based MODEL statement, which are familiar from SAS/STAT procedures (in particular, the GLM, GENMOD, LOGISTIC, GLIMMIX, and MIXED procedures) Nov 22, 2015 · The HPGENSELECT documentation is online and answers all of these questions. Lassoisusedforprediction. However if you're interested I can send you my Base SAS coding solution for lasso + elastic net for logistic and Poisson regression which I just by the random forest method) and logistic regression models (variables selected by the stepwise method) is demonstrated. I will post my SAS code at the end. Ordinal logistic regression becomes binary logistic regression if the target has 2 levels. 1 User's Guide documentation. The probit and the complementary log-log link functions are also appropriate for binomial data. I will be using this method and would appreciate the help. The HPGENSELECT procedure adds support for LASSO model selection for generalized linear models. Jul 9, 2021 · 特徴量選択でSTEPWISE法などのほか、LASSO（L1正則化）を視野に入れる人もいる（だろう）。 LASSOについては以前 Python の記事で触れた。 cochineal19. In ordinal logistic regression the target (or dependent variable) has 3 or more levels and these levels are ordered. In this setting the sample size is large and the model includes many predictors. For example, the following statements work: model sex(event="M") = height weight age / dist=binary; . 3 is required to allow a variable into the model (SLENTRY=0. Introduction PROC HPGENSELECT is invoked using events/trials syntax to fit the binary logit model to the grouped data. Jan 10, 2018 · Hi, I am new to hpgenselect procedure (used to logistic), however, I find it more efficient for stepwise method, but I can find how to output: Oddsratio for all selected variable with it`s coefficients, Pearson chi-square, Cox- Snell residuals, Nagelkerke residuals, R-square and ajdusted R-square For a logistic or probit model, the scoring process is greatly simplified in PROC LOGISTIC. The same Lassointro—Introductiontolasso4 Lassoisusedinthreeways: 1. I also believe LOGISTIC will have some more specific features such as AUC & ROC curves but I haven't checked that out. Nov 13, 2014 · Yesterday I attended a presentation by Robert Rodriguez at the SAS Global Forum on the latest version (SAS/STAT 13. 1) of the HPGENSELECT procedure. suppresses the computation of the covariance matrix and the standard errors of the regression coefficients. I am working on a dataset with 6 predictors (3 contiuous, 3 categorical) with binary outcome. For example, ordinal logistic regression applies to fitting a model where the target is a satisfaction rating (e. hatenablog. I also tried using HP Usage Note 60240: Regularization, regression penalties, LASSO, ridging, and elastic net Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. The –2 log likelihood at the converged estimates is 88. INTRODUCTION The primary purpose of this paper is the use of random forests for variable selection. The ALLVAL and ALLTEST sets containing decision tree predictions are supplied in the first regression run. 2. After some search can't find the appropriate code to substitute the variables for which the parameters made it on the regression. Note that the words logistic and logit are used interchangeably. I don't understand your question about "the default method for variable selection. 2 2 33 27 2. 2 User's Guide: High May 27, 2020 · In this case, I would suggest to switch to PRO C HPLOGISTIC procedure is a high-performance statistical procedure that fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance. strategies for estimating the out-of-sample validity of logistic regression models, Stat Methods Med Res. There are different models for this (RIDGE, LASSO) and I have found this function in linear regression in SAS (PROC REG) but not in logistic regression (PROC LOGISTIC). However, I am finding that the significance varies depending on which variables I include and exclude, and I believe that there is association and collinearity among the variables. Other updates include key functionality for Bayesian analysis and Oct 28, 2020 · A logistic regression for these data is a generalized linear model with response equal to the binomial proportion r/n. (2015) Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, 2nd Edition. 