Update: The p-values and S.E.'s are similar between SPSS and R if I change the parameter estimation method in SPSS to 'Hybrid' and the scale parameter method to 'Pearson Chi-square'. Does anyone now how to change these settings in R and what these settings actually mean?
I am trying to perform an GLM with a gamma log link function in R, to analyse a multiple imputation dataset.
However, when I compare the results from the same analysis in R and SPSS they are very different. This example is in a non-imputation dataset to make things easier to interpret. The SPSS result is as follows:
Parameter Estimates
Parameter B Std. Error 95% Wald Confidence Interval Hypothesis Test
Lower Upper Wald Chi-Square df Sig.
(Intercept) 3,263 ,2499 2,774 3,753 170,571 1 ,000
[Comorb=1] -,631 ,1335 -,893 -,369 22,331 1 ,000
[Comorb=2] -,371 ,1473 -,660 -,083 6,358 1 ,012
[Comorb=3] 0a . . . . . .
PAIDhoog ,257 ,1283 ,006 ,509 4,023 1 ,045
PHQhoog ,039 ,1504 -,256 ,334 ,068 1 ,794
[etndich=1,00] -,085 ,1125 -,306 ,135 ,575 1 ,448
[etndich=2,00] 0a . . . . . .
Leeftijd ,009 ,0035 ,002 ,016 6,588 1 ,010
(Scale) ,613b ,0470 ,528 ,712
Dependent Variable: totaalhealthcareutilization
Model: (Intercept), Comorb, PAIDhoog, PHQhoog, etndich, Leeftijd
a Set to zero because this parameter is redundant.
b Maximum likelihood estimate.
While the same analysis in R yields this result:
Call:
glm(formula = (totaalhealthcareutilization) ~ PAIDhoog + PHQhoog +
Comorb + Leeftijd + etndich, family = Gamma(link = log),
data = F)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1297 -0.7231 -0.3018 0.2075 3.1365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.006208 0.273817 10.979 < 0.0000000000000002 ***
PAIDhoog 0.201881 0.131777 1.532 0.1264
PHQhoog 0.126989 0.157416 0.807 0.4203
Comorbgeen -0.638842 0.144459 -4.422 0.0000128 ***
Comorb1 -0.348187 0.158484 -2.197 0.0286 *
Leeftijd 0.007311 0.003534 2.069 0.0392 *
etndich 0.151836 0.118872 1.277 0.2023
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.9432289)
Null deviance: 286.49 on 381 degrees of freedom
Residual deviance: 243.01 on 375 degrees of freedom
(71 observations deleted due to missingness)
AIC: 3156
Number of Fisher Scoring iterations: 6
How is this possible? The results keep differing, even if I use na.omit or na.exclude in R. I have used the function 'relevel' in R, to make sure that the same reference category is used for the categoric variables.
I hope you have any idea what I am doing wrong in R.
This is what a sample of my data looks like:
verrichtingen verpleegkanders Leeftijd HbA1c BMI Type_Treat DurationDM
1 0 0 26 69 26.7 Insulin 5
2 0 0 69 75 34.5 Insulin 17
3 0 0 67 62 24.3 Insulin 1
4 6 0 38 96 NA Insulin 10
5 0 0 29 NA 19.1 Insulin 25
6 0 0 50 86 37.9 Both 9
7 1 0 29 44 29.1 Both 33
451 4 0 68 113 37.9 Both 11
452 21 1 57 62 21.5 Insulin 1
453 0 0 37 54 25.4 Both 14
Socstatus PAID1 PAID2 PAID3 PAID4 PAID5 PAIDtot PHQ1 PHQ2 PHQ3
1 wel achterstandsw 0 1 2 1 0 4 0 0 0
2 geen achterstandsw 2 1 1 2 0 6 0 0 0
3 <NA> 0 0 0 0 0 0 0 0 0
4 geen achterstandsw 0 0 1 1 0 2 1 0 3
5 geen achterstandsw 0 0 0 0 0 0 1 1 3
6 wel achterstandsw 0 1 0 2 0 3 2 0 3
