NaN usually indicates some kind of bug. If I can get a reproduction case, then I have at least a chance of fixing it.
On Wed, Dec 20, 2023 at 2:17 AM Alan Mead <ame...@alanmead.org> wrote: > Tim, > > NaN looks like a numerical error. I'm curious, how may levels does the > variable have and how many dummy variables are you using? > > If the original variable has K levels, you should have K-1 dummy > variables. For example, if your variable were location (1=rural, > 2=suburban, 3=urban) then you would pick one level to be the reference and > create two dummy variables, perhaps: > > recode location (1=1) (else=0) into dum1. > > recode location (2=1) (else=0) into dum2. > > Then the coefficients of dum1 and dum2 tell you how living in a rural > (dum1) or suburban (dum2) area compares to living in an urban area. > > The model won't be defined if you use K variables for K levels. > > I notice that both of the zeros are for xxx_1 variables, so that suggested > possibly not coding the categorical variable correctly. But I don't know if > that's what you are seeing. You could also get zeros if there were no > instances of that dummy code, but you shouldn't see NaN values. It could > also be another problem, or a bug. In fact, I think it's probably a bug to > see NaN's... > > -Alan > > > On 12/20/23 3:46 AM, tim.goodsp...@btinternet.com wrote: > > A basic stat’s question and a specific PSPP query, please. Any help > gratefully received. I can’t see this in the archives anywhere (searching > for ‘categorical’ and ‘dummy’). > > > > For a linear regression, some variables are categorical and so included > using dummy coding (Coding Systems for Categorical Variables in > Regression Analysis (ucla.edu) > <https://stats.oarc.ucla.edu/spss/faq/coding-systems-for-categorical-variables-in-regression-analysis-2/#:~:text=Categorical%20variables%20require%20special%20attention,entered%20into%20the%20regression%20model.> > ). > > > > *basic stat’s question: *This results in a zero coefficient and zero > standard error for some variables, as shown in the example below. Is this > correct? There is little or no linear relationship to be found? > > > > *specific PSPP query: *if there is little relationship/the coefficient is > very small, is there a way to tell PSPP to show the very small value > instead of zero? > > > > Thanks in advance > > > > Table: Model Summary (adjRA1SR1) > > > > > > > > > > > > R > > R Square > > Adjusted R Square > > Std. Error of the Estimate > > > > > > > > 0.55723 > > 0.310505 > > 0.302797 > > 0.8359 > > > > > > > > > > > > > > > > > > > > > > > > > > Table: ANOVA (adjRA1SR1) > > > > > > > > > > > > > > Sum of Squares > > df > > Mean Square > > F > > Sig. > > > > > > Regression > > 619.25791 > > 22 > > 28.148087 > > 40.284698 > > 0 > > > > > > Residual > > 1375.0987 > > 1968 > > 0.698729 > > > > > > > > > > Total > > 1994.3566 > > 1990 > > > > > > > > > > > > > > > > > > > > > > > > > > > > Table: Coefficients (adjRA1SR1) > > > > > > > > > > > > > > Unstandardized Coefficients > > Standardized Coefficients > > t > > Sig. > > 95% Confidence Interval for B > > > > B > > Std. Error > > Beta > > > > > > Lower Bound > > Upper Bound > > (Constant) > > 8.163407 > > 0.310014 > > 0 > > 26.332394 > > 0 > > 7.555417 > > 8.771397 > > lnSTINC > > -0.036745 > > 0.011677 > > -0.088107 > > -3.146888 > > 0.002 > > -0.059645 > > -0.013845 > > RA1PKHSIZ > > -0.011834 > > 0.016218 > > -0.020561 > > -0.729708 > > 0.466 > > -0.043639 > > 0.019971 > > RA1PRAGE > > -0.039326 > > 0.011175 > > -0.550388 > > -3.519082 > > 0 > > -0.061242 > > -0.01741 > > sqPRAGE > > 0.000464 > > 0.000109 > > 0.666977 > > 4.258349 > > 0 > > 0.00025 > > 0.000678 > > RA1PRSEX > > 0.13709 > > 0.03935 > > 0.068446 > > 3.483888 > > 0.001 > > 0.059918 > > 0.214261 > > RA1PB19_1 > > 0 > > 0 > > 0 > > NaN > > NaN > > 0 > > 0 > > RA1PB19_2 > > -0.485628 > > 0.170694 > > -0.054029 > > -2.845015 > > 0.004 > > -0.820389 > > -0.150867 > > RA1PB19_3 > > -0.324574 > > 0.058981 > > -0.109094 > > -5.503011 > > 0 > > -0.440246 > > -0.208902 > > RA1PB19_4 > > -0.333625 > > 0.089807 > > -0.074169 > > -3.714896 > > 0 > > -0.509752 > > -0.157497 > > RA1PB1 > > -0.002888 > > 0.008407 > > -0.007002 > > -0.343559 > > 0.731 > > -0.019376 > > 0.0136 > > RA1SG17A_1 > > 0 > > 0 > > 0 > > NaN > > NaN > > 0 > > 0 > > RA1SG17A_2 > > -0.061221 > > 0.053837 > > -0.021822 > > -1.137147 > > 0.256 > > -0.166804 > > 0.044363 > > RA1PA1 > > -0.15082 > > 0.022182 > > -0.160102 > > -6.7991 > > 0 > > -0.194324 > > -0.107317 > > RA1PA2 > > -0.248882 > > 0.024367 > > -0.243609 > > -10.214077 > > 0 > > -0.29667 > > -0.201095 > > RA1SC1 > > -0.328042 > > 0.073134 > > -0.08782 > > -4.485512 > > 0 > > -0.471469 > > -0.184614 > > RA1PF3bin > > 0.003064 > > 0.041159 > > 0.001422 > > 0.074435 > > 0.941 > > -0.077655 > > 0.083783 > > RA1PF7A_2 > > 0.009538 > > 0.086914 > > 0.002111 > > 0.109735 > > 0.913 > > -0.160917 > > 0.179992 > > RA1PF7A_3 > > 0.14177 > > 0.166844 > > 0.016081 > > 0.849712 > > 0.396 > > -0.18544 > > 0.468979 > > RA1PF7A_4 > > -0.104009 > > 0.155971 > > -0.01266 > > -0.666848 > > 0.505 > > -0.409894 > > 0.201877 > > RA1PF7A_5 > > 0.173309 > > 0.59246 > > 0.005486 > > 0.292525 > > 0.77 > > -0.988606 > > 1.335224 > > RA1PF7A_6 > > 0.064264 > > 0.080864 > > 0.01504 > > 0.794712 > > 0.427 > > -0.094325 > > 0.222853 > > RA1PG2 > > -0.350528 > > 0.030049 > > -0.233421 > > -11.66509 > > 0 > > -0.40946 > > -0.291597 > > > > > > > > -- > > Alan D. Mead, Ph.D. > President, Talent Algorithms Inc. > > science + technology = better workers > https://talalg.com > > > Linus' Law: Given enough eyeballs, all bugs are shallow. > > > >