2 0 21 51 2. provides model-building syntax in the CLASS statement and the effect-based MODEL statement, which are familiar from SAS/STAT procedures (in particular, the GLM, GENMOD, LOGISTIC, GLIMMIX, and MIXED procedures) Jul 30, 2019 · Do I simply run HPGENSELECT one by one per variable, and choose those with p<0. CLASS y x1 x2 x3 x4 x5 x6 x7 x8 / PARAM=GLM See full list on support. Apr 14, 2017 · Hi, I am not advanced SAS user and I will need your help. if there is SAS program with the codes LASSO-CLR that will be awesome. 2 This paper focuses on the Dec 24, 2017 · I am trying to run a model with logistic regression containing about 20 independent variables, both categorical and continuous. 1? Or is there a way to loop the variables, or to integrate it into proc logistic (eg, stepwise?) I also notice that HPGENSELECT is done through linear regression. To use the same parameterization, change the LOGISTIC procedure to use the GLM parameterization by using. Jun 17, 2015 · What I want to perform is a penalized logistic regression in SAS (penaliz/shrink the size of the regression coefficients). Byprediction May 1, 2019 · The EFFECT encoding is the default for CATMOD, LOGISTIC, and SURVEYLOGISTIC. Any idea is greatly appreciated. 2 Modeling Binomial Data; 8. > , > > Amit Hi Amit You can do this in two steps: 1) Use GLMSELECT as if you had an OLS model, and get several sensible models 2) Try those models in LOGISTIC or whichever PROC you like for logistic regression. Finally, MLE is compared to fitting models by LASSO,1 provided by PROC HPGENSELECT. Many modeling procedures provide options in their CLASS statements (or in other statements) which allow you to specify reference levels for categorical predictor variables. com 今回は「SASで使ったみたい」＆「ロジスティック回帰で使ってみたい」という視点でまとめる。（需要は低いかもしれないが、）SASで機械 Nov 5, 2009 · If you have sas 9. , Sturdivant R. 0 1 56 51 1. Dec 2, 2024 · The HPGENSELECT procedure does the following: estimates the parameters of a generalized linear regression model by using maximum likelihood techniques provides model-building syntax in the CLASS statement and the effect-based MODEL statement, which are familiar from SAS/STAT procedures (in particular, the GLM, GENMOD, LOGISTIC, GLIMMIX, and PROC HPGENSELECT Features The HPGENSELECT procedure does the following: estimates the parameters of a generalized linear regression model by using maximum likelihood techniques provides model-building syntax in theCLASSstatement and the effect-basedMODELstatement, which are familiar from SAS/STAT procedures (in particular, the GLM, GENMOD This example shows how you can use PROC HPGENSELECT to perform model selection among Poisson regression models by using the LASSO method in single-machine and distributed modes. Table 8. Relative risk estimation by log-binomial regression. 1 in the HPGENSELECT procedure. It is possible to run a cox-regression in Proc HPGENSELECT, but it may not work very good: You need to make a aggregated dataset such information on each risk set is collected in the same records (one record for each different combination of covariates and Best practice recommends starting with simple regression. This example illustrates how you can use PROC HPGENSELECT to perform Poisson regression for count data. 1 provides the LASSO option in PROC HPGENSELECT , which provides shrinkage of the coeffiients. The focus of this example is to show how estimates the parameters of a generalized linear regression model by using maximum likelihood techniques . 3), and a significance level of 0. PROC HPGENSELECT runs in either single-machine mode or distributed mode. Sep 20, 2019 · Lasso variable selection is available for logistic regression in the latest version of the HPGENSELECT procedure (SAS/STAT 13. The subgroup analysis Oct 15, 2021 · Meaning that hpgenselect takes forever and reserves a high percentage of our CPU resources (other scheduled runs are queued, and this is unwanted). For example, it says that PROC LOGISTIC needs to manually create dummy variables, it cannot specify multiplicative terms (i. interaction) in the MODEL statement. " The LASSO method IS a variable-selection method, so the default method is LASSO. 7 0 17 14 1. ) Jan 25, 2019 · We describe a set of guidelines and heuristics for clinicians to use to develop a logistic regression-based prediction model for binary outcomes that is intended to augment clinical decision-making. It also does the following: provides model-building syntax with theCLASSand effect-basedMODELstatements, which are familiar from SAS/STAT analytic procedures (in particular, the GLM, LOGISTIC, GLIMMIX, and MIXED procedures) Oct 28, 2020 · estimates the parameters of a generalized linear regression model by using maximum likelihood techniques . regression trees, including cross validation and graphical displays. Its SCORE statement enables you to score a data set of new observations. 