7 geen achterstandsw 1 1 2 3 0 7 1 1 3
451 geen achterstandsw 0 0 1 0 0 1 0 0 0
452 wel achterstandsw 1 0 4 1 0 6 2 0 3
453 wel achterstandsw 1 1 2 3 2 9 1 0 1
PHQ4 PHQ5 PHQ6 PHQ7 PHQ8 PHQ9 Geslacht Etnicit HAPOH Bedrijfsarts MW
1 1 1 0 0 0 0 vrouw Overigwest NA NA NA
2 1 0 0 1 1 0 man Mar NA NA NA
3 0 0 0 0 0 0 man Overigwest NA NA NA
4 3 1 1 1 1 0 vrouw Overignietwest NA NA NA
5 0 0 0 3 0 0 man Overigwest NA NA NA
6 1 1 1 0 0 0 man Turk NA NA NA
7 3 0 0 2 0 0 vrouw Overigwest NA NA NA
451 0 0 0 0 0 0 man 4 NA NA NA
452 3 0 0 1 0 0 vrouw Mar NA NA NA
453 2 2 0 0 0 0 vrouw Mar NA NA NA
FysioErgo Diet Psychiat Psychol Dvk VPtot Internist Specialist ICUopname
1 NA 5 0 0 5 5 2 3 0
2 NA 2 0 0 2 2 3 8 0
3 NA 0 0 0 1 1 2 3 0
4 NA 0 1 2 11 11 6 25 0
5 NA 0 0 0 4 4 2 6 0
6 NA 1 0 0 2 2 2 0 0
7 NA 3 0 0 4 4 2 3 0
451 NA 0 0 0 1 1 3 7 0
452 NA 2 0 0 4 5 0 25 4
453 NA 1 0 0 2 2 0 5 0
Opnamegewoon SEH Comorb DMtype PAIDtotaal PHQtotaal PAIDhoog PHQhoog
1 0 0 geen DM1 4 2 0 0
2 0 0 geen DM2 6 3 0 0
3 0 0 geen DM1 0 0 0 0
4 1 0 geen DM2 2 NA 0 NA
5 0 0 geen DM1 0 8 0 0
6 0 0 geen DM2 3 8 0 0
7 0 0 geen DM2 7 10 0 0
451 18 2 <NA> DM2 1 0 0 0
452 34 3 <NA> DM1 6 9 0 0
453 0 0 <NA> DM2 9 6 1 0
interactPHQPAID paidtotaalimp PHQtotaalimp GADtotaalimp PAIDhoogimp
1 0 4 2 1 0
2 0 6 3 0 0
3 0 0 0 0 0
4 0 2 11 2 0
5 0 0 8 0 0
6 0 3 8 0 0
7 0 7 10 3 0
451 0 1 0 0 0
452 0 6 9 0 0
453 0 9 6 1 1
PHQhoogimp GADimphoog kostenopnames kosteninternist kostenspecialist
1 0 0 0 160 240
2 0 0 0 240 640
3 0 0 0 160 240
4 0 0 443 480 2000
5 0 0 0 160 480
6 0 0 0 160 0
7 0 1 0 160 240
451 0 0 7974 240 560
452 0 0 15062 0 2000
453 0 0 0 0 400
kostenhuisarts kostenMW kostenfysioergo kostendvk kostendietist
1 NA NA NA 240 240
2 NA NA NA 96 96
3 NA NA NA 48 0
4 NA NA NA 528 0
5 NA NA NA 192 0
6 NA NA NA 96 48
7 NA NA NA 192 144
451 NA NA NA 48 0
452 NA NA NA 192 96
453 NA NA NA 96 48
totaalkosten jaarHAPOH jaarbedrijfsarts jaarMW jaarfysioergo
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
6 NA NA NA NA NA
7 NA NA NA NA NA
451 NA NA NA NA NA
452 NA NA NA NA NA
453 NA NA NA NA NA
totaalverbruikjaar kostenHAjaar kostenMWjaar kostenjaarfysioergo
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
451 NA NA NA NA
452 NA NA NA NA
453 NA NA NA NA
kostenopnameICU kostenpsycholoog kostenpsychiater kostenvpanders
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 188 94 0
5 0 0 0 0
6 0 0 0 0
7 0 0 0 0
451 0 0 0 0
452 8060 0 0 48
453 0 0 0 0
kostenverrichtingen totaalutilization kostenseh totaalkostennieuw hypoangst
1 0 NA 0 880 1
2 0 NA 0 1072 1
3 0 NA 0 448 0
4 876 NA 0 4609 0
5 0 NA 0 832 0
6 0 NA 0 304 1
7 146 NA 0 882 5
451 584 NA 518 9924 0
452 3066 NA 777 29301 0
453 0 NA 0 544 3
contactprimarycare contactsecondarycare totaalhealthcareutilization
1 NA 15 15
2 NA 15 15
3 NA 6 6
4 NA 52 52
5 NA 12 12
6 NA 5 5
7 NA 13 13
451 NA 35 35
452 NA 94 94
453 NA 8 8
kostenprimarycare kostensecondarycare totaalkostenhealthcare etndich
1 NA 880 NA 1
2 NA 1072 NA 2
3 NA 448 NA 1
4 NA 4609 NA 2
5 NA 832 NA 1
6 NA 304 NA 2
7 NA 882 NA 1
451 NA 9924 NA 1
452 NA 29301 NA 2
453 NA 544 NA 2
The following reproduces your SPSS output.
