I want to ask a question on how to compute the p-value without a t-stat table, just by looking at the table, like on the first page of the pdf in the following link http://faculty.arts.ubc.ca/dwhistler/326UBC/stataHILL.pdf . Like if I don't know the value 0.062, how can I know it is 0.062 by looking at other information from the table?
You need to use the ttail() function, which returns the reverse cumulative Student's t distribution, aka the probability T > t:
display ttail(38,abs(_b[_cons]/_se[_cons]))*2
The first argument, 38, is the degrees of freedom (sample size less number of parameters), while the second, 1.92, is the absolute value of the coefficient of interest divided by its standard error, or the t-stat. The factor of two comes from the fact that Stata is doing a two-tailed test. You can also use the stored DoF with
display ttail(e(df_r),abs(_b[_cons]/_se[_cons]))*2
You can also do the integration of the t density by "hand" using Adrian Mander's integrate:
ssc install integrate
integrate, f(tden(38,x)) l(-1.92) u(1.92)
This gives you 0.93761229, but you want Pr(T>|t|), which is 1-0.93761229=0.06238771.
If you look at many statistics textbooks, you will find a table called the Z-table which will give you the probability that Z is beyond your test statistic. The table is actually a cumulative distribution function of the normal curve.
When people went to school with 4-function calculators, one or more of the questions on the statistics test would include a copy of this Z-table, and the dear students would have to interpolate columns of numbers to find the p-value. In your example, you would see the test statistic between .06 and .07 and those fingers would tap out that it was closer to .06 and do a linear interpolation to come up with .062.
Today, the p-value is something that Stata or SAS will calculate for you.
Here is another SO question that may be of interest: How do I calculate a p-value if I have the t-statistic and d.f. (in Perl)?
Here is a basic page on how to determine p-value "by hand": http://www.dummies.com/how-to/content/how-to-determine-a-pvalue-when-testing-a-null-hypo.html
Here is how you can determine p-value using Excel: http://ms-office.wonderhowto.com/how-to/find-p-value-with-excel-346366/
===EDIT===
My Stata text ("Microeconometrics using Stata", Revised Ed, Cameron & Trivedi) says the following on p. 402.
* p-values for t(30), F(1,30), Z, and chi(1) at y=2
. scalar y=2
. scalar p_t30 = 2 * ttail(30,y)
. scalar p_f1and30 = Ftail(1,30,y^2)
. scalar p_z = 2 * (1 - normal(y))
. scalar p_chi1 = chi2tail(1,y^2)
. display "p-values" " t(30)=" %7.4f p_t30
p-values t(30) = 0.0546
Related
I am using propensity score stratification method. I got some output but can't interpret. I am looking for a source how to interpret those results.
I have divided PS scores into 5 groups and got this output at the end after running some codes
obs =1
type =0
freq =10 sum_wt = 1010988.4 sum_diff= 0.0015572 mean-diff= 0.0015572 SE-diff= 0.0000994551
I know that frequency column stands for 2*5(number of groups), mean diff is equal to sum diff and SE diff is the sq rt of 1-sum of weights
Does it say that ranking PS scores into 5 groups is an appropriate approach ? Which of above criteria I should use for final decision?
I believe your output is just stating the distribution within the groups. You evaluate whether or not propensity score matching, in your case stratified matching, works by looking at the absolute standardized differences of the variables pre vs post-matching.
Here is a peer reviewed paper my colleagues and I published that incorporates propensity score matching. There is some details in the methodology section that I wrote which should answer your question on how to evaluate if your approach is working.
(cross-posted at http://www.statalist.org/forums/forum/general-stata-discussion/general/1370770-margins-plot-of-treatment-effect-rather-than-y-for-values-of-a-covariate)
I'm running a multivariate regression (outcome variable is continuous, happens to be GPA). The covariate of interest is a dummy variable for treatment status; another of the covariates is a pre-score. We want to look at how the treatment effect differs at various values of pre-score. The structure of the model is not complicated:
regress GPA treatment pre_score X3 X4 X5...
What I want is a graph that shows what the treatment effect is (values of Beta1) at various values of pre-score (X2). It's straightforward to get a graph with values of the OUTCOME at various values of X2:
margins, at(pre_score= (1(0.25)5)) post
marginsplot
I have consulted an array of resources and tried alternatives using marginscontplot, coefplot with recast, the dy/dx option, and so forth. I remain unsuccessful. But this seems like something that there must be a way to do; wanting to know if a treatment effect varies for values of a control (say, income) must be common.
Can anyone direct me to the right command, or options for Margins, to output values of Beta1 (coefficient on treatment dummy), rather than of Y (GPA), at values of the pre_score?
Question was resolved at Statalist. Turns out that Margins alone can't do what I was trying to; the model needs to be run with an interaction term. Then it's simple.