3 summarizes these options. In ordinal logistic regression the target has 2 or more levels and these levels have an ordering. (SBC). , Lemeshow S. */ title 'Example 2: Modeling Binomial Data'; data Ingots; input Heat Soak r n @@; Obsnum= _n_; datalines; 7 1. 35). For more information about the execution modes of SAS High-Performance Statistics procedures, see the section Processing Modes. The HP regression procedures all use the GLM encoding by default and support only PARAM=GLM or PARAM=REF. Examples: HPGENSELECT Procedure. Efron et al. However, PROC HPGENSELECT in SAS/STAT14. With a very minor modification of the statements used above for the logistic regression, a log-binomial model can be run to get the RR instead of the OR. For all these models, the HPGENSELECT procedure provides forward, backward, and stepwise variable selection. The following DATA step contains 100 observations for a count response variable ( Y ), a continuous Mar 20, 2013 · I assume you mean that you want to penalize and shrink large coefficients in the logistic regression model. Nov 12, 2024 · Even though they know the basics of linear regression and logistic regression, they are not that acquainted with more specialized regression models. 2 SAS® 9. It does not have the full functionality of GLMSELECT, but it may be fine for your needs. proc glmselect can do CV, but I don't know if it could do some logistic regression. . You didn't supply any data, but try to modify this code, which is taken from the LOGISTIC documentation for ordinal regression (Caution: untested! Didn't verify that this results in cumulative logit model. 1 Model Selection; 8. Oct 15, 2021 · Meaning that hpgenselect takes forever and reserves a high percentage of our CPU resources (other scheduled runs are queued, and this is unwanted). For this second audience, adopting the model building procedures involves learning about specialized regression models beyond standard linear and logistic regression. Feb 18, 2010 · > Regression. Dec 25, 2017 · I am trying to run a model with logistic regression containing about 20 independent variables, both categorical and continuous. 9 in F1 and balanced accuracy. 3 Tweedie Model Oct 17, 2022 · The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0. good, fair, poor). " Nov 28, 2016 · I wonder if you have looked at the HPGENSELECT procedure, which supports LASSO. Dec 13, 2024 · Hi all, I need dataset to apply the LASSO and conditional logistic regression . I have a question about my methodology. ; It maps any real value into another value within a range of 0 and 1. ( 2004 ) cite empirical evidence for doing this but do not give any mathematical justification for this choice. Nov 22, 2015 · Community. Applied Logistic Regression 3rd Ed. Ridge can help with problems besides multicollinearity: it penalizes coefficients to reduce overfitting and to avoid "complete separation" problems. Nov 6, 2022 · Hi! I'm trying to run a logistic regression and implement LASSO variable selection using PROC HPGENSELECT. 7 0 43 27 1. 1 For example, ordinal logistic regression applies to fitting a model where the target is a satisfaction rating (e. The HP procedures use the GLM parameterization as a default. g. 1 does offer selection=LASSO which gets around a lot of the difficulties with the other methods. 3. The HP regression procedures include HPFMM, HPGENSELECT, HPLMIXED, HPLOGISTIC, HPNLMOD, HPPLS, HPQUANTSELECT, and HPREG. However, I am finding that the significance varies depending on which variables I include and exclude, and I believe that there is association and collinearity among the va step-by-step approach to creating a predictive logistic regression model. Jan 30, 2023 · I have previously used glmselect and hpgenselect for linear and logistic regression model selection however I have come across a question. A modification of LASSO selection suggested in Efron et al. For methods other than LASSO, the only effect-selection criterion that the HPGENSELECT procedure supports is SELECT= SL, in which effects enter and leave the model based on an evaluation of the Select Regression Models and Score The SCORE statements allows for scoring of new data (Validation and Test) and adjusts oversampled data back to the population prior (PRIOREVENT=0. 