Note, it's all a matter of setting the reference levels of the categorical variables correctly, to match the SPSS encoding. In R the first level will be used as the reference level.
df <- within(F, {
Comorb <- relevel(Comorb, ref = "2 of meer"); # Reference level = "2 of meer"
etndich <- factor(etndich, levels = 2:1); # Reference level = 2
PAIDhoog <- factor(PAIDhoog, levels = 1:0); # Reference level = 1
PHQhoog <- factor(PHQhoog, levels = 1:0); # Reference level = 1
})
fit <- glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
Comorb + Leeftijd + etndich, family = Gamma(link = log),
data = df)
summary(fit)
#
#Call:
#glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
# Comorb + Leeftijd + etndich, family = Gamma(link = log),
# data = df)
#
#Deviance Residuals:
# Min 1Q Median 3Q Max
#-2.1297 -0.7231 -0.3018 0.2075 3.1365
#
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 3.638751 0.267741 13.591 < 2e-16 ***
#PAIDhoog0 -0.201881 0.131777 -1.532 0.1264
#PHQhoog0 -0.126989 0.157416 -0.807 0.4203
#Comorbgeen -0.638842 0.144459 -4.422 1.28e-05 ***
#Comorb1 -0.348187 0.158484 -2.197 0.0286 *
#Leeftijd 0.007311 0.003534 2.069 0.0392 *
#etndich1 -0.151836 0.118872 -1.277 0.2023
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#(Dispersion parameter for Gamma family taken to be 0.9432289)
#
# Null deviance: 286.49 on 381 degrees of freedom
#Residual deviance: 243.01 on 375 degrees of freedom
# (71 observations deleted due to missingness)
#AIC: 3156
#
#Number of Fisher Scoring iterations: 6
Compare with SPSS output
Parameter Estimates
Parameter B Std. Error 95% Wald Confidence Interval Hypothesis Test
Lower Upper Wald Chi-Square df Sig.
(Intercept) 3,639 ,2177 3,212 4,065 279,350 1 ,000
[PAIDhoog=0] -,202 ,1056 -,409 ,005 3,657 1 ,056
[PAIDhoog=1] 0a . . . . . .
[PHQhoog=0] -,127 ,1260 -,374 ,120 1,015 1 ,314
[PHQhoog=1] 0a . . . . . .
[Comorb=1] -,639 ,1148 -,864 -,414 30,940 1 ,000
[Comorb=2] -,348 ,1250 -,593 -,103 7,758 1 ,005
[Comorb=3] 0a . . . . . .
[etndich=1,00] -,152 ,0936 -,335 ,032 2,633 1 ,105
[etndich=2,00] 0a . . . . . .
Leeftijd ,007 ,0028 ,002 ,013 6,599 1 ,010
(Scale) ,581b ,0387 ,510 ,662
Dependent Variable: totaalhealthcareutilization
Model: (Intercept), PAIDhoog, PHQhoog, Comorb, etndich, Leeftijd
a Set to zero because this parameter is redundant.
b Maximum likelihood estimate.
Further comments on differences in the SPSS and glm output
The first thing to note is that parameter estimates from SPSS and R are identical: Both parameter sets correspond to the (unique) set of maximum likelihood (ML) estimates given the model and data.
In R, the standard errors are simply given as the square root of the diagonal elements of the estimated covariance matrix
sqrt(diag(vcov(fit))) #(Intercept) PAIDhoog0 PHQhoog0 Comorbgeen Comorb1 Leeftijd #0.267740656 0.131776659 0.157416176 0.144458874 0.158484265 0.003534017 # etndich1 #0.118871533Note that these values are identical to the reported se’s in
summary(fit).I don’t know SPSS, but it seems that SPSS' se's correspond to scaled square roots of the diagonal elements of the variance-covariance matrix.
