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sebai · ‎09-04-2018

oh ok, thank u so much for ur answer! Then if the p-value of the chi-square test is less than 0.05 ==> Can I assume that there is a collinearity between the two variables and if the p-value is higher than 0.05 ==> then the 2 variables are independent (no collinearity) Is this right!?

sebai · ‎09-04-2018

I'm creating a regression model and I want to test if some co-variables are linear or co-dependent? I am thinking, among other things, of '' access to land '' and '' possession of livestock ''. Which test can I use to show the collinearity between two categorical variables? Thanks

sebai · ‎05-16-2018

Actually, all my observations were used I have no missing data. By the percentages don't match: I mean that because 24% is higher than 19.1% then the OR should be more than 1, right !!?

sebai · ‎05-16-2018

I want to check the OR of some independent variables in a logistic model. I used this model : proc logistic data=results2 descending; where season between 1001 and 3254; class source_revenu_cat (param=ref ref='Aucune') betail (param=ref ref='auc') terres_acces (param=ref ref='Non') region (param=ref ref='1-Sud') v_urban_rural (param=ref ref='rural') etudes_ppt_cat (param=ref ref="A - Jamais allée à l'école") personnes_menage_nombre1 (param=ref ref='1 à 3') adultes_15_ans_plus (param=ref ref='1 ou plus'); model MDD_W_5 (event='Oui')= region v_urban_rural adultes_15_ans_plus source_revenu_cat terres_acces betail etudes_ppt_cat personnes_menage_nombre1 ; format season saison.; run; The table of the OR gave me this result; though the percentages don't seem to match !! did I do something wrong with the code or the interpretation ??

sebai · ‎04-16-2018

proc Logistic data=results2; class region season terres_acces; model IDE_groupe = region season terres_acces terres_acces * season region * season / cl ; lsmeans region*season / oddsratio ilink cl pdiff; lsmeans terres_acces*season / oddsratio cl pdiff; format season saison.; run; I used this code and I got this table: It doesn’t include all the levels for the 2 variables: for region, I only got 1-Sud, 2-GAE and not 3-GAO (I think it did a comparison with that level of the variable though I want it to be compared separately). I used Lsmeans with proc glimmix and it worked very well but it was for another dependent variable with 2 categories; proc glimmix didn’t work well for me with a dependent variable with 3 categories. proc glimmix data=results2; class region season terres_acces; model MDD_W_5 = region season terres_acces terres_acces * season region * season/ or dist=binary; lsmeans region*season / slicediff=region oddsratio ilink cl pdiff; lsmeans terres_acces*season / slicediff=terres_acces oddsratio cl pdiff; format season saison.; run; This is what I got with proc Glimmix for the dependant variable with 2 categories: (I want to find results for my new dependent variable presents in a similar way!!)

sebai · ‎04-16-2018

I want to use contrast in proc logistic, but I don’t seem to clearly understand where the numbers in the contrast statement come from. My model include: Region: variable with 3 categories (1-Sud; 2-GAE; 3-GAO) Land access: variable with 2 categories (‘NO’; ‘Wi’) Season: variable with 2 categories (Spring; Summer) AND my dependent variable IDE: with 3 categories I want to do contrast for the interaction between season and land access; and a second contrast statement for the interaction between region and season: My model (without the contrast) is: proc logistic data = results2; Class Land_acces (param=ref ref='Non') region (param=ref ref='1-Sud') season (param=ref ref='spring') / param=ref order=internal; model IDE_groupe(event='A - Peu ou pas de faim dans le ménage')= season Land_acces region Land_acces * season region * season/ expb; format season saison.; run; Should I use this table (I got after running the code for proc logistic) to write the contrast statement? What should I write in the contrast statement to have a table like this: Region*season Land_acces*season contrast 'region = 1-Sud season summer vs spring' region*season ……………… / estimate = exp ; contrast 'region = 2-GAE season summer vs spring' region*season ……………… / estimate = exp; contrast 'region = 3-GAO season summer vs spring' region*season ………………/ estimate = exp; contrast ' Land_acces = Non season summer vs spring' Land_acces*season ……………… / estimate = exp; contrast ' Land_acces = Wi season summer vs spring' Land_acces* season ………………/ estimate = exp;

