ISLANDS >mit <- glm(temp_media~Island, family=gaussian(link = "identity"), data=datayear) > Anova(mit) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) Island 33.256 1 8.08e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mit, ~ Island, adjust="sidak") > med Island lsmean SE df asymp.LCL asymp.UCL Lanzarote 20.5 0.210 Inf 20.0 20.9 Tenerife 19.0 0.147 Inf 18.7 19.3 Confidence level used: 0.95 Conf-level adjustment: sidak method for 2 estimates > mitma <- glm(temp_max~Island, family=gaussian(link = "identity"), data=datayear) > Anova(mitma) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) Island 10.434 1 0.001237 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mitma, ~ Island, adjust="sidak") > med Island lsmean SE df asymp.LCL asymp.UCL Lanzarote 28.5 0.325 Inf 27.7 29.2 Tenerife 27.2 0.228 Inf 26.7 27.7 Confidence level used: 0.95 Conf-level adjustment: sidak method for 2 estimates > mitmi <- glm(temp_min~Island, family=gaussian(link = "identity"), data=datayear) > Anova(mitmi) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) Island 37.189 1 1.072e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mitmi, ~ Island, adjust="sidak") > med Island lsmean SE df asymp.LCL asymp.UCL Lanzarote 15.3 0.233 Inf 14.8 15.8 Tenerife 13.6 0.163 Inf 13.2 13.9 Confidence level used: 0.95 Conf-level adjustment: sidak method for 2 estimates > mih <- glm(hum_media~Island, family=gaussian(link = "identity"), data=datayear) > Anova(mih) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) Island 4.7675 1 0.029 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mih, ~ Island, adjust="sidak") > med Island lsmean SE df asymp.LCL asymp.UCL Lanzarote 70.1 0.571 Inf 68.8 71.4 Tenerife 68.6 0.399 Inf 67.7 69.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 2 estimates > mip <- glm(prec~Island, family=gaussian(link = "identity"), data=datayear) > Anova(mip) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) Island 25.785 1 3.817e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mip, ~ Island, adjust="sidak") > med Island lsmean SE df asymp.LCL asymp.UCL Lanzarote 10.1 2.17 Inf 5.29 15 Tenerife 23.6 1.52 Inf 20.17 27 Confidence level used: 0.95 Conf-level adjustment: sidak method for 2 estimates ZONES > mtl <- glm(temp_media~location, family=gaussian(link = "identity"), data=datayear) > Anova(mtl) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) location 99.189 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtl, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 20.5 0.199 Inf 20.0 20.9 NT 18.2 0.171 Inf 17.8 18.6 ST 20.5 0.241 Inf 20.0 21.1 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtl,linfct = mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_media ~ location, family = gaussian(link = "identity"), data = datayear) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.25381 0.26191 -8.605 <1e-05 *** ST - LZ == 0 0.07586 0.31259 0.243 0.968 ST - NT == 0 2.32967 0.29555 7.882 <1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtml <- glm(temp_max~location, family=gaussian(link = "identity"), data=datayear) > Anova(mtml) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) location 37.635 2 6.726e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtml, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 28.5 0.317 Inf 27.7 29.2 NT 26.4 0.272 Inf 25.7 27.0 ST 28.8 0.385 Inf 27.9 29.7 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtml, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_max ~ location, family = gaussian(link = "identity"), data = datayear) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.0939 0.4181 -5.008 <1e-05 *** ST - LZ == 0 0.3442 0.4991 0.690 0.769 ST - NT == 0 2.4381 0.4719 5.167 <1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmIl <- glm(temp_min~location, family=gaussian(link = "identity"), data=datayear) > Anova(mtmIl) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) location 66.274 2 4.063e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmIl, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 15.3 0.227 Inf 14.8 15.8 NT 13.0 0.195 Inf 12.5 13.4 ST 14.7 0.276 Inf 14.1 15.4 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmIl, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_min ~ location, family = gaussian(link = "identity"), data = datayear) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.3236 0.2998 -7.751 <1e-04 *** ST - LZ == 0 -0.5575 0.3578 -1.558 0.263 ST - NT == 0 1.7661 0.3383 5.221 <1e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mhl <- glm(hum_media~location, family=gaussian(link = "identity"), data=datayear) > Anova(mhl) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) location 33.096 2 6.506e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mhl, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 70.1 0.557 Inf 68.8 71.4 NT 70.0 0.477 Inf 68.9 71.2 ST 65.6 0.674 Inf 64.0 67.3 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mhl, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = hum_media ~ location, family = gaussian(link = "identity"), data = datayear) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -0.06164 0.73277 -0.084 0.996 ST - LZ == 0 -4.43673 0.87414 -5.076 <1e-06 *** ST - NT == 0 -4.37508 0.82554 -5.300 <1e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mpl <- glm(prec~location, family=gaussian(link = "identity"), data=datayear) > Anova(mpl) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) location 