Confidence intervals are based on parameter and variance-covariance estimates; as explained in the previous points, parameter estimates are identical, but SPSS uses a scaled variance-covariance matrix, so confidence intervals for the parameters in the SPSS and R output will be different according to said scaling factor.
SPSS' documentation is regrettably diffuse, so I'm not sure how SPSS scales its variance-covariance matrix.
Sample data
F <- structure(list(HbA1c = c(69, 75, 62, 96, NA, 86, 44, 49, NA, 63, 43, 75, 48, 56, 79, 78, 67, 66, 75, 67, 65, 66, 34, 62, 79, 60, 91, 51, 84, 72, 65, NA, NA, 62, 61, 69, 63, NA, 85, 38, 42, 80, 59, 96, 59, 49, 62, 98, 71, 78, 50, 43, 44, 69, 56, 38, 59, 74, 115, 69, 67, 51, NA, 107, 71, 86, 78, 41, 60, 59, 74, 73, 49, 34, 71, 57, 55, 74, 67, 61, 48, 59, 70, NA, 55, 72, 69, 82, 40, 58, NA, 53, 46, 69, 60, 39, 76, 69, 61, 86, 58, 63, 66, 103, 73, 54, 59, 46, 58, 70, 57, 53, 49, 53, 58, 71, 60, 76, 64, 97, 60, 49, 53, 44, 53, 73, 59, 75, 61, 55, 68, 56, 51, 91, 92, 76, 51, 55, 61, 83, 52, 62, 71, 75, 54, 64, 90, 65, NA, 69, 70, 70, 59, 62, 60, 63, 58, 58, 63, 60, 49, 62, 95, 42, 99, 67, 117, 68, 55, 55, 70, 60, 61, 91, 33, 89, 60, 47, 62, 72, 40, 88, 59, 56, 57, 59, 74, 41, 53, 76, 48, 73, 65, 96, 58, 55, 67, 45, 45, 69, 72, 44, 59, 43, 90, 69, 69, 71, 93, 42, 87, 54, 83, 60, 48, NA, 53, 56, 57, 77, 63, NA, 63, 60, 68, 51, 48, 65, 61, 79, 63, 62, 53, 67, 53, 53, 63, 55, 61, 51, 53, 46, NA, 78, 76, 73, 51, 49, 68, 86, 71, 55, 57, 113, 63, 68, 94, NA, 38, 50, NA, 42, 60, 57, 49, 60, 81, 69, 55, 82, 64, 55, 74, 71, 56, 60, NA, 47, 49, 98, 55, 80, 71, 69, 35, 53, 90, 64, 82, 132, 64, 70, 65, 34, 65, 54, NA, 68, 58, 76, 82, 66, 74, 66, NA, 54, 53, 78, 62, 88, 69, 49, 83, 54, 55, 56, 66, 84, 47, 82, 53, 62, 163, 41, 55, 89, 76, 81, 45, 50, 89, 72, 90, 47, 38, 83, NA, 53, 74, 55, 47, 49, 56, 74, 107, 86, 48, 59, 86, 44, 55, 64, 81, 66, 63, 98, 51, NA, 60, 50, 55, 52, 79, 58, 50, 89, NA, 36, 50, 70, NA, 86, 57, 60, 78, 53, 70, 79, 49, 78, 83, 66, 57, 62, 80, 70, NA, 67, 80, 46, 79, 47, 145, 87, 53, 65, 73, 75, 53, 50, 71, NA, 65, 106, 123, 51, 55, 43, 48, 86, 61, 64, 55, 71, 61, 96, 80, 69, 66, 74, 88, 48, 68, 55, 52, 58, 69, 66, 44, 45, 64, 84, 72, 49, NA, 71, 70, 104, 78, 73, 47, 75, 45, 57, 88, 86, 55, 72, 47, 53, 113, 62, 54), BMI = c(26.7, 34.5, 24.3, NA, 19.1, 37.9, 29.1, 27.1, NA, 21.1, 48.5, 26.2, 26.9, NA, 25.5, 25.3, 44.3, 25.2, 26.7, NA, 25.5, 25.9, 31.2, 33, 21.8, 23.7, 32, 23.6, 32.4, 29.7, NA, 22.9, 24.4, 33.9, 35.4, 41.2, 20.4, NA, 30.1, 21, NA, NA, 29.5, 16.6, 38.1, 23.9, 19.1, 35.4, 24.2, NA, 26.1, 