4). If you request confidence intervals by specifying the CL option in the MODEL statement, confidence limits for regression parameters are produced for the estimate on the linear scale. I looked for sample data but did not find any. When the response 70 F Chapter 4: Introduction to Regression Procedures HPNLMOD is a high-performance procedure that uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models. All variable sin the model, including the dependant are binary 1 or 0. Throughout the paper there are citations to these two references: LRuS: Allison, P. The log states that: WARNING: The information matrix is singular and thus the convergence is questionable. Nov 19, 2015 · HPGENSELECT supports the DIST=BINARY and DIST=BINOMIAL options for logistic regression. Elastic net isn't supported quite yet. The LOGISTIC procedure provides a few classical variable selection algorithms, but I recommend the newer HPGENSELECT procedure, which supports much of the same functionality as PROC GLMSEELCT. But how? I am new to SAS, having come from the world of R. (2012a), ndLogistic Regression Using SAS®: Theory and Application 2 Ed ALR: Hosmer D. (want: color 1 weight 2. 0 0 31 27 1. 1 included in Base SAS 9. The proc is designed for Big Data problems, but runs on any data set. 9. Following are some common logistic models. 35 is required for a variable to stay in the model (SLSTAY=0. For more information about the execution modes of SAS High-Performance Statistics procedures, see the section Processing Modes in SAS/STAT 14. Lassoisusedforinference. 072). performs stepwise regression. PDF EPUB Feedback Specify an ODS OUTPUT= statement and the CLB option in the MODEL statement to save the ParameterEstimates table containing the parameter estimates and statistics in a data set. Introducing the HPGENSELECT Procedure: Model Selection for Aug 18, 2021 · Hello fellow SAS users and SAS support, I have been using HPGENSELECT with LASSO selection for a binary dependent variable, and was hoping for clarification regarding the details of the LASSO penalization method and the resulting coefficients. Apr 26, 2019 · The LOGISTIC procedure uses an EFFECT parameterization to build the design matrix. For these data, drug and x are explanatory variables. 4 and SAS® Viya® 3. In addition, PROC HPGENSELECT fits multinomial models for ordinal and nominal responses, and it fits zero-inflated Poisson and negative binomial models for count data. Local Regression Using LOESS Local Polynomial Regression are a group of non-parametric regression methods that combine multiple regression runs into a meta-model. (2004) uses the LASSO algorithm to select the set of covariates in the model at any step, but uses ordinary least squares regression with just these covariates to obtain the regression coefficients. I am looking to use it for variable > selection. 3 Programming Documentation . When the model contains many variables (thousands), the inversion of the Hessian matrix to derive the covariance matrix and the standard errors of the regression coefficients can be time-consuming. The value of the logistic regression must be between 0 and 1, which cannot go beyond this limit, so it forms a curve like the “ S ” form. Feb 4, 2019 · Yes. 5) Nov 30, 2020 · CLASS 语句的 PROC LOGISTIC 和 HPGENSELECT 中的默认值都是 GLM，但由于您没有显示代码，我不知道 LOGISTIC 过程中指定了哪些选项。 无论哪种方式，将参数化更改为 REF，您将获得参考编码。 class Treatment Sex / param=REF; PS。 如果您想确保它相同，请明确检查优势比。 Jul 4, 2011 · I am using the book: Logistic Regression Using SAS: Theory and Application, by Paul D. for education purpose. Keywords: Review; logistic regression; predictive model. I am trying to do the followings: 1. And new software implements generalized additive models by using an approach that handles large data easily. e. I am working on a case/control (1:3) conditional logistic regression with a high number of parameters and would like to perform dimensional reduction, like lasso. The three basic categories of logistic models are the binary, ordinal, a This example shows how you can use PROC HPGENSELECT to perform model selection among Poisson regression models by using the LASSO method in single-machine and distributed modes. The probability distribution is binomial, and the link function is logit. (2013). Allison. 