101.92 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mpl, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 10.13 2.03 Inf 5.276 15.0 NT 32.16 1.75 Inf 27.994 36.3 ST 6.37 2.47 Inf 0.473 12.3 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mpl, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = prec ~ location, family = gaussian(link = "identity"), data = datayear) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 22.031 2.681 8.217 <1e-04 *** ST - LZ == 0 -3.762 3.200 -1.176 0.466 ST - NT == 0 -25.793 3.026 -8.525 <1e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) ZONES/SEASONS Mean Temperature > spring<-subset(datayear, season=="spring") > winter<-subset(datayear, season=="winter") > summer<-subset(datayear, season=="summer") > autumn<-subset(datayear, season=="autumn") > m1 <- glm(temp_media~location, family=gaussian(link = "identity"), data=spring) > Anova(m1) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) location 94.621 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(m1, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 19.1 0.228 Inf 18.5 19.6 NT 16.5 0.195 Inf 16.0 16.9 ST 19.0 0.276 Inf 18.3 19.6 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(m1, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_media ~ location, family = gaussian(link = "identity"), data = spring) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.58591 0.29980 -8.626 <1e-05 *** ST - LZ == 0 -0.08425 0.35764 -0.236 0.97 ST - NT == 0 2.50167 0.33775 7.407 <1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mw <- glm(temp_media~location, family=gaussian(link = "identity"), data=winter) > Anova(mw) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) location 48.341 2 3.183e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mw, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 17.3 0.228 Inf 16.8 17.9 NT 15.9 0.193 Inf 15.4 16.4 ST 18.1 0.272 Inf 17.4 18.7 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mw, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_media ~ location, family = gaussian(link = "identity"), data = winter) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -1.4225 0.2982 -4.771 <0.001 *** ST - LZ == 0 0.7374 0.3550 2.077 0.0939 . ST - NT == 0 2.1598 0.3337 6.472 <0.001 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > ms <- glm(temp_media~location, family=gaussian(link = "identity"), data=summer) > Anova(ms) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) location 95.321 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(ms, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 23.0 0.229 Inf 22.5 23.6 NT 20.4 0.198 Inf 19.9 20.9 ST 22.9 0.280 Inf 22.2 23.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(ms, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_media ~ location, family = gaussian(link = "identity"), data = summer) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.6635 0.3025 -8.804 <1e-05 *** ST - LZ == 0 -0.1817 0.3616 -0.502 0.87 ST - NT == 0 2.4818 0.3430 7.235 <1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > ma <- glm(temp_media~location, family=gaussian(link = "identity"), data=autumn) > Anova(ma) Analysis of Deviance Table (Type II tests) Response: temp_media LR Chisq Df Pr(>Chisq) location 54.128 2 1.763e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(ma, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 22.2 0.254 Inf 21.6 22.9 NT 20.1 0.220 Inf 19.6 20.6 ST 22.3 0.311 Inf 21.5 23.0 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(ma, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_media ~ location, family = gaussian(link = "identity"), data = autumn) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.172949 0.336419 -6.459 < 1e-08 *** ST - LZ == 0 0.002384 0.402097 0.006 1 ST - NT == 0 2.175333 0.381463 5.703 2.36e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) Maximum temperature > mtmas <- glm(temp_max~location, family=gaussian(link = "identity"), data=spring) > Anova(mtmas) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) location 16.393 2 0.0002755 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmas, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 27.4 0.571 Inf 26.0 28.7 NT 24.6 0.489 Inf 23.5 25.8 ST 27.2 0.691 Inf 25.6 28.9 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmas, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_max ~ location, family = gaussian(link = "identity"), data = spring) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.7195 0.7514 -3.619 < 0.001 *** ST - LZ == 0 -0.1450 0.8963 -0.162 0.98562 ST - NT == 0 2.5745 0.8465 3.041 0.00671 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmaw <- glm(temp_max~location, family=gaussian(link = "identity"), data=winter) > Anova(mtmaw) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) location 22.878 2 1.077e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmaw, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 24.0 0.401 Inf 23.1 25.0 NT 23.6 0.339 Inf 22.8 24.4 ST 26.3 0.480 Inf 25.2 27.