sebai · ‎04-15-2018

I want to create a crosstabulation table between Region and season: Spring Summer p-value Region 1 .. % ..% P1 Region 2 .. % .. % P2 Region 3 ..% .. % P3 I want to: Check if the % of people in region 1 is different between spring and summer (p-value or OR) Check if the % of people in region 2 is different between spring and summer (p-value or OR) Check if the % of people in region 3 is different between spring and summer (p-value or OR) What test should I use to get the 3 p-values needed?

sebai · ‎04-09-2018

@PaigeMiller I want to compare: Minimum dietary diversity=NO in spring and Minimum dietary diversity=NO in summer (compare 76% and 79%) Minimum dietary diversity spring summer p-value No 76.79% 79.03% ……….. And then do another comparison: Between Minimum dietary diversity=Yes in spring and Minimum dietary diversity=Yes in summer Minimum dietary diversity spring summer p-value Yes 23.21% 20.97% The minimum dietary diversity (MDD) is another variable that I created in my programme: MDD = MDD_W_1_grains_root_tuber + MDD_W_2_pulses + MDD_W_3_nuts_seeds + MDD_W_4_dairy + MDD_W_5_meat_fish + MDD_W_6_eggs + MDD_W_7_leafy_veg + MDD_W_8_vit_a_veg_fruit + MDD_W_9_other_veg + MDD_W_10_other_fruit; IF MDD < 5 THEN MDD_W_5 = "No"; IF MDD GE 5 THEN MDD_W_5 = "Yes";

sebai · ‎04-09-2018

Thank you so much for all your answers. I just need to confirm one last thing if you don't mind... As I said in my post: https://communities.sas.com/t5/Base-SAS-Programming/Proc-logistic-p-value/td-p/452359 I want to create a table like this: So I thought I need 2 p-values to compare people from the 2 rows: women who achieved dietary diversity during both seasons and women who didn't during both seasons. Minimum dietary diversity spring summer p-value No 76.79% 79.03% ....... Yes 23.21% 20.97% ...... By using LSMEANS I got only one p-value for the variable season (spring/summer) so my question is what can I do to create the table I want? this is the code I used : proc logistic data = results2; Class terres_acces (param=ref ref='Non') region (param=ref ref='1-Sud') v_urban_rural (param=ref ref='rural') season (param=ref ref='spring'); model MDD_W_5(event='Oui')= terres_acces region season v_urban_rural terres_acces * season region * season v_urban_rural * season/ expb; lsmeans terres_acces * season region * season v_urban_rural * season / lines; format season saison.; run;

sebai · ‎04-08-2018

Yes, I know and it was really helpful Thank you. But last time I asked about the independent variables and this time I wanted to do the same thing for the dependent variable. For the other question I used contrast instead of LSMEANS to obtain my results and it worked but not for the dependant variable in both seasons!! I used this Code : proc logistic data = results2; Class Land_acces (param=ref ref='Non') region (param=ref ref='1-Sud') v_urban_rural (param=ref ref='rural') season (param=ref ref='spring'); model MDD_W(event='Yes')= Land_acces region v_urban_rural Land_acces * season region * season v_urban_rural * season season/ expb; contrast 'region = 1-Sud season summer vs spring' region 0 0 season 1 / estimate = exp ; contrast 'region = 2-GAE season summer vs spring' region 1 0 season 1 / estimate = exp; contrast 'region = 3-GAO season summer vs spring' region 0 1 season 1 / estimate = exp; contrast 'v_urban_rural = rural season summer vs spring' v_urban_rural 0 season 1 / estimate = exp; contrast 'v_urban_rural = urban season summer vs spring' v_urban_rural 1 season 1 / estimate = exp; contrast 'Land_acces = Non season summer vs spring' Land_acces 0 season 1 / estimate = exp; contrast 'Land_acces = Wi season summer vs spring' Land_acces 1 season 1 / estimate = exp; contrast 'summer v spring' season 0 / estimate = exp; contrast 'summer v spring' season 1 / estimate = exp; format season saison.; TITLE'COMP MDD_W PRINTEMPS ÉTÉ'; run; I tried to add the code in red to answer my question but it didn't seem to work !! Will it work with LSMEAN for the dependent variable ???

