/*** # Introduction To create a CI plot of crude estimates using Mata matrices and coefplot. In this example we use hazard ratio estimates for the coefplot. Further it is shown how to add the estimates and CI limits as labels in the ciplot. ## The command coefplot Note that coefplot might be installed on your version of Stata. If coefplot is not installed, run the command: ssc install coefplot To see if coefplot is installed see if the command below returns a help page: help coefplot ## The data We use the Stata example dataset stan3 (Heart transplant survival data from Stanford). ***/ /**/webuse stan3, clear /**/label variable posttran "After tranplantation" /**/label define posttran 0 "No" 1 "Yes" /**/label values posttran posttran metadata id year age died stime surgery transplant wait posttran /*** The dataset is already set for Cox regression: ***/ stset /*** The dataset will be used without taking the case story of the dataset into account. So the results as such are pure rubish. # Howto create a ciplot using coefplot Before creating the ciplot we gather (crude) estimates and CI limits in the Mata matrix estimates. Also we gather the variable names for which estimates are found in the Stata local rownames. In order to use coefplot a Stata matrix is needed so at the end the Mata matrix and the local rownames are transformed into a Stata matrix. Then the coefplot command is used to generate a ciplot. Finally it is shown how a label with estimates can be added the coefplot. ## Step 1: Reset and start Both the local and the Mata matrix has to be reset before start: ***/ local rownames "" mata: estimates = J(0,3,.) /*** ## Step 2: Gather estimates in local "rownames" and Mata matrix "estimates" A foreach loop is used to loop through the variables for which the estimates are needed. In the loop the regression of choice is made. The regression is formulated referring to Stata local var. All Stata regression returns a table of regression estimates in the Stata named matrix r(table). In the r(table) regression estimates are in row 1 and lower and upper CI limits are in rows 5 and 6, respectively. It is easier to extract data from Mata matrices, so the values of r(table) are copied into a Mata matrix (st_matrix("r(table)")), transposed (') and columns 1, 5 and 6 are added to the matrix estimates. Since estimates from the regression comes in the order of the independent regression variables ending with constant estimates only the first row of the transposed r(table). The variable names are gathered in the local rownames. They are needed as labels on the y-scale: foreach var of varlist year surgery transplant wait posttran { stcox `var' mata: estimates = estimates \ st_matrix("r(table)")'[1, (1,5,6)] local rownames `rownames' `var' } ***/ //OFF foreach var of varlist year surgery transplant wait posttran { stcox `var' mata: estimates = estimates \ st_matrix("r(table)")'[1, (1,5,6)] local rownames `rownames' `var' } //ON /*** ## Step 3: Move estimates back into a Stata matrix "estimates" The values from the Mata matrix are moved into the Stata matrix with the same name. Rownames (from Stata local rownames) and column names (HZ, LL, and UL) are added to make the coefplot more readable. ***/ mata: st_matrix("estimates", estimates) matrix rownames estimates = `rownames' matrix colnames estimates = HZ LL UL /*** The Stata matrix "estimates" looks like ***/ matprint estimates, d(3) /*** ## Step 4: Doing the ciplot To make a ciplot based on the Stata matrix "estimates" using coefplot it is needed to tell in which column the estimates are [matrix(estimates[,1])] and in which columns the ci limits are [ci((estimates[,2] estimates[,3]))]. ***/ coefplot matrix(estimates[,1]), ci((estimates[,2] estimates[,3])) xline(1) //OFF graph export simple_ciplot.png, replace //ON /*** ## Step 5: Adding estimates and ci limits as labels First generate a variable to keep the estimate and CI labels: ***/ /**/generate lbl = "" /*** Then run a slightly modified version of coefplot keeping the variables behind the graph (generate) and telling how to use the variable lbl as label [mlabel(lbl) mlabpos(12)]: ***/ /**/coefplot matrix(estimates[,1]), ci((estimates[,2] estimates[,3])) xline(1) /// generate mlabel(lbl) mlabpos(12) /*** Replace the variable lbl with how the labels should look like: ***/ /**/replace lbl = string(__b, "%6.2f") + " (" + string(__ll1, "%6.2f") + ", " /// + string(__ul1, "%6.2f") + ")" /*** Run twoway graph command generated by coefplot again with variable lbl updated: ***/ `r(graph)' /*** Clean up: ***/ drop lbl drop __* label drop __by __plot //OFF graph export simple_ciplot_lbls.png, replace graph drop _all //ON /*** ***/