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmaw, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_max ~ location, family = gaussian(link = "identity"), data = winter) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -0.4163 0.5249 -0.793 0.706 ST - LZ == 0 2.3284 0.6250 3.726 <0.001 *** ST - NT == 0 2.7447 0.5874 4.672 <0.001 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmasu <- glm(temp_max~location, family=gaussian(link = "identity"), data=summer) > Anova(mtmasu) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) location 20.724 2 3.16e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmasu, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 32.0 0.560 Inf 30.6 33.3 NT 28.7 0.485 Inf 27.6 29.9 ST 31.1 0.686 Inf 29.5 32.8 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmasu, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_max ~ location, family = gaussian(link = "identity"), data = summer) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -3.2316 0.7409 -4.362 <0.001 *** ST - LZ == 0 -0.8326 0.8855 -0.940 0.6135 ST - NT == 0 2.3990 0.8401 2.856 0.0119 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmaa <- glm(temp_max~location, family=gaussian(link = "identity"), data=autumn) > Anova(mtmaa) Analysis of Deviance Table (Type II tests) Response: temp_max LR Chisq Df Pr(>Chisq) location 10.351 2 0.005654 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmaa, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 30.3 0.507 Inf 29.0 31.5 NT 28.5 0.439 Inf 27.4 29.5 ST 30.5 0.621 Inf 29.0 32.0 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmaa, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_max ~ location, family = gaussian(link = "identity"), data = autumn) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -1.7868 0.6705 -2.665 0.0208 * ST - LZ == 0 0.2476 0.8013 0.309 0.9485 ST - NT == 0 2.0343 0.7602 2.676 0.0202 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) Minimum temperature > mtmis <- glm(temp_min~location, family=gaussian(link = "identity"), data=spring) > Anova(mtmis) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) location 44.662 2 2.004e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmis, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 13.7 0.297 Inf 13.0 14.4 NT 11.3 0.254 Inf 10.7 11.9 ST 13.2 0.359 Inf 12.3 14.1 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmis, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_min ~ location, family = gaussian(link = "identity"), data = spring) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.4649 0.3904 -6.314 <1e-04 *** ST - LZ == 0 -0.5296 0.4657 -1.137 0.49 ST - NT == 0 1.9353 0.4398 4.400 <1e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmiw <- glm(temp_min~location, family=gaussian(link = "identity"), data=winter) > Anova(mtmiw) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) location 20.92 2 2.866e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmiw, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 12.0 0.291 Inf 11.33 12.7 NT 10.5 0.246 Inf 9.86 11.0 ST 11.9 0.348 Inf 11.06 12.7 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmiw, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_min ~ location, family = gaussian(link = "identity"), data = winter) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -1.5730 0.3810 -4.129 < 0.001 *** ST - LZ == 0 -0.1379 0.4536 -0.304 0.95013 ST - NT == 0 1.4352 0.4264 3.366 0.00231 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmisu <- glm(temp_min~location, family=gaussian(link = "identity"), data=summer) > Anova(mtmisu) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) location 74.725 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmisu, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 18.4 0.272 Inf 17.8 19.1 NT 15.4 0.236 Inf 14.8 16.0 ST 17.3 0.334 Inf 16.5 18.1 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmisu, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_min ~ location, family = gaussian(link = "identity"), data = summer) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -3.0558 0.3603 -8.481 <0.001 *** ST - LZ == 0 -1.1134 0.4307 -2.585 0.026 * ST - NT == 0 1.9423 0.4086 4.754 <0.001 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mtmia <- glm(temp_min~location, family=gaussian(link = "identity"), data=autumn) > Anova(mtmia) Analysis of Deviance Table (Type II tests) Response: temp_min LR Chisq Df Pr(>Chisq) location 25.442 2 2.987e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mtmia, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 16.8 0.328 Inf 16.0 17.6 NT 14.8 0.284 Inf 14.1 15.5 ST 16.5 0.402 Inf 15.6 17.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mtmia, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = temp_min ~ location, family = gaussian(link = "identity"), data = autumn) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -2.0207 0.4341 -4.655 < 0.001 *** ST - LZ == 0 -0.2690 0.5189 -0.518 0.86174 ST - NT == 0 1.7517 0.4922 3.559 0.00114 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) Mean humidity > mh2 <- glm(hum_media~location, family=gaussian(link = "identity"), data=spring) > Anova(mh2) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) location 22.453 2 1.332e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mh2, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 68.9 0.769 Inf 67.1 70.8 NT 71.6 0.658 Inf 70.0 73.2 ST 66.3 0.931 Inf 64.1 68.