sebai · ‎04-08-2018

The purpose of our study is to compare our results of minimum dietary diversity for women during 2 seasons: spring and summer. We want to check if the number of women who achieve dietary diversity is different between spring and summer and if the number of women who do not achieve minimum dietary diversity is different between spring and summer. We want to create a table like this: Minimum dietary diversity spring summer p-value No ......% .....% Yes ......% .....% We used a proc logistic to take into account the confounding variables: proc logistic data = results2; Class Land_acces (param=ref ref='No') region (param=ref ref='1-South') v_urban_rural (param=ref ref='rural') season (param=ref ref='spring') model MDD_W (event='Yes')= Land_acces region season; v_urban_rural Land_acces * season region * season v_urban_rural * season format season saison. ; run; This proc logistic gave us just one p-value for both seasons!! How can I obtain the p-value for women who achieve minimum dietary diversity and women who do not while taking into account the confounding variables?

sebai · ‎04-01-2018

Hi, The purpose of our study is to compare our results for the dietary diversity for women during 2 seasons: spring and summer. We want to check if our confounding variables for women’s dietary diversity interact during the 2 seasons. We used the following proc logistic to answer our question (I’m using SAS Enterprise Guide 6.1): proc logistic data=results2 ; class region (param=ref ref='1-Sud') v_urban_rural (param=ref ref='rural') season; model Dietary_diversity (event='Yes')= season region v_urban_rural region*season v_urban_rural*season ; format season saison.; run; The code gave us the p-value for one group of the variable example: p-value for urban but it didn’t give us the p-value for rural !!! though we want to check the differences between the 2 seasons for all groups and not compare urban-rural between the 2 periods. So how can I ask for such information!!??

sebai · ‎03-16-2018

Thank u so much for your answer. You said, "If you want to assume it does not interact with one of them, then you can get the odds ratios at just the levels of the other." can you please explain to me what do u mean by that, how can I get the odds ratio at the level of the other? + I wanted to make a table like this, isn't it possible to make with proc logistic when entering 2 interactions (the region with spring_summer and rural_urban with spring_summer)!!? Odds Rtio Spring vs summer in region=SUD Spring vs summer in region=GAE Spring vs summer in rural_urban=rural Spring vs summer in rural_urban=urban Spring vs summer livestock=yes Spring vs summer livestock=no

sebai · ‎03-15-2018

The purpose of our study is to compare our results for minimum dietary diversity for women during 2 seasons: spring and summer. We want to check if our confounding variables for women’s dietary diversity interact during the 2 seasons. I have a question regarding the way we should include interactions in proc logistic when comparing two groups? We felt that it was possible that more than one confounding factor affects the way seasons influence dietary diversity. (For instance, maybe women with greater wealth have lower dietary diversity in the spring as compared to summer because foods are cheaper in the summer. In contrast, maybe women with lesser wealth must sustain on the same types of low-cost food all year-long.) We used the following program, specifying the types of interactions for which we need to know the odds ratio: spring_summer* v_region_pub and spring_summer * v_urban_rural in this case. proc logistic data=results2; class v_region_pub (param=ref ref='1-Sud') v_urban_rural (param=ref ref='rural') spring_summer (param=ref ref='spring'); model MDD_W_5 (event='Oui')= v_region_pub v_urban_rural spring_summer spring_summer* v_region_pub spring_summer * v_urban_rural ; oddsratio spring_summer / diff=ref at (v_region_pub=all) cl=pl; oddsratio spring_summer / diff=ref at (v_urban_rural =all) cl=pl; run; Though the oddsratio step specifies that we would like the program to generate the odds ratio for : spring_summer* v_region_pub and spring_summer * v_urban_rural it generates a table that presents all permutations of spring_summer* v_region_pub * v_urban_rural

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