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Is there a way to drop lines other than the _cons line? I am trying to plot the results of a logistic regression with several control variables, and when I use the code as shown here ALL of the control variables end up in the plot, which I don't want.

Ma sha Allah

Amazing vidéo Thanks

After comp1, is the procedure the same for all other components?

For clear understanding I have a few questions? Let's say, I have 3 groups of intervention and one group is control. 1. Since the sample size is 117 here, do I have to have 117 participants in each group or it is 117/4 ? 2. And is the calculation always comes like 117 or it depends on power of the study? 3. To get a larger sample size what would be the formula?

Think Keu Sir

Ma Shallah

salam sir can i find full video in the youtub channel?

think Keu Sir

think Keu Sir

মাশাআল্লাহ

Dear sir, would you mind share us a video of HIFAS questionnaire found in excel and how to import it on STATA? Thanks

Think Keu Sir

Think Keu Sir

ভাই খুজতে খুজতে দেখছি fies নিয়ে আপনি ভিডিও বানিয়েছেন। কয়েকদিন আগে বিবিএস থেকে ডেটাসেট পেলাম।

Mash alla think Keu Sir

Positivity is contagious. Spread it! 🌈

Excellent

think Keu Sir

ভাইয়া, Epidemiology ও এর স্টাডি ডিজাইনের উপর যদি টিউটোরিয়াল দিতেন তাহলে উপকৃত হতাম।

Great video. Thanks so much. My question is why "predict comp1" only? What if comp2 and comp3 have eigen values greater than 1, do we also run the "predict comp2" and "predict comp2" command.

please i have this message : estat kmo correlation matrix is singular

Thanks! This is actually simpler than I anticipated it to be. Great work! I will try it out. However, I was wondering, do you not think that we are losing a nuance in the data by converting it into a binary variable? For example, maybe there's some important information between "having an item (1) and not having that item (0), which is most likely lost when categorising into a binary variable?

First of all, thank you so much for this video!!!, it really helped that it wasn´t edited so I could understand all the process of analyzing case-control data. Im new in the field of data analysis and biostatistics, but I have rather a silly rookie question why OR doesn't appear in No smoking, Rural, Normal BMI, Female and Service holder? Also would you mind showing how to do a OR Forest Plot for this data? It would be really helpful

I love it especially for education purposes

#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()

#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()

Good Morning Sir @amalsedekah

* Set variable labels label variable main_id "Study ID by SRL" label variable v012 "Respondent's current age" label variable mH "Maternal Height in cm" label variable mW "Maternal Weight in kg" label variable bmiCAT "Maternal underweight (BMI<18.5)" label variable edu "Education" label variable DV "Attitudes to domestic Violence" label variable anc2 "At least 4 ANC from Medically trained" label variable b4 "Sex of child" label variable ChildAge "Child's Age in Months" label variable stunting "Childhood stunting" label variable v024 "Division" label variable v025 "Type of place of residence" label variable v190 "Wealth index" label variable toilet2 "Type of toilet facility" label variable v021 "Primary sampling unit" * Set value labels label define bmiCAT_lbl 0 "Non-underweight" 1 "Underweight" label define edu_lbl 0 "Below secondary" 1 "Secondary and above" label define DV_lbl 0 "Non-violent" 1 "Violent" label define anc2_lbl 0 "Less than 4" 1 "At least 4" label define b4_lbl 1 "Male" 2 "Female" label define stunting_lbl 0 "Non-stunted" 1 "Stunted" label define v024_lbl 1 "Barisal" 2 "Chittagong" 3 "Dhaka" 4 "Khulna" 5 "Mymensingh" 6 "Rajshahi" 7 "Rangpur" 8 "Sylhet" label define v025_lbl 1 "Urban" 2 "Rural" label define v190_lbl 1 "Poorest" 2 "Poorer" 3 "Middle" 4 "Richer" 5 "Richest" label define toilet2_lbl 0 "Improved" 1 "Unimproved" * Apply value labels label values bmiCAT bmiCAT_lbl label values edu edu_lbl label values DV DV_lbl label values anc2 anc2_lbl label values b4 b4_lbl label values stunting stunting_lbl label values v024 v024_lbl label values v025 v025_lbl label values v190 v190_lbl label values toilet2 toilet2_lbl

does coefplot work also with HR from cox regression?

Saved me a lot of stress. thank you so much

Very helpful. thanks

thank you

Sir,I need the reference source of the formula for my research study..can you pls help?

woow this was perfect

Learn to speak English first then make videos.. Waste of my time

There is no link in the description

this is gold mine for me! thanks

Thank you very much for your video. It was very helpful. Can i get the do file? Thanks