20, 28.7, 30.7, 25.4, 29.6, 25.4, 26.2, 18.3, 31, NA, NA, 31.5, 32, 35.6, 24.3, 33.3, 35.5, NA, 24.1, NA, 33.4, 28.4, NA, 25.9, 26.7, 35.5, 31.6, 25, 25.5, 22.2, 22.3, 23.4, 35.3, 26.1, 32.6, 20.9, 35.9, 29.1, 32.8, 32.2, 28.9, 28.9, 28.8, 19.7, 29.4, 28.8, 28.2, 20.9, 33.5, 17.6, 38.6, 27.1, NA, 29, 25.6, 22.5, 30.6, 35.6, 32.5, 23.4, 27.2, 23.6, 26.6, 23.5, 30.3, 30.6, 26.4, 38.1, 34.7, NA, 24.6, 22.2, 39.8, 23, 35.8, 31.4, 22.8, 29.3, 27, 31.1, NA, NA, 32.4, 36, NA, 52.8, 22, 27.1, 23.3, 22.7, 25, 42.6, 30.2, 25.3, 30.5, 25.3, 28.4, 30.1, 32.4, NA, 32, 18.8, 23.1, 28.5, 25.1, 22.8, 23.6, 18.5, NA, 27.1, 25.3, 19.8, 20.8, 32.7, 30.1, 34.8, 37.5, NA, 28.1, 46, 23.5, 26.3, 22.2, 28.2, 29.3, 24.2, 29.7, 28.9, 28, 31.3, 28.6, 29.1, 28.4, 23.1, 34.9, 22.7, 26.9, 28.9, 35.9, 23, 25.8, 22.8, 19.2, 27.9, 29.2, 35, 25.1, 20.5, 23.9, 34.3, 23.1, 25.1, 20.5, 24.6, 24.4, 23.7, 22.4, 40.1, 21.9, 50, 34.2, 30.5, 20.7, 29.3, 32.6, 32.1, 23.9, NA, 34, 22.6, 30.2, 28.6, 27.5, 33, 24, 28.8, NA, 32.8, 21.8, NA, 37.8, 26.4, 36.2, 20.8, 24.4, 31, 31.9, 27.6, 25.4, 22.7, NA, 27.7, 32.4, 34, 26.2, 26.7, 23.7, 32, 24.1, 35.8, 23.5, 38.9, 35.3, NA, 23.9, 30.2, 24.4, 24.4, 27.9, NA, 25.7, 25.6, 25.8, 47.9, 25.6, 36.1, NA, 24.2, 24.8, 21.4, 22.3, 24.3, 24.7, 22.5, 25.9, 30.1, 27.4, 27.8, 22.6, 24.4, NA, 33.8, 41.9, 21.4, 32.5, 41.1, 27.2, NA, 37.8, 29, 23.2, 28.7, 25.2, 32.6, 29, 24.4, 23.1, 22.8, 23.1, 39.8, 26.6, 25.3, 53.5, 25, 22.9, 22.2, 30.2, 27.4, 27.4, NA, 25.2, 22.4, 20.2, 23.9, 23.3, 31.2, 24, 23.5, 38.8, 30, 30.6, 28.9, 23.1, 34.4, 28.7, 30.8, 21.6, 24.1, 25.5, 39.2, 29.3, 36.2, 28.3, NA, NA, NA, 29.5, 33.1, 23.4, 23.5, 25.1, 34.4, 24.5, 29.7, 22.2, 25.5, 23.3, 37.5, 26.8, 44.5, 32.4, 26.1, 21.4, 26.5, 32.7, 26.9, NA, 27.4, 36.3, 25.1, 37.7, NA, 27.6, 24.2, 46.9, 30.8, 29.3, 25.4, 35.7, 36.8, 35, 22.3, 28.3, 20.4, 25, 35, NA, 39.4, 25.2, 22.5, 34.5, NA, 21.6, 30.1, 25, NA, 28.3, 19.7, 22.3, 33.2, NA, 24.6, 23.9, 22.8, 24.1, 31.7, 28.4, 34.5, 30.1, 33.3, 28, 38, 35.9, 30.6, 33.5, 29.5, 21.4, 24.4, 27.5, 31.7, 23.8, NA, 21.8, 28.7, 33.5, 23.5, 27.3, 28.7, NA, 25.6, 26.7, 44.8, 26.2, 27.1, 39.7, 24.1, 21.3, 29.5, 30, NA, 27, NA, 23.6, 22.3, 32.6, 51.9, 27.7, 28.7, 35.2, 27.2, 29.6, 22.8, 19.6, 25.7, 28.3, 31.2, 21.7, 36.2, 26.9, 37.9, 21.5, 25.4), Comorb = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA), .Label = c("2 of meer", "geen", "1"), class = "factor"), PAIDhoog = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, NA, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1), PHQhoog = c(0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, NA, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, NA, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, NA, NA, 1, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, NA, 0, 0, NA, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 1, 1, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, NA, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, NA, NA, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 1, 1, 1, 0, 1, NA, NA, 0, 1, 0, 0, 1, 1, NA, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, NA, 0, 0, 0, 0, 0, 1, NA, 0, 1, 1, 0, 0, NA, 0, 0, 1, 1, 0, 0, 0, NA, 0, 1, 0, 0, 0, 0), totaalhealthcareutilization = c(15, 15, 6, 52, 12, 5, 13, 15, 13, 8, 10, 4, 9, 8, 6, 5, 8, 42, 15, 21, 6.3, 9, 5, 5, 14, 24, 8, 15, 25, 12, 29, 21, 6, 11, 8, 7, 29, 7, 7, 19, 14, 25, 16, 7, 20, 13, 17, 12, 5, NA, 9, 11, 14, 57, 12, 10, 37, 8, 12, 57, 8, 11, 14, 11, 49, 10, 10, 11, 19, 20, 21, 5, 1, 2, 2, 3, 3, 6, 4, 3, 4, 6, 5, 4, 4, 5, 7, 6, 6, 8, 5, 7, 8, 5, 6, 6, 6, 8, 7, 6, 6, 9, 11, 7, 9, 7, 7, 7, 7, 8, 10, 10, 10, 9, 9, 9, 11, 8, 10, 9, 9, 11, 13, 8, 12, 12, 9, 11, 7, 8, 10, 10, 9, 10, 10, 12, 12, 16, 9, 5, 10, 7, 13, 13, 13, 15, 16, 11, 11, 17, 13, 12, 22, 19, 15, 14, 11, 12, 19, 13, 15, 13, 14, 11, 17, 12, 17, 10, 13, 15, 12, 13, 13, 20, 16, 21, 17, 25, 22, 18, 18, 17, 15, 19, 10, 15, 20, 33, 22, 26, 23, 27, 20, 21, 21, 13, 24, 45, 27, 27, 19, 19, 25, 43, 16, 16, 13, 24, 29, 17, 24, 25, 32, 27, 29, 22, 35, 56, 26, 45, 23, 54, 26, 33, 23, 39, 35, 24, 36, 37, 37, 74, 53, 36, 60, 33, 35, 26, 44, 78, 22, 26, 77, 62, 121, 51, 28, 68, 63, 43, 64, 81, 120, 95, 98, 23, 11, 21, 10, 7, 41, 7, 33, 6, 40, 20, 2, 31, 23, 23, 13, 68, 9, 8, 41, 19, 27, 29, 46, NA, 35, 16, 12, 9, 14, 20, 7, 2, 4, 6, 6, 6, 4, 9, 6, 8, 9, 12, 9, 7, 8, 12, 11, 11, 14, 12, 14, 12, 16, 15, 22, 23, 19, 11, 12, 13, 17, 18, 19, 27, 15, 9, 17, 18, 19, 17, 19, 12, 16, 54, 21, 30, 23, 25, 24, 37, 35, 27, 47, 22, 27, 27, 30, 32, 32, 31, 39, 28, 36, 54, 50, 45, 42, 88, 56, 63, 82, 60, 70, 139, 122, 71, 130, 84, 33, 111, 111, 246, 157, 54, 24, 41, 22, 7, 33, 15, 9, 6, 16, 67, 3, 22, 48, 15, 57, 25, 48, 74, 40, 25, 18, 21, 3, 6, 7, 7, 14, 9, 11, 16, 14, 14, 14, 28, 18, 22, 21, 26, 39, 24, 22, 18, 22, 19, 19, 45, 15, 13, 22, 31, 29, 46, 37, 23, 35, 68, 39, 51, 35, 50, 80, 69, 51, 41, 90, 43, 32, 48, 34, 53, 25, 66, 39, 83, 70, 237, 81, 126, 95, 170, 35, 94, 8), etndich = c(1, 2, 1, 2, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, NA, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, NA, 2, 