About Birgithj

Birgithj · ‎12-20-2019

Hi again Jag, Unfortunately the issue is the same: I can get a correct legend, but the color mapping disappears. I was though able to resolve the issue based on another solution in this thread. It is a very manual solution, but . nonetheless solved the issue. But thank you for your help!

Birgithj · ‎12-20-2019

Hi Jag, Thank you for your reply. While this solves the problem with the key legend, unfortunately this solution disables the custom colouring of the stacked bar chart using the 'myattrmap' code, which is vital for the figure. Is there a way to avoid this?

Birgithj · ‎12-19-2019

Hi, I am oping you are able to help me with below problem. I have a dataset containing 11 variables. have plotted the variables using a stacked bar chart with the date on the x-axis. Now I want to change the name of the variables in the keylegend box (i.e. Bakk = Bakken (ND & MT), Eagl=Eagle Ford (TX) etc). How can I do this? Below I have pasted my code as well as the figure. ***Import data***; PROC IMPORT DATAFILE="/folders/myfolders/Speciale/oil/shale oil by basin.xlsx" OUT=shale_prod DBMS=XLSX REPLACE; ***Convert from long to wide format***; proc sort data=shale_prod out=Wide; by date; run; proc transpose data=Wide out=Long(rename=(Col1=Value)) name=Source; by date; var Eagl Spra Bakk Wolf Bone Niob Miss Aust Wood Rest; run; ***Assign colors to variables***; data myattrmap; length linecolor $ 9 fillcolor $ 9; input ID $ value $ linecolor $ fillcolor $; datalines; myid Eagl VLIGB VLIGB myid Miss brgr brgr myid Rest vib vib myid Spra MOGB MOGB myid Bakk vigb vigb myid Bone BLUE BLUE myid Aust BIGB BIGB myid Niob PKGR PKGR myid Wood STOY STOY myid Wolf PAB PAB ; ***Plot stacked bar chart***; ods graphics on / width=7in height=4.5in maxlegendarea=60; proc sgplot data=Long dattrmap=myattrmap; vbar date / response=Value group=Source groupdisplay=stack grouporder=data attrid=myid; xaxis type=time thresholdmin=0 max=21100 display=(nolabel); yaxis grid values=(0 to 7) GRIDATTRS=(color=white) label="Million barrels per day"; keylegend /title=""; run;

Birgithj · ‎11-14-2019

Thank you for your answer. Do you know if this can be done without writing each calculation manually? I ask because I have a dataset containing 90 variables, and manual calculation will be very time consuming.

Birgithj · ‎11-14-2019

Hi, I hope someone is able to help me with below. I have two datasets: Dataset 1 contains raw data for a number of variables and dataset 2 contains a set of weights for the same variables. Example is shown below: Dataset 1: Raw data A B C D 8 2 4 7 1 3 5 6 7 3 3 10 4 3 7 5 1 6 7 8 4 1 7 9 2 7 1 2 1 8 10 10 7 7 6 10 Dataset 2: weights B A D C 0.2 0.3 0.1 0.4 I would like to multiply all observations A in dataset 1 with the weight A in dataset 2. The same goes for variables B, C and D. The variables are though not listed in the same order. Can someone please help me with this? *** Actual dataset contains a large number of variables, so solutions without manual calculation for each variable would be greatly appreciated***

Birgithj · ‎11-07-2019

Thank you so much. Once again you have saved my day!

Birgithj · ‎11-07-2019

Hi, I have dataseries containing annual percentage growth, and from this I would like to create a new column showing corresponding price development (assuming a price of 100 in year 0). An example can be seen below. Year Annual return Stock price development 1 7,00% 107,0 2 6,00% 113,4 3 2,00% 115,7 4 1,00% 116,8 5 2,00% 119,2 6 4,00% 123,9 7 5,00% 130,1 I have used the following code, but I am not able to successfully do the calculation. data want; set have; if first.return then Price=100; Price*(1+return); run; Can someone please help me?

Birgithj · ‎10-10-2019

Hi Paige, As shown in my additional post, the same data has been structures in two ways: Date ORCL KO GM 201801 1% 2% 3% 201802 1% 2% 3% 201803 1% 2% 3% In this minor example, the variables are ORCL, KO an GM (company tickers). Here PCA works fine. My question is whether or not it is possible to run the same analysis when data instead is structures as below: Date Ticker Company name Industry classification Returns 201801 ORCL Oracle Corp 7372 1% 201802 ORCL Oracle Corp 7372 1% 201803 ORCL Oracle Corp 7372 1% 201801 KO Coca Cola comp 2086 2% 201802 KO Coca Cola comp 2086 2% 201803 KO Coca Cola comp 2086 2% 201801 GM General Motors 3711 3% 201802 GM General Motors 3711 3% 201803 GM General Motors 3711 3% I am aware that the returns for all companies are placed in the same column and that the same goes for company tickers, but we thought perhaps it was possible to work around it so that we could keep the information stated in the additional columns. I know this i possible when running the analysis i python (using ID variables), but I was wondering it the analysis could be done i SAS using the above data structure.