I am fairly new to Stata so some of my questions may be pretty basic, but I appreciate any help I can get. An example of my data is below
wage surveyweights Constant Sex
32 14 .56 1
56 45 .96 1
77 88 .25 0
I have survey data and I am trying to bootstrap the difference in mean wages for women ONLY. I want to find out the difference in mean outcome with [survey weights] and with [survey weights * constant] that is Manual calculation
mean x [pw=wt] if sex==1
mat x1=e(b)
mean x [pw=wt*constant] if sex==1
mat x2= e(b)
mat dd=x1-x2
**my outcome of interest is the point estimate and bootstrapped SE of dd
I have written the following program, but I am not getting the results I want. In particular, my SE column turns up blank and my point estimates for the means and the difference in means do not match with what I get with manual calculation.
program define meandiff, eclass properties (svyb)
args vars
mean `vars'
mat x1=e(b)
mean `vars' [pw=constant]
mat x2=e(b)
mat dd = x1-x2
ereturn scalar dd=e1(dd,1,1)
end
local vars wage
svy bootstrap e(dd), subpop(sex): means `vars'
I have already svyset my data using bootstrap weights. My questions are as follows:
When I type svy: mean 'vars' the program seems to start running bootstrap replications, and when I type svy bootstrap: means `vars' also the program seems to start replications. What is the difference between the two commands?
When I do mean x with regular survey weights do I need to do [pw=wt] or will svy command automatically apply the survey weights?
If I do have to write [pw=wt] in the first mean then do I need to create a variable called, say, gen wtxcons = wt * constant to do [pw=wtxcons] when I calculate the second mean?
How do I calculate the bootstrap SE and point estimates for my outcome of interest, which is the difference in means. Why are my point estimates not matching my manual calculation?
I've estimated a model via maximum likelihood in Stata and was surprised to find that estimated standard errors for one particular parameter are drastically smaller when clustering observations. I take it from the Stata manual on robust standard error estimation in ML that this can happen if the contributions of individual observations to the score (the derivative of the log-likelihood) tend to cancel each other within clusters.
I would now like to dig a little deeper into what exactly is happening and would therefore like to have a look at these score contributions. As far as I can see, however, Stata only gives me the total sum as e(gradient). Is there any way to pry the individual summands out of Stata?
If you have written your own command, you can create a new variable containing these scores using the ml score command. Official Stata commands and most finished user written commands will often have score as an option for predict, which does the same thing but with an easier syntax.
These will give you the score of the log likelihood ($\ell$) with respect to the linear predictor, $x\beta = \beta_0 + \beta_1 x_1 + \beta_2 x_2 \elipses$. To get the derivative of the log likelihood with respect to an individual parameter, say $\beta_1$, you just use the chain rule:
$\frac{\partial \ell}{\partial \beta_1} = \frac{\partial \ell }{\partial x\beta} \frac{\partial x\beta}{\partial \beta_1}$
The scores returned by Stata are $ \frac{\partial \ell }{\partial x\beta}$, and $\frac{\partial x\beta}{\partial \beta_1} = x_1$.
So, to get the score for $\beta_1$ you just multiply the score returned by Stata and $x_1$.
I am trying to estimate a maximum likelihood model and it is running into convergence problems in Stata. The actual model is quite complicated, but it converges with no troubles in R when it is supplied with appropriate starting values. I however cannot seem to get Stata to accept the starting values I provide.
I have included a simple example below estimating the mean of a poisson distribution. This is not the actual model I am trying to estimate, but it demonstrates my problem. I set the trace variable, which allows you to see the parameters as Stata searches the likelihood surface.
Although I use init to set a starting value of 0.5, the first iteration still shows that Stata is trying a coefficient of 4.
Why is this? How can I force the estimation procedure to use my starting values?
Thanks!
generate y = rpoisson(4)
capture program drop mypoisson
program define mypoisson
args lnf mu
quietly replace `lnf' = $ML_y1*ln(`mu') - `mu' - lnfactorial($ML_y1)
end
ml model lf mypoisson (mean:y=)
ml init 0.5, copy
ml maximize, iterations(2) trace
Output:
Iteration 0:
Parameter vector:
mean:
_cons
r1 4
Added: Stata doesn't ignore the initial value. If you look at the output of the ml maximize command, the first line in the listing will be titled
initial: log likelihood =
Following the equal sign is the value of the likelihood for the parameter value set in the init statement.
I don't know how the search(off) or search(norescale) solutions affect the subsequent likelihood calculations, so these solution might still be worthwhile.
Original "solutions":
To force a start at your initial value, add the search(off) option to ml maximize:
ml maximize, iterate(2) trace search(off)
You can also force a use of the initial value with search(norescale). See Jeff Pitblado's post at http://www.stata.com/statalist/archive/2006-07/msg00499.html.