1, NA, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, NA, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, NA, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, NA, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, NA, NA, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, NA, 2, 1, 1, 1, 1, NA, 1, 1, 2, 1, 1, 1, 2, 1, NA, 1, 1, 1, 1, 1, 1, 1, NA, NA, 2, 1, 1, 2, 2, NA, 2, NA, 2, 2, 1, NA, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2), Leeftijd = c(26, 69, 67, 38, 29, 50, 29, 23, 52, 39, 50, 29, 36, 52, 43, 53, 47, 33, 52, 55, 43, 64, 35, 24, 51, 39, 50, 51, 46, 51, 30, 32, 28, 25, 52, 48, 60, 31, 61, 47, 46, 56, 38, 72, 88, 34, 56, 27, 27, 56, 52, 49, 34, 25, 22, 60, 61, 42, 45, 51, 42, 61, 69, 57, 35, 50, 42, 50, 51, 46, 28, 34, 52, 33, 30, 64, 65, 35, 31, 57, 75, 43, 46, 35, 65, 29, 29, 75, 49, 31, 57, 29, 40, 75, 30, 34, 58, 47, 37, 43, 34, 47, 46, 42, 49, 57, 46, 36, 51, 80, 45, 47, 48, 23, 51, 53, 44, 64, 44, 33, 40, 42, 29, 60, 28, 47, 47, 39, 25, 41, 39, 27, 57, 66, 42, 22, 59, 27, 43, 53, 65, 52, 41, 50, 55, 29, 55, 39, 41, 25, 74, 68, 55, 29, 77, 45, 18, 34, 49, 74, 44, 33, 48, 82, 61, 54, 46, 30, 33, 65, 51, 44, 50, 57, 27, 56, 85, 52, 31, 62, 62, 34, 48, 28, 28, 63, 30, 40, 44, 37, 73, 70, 39, 59, 56, 61, 40, 43, 33, 58, 44, 62, 26, 72, 67, 59, 48, 37, 52, 37, 57, 53, 59, 44, 71, 81, 33, 61, 50, 33, 48, 50, 63, 46, 60, 58, 40, 63, 39, 71, 38, 40, 56, 36, 52, 61, 83, 59, 43, 69, 50, 57, 38, 50, 27, 43, 46, 30, 50, 34, 68, 53, 48, 84, 41, 57, 61, 72, 27, 80, 71, 69, 61, 43, 67, 60, 58, 67, 72, 40, 79, 52, 80, 33, 25, 80, 67, 56, 66, 54, 50, 65, 39, 36, 69, 39, 34, 41, 36, 61, 33, 42, 43, 45, 48, 67, 69, 66, 37, 28, 64, 65, 68, 62, 84, 82, 59, 61, 74, 52, 41, 30, 33, 55, 55, 26, 53, 33, 64, 65, 74, 67, 70, 58, 51, 62, 67, 52, 40, 57, 57, 57, 59, 56, 61, 58, 45, 63, 61, 50, 70, 32, 50, 74, 70, 49, 42, 71, 51, 67, 46, 45, 75, 54, 75, 45, 46, 64, 60, 55, 61, 65, 68, 71, 43, 78, 53, 63, 85, 75, 66, 67, 54, 63, 68, 84, 58, 72, 70, 58, 29, 63, 83, 64, 75, 59, 76, 61, 62, 65, 61, 72, 20, 43, 67, 33, 62, 63, 51, 34, 68, 68, 60, 67, 44, 64, 69, 53, 69, 47, 41, 38, 57, 71, 70, 68, 25, 60, 71, 48, 64, 62, 72, 60, 45, 67, 59, 73, 27, 64, 66, 57, 72, 71, 77, 58, 56, 65, 74, 44, 22, 63, 42, 80, 52, 66, 60, 56, 54, 42, 68, 57, 37)), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd"), row.names = c(NA, -453L), variable.labels = structure(c("HbA1c", "BMI level", "", "", "", "", "", ""), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd")), codepage = 65001L, class = "data.frame")
来源：https://stackoverflow.com/questions/51423002/different-results-gamma-generalized-linear-model-r-and-spss