3. Home; Welcome. If I ever manage to fit the hpgenselect model, I dont want to do it again very The following invocation of PROC LOGISTIC illustrates the use of stepwise selection to identify the prognostic factors for cancer remission. The lasso variable selection procedure is available for logistic regression (in fact that was one of the examples in his slides), although I can't speak for least angle regression. The default in PROC LOGISTIC and HPGENSELECT for the CLASS statement is both GLM, but since you didn't show the code I don't know what options were specified in the LOGISTIC procedure. The focus of this example is to show how Nov 30, 2020 · What you're referring to from the logistic regression is referential coding. Below is the code for the LASSO with a binary outcome: proc hpgenselect data =dataset; The model degrees of freedom that PROC HPGENSELECT uses at any step of the LASSO are simply the number of nonzero regression coefficients in the model at that step. You can use this value to compare the model to nested model alternatives by means of a likelihood-ratio test. Either way, change the parameterization to REF and you'll get referential coding. Sep 26, 2021 · まとめ用。随時更新。 教師あり学習 最小二乗回帰（OLS：Ordinary Least Squares regression、線形回帰） ロジスティック回帰（Logistic regression） 四分位回帰（Quantile regression） サポートベクターマシーン（SVM: Support vector machine） 決定木（Decision tree） ランダムフォレスト（Random Forest） 勾配ブースティング Sep 20, 2021 · $\begingroup$ @user54285 SAS/STAT has implemented LASSO for logistic regression since version 14. (1) If I want to control for age and gender, do I exclude them from the lasso selection but include them in the logistic regression? Or do I include them in both the lasso and logistic regression? Feb 20, 2017 · A model from GLMSELECT does not have to be a LOGISTIC regression. The variables to be considered for inclusion in a model can be ranked in order of their importance. SAS/STAT 15. Split the data into 50% training and 50% validating datasets and then compare their ROC curves, SAS code: pro Aug 29, 2019 · Hi All, I have been working on a Lasso Logistic regression with binary response and 20 predictor varaibles (a mix of categorical and continuous ) and have read a lot on using GLMSELECT procedure and coding the outcome ±1, and applying a cutoff (usually 0) to the predictions. 7 0 1 7 2. For exponential family models, the distribution variance is Var( Y ) = φ V( μ ), where V( μ ) is a variance function that depends only on μ . Confidence limits for the dispersion parameter of those distributions that possess a dispersion parameter are produced on the log scale, because the dispersion Feb 10, 2017 · I have the same need, but came to the conclusion that it is not in SAS (yet). 4 / Viya 3. 7007. 95 in AUC and around 0. Aug 17, 2015 · The problem is exacerbated for logistic regression. SAS/STAT 14. Then, as strong interactions are observed, both Ridge and LASSO are tried and the results compared to identify the best solution. The “ Fit Statistics ” table is shown in Figure 5. 2 0 7 14 2. LOGISTIC REGRESSION: A BRIEF OVERVIEW Logistic regression is primarily used for binary classification problems where the outcome or dependent variable has two possible categories, often denoted by 0 and 1. New York: Springer. •(RMS) Harrell, F. Panji proc hpgenselect data=mydata; clas Usage Note 60240: Regularization, regression penalties, LASSO, ridging, and elastic net Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. 3® User’s Guide Introduction to Regression Procedures SAS® Documentation February 26, 2025 Dec 18, 2023 · The HPGENSELECT procedure parameterizes models in terms of the regression parameters β and either the dispersion parameter φ or a parameter that is related to φ, depending on the model. Lassoisusedformodelselection. All that needs to be changed is the link function between the covariate(s) and outcome. selection method=lasso; run; Please post the syntax that is giving you the error. Mar 21, 2017 · How can one do Logistic Regression optimized with a ridge regression, in SAS? According to comments here and here this should already be implemented in SAS with PROC HPGENSELECT. sas. In marketing or credit risk a model with binary target is often fitted by logistic regression. However, SAS did not produce confidence limits for the regression estimates even though I have requested it. Look at the SELECTION statement to see various defaults. Oct 11, 2016 · INFLUENCE option of model can do Regress Diagnoise. Still, consider the result of putting things on a logit link, and what might happen with fewer than 10 events per predictor. 0 0 10 14 1. com There are many types of models in the area of logistic modeling. tgblw nkbwov utyi ztyep akg pigk notypydv ibyx ppk inte phqd vlwl isg oqy uklmma