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mh2, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = hum_media ~ location, family = gaussian(link = "identity"), data = spring) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 2.652 1.012 2.621 0.0235 * ST - LZ == 0 -2.641 1.207 -2.188 0.0727 . ST - NT == 0 -5.293 1.140 -4.643 <0.001 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mhw <- glm(hum_media~location, family=gaussian(link = "identity"), data=winter) > Anova(mhw) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) location 32.787 2 7.592e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mhw, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 68.3 1.138 Inf 65.6 71.0 NT 64.2 0.963 Inf 61.9 66.5 ST 58.1 1.362 Inf 54.9 61.4 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mhw, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = hum_media ~ location, family = gaussian(link = "identity"), data = winter) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -4.072 1.491 -2.731 0.0172 * ST - LZ == 0 -10.163 1.775 -5.725 <0.001 *** ST - NT == 0 -6.091 1.669 -3.650 <0.001 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mhs <- glm(hum_media~location, family=gaussian(link = "identity"), data=summer) > Anova(mhs) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) location 7.4007 2 0.02471 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mhs, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 72.2 0.878 Inf 70.1 74.3 NT 74.2 0.760 Inf 72.4 76.0 ST 70.8 1.075 Inf 68.2 73.3 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mhs, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = hum_media ~ location, family = gaussian(link = "identity"), data = summer) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 1.976 1.161 1.701 0.2035 ST - LZ == 0 -1.459 1.388 -1.051 0.5431 ST - NT == 0 -3.435 1.317 -2.608 0.0244 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mha <- glm(hum_media~location, family=gaussian(link = "identity"), data=autumn) > Anova(mha) Analysis of Deviance Table (Type II tests) Response: hum_media LR Chisq Df Pr(>Chisq) location 3.7961 2 0.1499 > med<-lsmeans(mha, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 70.8 1.148 Inf 68.1 73.6 NT 70.1 0.983 Inf 67.8 72.5 ST 67.4 1.390 Inf 64.1 70.8 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mha, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = hum_media ~ location, family = gaussian(link = "identity"), data = autumn) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 -0.7121 1.5116 -0.471 0.884 ST - LZ == 0 -3.3946 1.8032 -1.883 0.143 ST - NT == 0 -2.6825 1.7029 -1.575 0.255 (Adjusted p values reported -- single-step method) Precipitation > mp3 <- glm(prec~location, family=gaussian(link = "identity"), data=spring) > Anova(mp3) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) location 102.3 2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mp3, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 5.32 2.44 Inf -0.516 11.2 NT 33.56 2.09 Inf 28.558 38.6 ST 4.71 2.96 Inf -2.363 11.8 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mp3, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = prec ~ location, family = gaussian(link = "identity"), data = spring) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 28.2354 3.2180 8.774 <1e-06 *** ST - LZ == 0 -0.6155 3.8389 -0.160 0.986 ST - NT == 0 -28.8508 3.6254 -7.958 <1e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mpw <- glm(prec~location, family=gaussian(link = "identity"), data=winter) > Anova(mpw) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) location 48.964 2 2.331e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mpw, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 17.12 3.43 Inf 8.94 25.3 NT 40.32 2.90 Inf 33.39 47.2 ST 8.66 4.10 Inf -1.13 18.4 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mpw, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = prec ~ location, family = gaussian(link = "identity"), data = winter) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 23.198 4.488 5.169 <1e-04 *** ST - LZ == 0 -8.461 5.343 -1.584 0.251 ST - NT == 0 -31.660 5.022 -6.304 <1e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mps <- glm(prec~location, family=gaussian(link = "identity"), data=summer) > Anova(mps) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) location 37.981 2 5.657e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mps, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 1.15 1.46 Inf -2.35 4.64 NT 11.93 1.27 Inf 8.91 14.96 ST 1.96 1.79 Inf -2.32 6.24 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mps, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = prec ~ location, family = gaussian(link = "identity"), data = summer) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 10.786 1.936 5.572 < 1e-05 *** ST - LZ == 0 0.812 2.314 0.351 0.934 ST - NT == 0 -9.974 2.195 -4.544 1.53e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) > mpa <- glm(prec~location, family=gaussian(link = "identity"), data=autumn) > Anova(mpa) Analysis of Deviance Table (Type II tests) Response: prec LR Chisq Df Pr(>Chisq) location 15.861 2 0.0003596 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > med<-lsmeans(mpa, ~ location, adjust="sidak") > med location lsmean SE df asymp.LCL asymp.UCL LZ 17.2 6.26 Inf 2.21 32.1 NT 42.9 5.42 Inf 29.91 55.8 ST 10.2 7.66 Inf -8.14 28.5 Confidence level used: 0.95 Conf-level adjustment: sidak method for 3 estimates > summary(glht(mpa, mcp(location="Tukey")))#Comparaciones por pares Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = prec ~ location, family = gaussian(link = "identity"), data = autumn) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) NT - LZ == 0 25.702 8.278 3.105 0.00535 ** ST - LZ == 0 -6.987 9.894 -0.706 0.75885 ST - NT == 0 -32.689 9.386 -3.483 0.00157 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method)