Birgithj · ‎10-10-2019

Hi Paige, I am sure that principal component analysis is the right method to my problem. Maybe, perhaps, I have not been clear enough regarding what the purpose of the analysis is, and hence what I am trying to analyse. The situation is, that I have a large dataset consisting of around 500 stocks and their respective returns for the period 2000-2019. Based on PCA I am: 1) trying to reduce dimensionality of the observed variation in returns to a limited number of principal components explaining e.g. 70% of the total variation in stock returns. and 2) trying to analyse the composition of each principal component that we choose to keep e.g. in finance this could be interpreted as risk factors. In this case you could e.g. expect PC1 to represent general market risk, PC2 and so on to represent other sources of risk. In order to interpret on the principal components it is vital that we can keep information regarding company specifics in the dataset. So far we have not been able to do so when data is structured in wide-form, which is why we want to know if PCA can be performed in above shown long-form. Other structures of data are also fine, but it is nonetheless vital that we are able to keep information about e.g. sector classification for subsequent analysis.

Birgithj · ‎10-10-2019

Hi. Thanks, that's very helpful. We are already able to transform the data to the wide form as in below example. Our problem is, that we are not able to figure out how to keep the information regarding industry classification in the dataset, while still being able to perform the PCA. Date ORCL KO GM 201801 1% 2% 3% 201802 1% 2% 3% 201803 1% 2% 3% So, would it be possible to get the Industry Code (in addition to ticker) in wide form together with the return data, so that we would be able to infer which stocks are in which industries from the PCA output, without having to go through each stock manually one by one?

Birgithj · ‎10-10-2019

Hi Paige thanks for your answer. The purpose of the analysis is to perform PCA on stock returns for a number of stocks over a certain period of time. Once I have identified the principle components and their eigenvalues, I would like to be able to analyse the principal components according to the industry that the underlying stocks are in. e.g. PCA 3 predominantly contain stocks that are in Healthcare etc. which I am not able to do when data is structured as in the first example.

Birgithj · ‎10-10-2019

Hi all, I have managed to perform a PCA when data is structured as the small example below. This I have done by using the following code: ods select NObsNVar SimpleStatistics Corr Eigenvalues Eigenvectors ScreePlot PatternProfilePlot; proc princomp data=example; var ORCL KO GM; run; Date ORCL KO GM 201801 1% 2% 3% 201802 1% 2% 3% 201803 1% 2% 3% My issue is that I do not have information regarding the Industry Classification code and Company Name of each variable (e.g. ORCL, KO, GM...). I would therefore like to perform the PCA when data is structured as the example below instead: Date Ticker Company name Industry classification Returns 201801 ORCL Oracle Corp 7372 1% 201802 ORCL Oracle Corp 7372 1% 201803 ORCL Oracle Corp 7372 1% 201801 KO Coca Cola comp 2086 2% 201802 KO Coca Cola comp 2086 2% 201803 KO Coca Cola comp 2086 2% 201801 GM General Motors 3711 3% 201802 GM General Motors 3711 3% 201803 GM General Motors 3711 3% Can anyone help with a code to do this? Thank you!

Birgithj · ‎10-02-2019

Thank you so much! This solved my problem 🙂

Birgithj · ‎10-02-2019

Hi, I am hoping you can help me. I have a large set of variables, where some of the variables have missing values (small example shown below). I only want to keep variables where there are no missing values, e.g. I need to delete variable B below. How can I delete all variables with missing values? Var A Var B Var C 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 missing 3 1 missing 3 Thanks!

Birgithj · ‎09-20-2019

FYI, With a small modification I was able to get the code to work! Thank you for leading me in the right direction 🙂 The code I used was proc sort data=have; by Date; run; proc transpose data=have out=new; by date; id stock; var return; run;

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Re: How to change legend label for stacked bar chart in proc sgplot

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Re: Multiply variables across datasets based on condition

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Re: How to calculate a price series from annual returns

How to calculate a price series from annual returns

Re: How perform PCA with multiple column of data

Re: How perform PCA with multiple column of data

Re: How perform PCA with multiple column of data

Re: How perform PCA with multiple column of data

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Re: How to delete variables with missing values

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Re: Help: All variables in a single column